DE10341764A1 - Integrated model prediction control and optimization within a process control system - Google Patents

Abstract

A process control configuration system for use in fabricating or viewing an integrated optimization and control block is provided that implements an optimization program and a multiple input / multiple output control program. The configuration system may allow a user to view or configure the optimization or control program. A memory program may store data relating to multiple control and auxiliary variables and a plurality of manipulated variables to be used by the optimizer and / or the control program. A viewer may provide a user with an indication of the data pertaining to the multiple control and auxiliary variables and the plurality of manipulated variables.

Description

The login is a partial tracking from and claims priority from U.S. Patent Application No. 10 / 310,416 entitled "Configuration and Viewing Display for Integrated Model Predictive Control and Optimizer Function Block ", filed on December 5, 2002, which is a follow-up to the US patent application No. 10 / 241,350 entitled "Integrated Model Predictive Control and Optimization within a Process Control System ", filed on September 11, 2002, and priority is claimed from this. This Registrations are universally included in their entirety by reference.

The present invention relates generally on process control systems and in particular on an optimized model prediction controller as part of a process control system.

Process control systems such as distributed or scalable process control systems such as those in Chemical, petroleum or other processes typically include one or more communicatively with each other, with at least one Home or server workstation and with one or more Field devices via analog, digital or combined analog / digital buses coupled controller. The field devices, These are, for example, valves, valve actuators, switches and transducers (e.g., temperature, pressure and flow rate sensors) can act, fulfill Functions within the process such as opening and closing of Valves and measuring process parameters. The process controller receives signals the for Process measurements carried out by the field devices, and / or others, the field devices related data, uses this data to a control program to initiate, and then generates control signals over the Buses to the field devices be sent to control the flow of the process. dates from the field devices and the controller will typically be one or more applications to disposal set up by the operator workstation, around an operator any desired Function regarding the process such as monitoring the current process stage, amend of the process flow, etc. allow.

Process controllers are typical programmed to use different algorithms, subroutines or control loops (which are all control routines) for each one Number of different circles that determines for a process or are included, such as flow control loops, temperature control loops, Pressure control loops, etc. Generally speaking, each includes such control loop one or more input blocks such as a function block analog input (AI), a single output control block, such as a proportional, integral, derivative (PID) rule block or a fuzzy rule function block, and a single output block, like a function block analog output (AO). These control circuits to lead typically a single input / single output control, because the control block generates a single control output, used to process a single process input, such as a valve position, etc. to control. In some cases however, is the use of multiple independently operating single-input / single-output control circuits not useful, because the process variables to be controlled by more than a single Process input are affected and each process input on can affect the status of many process outputs. An example of this could be, for example occur in a process in which a tank passes through two supply lines filled and over a single outlet conduit is to be emptied, each conduit controlled by another valve, and at the temperature, Pressure and flow rate of the tank are regulated so that they are are at or near target values. As stated above, the Control of the flow rate, the temperature and the pressure of the tank using a separate throughput loop, a separate one Temperature control circuit and a separate pressure control loop are performed. In this situation, however, the operation of the temperature control loop when changing adjusting one of the supply valves to the temperature in the tank to regulate, cause that the pressure in the tank rises, which, for example, the pressure circuit caused the exhaust valve to open to lower the pressure. This process can then cause the throughput loop to one close the inlet valves, which affects the temperature and the temperature control loop is prompted to take any further action. How out In this example, the single input / single output control loops bring the process outputs (in this case throughput, temperature and pressure) to act in an unacceptable manner in which the Expenditures fluctuate without ever achieving a steady state.

Model Prediction Control (MPC Model Predictive Control) or other types of advanced control were used to perform process control in situations which changes At a given controlled process size, more than one process variable or Affect process output. Since the late 70s of the twentieth Century was over many successful implementations of model prediction control reported, and MPC became the primary form of advanced multivariable control in the process industry. Furthermore MPC control has become distributed in distributed control systems Control system multi-layer software implemented. US Pat. 4,616,308 and 4,349,869 generally describe MPC controllers that can be used in a process control system.

Generally speaking, MPC is one Multiple input / multiple output control strategy when the effects of changing several process inputs each to several process outputs measured and then uses these measured responses to create a control matrix or model of the process. The process model or the control matrix (which in general is the Steady state of the process defined), is mathematically inverted and then in one or more than one multiple input / multiple output controller used to control the process outputs based on changes made to the process inputs are made. In some cases the process model as a process output response curve (typically a step response curve) for Each of the process inputs is represented, and these curves can be based on on a series of, for example, pseudo-random step changes generated, which are delivered to each of the process inputs. These response curves can be used to model the process in a known manner display. Model prediction control is in the art and therefore their specific characteristics are not described here. However, MPC is generally in Qin, S. Joe and Thomas A. Badgweil, "An Overview of Industrial Model Predictive Control Technology, AIChE Conference, 1996 described.

MPC turned out to be a very effective one and useful Control technology out and was related to process optimization used. To optimize a process that uses MPC, An optimizer determined by the MPC program minimizes or maximizes One or more process input variables to complete the process to run optimal operation point. While this technique of the Computational possible is, must the process variables are selected for example, a significant impact on the improvement the economic flow of the process (e.g., process throughput or Quality) have to do the process of an economic Point of view to optimize. To the process of a financial or economic point of view at an optimal operation point have to expire many process variables, and not just a single process variable, taken together become.

An optimization by means of quadratic Programming techniques or more common Techniques such as floating-point techniques have been proposed as a solution to provide dynamic optimization with MPC. With these Method, an optimization solution is determined, and the optimization provides the controller with conversions in the controller outputs (i.e. the manipulated variables of the Process) the process dynamics, existing boundary conditions and optimization goals. Nevertheless, this solution shows a huge computational burden on and is with the current state the technology is not feasible.

In most cases it is in use of MPC the number of process variables that are in the process to disposal are greater than (i.e., the control outputs of the MPC program) the number of control variables of the process (i.e., the number of process variables that need to be controlled to be at a specific setpoint). In the result exist usually several Degrees of freedom, to the execution of optimization and boundary conditions available are. Theoretically, to carry out such optimization Values are calculated by process size, constraints, limits and economic Factors expressed which determine an optimal operation point of the process. In many cases are these process variables compulsory given sizes, because them limits in terms of the physical properties of the Process they concern, within which they are placed must be kept. For example, a process variable is the tank level represents, on the physically achievable maximum and minimum level limited to the respective tank. An optimization function can calculate costs and / or benefits associated with each of the forcibly given or auxiliary sizes are to fill a level to work, maximizing benefits, minimizing costs are, etc. The measurements of these auxiliary quantities can then the MPC program as inputs available provided by the MPC program as control variables with a setpoint equal the operation point for the auxiliary size, the is defined by the optimizer to process.

MPC delivers the best performance that often by the application only for the square control is needed where the number of control inputs into the process (i.e. Control program formed manipulated variables) the number of control variables to be controlled (i.e., the inputs in the controller). In most cases, however, the number is at forcibly specified auxiliary quantities plus the number of process control variables greater than the number of manipulated variables. A Implementation of MPC for Such non-square configurations result in an unacceptably bad configuration Power.

It is believed that others have attempted to overcome this problem by selecting a set of control and forced sizes equal to the number of manipulated variables and generating the controller online or during the process to estimate the next movements in the manipulated variables determine. However, this technique is computationally expensive because it employs a matrix inversion and, in some cases, can not be used like MPC, which is incorporated as a functional block in a process control device. Equally important, some combinations of inputs and outputs of the generated controller can result in a badly-conditional controller, resulting in unacceptable operation. While the state of the controller can be checked and improved when the Control When the offline configuration is installed offline, this task is too much for on-line operation and virtually impossible at the controller level.

It becomes a process control configuration system for use in creating or viewing an integrated Optimization and control blocks provided an optimization program and Run a multiple input / multiple output control program. The Configuration system may allow a user to optimize or to view or configure the control program. For example a memory program can store data regarding multiple control and data Auxiliary sizes and store several manipulated variables, which are used by the optimizer and / or the control program and a viewer can give an ad to a user presenting the data concerning the multiple control and auxiliary sizes and relate to several variables.

In one embodiment, the memory program stores Response data or response data for each of at least some the tax and Auxiliary variables. The Response data for a control or auxiliary size can be data include that for a respective response of the control or auxiliary variables respective manipulated variables are available. This response can be, for example, step behavior, pulse behavior, Rise behavior, etc. The display program can be the user Show response data. For example, the user define a manipulated variable, and the viewer may have the responsiveness of one or more the tax and auxiliary sizes show the specified manipulated variable.

In another aspect a process control system for controlling a process of a multiple input / multiple output controller and an optimizer. The multi-input / multi-output controller generated during multi-tax expenditures for each cycle of the process control system, which are configured to run the process based on multiple measurement inputs control from the process and based on a set of target values, the multi-input / multi-output controller during each Operation cycle of the process control system are provided. The optimizer makes the set of targets for use the multi-input / multi-output controller during each Operation cycle of the process control system. The optimizer tries to do a Goal function to minimize or maximize and at the same time one Set of control variables within predetermined Setpoint limits, a set of auxiliary quantities within a set predetermined auxiliary size limits and a set of manipulated variables within to keep a set of predetermined manipulated variable limits. If optimizing no solution Optimize attempts to minimize the objective function or to maximize while allowing at least one the setpoint limits are violated.

In yet another aspect a process control method for controlling a process with multiple Manipulated variables and several control and auxiliary sizes the Choose a subset of control and auxiliary quantities for use in the execution process control, whereby at least one of the selected control and auxiliary sizes based selected on it is that it responds the most to one of the manipulated variables. A tax matrix is using the selected Tax and auxiliary sizes and generates the manipulated variables, and a controller matrix is generated from the control matrix. entries in the controller include the selected control and auxiliary quantities, and Outputs of the controller include the manipulated variables. An optimization becomes carried out, by selecting a process operation point, the process operation point through a set of target values for the selected tax and auxiliary sizes determined is used to minimize or maximize an objective function. Of the Controller is used to multi-input / multiple output control method perform, to form a set of manipulated variable values from the target values.

The following are preferred embodiments described with reference to the drawings. Show it:

1 FIG. 12 is a block diagram of a process control system including a control module having an advanced controller functional block including an optimizer with an MPC controller; FIG.

2 is a block diagram of the advanced controller functional block of 1 with integrated optimizer and MPC controller;

3 FIG. 10 is a flowchart illustrating one way of using the integrated optimizer and the MPC controller functional block of FIG 2 to set and install;

4 is a flow chart illustrating the operation of the integrated optimizer and MPC controller of 2 during an online process flow;

5 Fig. 11 is a screen of a configuration program illustrating an advanced control block in a control module that performs process control;

6 is a screen of a configuration utility that displays a dialog box showing the properties of the advanced control block of 5 displays;

7 is a screen display of a configuration program that provides a way of inputting to or outputting from one in the display of 5 to select or input the advanced control function block shown;

8th is a screen display provided by a configuration utility that does it allows a user or operator to select one of a set of target functions for use in determining an advanced control block;

9 Fig. 10 is a screen display of a test screen that may be used to enable a user to test and generate a process model while setting an advanced control block;

10 Fig. 11 is a screen of a configuration program illustrating multiple step responses indicating the response of various control and auxiliary quantities to a particular manipulated variable;

11 is a screen of a configuration program that shows a way of selecting the control or auxiliary quantities from 9 represents that were previously assigned to the manipulated variable;

12 Fig. 11 is a screen of a configuration program illustrating multiple step responses indicating the response of the same control or auxiliary variable to other ones of the manipulated variables;

13 Fig. 12 is a screen of a configuration program illustrating another way of selecting control or auxiliary variables to be associated with manipulated variables;

14 Figure 12 is a screen of a configuration program that was another way of selecting control or auxiliary quantities to be assigned to manipulated variables;

15 is a screen display which is the way to copy one of the step responses of a model to be copied for use in another model;

16 is a screen display that represents the way to view and change a step response curve;

17 Fig. 11 is a screen displaying a dialog screen which provides data to the operator during operation of the advanced control block; and

18 is a screen that displays a diagnostic screen that may be provided to a user or operator to diagnose the advanced control block.

Now comprising with respect to 1 a process control system 10 a process controller 11 who is communicative with a data collection 12 and one or more major workstations or computers 13 (which may be of any type of PC, workstation, etc.), each of which has a display screen 14 having. The controller 11 is via input / output cards (I / O cards) 26 and 28 with field devices 15 - 22 connected. The data collection 12 may be of any desired type of data collection unit with any desired type of memory and any desired or known software, hardware or firmware for storing data, and may be separate (as in 1 shown) or a part of one of the workstations 13 his. The controller 11 which may, for example, be the DeltaV ^™ controller marketed by Fisher-Rosemount Systems, Inc., is the host 13 and the data collection 12 for example via an Ethernet connection or any other communication network 29 connected. In the communication network 29 it may be a local area network (LAN), a supra-regional network (WAN), a telecommunications network, etc., and it may be implemented using hardwired or wireless technology. The controller 11 is communicative with the field devices by any desired hardware and software associated, for example, with standard 4-20 ma devices and / or any intelligent communication protocol such as FOUNDATION fieldbus protocol (Fieldbus), HART protocol, etc. 15 - 22 connected.

With the field devices 15 - 22 These can be any type of devices, such as sensors, valves, transducers, actuators, etc., while the I / O cards 26 and 28 can be any type of I / O device that meets any desired communication or controller protocol. In the in 1 illustrated embodiment, the field devices 15 - 18 standard 4-20 ma devices that have analog lines with the I / O card 26 communicate while the field devices 19 - 22 Smart devices are, like fieldbus field devices, that have a digital bus with the I / O card 28 communicate using Fieldbus protocol communication. Of course, the field devices could 15 - 22 also comply with any other standard or any other standards or protocols, including those that will be developed in the future.

The controller 11 which is one of many distributed controllers within the plant 10 may include at least one processor, implementing or monitoring one or more process control programs that may include control circuits stored therein or otherwise associated therewith. The controller 11 also communicates with the devices 15 - 22 , the main computers 13 and the data collection 12 to control a process in any desired way. It should be noted that any control programs or elements described herein may include portions thereof implemented or executed by other controllers or devices if desired. Likewise, the control programs or elements described herein may be used in the process control system 10 be implemented, take any form that includes software, firmware and hardware, etc. For purposes of this discussion, a process control element may be any part or portion of a process control A system comprising, for example, a program, a block or a module stored on any computer-readable medium. Control programs, which may be modules or any part of a control process, such as a subroutine, subprogram parts (such as command lines), etc., may be implemented in any desired software format, such as LAD logic, sequence function curves, function block diagrams, object oriented programming or use any other software programming language or any other software design paradigm. Likewise, the control programs may, for example, be hard-coded into one or more EPROMs, EEPROMs, application specific integrated circuits (ASICs), or any other hardware or firmware elements. Moreover, the control programs may be defined by any design tools including graphical design tools or any other type of software / hardware / firmware programming or design tools. That way, the controller can 11 be configured to implement a control strategy or program in any desired manner.

In one embodiment, the controller implements 11 a control strategy that employs what are generally referred to as function blocks, where each function block is a part or subject of a parent control program and works with other function blocks (via communications called links or "links") to control process circuits within the system process control Systems 10 to implement. The functional blocks typically perform an input function, such as that associated with a transmitter, sensor or other process parameter measuring device, a control function such as that associated with a control program that performs PID, fuzzy control, etc., or one Output function that controls the operation of any device, such as a valve, to perform any physical function within the process control system 10 to fulfill. Of course, there are hybrid and other types of function blocks. Function blocks can be in the controller 11 stored and drained, which is typically the case when these functional blocks are used for or in conjunction with standard 4-20 ma devices and some types of smart field devices such as HART devices, or may be stored in the field devices themselves and can be implemented by them, which can be the case with Fieldbus devices. While the description of the control system is described herein using a functional block control strategy employing an object-oriented programming paradigm, the control strategy or control circuits or modules could also be implemented or designed using other programming conventions, such as ladder logic, sequence function curves, etc., or using any other programming language or programming paradigm.

As by the extended block 30 from 1 is shown, the controller can 11 Several single-circuit control programs include programs 32 and 34 and may implement one or more advanced rule / control circuits acting as a control circuit 36 is shown / are. Each such circuit is typically referred to as a control module. The single-circuit control programs 32 and 34 are shown performing a single loop control using a single input / single output fuzzy control block or a single input / single output PID control block connected to appropriate analog input (AI) and analog output (AO) function blocks; which process control devices such as valves, gauges such as temperature and pressure transducers or any other device within the process control system 10 can be assigned. The advanced control loop 36 is shown as having a progressed control block 38 whose inputs are communicatively connected to numerous AI function blocks and outputs communicatively connected to numerous AO function blocks, although the inputs and outputs of the advanced control block 38 also communicatively connected to any other desired functional blocks or controls to receive other types of inputs and to provide other types of control outputs. As will be further described, the advanced control block 38 a control block that includes a model prediction control program with an optimizer to perform optimized control of the process or part of the process. While the advanced control block 38 Described here as including a model prediction control block (MPC block) could be the advanced control block 38 Also, any other multiple input / multiple output control program or procedure, such as a neural network modeling or control program, a multivariable control program in fuzzy program logic, etc. It will be appreciated that the in 1 displayed function blocks that the advanced control block 38 include, from the controller 11 can be performed, or alternatively can be arranged in and executed by any other processing device, such as one of the workstation 13 or even one of the field devices 19 - 22 ,

As in 1 illustrated includes one of the workstations 13 a generator program 40 for an advanced control block used to control the advanced control block 38 set, download and implement. While the generator program 40 for the advanced control block in a memory within the workstation 13 stored and in it may be executed by a processor, this program (or any part thereof) may additionally or alternatively be in any other device within the process control system 10 if so desired, stored and executed by it. Generally speaking, the generator program includes 40 for the advanced control block, a control block setting program 42 setting an advanced control block, as will be described later herein, and incorporating this advanced control block into the process control system, a process modeling program 44 generating a process model for the process or a part thereof, based on data acquired by the advanced control block, a control logic parameter setting program 46 , which generates control logic parameters for the advanced control block from the process model, and stores and downloads those control logic parameters in the advanced control block for use in process control, and an optimizer 48 setting an optimization program for use with the advanced control block. Of course, the programs can 42 . 44 . 46 and 48 consist of a number of different programs, such as a first program defining an advanced control, whose control inputs are designed to receive process outputs, and whose control outputs are adapted to send control signals to the process inputs, a second program to a user allows the advanced control within the process control program (which may be any desired configuration program) to be downloaded and communicated, a third program using the advanced control to give each of the process inputs energizing waveforms, a fourth program using the advanced control to do so; to capture data that reflects the response of each of the process outputs to the excitation waveforms, a fifth program that selects itself or allows a user to make a set of entries for the advanced waveform ridden control block, a sixth program defining a process model, a seventh program forming advanced control logic parameters from the process model, an eighth program introducing the advanced control logic and, if necessary, the process model into the advanced control allowing the advanced control to control the process and a ninth program choosing itself or allowing a user to use an optimizer in the advanced control block 38 select.

2 FIG. 12 illustrates a more detailed block diagram of one embodiment of the advanced control block. FIG 38 communicating with a process 50 It will be appreciated that it will be understood that the control block 38 generates a set of manipulated variables MVs that are provided to other function blocks, in turn, to control inputs of the process 50 are connected. As in

2 is shown, includes the advanced control block 38 an MPC controller block 52 , an optimization 54 , a target conversion block 55 , a step response model or a control matrix 56 and an input processing / filtering block 58 , In the MPC controller 52 may be any standard, an M times M MPC program or procedure (where M can be any number greater than one) with the same number of inputs and outputs. The MPC controller 52 receives as inputs a set of N control and auxiliary quantities CV and AV (which are vectors of values) as they are in the process 50 be measured, a set of disturbances DV, which are known or expected changes or disturbances with which the process 50 at any time in the future, and a set of steady state target and auxiliary variables CV _T and AV _T generated by the target conversion block 55 to be provided. The MPC controller 52 uses these inputs to set the set of M manipulated variables MV (in the form of control signals) and supplies the signals of the manipulated variable MV to the process 50 to control.

In addition, the MPC controller calculates 52 even a set of prediction steady state control variables CV _SS and forecast steady state auxiliary variables AV _SS and provides, along with a set of prediction steady state manipulated variables MV _SS representing the prediction values of the control variables CV, the auxiliary variables AV and the manipulated variables MV, the prediction horizon the Eingabeverarbeitungs- / filter block 58 to disposal. The input processing / filter block 58 processes the determined prediction steady-state values of the control, auxiliary, and rapid variables CV _SS , AV _SS, and MV _SS to reduce the effects of noise and unpredicted noise on these quantities. Of course, the input processing / filter block 58 include a low pass filter or any other input _processing that reduces the effects of noise, modeling errors, and noise on these values, and provides the filtered control, auxiliary, and manipulated variables CV _SSfil , AV _SSfil, and MV _SSfil to the optimizers 54 ,

The optimizer 54 in this example, is a linear programming (LP) optimizer that has an objective function (OF) that is derived from a selection block 62 can be provided used to perform a process optimization. Alternatively, the optimizer could 54 a quadratic programming optimizer, which is an optimizer with a linear model and a quadratic objective function. Generally speaking, the objective function OF determines costs and benefits associated with each of a number of control, auxiliaries and manipulated variables, and the optimizer 54 sets target values for these quantities by maximizing or minimizing the objective function. The selection block 62 can select the objective function OF, which is the optimizer 54 as one of a set of pre-stored objective functions 64 which mathematically provides various ways of defining the optimal course of the process 50 represent. For example, one of the pre-stored objective functions 64 be configured to maximize the asset's return, another of the objective functions 64 can be configured to minimize the use of a particular raw material that is short of supply while still another of the objective functions 64 can be configured to match the quality of the process 50 product to be maximized. Generally speaking, the objective function uses costs or benefits associated with each movement of a control, aux and manipulated variable to determine the most optimal process operation point within a set of acceptable points, as determined by the set values or ranges of the control variables CV and the limits of the control variables Auxiliary and manipulated variables AV and MV are defined. Of course, any desired objective function may be used in place of or in addition to those described herein, including objective functions, each of which optimizes several issues such as raw material usage, profitability, etc. in some way.

To one of the objective functions 64 a user or operator may provide an indication of the objective function to be used 64 by performing this target function on an operator or user terminal (such as one of the workstations 13 from 1 ) selects which selection via an input 66 the selection block 62 is made available. In response to the input 66 represents the selection block 62 the selected objective function OF the optimizer 54 to disposal. Of course, the user or operator may change the objective function used during the process. If desired, a default target function may be used in cases where the user does not provide or select a target function. One possible standard objective function will be explained in more detail below. Although as part of the advanced control block 38 The various target functions can also be displayed in the operator terminal 13 from 1 stored, and one of these objective functions can the advanced control block 38 be provided during the setting or generation of this block.

In addition to the objective function OF receives the optimizer 54 as inputs, a set of control variable setpoints (which are typically operator set setpoint values for the control variables CV of the process 50 and can be changed by the operator or another user) and a range and weighting or priority associated with each of the control quantities CV. The optimizer 54 also obtains a set of range or constraints and a set of weights or priorities for the auxiliary quantities AV and a set of limits for the manipulated variables MV used to control the process 50 be used. Generally speaking, the ranges for the auxiliaries and manipulated variables define the limits (typically based on the physical properties of the system) for the auxiliary and manipulated variables, while the ranges for the control variables provide a range in which the control variables for a satisfactory control of the process can act. The weights for the control and auxiliary quantities determine the relative importance of the control and auxiliary quantities in the optimization process with respect to each other, and can under some circumstances be used by the optimizer 54 to enable a control target solution to be generated if some of the constraints are violated.

During operation, the optimizer can 54 use a linear programming technique (LP programming technique) to perform the optimization. As is well known, linear programming is a mathematical method for solving a set of linear equations or inequalities that maximizes or minimizes a particular additional function called the objective function. As previously explained, the objective function may express an economic value such as cost or benefit, but may instead express other goals. Moreover, as is assumed, the steady state gain matrix defines the steady state gain for each possible pair of manipulated variables and the control and auxiliary quantities. In other words, the steady state gain matrix defines the steady state gain in each control and auxiliary variable for a change unit in each of the set and disturbances. This steady state gain matrix is generally an N by M matrix, where N is the number of control and auxiliary quantities and M is the number of manipulated variables used in the optimization routine. In general, N may be greater than, equal to, or less than M, the most common case being that N is greater than M.

Using any known or standard LP algorithm or method, the optimizer repeats 54 the determination of the set of target manipulated variables MV _T (as determined by the steady state gain matrix) which maximize or minimize the selected objective function OF and at the same time lead to a process flow comprising the setpoint range limits of the control variable CV, the constraints of the auxiliary variable AV and the limits of the manipulated variable MV meets or is located within this. In one embodiment, the optimizer determines 54 even the change of the manipulated variables and uses the indication of the prediction steady state control quantities, auxiliary quantities and manipulated variables CV _SSfil , AV _SSfil and MV _SSfil to determine the changes in the process flow from its present process, ie the dynamic flow of the MPC control program during the reaching process of the target or optimal process operation point. This dynamic process is important because it ensures must be that during the movement from the current operation point to the target operating point none of the constraints is violated.

In one embodiment, the LP optimizer 54 be designed to minimize an objective function of the following form: Q = P t · A · ΔMV + C t delta MV wherein
Q = total cost / benefit
P = utility vector in the context of the AVs and CVs
C = cost vector associated with MVs
A = gain matrix
ΔMV = vector for the calculated change in MVs

The utility values are positive numbers and the cost values are negative numbers to indicate their impact on the goal. Using this objective function, the LP optimizer computes 54 the changes in the manipulated variables MV that minimize the objective function while ensuring that the control variables CV remain within a range of their target setpoint, that the auxiliary variables AV are within their upper and lower constraints, and that the manipulated variables MV are within their upper limits and lower limits.

vorhergesagte Änderungen in Ausgaben am Ende des Vorhersagehorizonts (t + p) bezeichnet,

der Prozessbeharrungszustand m mal n Verstärkungsmatrix ist,

Veränderungen bei den Stellgrößen am Ende des Steuerhorizonts (t + c) bezeichnet.In an optimization method that can be used, incremental values of manipulated variables at the current time (t) are used and a sum of manipulated variables over the control horizon is used, with the incremental values of the control and auxiliary variables at the end of the prediction horizon and no current ones As typical in LP applications. Of course, the LP algorithm for this variant can be modified appropriately. The LP optimizer 54 can definitely use a steady state model, and as a result, a steady state condition is required for this application. With a predictive horizon normally used in MPC development, a future steady state for a self-regulation process is guaranteed. One possible process prediction steady state equation for an m-by-n input-output process with a prediction horizon p, a control horizon c, expressed in incremental form, is: ΔCV (t + p) = A · ΔMV (t + c) wherein:

predicted changes in outputs at the end of the forecast horizon (t + p),

the process steady state is m times n gain matrix,

Changes in the manipulated variables at the end of the tax horizon (t + c).

The vector ΔMV (t + c) represents the sum of the changes over the control horizon made by each controller output mv _i such that

The changes should have limits both for the manipulated variables MV and the control variables CV correspond (here the auxiliary quantities are treated as control variables): MV Wed. n ≤ MV currently + ΔMV (t + c) ≤ MV Max CV Wed. n ≤ CV predicted + ΔCV (t + p) ≤ CV Max

worin:
UCV der Kostenvektor für eine Änderungseinheit beim Prozesswert der Steuergröße CV ist; und
UMV der Kostenvektor für eine Änderungseinheit beim Prozesswert der Stellgrößen MV ist.In this case, the objective function of maximizing product value and minimizing raw material costs can be defined together as:

wherein:
UCV is the cost vector for a change unit in the process value of the control variable CV; and
UMV is the cost vector for a change unit in the process value of the manipulated variables MV.

Using the above first equation, the objective function can be expressed in the form of manipulated variables MV as:

To find an optimal solution, the LP algorithm calculates the objective function for an initial vertex in the range defined by this equation, and improves the solution at each next step until the algorithm reaches the vertex with the highest (or lowest) value of the objective function optimal solution determined. The determined optimal manipulated variable values are applied as the target manipulated variables MV _T to be achieved within the control horizon.

Generally speaking, running the LP algorithm on the prepared matrix gives three possible results. First, there is a single solution for the target manipulated variables MV _T. Second, the solution is unlimited, which should not be the case if the helper size has a high and a low limit. Third, there is no solution, which means that the limits or constraints imposed on the auxiliary quantities are too narrow. To handle the third case, the constraints can be relaxed to get a solution. The basic assumption is that the limits set for the manipulated variables (high / low limits) can not be changed by the optimizer. The same applies to the boundary conditions or limits of the auxiliary quantities (high / low limits). Nevertheless, the optimizer may refrain from driving the control quantity CV to the setpoint values (CV setpoint control) and the control quantities to any other values within a range of or around the setpoint value (CV range control). In this case, the values of the control variables can be settled in a range rather than a specific setpoint. If there are several auxiliary quantities AV that violate their constraints and skip from CV setpoint control to CV range control, it is also possible to relax or ignore the constraints of the auxiliary sizes based on the provided weighting or priority assignments. In one embodiment, a solution could be determined by minimizing the squared error of the auxiliary quantities, thereby allowing each of them to violate their respective constraints, or by sequentially relinquishing the constraints of the lowest priority auxiliary variables.

As stated above, the objective function OF may be controlled by the control block generator program 40 be selected or set by default. A method for specifying such a default is shown below. In particular, while it is desirable to provide the optimization capability, many situations may require that only setpoint values for the control quantities be maintained in a manner that still takes into account the outflow constraints of the auxiliaries and manipulated variables. For these applications, the block 38 be configured to act solely as an MPC function block. To provide for this ease of use, a standard objective function "Operate" can be generated automatically, with standard costs being assigned to the various variables occurring therein together with standard weights for the auxiliary quantities AV These standards can set all the costs for the auxiliary quantities AV and the manipulated variables MV equal or can provide any other cost allocation to the auxiliary and manipulated variables AV and MV If a professional option is selected, the user can specify additional optimization selections and their associated costs for different objective functions 64 define. The expert user then also has the possibility to change the weights of the standard auxiliary and control variables AV and CV of the standard target function.

In one embodiment, if, for example, no economic constraints for the process configuration are defined, the target function is automatically built from the MPC configuration. In general, the objective function can be built using the following formula:

The sizes C _j and p _j can be defined from the configuration settings. In particular, assuming that the target value of the control variable CV can only be set to LL (Low Limit) or HL (High Limit), the value p _j is defined as follows:
p _j = -1 if the setpoint has been set to low limit or minimization has been selected; and
p _j = 1 if the setpoint has been set to High Limit or Maximize has been selected.

Assuming that no configuration data is input for the auxiliary quantities AV, then for all auxiliary quantities p _j = 0. Similarly, for the manipulated variables MV, the value C _j depends on whether the preferred target manipulated variable MV _{T is} defined or not. If the preferred target manipulated variable MV _{T is} fixed, the following applies:
C _j = 1 if MV _{T was set} to the high limit or Maximize was selected;
C _j = -1, if MV _{T was set} to the low limit or Minimize was selected, and
C _j = 0 if MV _{T is} not defined.

If desired, the choice of using the optimizer 54 in connection with the MPC controller 52 be adjustable to thereby provide an optimization level. To perform this function, the change may be made by the controller 52 used manipulated variables MV changed by that on the by the MPC controller 52 and the optimizer 54 certain changes in the manipulated variables MV different weights are placed. Such a weighted combination of the manipulated variables MV is referred to here as effective MV (MV _eff ). The effective MV _eff can be determined as: delta MV eff = MV mpc (α / S) + ΔMV opt (1 - α) 0 ≦ α ≦ 1, where S is selected arbitrarily or heuristically. Typically, 5 is greater than one and may be in the region of ten.

Here the optimizer increases with α = 0 effective output at how it was set when setting. With α = 1 carries the controller only adds a dynamic MPC control. Of course supplies the range between 0 and 1 different optimizer and MPC control contributions.

The standard objective function described above can be used to produce the optimizer's operation in various possible modes of operation. In particular, if the number of control variables CV matches the number of manipulated variables MV, the expected behavior in the default setting is that the setpoint values of the control variable CV are maintained as long as the auxiliary variables AV and manipulated variables MV are expected to be within their limits. If it is predicted that an auxiliary quantity or manipulated variable will violate its limit, the control variable work setpoints will be changed within their range to prevent these limits from being violated. If in this case the optimizer 54 can find no solution that adheres to the auxiliary and manipulated variable limits, while at the same time the control variables are kept within their range, the control variables are kept within their range, while the auxiliary variables may deviate from their constraints. In finding the best solution, then those auxiliary quantities AV likely to violate a boundary are treated equally and their average limit deviation is minimized.

To achieve this behavior, become the standard costs / benefits used by the objective function automatically adjusted so that the control variables CV a Get assigned benefits of 1 if the area is defined as that it allows a deviation below the set point, and a benefit of -1 if the area is defined as having a deviation over the Setpoint permits. The auxiliary quantities AV within the limits are used from 0 and the manipulated variables cost allocated from 0.

Is the number of control variables CV lower as the number of manipulated variables MV, then can the additional Degrees of freedom are used to meet the requirements, with the final Rest position of the configured manipulated variable MV related. Here become the control variable setpoints (if control variables CV defined are retained as long as the auxiliary and control values are expected stay within their limits. The average deviation the manipulated variables of the configured final resting position is minimized. It is predicted that one or more of the auxiliary and their manipulated variables Limit will / will violate, then the control variable work setpoints changed within their areas to to prevent these limits from being violated. If it is under this condition gives several solutions, then the one used for the control becomes the average deviation the manipulated variables of the configured final Minimize resting position.

Can the optimizer 54 find no solution (ie there is no solution), the auxiliary and manipulated variables adherence to limits and at the same time keeps the tax limits within its range, then the control variables are kept within the range, while the auxiliary quantities may deviate from their constraints. When finding the best solution, then those helper sizes likely to violate a boundary are treated equally and their average limit deviation is minimized. To accomplish this behavior, the standard cost / benefits used by the objective function are automatically adjusted such that the control values are assigned a benefit of 1, if the range is defined to allow deviation below the setpoint, and a benefit of -1 if the range is defined to allow deviation from the setpoint. The auxiliary quantities get a benefit of 1 or -1 and the manipulated variables are allocated costs of 0.1.

Anyway, after the expiration of the optimizer 54 the target conversion block 55 provides a set of optimal or target manipulated variables MV _T which uses the steady state gain matrix to determine the target steady state control quantities and auxiliaries resulting from the target manipulated variables MV _T. This implementation is computationally straightforward, since the steady state gain matrix defines the interactions between the manipulated variables and the control and auxiliary quantities and thereby can be used to only the target control variables and auxiliary variables CV _T and AV _T from the defined target (steady state) variables MV _T determine.

Once determined, at least a subset N of the target control quantities and auxiliary variables CV _T and AV _{T become} the MPC controller 52 As previously stated, these target values CV _T and AV _T are used to determine a new set of steady state manipulated variables MVss (over the control horizon) which matches the current control and auxiliary quantities CV and AV at the end of the Prediction horizon on the target values CV _T and AV _T drives. Of course, the MPC controller is known to alter the manipulated variables in steps, in an attempt to achieve the steady state values for these MVsss, theoretically those of the optimizer 54 certain target variables MV _T be. Because the optimizer 54 and MPC controllers 52 are as described above during each process scan in operation, the target values of the manipulated variables MV _T of scanning can change to sampling, as a result of the MPC controller can never really reach a certain of these sets of target manipulated variables MV _T, especially not if noise , unexpected errors, changes in the process 50 , etc. are added. Nevertheless, the optimizer is driving 54 the controller 52 always to move the manipulated variables MV to an optimal solution.

The MPC controller 52 As is known, it includes a control process prediction model 70 which may be an N by M + D step response matrix (where N is the number of control variables CV plus the number of auxiliary quantities AV, M is the number of manipulated variables MV and D is the number of disturbances DV). The control process prediction model 70 generated in an output 72 a previously calculated prediction for each of the control and auxiliary quantities CV and AV, and a vector summer 74 subtracts these current time prediction values from the actual measured values of the control and auxiliary quantities to input 76 to generate an error or correction vector.

The control process prediction model 70 then uses the N by M + D step response matrix to predict, for each of the control and auxiliary quantities CV and AV, a future control parameter over the prediction horizon based on the disturbance and manipulated variables, the other inputs of the control process prediction model 70 to be provided. The control process prediction model 70 also provides the prediction values of the steady state control and auxiliary variables CV and AV _{SS SS} on the Eingabeverarbeitungs- / filter block 58 ,

A destination control block 80 determines a target control vector for each of the N from the target conversion block 55 using one previously for the block 38 created trajectory filter 82 provided target control variables and auxiliary variables CV _T and AV _T. In particular, the trajectory filter provides a unit vector that determines the manner in which the control and auxiliary variables are to be driven to their target values over time. The destination control block 80 uses this unit vector and the target values CV _T and AV _T to generate a dynamic target control vector for each of the control and auxiliary quantities that defines the changes in the target values CV _T and AV _T over the period of time determined by the time of the prediction horizon , A vector sizer 84 then subtracts the future control parameter vector for each of the control and auxiliary quantities CV and AV from the dynamic control vectors to define an error vector for each of the control and auxiliary quantities CV and AV. The future error vector for each of the control and auxiliary quantities CV and AV is then provided to the MPC algorithm, which acts to select the steps of the manipulated variable MV, such as the least squared error over the control horizon for the manipulated variables MV and the prediction horizon for the control and auxiliary variables CV and AV minimized. Of course, the MPC algorithm or controller uses an M by M process model or a Mmal M control matrix, which is the ratio between the N in the MPC controller 52 entered control and auxiliary quantities and the M from the MPC controller 52 output manipulated variables is formed.

More specifically, the optimizer MPC algorithm has two main goals. First, the MPC algorithm attempts to reduce the control size control error with minimal manipulated variable movements in secondly, to minimize optimum steady state setpoint values set by the optimizer and the target control size values calculated directly from the optimal steady state setpoint values.

worin:
CV(k) der gesteuerte, ausgegebene Vorhersagevektor ist, der um einen p-Schritt voraus ist;
R(k) der Referenztrajektorien(sollwert)vektor ist, der um einen p-Schritt voraus ist;
ΔMV(k) der inkrementale Steuerbewegungsvektor ist, der um einen c-Schritt voraus ist;
Γ^y = diag{Γ^y ₁, ..., Γ^y _p} eine Penalty-Matrix für den gesteuerten Ausgabefehler ist, Γ^u = diag{Γ^u ₁, ..., Γ^u _c} eine Penalty-Matrix für die Steuerbewegungen ist,
p der Vorhersagehorizont (Schrittanzahl) ist;
c der Steuerhorizont (Schrittanzahl) ist; und Γ⁰ eine Penalty für den Fehler der Controllerausgabenbewegungen über den Steuerhorizont in Relation zur optimalen Zielveränderung von MV, die vom Optimierer definiert wurde. Der einfacheren Darstellung halber ist die Zielfunktion für eine Einzeleingabe-/Einzelausgabesteuerung (SISO-Steuerung) gezeigt.To satisfy these goals, the original unrestricted MPC algorithm can be extended to include the MV targets in the least square solution. The objective function for this MPC controller is:

wherein:
CV (k) is the controlled output prediction vector that is one p-step ahead;
R (k) of the reference trajectories (setpoint) is vector ahead of a p-step;
ΔMV (k) is the incremental control motion vector ahead by one c-step;
Γ ^y = diag {Γ ^y ₁ , ..., Γ ^y _p } is a penalty matrix for the controlled output error, Γ ^u = diag {Γ ^u ₁ , ..., Γ ^u _c } is a penalty matrix for the Tax movements is,
p is the forecast horizon (number of steps);
c is the tax horizon (number of steps); and Γ ⁰ a penalty for the controller output movement error over the control horizon in relation to the optimal target change of MV defined by the optimizer. For ease of illustration, the objective function for single input / single output (SISO) control is shown.

As it becomes clear, the first two Terme the objective function for the unrestricted MPC controller while the third term is an additional one Condition that sets the sum of the controller output movements equals the optimal goals. In other words, the first both terms goals for a dynamic controller operation on, while the third term steady state optimization goals sets up.

worin:
ΔMV(k) die Veränderung bei der MPC-Controllerausgabe zu einem Zeitpunkt k ist;
K_ompc die optimierte MPC-Controllerverstärkung ist;
S^u ist die Prozessdynamikmatrix, die aus den Schrittantworten der Dimension p × c für ein SISO-Modell und p*n × c*m für ein Mehrfacheingabe-/Mehrfachausgabe-MIMO-Modell mit m gestellten Eingaben und n gesteuerten Ausgaben aufgebaut ist.It should be noted that the general solution for this controller, similar to that for the unrestricted MPC controller, can be expressed as follows:

wherein:
ΔMV (k) is the change in the MPC controller output at a time k;
K _{ompc is} the optimized MPC controller _gain ;
S ^u is the process dynamics matrix made up of the step responses of the dimension p × c for a SISO model and p * n × c * m for a multi-input / multi-output MIMO model with m inputs and n controlled outputs.

For an optimized MPC, the dynamics matrix is extended to the size (p + 1) × m for the SISO model and (p + m) * n × c * for the MIMO model to accommodate the MV error. E _{p + 1} (k) is the CV error vector over the forecast horizon and the error of the sum of the controller output movements over the control horizon related to the optimal target change of MV. The matrix T summarizes the matrix Γ ^y and Γ ⁰ and is a square matrix of the dimension (p + 1) for a SISO controller and [n (p + m)] for the multivariable controller. The superscript T denotes a transposed matrix.

It was determined that, since the optimizer 54 based on all control and auxiliary quantities CV and AV optimized to determine a target set of manipulated variables MV _T , which define a single optimal operation point, it does not matter that the MPC controller 52 only using a subset of the control and auxiliary variables CV and AV in its control matrix to actually produce the manipulated variables output therefrom, because if the controller 52 the selected subset of control and auxiliary quantities CV and AV will travel to their assigned destinations, the others of the complete set of control and auxiliary quantities will also be at their target values. As a result, it was found that a quadratic (M by M) MPC controller with an M by M control matrix can be used with an optimizer using a rectangular (N by M) process model to perform process optimization. This allows standard MPC control methods to be used with standard optimization techniques without having to invert a non-square matrix with the concomitant approximations and hazards associated with such controller conversion techniques.

In one embodiment, when the MPC controller is squared, ie, the number of manipulated variables MV is equal to the number of control variables CV, the target manipulated variable MV can be effectively achieved by changing the CV values as follows: ΔCV = A * ΔMVT ΔMVT - optimal target change of MV
ΔCV - CV change to obtain optimal MV. CV modification is implemented using CV setpoints.

In operation, the optimizer provides 54 Steady state targets for the unrestricted MPC controller at each scan and update them. Thus, the MPC controller performs 52 the unrestricted algorithm. Since the targets CV _T and AV _{T are determined} in a way that considers constraints as long as a feasible solution exists, the controller operates within constraints. Therefore, optimization is an integral part of the MPC controller.

The 3 and 4 set a flowchart 90 which shows the steps taken to perform integrated model prediction control and optimization. The flowchart 90 is generally in two sections 90a ( 3 ) and 90b ( 4 ), which show functions that appear before the process flow ( 90a ), and functions that appear during the process ( 90b ), eg during each sampling of the process sequence. Prior to the process flow, an operator or technician takes a number of steps to complete the advanced control block 38 including an integrated MPC controller and optimizer. In particular, at a block 92 an advanced control template may be selected for use as advanced control block 38. The template may be in a library in a configuration application in the UI 13 can be stored or copied from it, and the general mathematical and logical functions of the MPC controller program 52 and the optimizer 54 without the special MPC, process models and steady state boost or control matrices and special target function. This advanced control template can be stored in a module with other blocks, such as input and output blocks, that are configured to interface with devices within the process 50 , as well as other types of functional blocks such as control blocks, including PID, neural network and fuzzy control blocks. It will be appreciated that in one embodiment, the blocks within a module are each objects in an object-oriented programming paradigm whose in and outputs are interconnected to accomplish communication between the blocks. During the process, the processor that processes the module performs each of the blocks in turn at a different time, and uses the inputs to the blocks to produce outputs from the blocks, which are then input to other blocks as determined by the blocks Communication links defined between the blocks are provided.

At a block 94 The operator specifies the special manipulated variables, control variables, boundary conditions and disturbance variables that are displayed in the block 38 should be used. If desired, the user can in a configuration program such as the program 40 from 1 , sift through the control template, select inputs and outputs to be named and configured, browse any standard browsers in the configuration environment to find the current inputs and outputs within the control system, and select these current control values as control template input and output controls. 5 Fig. 11 is a screen generated by a configuration program showing a DEB_MPC control module with several interconnected functional blocks including multiple analog input (AI) and analog output (AO) function blocks, multiple PID control function blocks, and one MPC PRO function block. which is an advanced function block. The tree structure on the left side of the display of 5 represents the function blocks within the DEB_MPC module, for example the block 1 , C4_AI, C4_DGEN, etc.

As will be appreciated, the user may specify the inputs to and outputs from the MPC PRO function block by drawing lines between these inputs and outputs and the inputs and outputs of other function blocks. Alternatively, the user can select the MPC-PRO block to gain access to the properties of the MPC-PRO block. A dialog like the one from 6 , can be displayed to allow a user to view the properties of the MPC-PRO block. As in 6 For each of the control, setting, disturbing, and constraining quantities (auxiliary quantities), another table may be provided to order these quantities, which is particularly necessary when numerous quantities such as 20 or more of each, the advanced one control / control block 38 assigned. Within the table for a particular type of size, a description, a low and high limit (boundary conditions), and a path name may be provided. In addition, the user or operator may determine what the block should do in the event of a missed condition, such as taking no action, using the simulated value of size rather than the measured value, or allowing manual entry. In addition, the operator may also decide whether to minimize or maximize this size for optimization, and may specify the priority or weight and the utility values associated with that size. These fields must be completed if no default target function is used. Of course, the user can add, move, modify or delete data or sizes using the appropriate buttons on the right side of the dialog box.

The user can set or change the data of one or more of the sizes by using the Size selects. In this case, the user can see a dialog box like that of 7 for the REFLUX command value (REFLUX FLOW). The user can change the data in their various fields and set data such as the pathname of the size (ie their input and output connection) by browsing. By means of the screen of 7 For example, the user may select an internal search button or an external search button to search within the module or outside the module in which the MPC-PRO block is housed. Of course, if desired, the operator or user may manually provide an address, path or label name, etc. defining the connections to and from the inputs and outputs of the advanced control block, if so desired.

After selecting the inputs and outputs to and from the advanced control function block, the user may define the setpoints associated with the control quantities, the ranges or limits associated with the control, auxiliaries and manipulated variables, and those associated with each of the control and auxiliary functions - and manipulated variables related weights. Of course, some of these data, such as constraints or ranges, may already be associated with these quantities because these quantities have been selected from or found in the configuration environment of the process control system. If desired, the operator at a block 96 from 3 Configure the one or more functions to be used in the optimizer by specifying the unit cost and / or unit benefit for the manipulated variables, control quantities, and auxiliary quantities, respectively. Of course, the operator may choose to use the default target function as previously described. 8th FIG. 10 is a screen display provided by a configuration program that allows a user or operator to select one of a set of targeting functions to produce an advanced control block. It is understood that the user can see the screen like the one in 8th is intended to use for selecting a set of previously stored target functions, here as the default target function and target functions 2-5 shown.

After the inputs (control, auxiliary and disturbance variables) are named and linked to the advanced control template, and the weights, limits and setpoints in a block 98 from 3 have been associated with, the advanced control template is downloaded to a selected controller within the process as a functional block to be used for control. The general nature of this control block and the manner of configuring this control block is described in U.S. Patent No. 6,445,963 entitled "Integrated Advanced Control Blocks in Process Control Systems", assigned to the assignee of While this patent describes the manner of making an MPC controller in a process control system and does not describe the manner in which an optimizer can be connected to that controller, it will be understood that the present application is hereby incorporated by reference the general steps taken to connect and configure the controller for the control block described here 38 can be used, the template all of the logic elements discussed here for the control block 38 and not just those described in this patent.

Anyway, the operator at a block 100 After the advanced control template has been downloaded to the controller, select to run a trial phase of the control template to create the step response matrix and the process model to be used in the MPC controller algorithm. As described in the above referenced patent, during the test phase, the control logic is in the advanced control block 38 provide the process with a series of pseudorandom waveforms as manipulated variables and observe the changes in the control and auxiliary quantities (which are essentially treated as control variables by the MPC controller). If desired, the control and disturbance variables as well as the control and auxiliary variables can be obtained from the data collection 12 from 1 be collected, and the operator can configure the configuration 40 ( 1 ) to build this data from the data collection 12 retrieve and somehow trend-examine these data to obtain or determine the step response matrix, each step response identifying the timing of one of the control or auxiliary quantities to a unit change at one (and only one) of the control and manipulated variables. This unit change is generally a step change, but could be another type of change, such as a pulse or slope change. On the other hand, the control block 38 if desired, generate the step response matrix in response to the data detected when the pseudo-random waveforms on the process 50 be applied, and then provides these operator interface waveforms 13 which is used by the operator or user to the advanced control block 38 manufacture and install.

9 FIG. 12 illustrates a screen display that may be provided by the test program to provide the operator with graphical representations of the data collected and trend-examined to enable the operator to guide the creation of the step response curves, and thus the process model or control matrix to be used in the MPC controller of the advanced control block. In particular, provides a graphics rendering area 101 the data for multiple inputs or outputs or other data (as previously determined by the operator) in response to the test waveforms Bar chart area 102 provides a bar graph for each of the size data examined for its trend, representing, for each of the quantities examined for its trend, the name of the size, the current value of the size in bar graph form, optionally a setpoint (indicated by a larger triangle above the bar chart) and possibly boundaries (indicated by smaller triangles above the bar chart). Other areas of the display represent other information about the advanced control block, such as the target and current mode of operation of the block ( 104 ) and the configured time to steady state ( 106 ).

Before creating a process model for the advanced control block, the operator can graphically specify the data resulting from the trend-indicating graphs 101 should be used. In particular, the operator can start and end points 108 and 110 the graphic representation 102 as the data to be used to make the step response curve. The data in this area may be hatched in another color, such as green, to visually display the selected data. Likewise, the operator may specify areas within the hatched area that are to be excluded (because they are not representative, an effect of noise or an unwanted disturbance, etc.). This area is between the lines 112 and 114 and, for example, may be hatched in red to indicate that these data should not be included in the creation of the step responses. Of course, the user could include or exclude any desired data and can perform these functions for each of several graphical trend representations ( 9 illustrates that there are eight graphical trend plots available in this case), where the different graphical trend plots are associated, for example, with different control, pilot, auxiliary, and so on.

To make a set of step responses, the operator can see on the screen of 9 the model making button 116 and the manufacturing program uses the data selected from the graphical trend plots to produce a set of step responses, each step response indicating the response of one of the control or auxiliary quantities to one of the manipulated variables. This manufacturing process is well known and will not be described in more detail here.

Again with respect to 3 After the step response matrix (or pulse, slope response matrix, etc.) has been established, in the case where the control and auxiliary quantities outnumber the manipulated variables, the step response matrix (or pulse, slope response matrix, etc.) is used to select the subset of control and auxiliary variables used in the MPC algorithm as the M by M process model or the control matrix inverted and in the MPC controller 52 should be used. This selection process can be done manually by the operator or automatically by a program, for example in the user interface 13 which has access to the step response matrix. Generally speaking, a single one of the control and auxiliary quantities is identified as being most closely related to a single one of the manipulated variables. Thus, a single and unique (ie other) of the control or auxiliary variables (which are inputs to the process controller) is associated with each of the other manipulated variables (which are outputs from the process controller) (eg, paired together), so that the MPC algorithm can rely on a process model made from a set of M-by-M step responses.

In one embodiment, which is a heuristic approach used by providing pairing, the automatic program selects or the operator the set of M control and auxiliary quantities (at M is equal to the number of manipulated variables) in an attempt, to select the single control or auxiliary size that a certain combination from greatest reinforcement and fastest response time to a unit change for a single having the manipulated variables, and from these two sizes a pair to build. Naturally can in some cases a single control or auxiliary size a large gain and fast response time to have several manipulated variables. Here, the control or auxiliary variable with any of the associated control variables to a Pair can be summarized and can also with a manipulated variable a pair form, which is not the biggest reinforcement and Response time produces, because in the group the manipulated variable, the the lower gain or slower response time, no other control or Auxiliary size in one influence the acceptable level can. Thus, the pairs of manipulated variables on the one hand and the control or auxiliary variables on the other all in all so selected that the manipulated variables with the subset of control and auxiliary quantities that represent the control variables, which are the most responsive to the manipulated variables, be combined into pairs.

The automatic program or the Operator can also try control and auxiliary variables CV and Include AV that is uncorrelated, highly correlated, minimal correlated, etc. are. In addition it does not matter, if not all of the control variables as one of the subset of M control and auxiliary variables are selected and therefore the MPC controller does not receive all of the control variables as inputs to it because of the Set of target tax and auxiliary sizes of Optimizer selected is to represent an operation point of the process in which the unselected Control variables (such as also the unselected Auxiliary quantities) themselves at their target value or within their intended operating range are located.

Since there are tens and even hundreds of tax and auxiliary sizes on the one hand and dozens or hun On the other hand, given the manipulated variables on the other hand, it may of course be difficult, at least from the point of view of visualization, to select the set of control and auxiliary variables which have the best response to the various manipulated variables. To tackle this problem can be the generator program 40 for the advanced control block in the server work surface 13 comprise a set of on-screen displays or to present to the user or operator to allow the operator to make an appropriate selection of the control and auxiliary sizes, as a subset of those in the MPC controller 52 to be used during operation control and auxiliary variables.

In this way, at an in 3 block 120, the operator is presented with a screen in which the operator can view the response of each of the control and auxiliary quantities to a specific or selected one of the control variables. Such a screen is in 10 representing the response of each of a plurality of control and auxiliary quantities (referred to as constraints) to a manipulated variable called TOP_DRAW. The operator can scroll through the manipulated variables one by one and look at the step responses of each of the control and auxiliary quantities to each of the different manipulated variables, and during the process select the one control or auxiliary variable that best responds to that manipulated variable. Typically, the operator will attempt to select the control or auxiliary size that has the best combination of highest steady state gain and fastest response time to the manipulated variable. As in 11 1, one of the control and auxiliary quantities can be selected using the dialog box as the most significant for that manipulated variable. If desired, as in 11 4, highlighting the size selected from the control and auxiliary sizes in another color, such as red (while highlighting the previously selected, ie, control and auxiliary sizes selected for other manipulated variables, in a different color, such as yellow can). In this embodiment, the control program 40 , which of course stores the previously selected control and auxiliary quantities in a memory, perform a check to ensure that the operator does not select the same control or auxiliary size as the variable associated with two different control variables. If the user or operator selects a control or auxiliary size that has already been selected for another manipulated variable, the program can 40 provide the user or operator with an error message to inform the user or operator of the selection of a previously selected control or auxiliary size. In this way the program prevents 40 the selection of the same control or auxiliary variable for two or more different control variables.

As in 12 is shown, the operator or user may choose to view the various step responses for each of the different set and disturbances. 12 Represents the step responses of the TOP_END_-POINT to each of the manipulated and disturbing variables previously set for the advanced control block to be created. Of course, the operator the screen of 12 Use to select one of the manipulated variables that is associated with the control variable TOP_END_POINT.

The referring to the 10 - 12 The selection procedure described is based on graphical displays in which a control or auxiliary variable can be selected from a display as the most significant. Additionally or alternatively, the data may be presented to the operator in a different manner, such as in tabular form, to assist in completing an M-by-M controller configuration. An example of a screen display that helps complete an M-by-M controller configuration and provides the data in tabular form is shown in FIG 13 shown. In this example, the sizes available to the control matrix (control CV, auxiliary AV and manipulated variables MV), the condition numbers of the matrix configuration, etc. are provided in tabular form. In a section 204 the screen 200 are indications of control and auxiliary quantities that are not yet part of a control matrix configuration, along with response parameters associated with the control and auxiliary quantities (eg, gain, dead time, priority, time constant, etc.) listed for each of the process manipulated variables. In a section 208 an indication of the current configuration of the MPC controller is displayed. A column 212 provides information about available manipulated variables, and a column 216 provides information about output quantities (eg, control or auxiliary quantities) currently included in the MPC control matrix.

The display example 200 shows a square controller with the steepnesses MV TOP_DRAW, SIDE_DRAW and BOT_REFLUX and controller inputs including the control and auxiliary quantities BOT_TEMP, SIDE_END_POINT, TOP_END_POINT. If a user selects one of the manipulated variables using an input device such as a mouse, a trackball, a touchscreen, etc., the selected manipulated variable can be highlighted. For example, in the example screen 200 the manipulated variable TOP_DRAW is highlighted. Additionally, response parameters are available with the ones available in the section 204 The displayed control and auxiliary variables are related and correspond to the selected manipulated variable. For example, in the example screen 200 reinforcements 220 and dead times 224 displayed, which correspond to the manipulated variable TOP_DRAW and the available control and auxiliary variables 228 assigned.

• – Prozessmatrix: eine vollständige N-mal-M-Prozessmatrix mit Steuerund Randbedingungsgrößen entlang einer ersten Achse und den Stellgrößen entlang einer zweiten Achse;
• – Aktuelle Konfiguration: die M-mal-M-Konfiguration, die aktuell vom Bediener ausgewählt wurde und in der Tabelle „MPC Controller Input-Otput Configuration" angezeigt wird;
• – Automatische Konfiguration: die automatisch von einem Auswahlprogramm in der MPC-Anwendung ausgewählte M-mal-M-Konfiguration.

The ad 200 also includes an add button 232a and a delete key 232b to control and Auxiliary quantities (boundary conditions) between the sections 204 and 208 to move. The figure also shows the number of condition of matrix configurations with different gain:

• Process matrix: a complete N-by-M process matrix with control and constraint quantities along a first axis and the manipulated variables along a second axis;
• - Current configuration: the M by M configuration currently selected by the operator and displayed in the "MPC Controller Input-Otput Configuration"table;
• - Automatic configuration: the M-by-M configuration automatically selected by a selection program in the MPC application.

The ad 200 also includes a button 236 to reset to a configuration that has been determined automatically. Thus, after making changes to an automatic configuration and wanting to return to automatic configuration, an operator may press the key 236 choose.

Using the data in an ad as shown in the ad 200 and knowing the process, an operator can construct a square matrix in any desired manner.

As you can see, the screen displays of the 10 - 13 allow the operator to visualize and select the subset of M control and auxiliary quantities that are used as inputs to the MPC control algorithm (Block 120 from 3 ), which is particularly useful when there are many of these sizes. Also, the set of control and constraint sizes used in block 120 be determined automatically or electronically based on certain predetermined criteria or a selection program. In one embodiment, a selection program may select the input quantities that may be determined based on any combination of response parameters (one or more gains, dead time, priority, time constant, etc.) as determined from the step responses (or pulse, slope responses, etc.) were to be used for the control boundary conditions and the manipulated variables. In another embodiment, a selection program may use some form of time series analysis of the values of the input and output parameters of the controller. For example, a cross-correlation between the control and auxiliary quantities may be used to use the most responsive control or auxiliary size as the controller input. As another example, a cross-correlation between the control and auxiliary quantities may be used to remove co-linear (ie, correlated) controller inputs from the matrix. A program may also include any heuristic set formed from model analysis or process knowledge.

In another embodiment An automatic selection program may first create a control matrix determine by an input / output matrix based on the number of conditions the matrix selected is, i. by minimizing the number of conditions to either desired Measure, and then a controller configuration is formed from the control matrix become.

In this example, for a process gain matrix A, the condition number of the matrix A ^T A can be determined to check the controllability of the matrix. A small number of conditions generally means better controllability, while a larger number of conditions mean less controllability and more control steps or larger movements during the dynamic control process. There are no strict criteria to establish an acceptable level of controllability, and therefore the number of conditions can be used as a relative comparison of various potential tax matrices and as a test for mismatched matrices. As we know, a number of conditions for a bad matrix of infinity approaches. Mathematically, poor conditionality occurs in the case of colinear process variables, ie, due to co-linear rows or columns in the control matrix. Thus, an important factor affecting the number of conditions and controllability is the cross-correlation between matrix rows and columns. Careful selection of the input / output variables in the control matrix can reduce condition problems. In practice, it should be relevant whether the number of conditions of a control matrix is in the hundreds (eg 500) or above. With such a matrix, the movements of the controller control variables can be extremely excessive.

As discussed previously, the control matrix solves the problem the dynamic control while the LP optimizer solves the problem of steady state optimization triggers, and the control matrix must be a quadratic input / output matrix, even if the MPC controller block has an unequal number of MVs and CVs (including AVs). To deal with the selection of inputs and outputs for the control matrix to begin with, which are used in the manufacture of the controller should be, all available MVs typically recorded or selected as controller outputs. After the selection of outputs (of the MVs), the process output sizes (i.e. the CVs and AVs) that have become part of the dynamic control matrix are, so selected be that a square control matrix is created, not bad condition.

A method to automatically or manually select CVs and AVs as inputs to the control matrix will now be discussed, assuming that other methods may be used as well. This embodiment enhances the robustness of the resulting controller by adopting a technique called "MV wrap arounod" (or self-controlling MVs) and automatically estimates the penalty factor on motion factors for the MVs of the MPC controller.

Step 1 - The CVs are selected until, if possible, the number of CVs is equal to the number of MVs (i.e., the number of controller outputs). If there are more CVs than MVs, the CVs in any order based on any desired Criteria such as priority, reinforcement or phase response, user input, correlation, Analysis etc. selected become. Is the entire possible Number of CVs equal to the number of MVs, proceed to step 4, around the resulting condition number of the square control matrix Acceptability. is the number of CVs less than the number of MVs, AVs can selected as described in step 2. There are no defined ones CVs, the AV with the highest reinforcement selected in relation to a MV and then proceeded to step 2.

Step 2 - It will determine the number of conditions one after the other, for every possible one AV computes the already selected control matrix by the previously selected CVs and AVs has been added. As it becomes clear, that contains the selected ones CVs set a row for each selected CV and AV, which gives the steady state gain for that CV or AV to each the previously selected MVs sets.

Step 3 - The procedure defined in step 2 AV is determined, which is the minimum condition number for the resulting Matrix yields, and the matrix becomes underneath like the previous matrix addition the selected one AV set. If the number of MVs is now equal to the number of selected CVs, plus the number of selected ones AVs (that is, the matrix is now square), then go to step 4 passed. Otherwise, return to step 2.

Step 4 - The number of conditions for the established square control matrix A _c is calculated. If desired, the condition number calculation for the matrix A _{c may be used in} place of the matrix A _c ^T A _c because the number of conditions for these different matrices are related as the square root of the others. If the number of conditions is acceptable, skip steps 5 and 6 and move to step 7.

Step 5 - A recirculation procedure is performed for each of the selected MVs and the number of conditions of the matrix calculated from each recirculation procedure is calculated. In essence, a wrap-around procedure can be performed by successively setting a unit response (Gain = 1.0, Dead Time = 0, Time Constant = 0) for each of the different MVs to the location of the eliminated AV (or CV). The unit response will be at one of the locations in the array's row and zero elsewhere. In essence, each of the individual MVs in this case is used instead of the AV (or CV) as an input and an output to form a well-conditioned quadratic control matrix. As an example, for a four by four matrix, the combinations 1000 . 0100 . 0010 and 0001 is set in the row of the eliminated AV line in the gain matrix A _c .

Step 6 - After performing a Circulation procedure for Each of the MVs is selected the combination that the minimum condition number results. If there is no improvement compared to the number of conditions which was obtained in step 4, becomes the original one Retain matrix.

Step 7 - At this point, each one will selected CV and selected AV associated with an MV by the CV or AV with the best Response (maximum gain, fast response time) with respect to a particular MV is selected, exclusively the MV used for self-control (i.e., the MV, the went through the round). It is to ensure that each one MV combined with a single CV (or AV) to a pair in case more than one MV has a high amplification and fast response with a single CV (or AV) or vice versa for the CV (AV) parameters Has. The bypassed MV is associated with itself. If the Pairing of all parameters is done is the selection process completed.

Of course, the through this Procedure formed control matrix and the resulting condition number presented to the user, and the user can be educated Adopt control matrix for use in the manufacture of the controller or reject.

It should be noted that in the automatic procedure described above, at most only one (ie, recirculating) MV was selected for self-control to improve controllability. In the manual process, the number of recirculating MVs can be arbitrary. For example, with reference to 11 the selection of the most significant ("Most Significant") CVs or AVs will be repealed 13 Can CVs or AVs using the delete key 232b be removed. In these examples, the self-selected MVs are unaffected by the absence of a corresponding output size selection in the controller configuration. For example 14 the ad 200 from 13 but in which the manipulated variables TOP_DRAW and SIDE_DRAW do not contain a corresponding control variable CV. In this way, the manipulated variables TOP_DRAW and SIDE_DRAW are circulated. The ad 200 from 13 could be on the in 14 Modified by first the pair BOT_TEMP and TOP_DRAW in the display 200 and then the delete key 232b is selected. As Next could be the pair SIDE_END_POINT and SIDE_DRAW in the ad 200 and then the delete key 232b to get voted. In the advertising 200 from 14 then the sizes BOT_TEMP and SIDE_END_POINT are placed in the column 228 set of available sizes. In addition, the condition number of the current, in 14 illustrated configuration of the number of conditions of the automatic configuration.

Also, more MVs than recirculation variables can Control be used if the number of MVs is greater as the number of total CVs plus AVs. In this way the controller still has a square control matrix at the end with each of the MVs as expenditures available. It becomes clear that that Procedure, circulations perform and used means that the number of CVs selected for the control matrix and AVs can be less than the number of controllers controlled by the controller MVs, with the difference in the number of cycles of the MVs as inputs exists in the control matrix. About that In addition, this orbital procedure can be used in a process the less CVs plus AVs as MVs has.

Of course, the above condition number becomes calculated using the steady state gains, and therefore The control matrix also essentially defines the controllability for the Steady state. The process dynamics (dead time, caster, etc.) and modeling uncertainty also have an impact on the dynamic Controllability, and this impact can be taken into account be that priority of process variables (e.g. Control and auxiliary variables) is changed, what their inclusion in the control matrix due to the effects, that they have on the dynamic control can command.

It is also possible other heuristic To use methods that both the steady state and to improve dynamic controllability. Such a procedure would typically a number of heuristic criteria, possibly some that are contradictory, which are applied in multiple phases to a control matrix and thereby form an appropriate set of controller inputs select which provide some improvements to the control matrix. In one such heuristic procedures are the CVs and AVs MV based on the highest Reinforcement ratio grouped. Then it will be for each MV grouping the one process output with the fastest Dynamics and significant amplification selected. This selection process can take into account a confidence interval and the CVs in preference to the AVs (otherwise all the same are). The process model generator program then uses the parameters the while the production of MPC control from each group. As only one parameter for each MV selected is, the response matrix is square and can be inverted.

worin DT_i die Totzeit in MPC-Abtastungen für ein MVi-CVj-Paar ist, G_i die Verstärkung (dimensionslos) für das MVi-CVj-Paar ist, und die Paarbildung diejenige ist, die während der quadratischen Controllerkonfiguration aufgebaut wird. Die Quadratmatrixpaarbildung liefert deshalb PM-Werte, die dazu beitragen, die miteinander in Konflikt stehenden Controllerbedürfnisse nach Leistung und Robustheit zu erfüllen.In any case, after selecting the subset of M (or less) control and auxiliary size inputs into the MPC controller, a block is generated 124 from 3 from the particular quadratic control matrix, the process model or controller that is in the MPC control algorithm 86 from 2 should be used. As is known, this controller manufacturing step is a computationally intensive procedure. Primary tuning factors for controller manufacturing are "Penalty on Move" (PM) parameters of the controller manipulated variables An analysis has shown that dead time is an important factor in the calculation of PM, while amplification naturally has a major impact on controller movements. The following experimental formula accounts for both dead time and gain in estimating a PM factor, which provides a stable and responsive MPC flow for model errors of up to 50%:

where DT _{i is} the dead time in MPC samples for an MVi-CVj pair, G _{i is} the gain (dimensionless) for the MVi-CVj pair, and pairing is the one established during the quadratic controller configuration. Square matrix pairing therefore provides PM values that help meet the conflicting controller needs for performance and robustness.

A block 126 then loads this MPC process model (with the inherent control matrix) or the controller and, if necessary, the step responses and the steady state step response gain matrix onto the control block 38 down, and these data will be in the control block 38 integrated for the operation. At this time, the control block is 38 ready for online operation as part of the process 50 ,

If desired, the process step responses may be reconfigured or provided in some other way than these step responses were generated. For example, one of the step responses may be copied out of different models and into the screen masks, for example, the 10 - 12 to set the step responses of a particular control or auxiliary quantity to a manipulated or disturbing variable. 15 FIG. 12 illustrates a screen display where the user can select and copy one of the step responses of a particular process or model and then drag or paste the same response into another model and manually insert that step response into the new model, thereby enabling the user to specify a step response model. Of course, the user can be part of this Delete one or more of the automatically generated step response models as described above.

16 represents a screen display in which the user can in particular view one of the step responses (here for the step response TOP_END_POINT as opposed to TOP_DRAW). The parameters for this step response, such as steady state gain, response time, first order time constant, and squared errors are presented to the user or operator for convenient reference to the display. If desired, the user may view or change the properties of this step response by specifying other parameters, such as another gain or time constant, if so desired. If the user sets a different gain or other parameter, the step response model may be mathematically regenerated to include this new parameter or parameter set. This process is useful when the user knows the parameters of the step response and must change the generated step response to match or meet these parameters. Changes to the step response model are in turn reflected in the pairing and production of the square control matrix because the gain and response dynamics are used.

Well, with respect to 4 the general steps shown during each cycle of operation or each scan of the advanced control block 38 as using the flowchart 90a from 3 produced, performed during the process 50 runs online. At a block 150 receives and processes the MPC controller 52 ( 2 ) the measured values of the control and auxiliary variables CV and AV. In particular, the control prediction process model processes the CV, AV and DV measurements or inputs to produce the future control parameter vector as well as the predicted steady state control variables and auxiliary variables CV _SS and AV _SS .

Next, it processes or filters on a block 152 the input processing / filter block 58 ( 2 ) the predicted, from the MPC controller 52 formed control, auxiliary and manipulated variables CV _SS , AV _SS and MV _SS and provides the optimizer 54 these filtered values are available. At a block 154 leads the optimizer 54 standard LP techniques to provide the set of M target manipulated variables MV _T that maximize or minimize the selected or default objective function while not violating any of the limits of the auxiliaries and manipulated variables, and while the control variables are at or within their setpoint Sizes fixed areas. Generally speaking, the optimizer calculates 54 a target size solution MV _T , by forcing the control and auxiliary variables in each case. As stated above, in many cases there will be a solution where the control quantities are each at their setpoint (which is initially treated as an upper limit set to the control amount) while the auxiliary quantities each remain within their respective constraints. If that's the case, the optimizer needs to 54 only the specific target manipulated variables MV _T spend that provide an optimal result for the objective function.

In some cases, however, due to constraints that are too narrow for some or all of the auxiliaries or manipulated variables, it may be impossible to find an operation point where all the control variables are at their setpoint and all the auxiliary variables are within their respective constraints. because such a solution does not exist. In these cases, as previously noted, the optimizer 54 allow relaxation of the setpoint setpoint ranges of the control variables to attempt to find an operation point where the auxiliary variables operate within their respective limits. If there is no solution in this case, the optimizer may drop one or more of the auxiliary force constraint limits as a solution and / or may drop the control setpoint range within the solution and instead determine the optimal process operation point and the dropped auxiliary force constraints and / or ignore the dropped control value setpoint range. Here, the optimizer chooses which auxiliary or control quantity to drop based on the respective weights provided for the control and auxiliary quantities, respectively (for example, the lowest weighting or highest priority being dropped first). The optimizer 54 Continues to drop helper or control quantities based on their intended weights and priorities until it finds a target size solution (MV _T solution) for which the other higher priority control or auxiliary quantities, all setpoint ranges for the control quantities, and the Limits for the auxiliary sizes are met.

Next used in a block 156 the target conversion block 55 ( 2 ) the steady state step response gain matrix to determine the target values of the control and auxiliary quantities CV _T and AV _T from the target values for the manipulated variables MV _T , and sets the selected N (where N is less than or equal to M) subset of these values to the MPC controller 52 as destination input available. At a block 158 uses the MPC controller 52 the derived control matrix or logic to operate as an unrestricted MPC controller as previously described to determine the future CV and AV vector for those target values performs vector subtraction on the future control parameter vector to produce the future error vector. The MPC algorithm operates in a known manner to determine the steady state manipulated variable MV _SS based on the process model formed from the M by M step responses, and places these MV _SS values into the input processing / filtering block 58 ( 2 ) to disposal. At a block 160 The MPC algorithm also determines the MV steps involved in the process 50 and gives the first of these steps to the process in some appropriate way 50 out.

During the process, one or more monitoring applications are running, for example, in one of the workstations 13 from, data from the advanced control block or other associated function blocks may be either directly or indirectly via the data collection 12 one or more visual or diagnostic screens are provided to the user or operator to indicate the operating status of the advanced control block. Function block technology includes cascade inputs (CAS_IN) and cascade remote inputs (RCAS_IN), as well as corresponding backscaling outputs (BCAL_OUT and RCAS_OUT) on both the control and output function blocks. Using these links, it is possible to lay an optimized MPC supervisory control strategy over the existing control strategy, and this monitored control strategy can be presented using one or more visual screens or one or more displays. Likewise, targets for the optimized MPC controller may be changed from a strategy if so desired.

17 FIG. 10 is an exemplary screen display that may be generated by one or more visual applications that represent an optimizer dialog screen that provides the operator with data pertaining to the operation of the advanced control block during its expiration. In particular, the inputs to the process (the manipulated variables MV) and the outputs (the control and auxiliary variables CV and AV) are shown separately. For each of these sizes, the screen displays the name (descriptor) of the size, the current value as measured, optionally a setpoint, the target value as calculated by the optimizer, the units and unit values of resizing, and an indication of the actual size values The output sizes also include an indication of whether this size is one of the selected sizes used in the MPC controller, the predicted value of that size as determined by the MPC controller, and the default priority for that size. This screen allows the operator to view the current operating state of the advanced control block and the manner in which the advanced control block performs the control. In addition, the user can configure some remote remediation capability control parameters so that external applications can set throughput goals for throughput coordination.

18 is a screen display that may be generated by a diagnostic application that represents a diagnostic screen mask that may be provided to a user or operator to make a diagnosis on the advanced control block. In particular, the diagnostic screen of 18 For each, the name or descriptor is supplied with an indication (in the first column) of whether an error or warning condition exists for that quantity. Such an error or warning may be displayed graphically using, for example, a green tick or a red "x" or in any other desired manner, and a value or status is also given for each of these quantities It will be appreciated that this screen can be used to diagnose the advanced control block by providing the operator with the necessary data to Of course, the operator may be provided with other types of screens and data to enable him to view the operation of the advanced control block and the module in which it is implemented to make a diagnosis on it.

While the advanced control block is shown here as an optimizer located in the same functional block and therefore embodied in the same device as the MPC controller, it is also possible to accommodate the optimizer in a separate device. In particular, the optimizer may be in another device, such as one of the user workstations 13 be accommodated and connected with the MPC controller as related to 2 described during each execution or sampling to calculate the target manipulated variables (MV _T ) or the subset of the control and auxiliary variables (CV and AV) and to provide the MPC controller, which were calculated therefrom. Of course, a special interface, such as a known OPC interface, may be used to provide the communications interface between the controller and the functional block including the MPC controller and the workstation or other computer that has installed or is running the optimizer. As for in terms of 2 described embodiment, the optimizer and the MPC controller during each scanning cycle still need to communicate with each other to perform an integrated, optimized MPC control. In this case, however, other desired types of optimizers may be used, such as known or real-time optimizers, which may already be present in a process control environment. This feature can also be used to advantage if the optimization problem is not linear and the solution is non-linear tioning techniques.

Of course, while the advanced control block and other blocks and programs described herein have been described as being used with Fieldbus and standard 4-20 ma devices, they may also be implemented using any other process control communication protocol or programming environment and can be used with any other types of devices, function blocks or controllers. Although the advanced control blocks and associated generation and testing programs described herein are preferably implemented in software, they may also be implemented in hardware, firmware, etc., and may be executed by any other processor associated with a process control system. Thus, the program described here 40 can be implemented in a standard general-purpose CPU or custom hardware or firmware, such as ASICs, if so desired. When implemented in software, the software may be stored in any computer-readable memory such as a magnetic disk, laser disk, optical disk or other storage medium, RAM or ROM of a computer or processor, etc. Likewise, this software may be provided to a user or process control system via any known or desired handover method, such as on a computer readable floppy disk or any portable computer storage mechanism, or modulated over a communication channel such as a telephone line, the Internet, etc. (which may be considered equivalent or is considered interchangeable with the provision of such software via a portable storage medium).

While thus the present invention with reference to specific examples has been described, which illustrate the invention and not restrict should be clear to the skilled person that changes, additions or Leaks to the disclosed embodiments can be made without that deviated from the content and scope of the invention would.

LIST OF REFERENCE NUMBERS

Claims (35)

Process control configuration system for use during the new creation or sighting of a control block with an integrated optimizer and a multiple input / multiple output control routine, wherein the process control system comprises: a computer readable Medium: a configuration routine that is on the computer readable Stored medium and is designed to run on a processor wherein the configuration routine includes: a memory routine, the information stores which multiple control and auxiliary sizes and concerns several manipulated variables, which of the optimizing and / or the multiple input / multiple output control routine be used, the information provided by the multiple control and auxiliary sizes and which concern several manipulated variables, Answer information for include each of at least some of the control and auxiliary quantities, the answer information from each answer for each of indicate the at least some of the control and auxiliary variables to respective manipulated variables; and a display routine designed for a user an indication of one or more of the tax, assistance and to offer manipulated variables, wherein the display comprises a subset of the response information, wherein the subset of response information comprises answer information, the answers from each of the at least some of the control and auxiliary sizes Show at least one of the manipulated variables. Process control configuration system according to claim 1, wherein the configuration routine further includes a first routine to it to enable the user one of the manipulated variables to select several manipulated variables. Process control configuration system according to claim 2, wherein the configuration routine further includes a second routine to it to enable the user the one of the manipulated variables of a attributable to the control and auxiliary quantities. Process control configuration system according to claim 3, wherein the configuration routine further includes a third routine to do it to enable the user the assignment of one of the control and auxiliary variables, which is assigned to one of the manipulated variables was to pick up again. Process control configuration system after a the claims 1 to 4, wherein the display routine is adapted to provide information of to display several manipulated variables and for each manipulated variable one Specifying the assigned multiple control and auxiliary quantities, if available to display. Process control configuration system after a the claims 1-5, wherein the display routine is adapted to provide an indication display a current configuration condition number using a configuration, which corresponds to the currently assigned control values and control and auxiliary quantities. Process control configuration system after a the claims 1-6, wherein the display routine is adapted to provide an indication to indicate an automatic configuration condition number with an automatically generated configuration. Process control configuration system after a of the preceding claims, wherein the display routine is configured to provide an indication of a process matrix configuration condition number that is related to a configuration that a process matrix corresponds to all of the multiple control and Auxiliary sizes along a first axis of the process matrix and all of the manipulated variables along a second axis of the process matrix. Process control configuration system after a of the preceding claims, wherein the display routine is adapted to provide indications of available quantities of to display several control and auxiliary quantities, being the available ones Assign variables to manipulated variables are. Process control configuration system after a of the preceding claims, wherein the display routine is configured to provide information of response information for every the available Display sizes, the response information includes responses from each of the available sizes which display one of the manipulated variables. Process control configuration system after a of the preceding claims, wherein the answer information for each of the available sizes at least an indication of reinforcement, an indication of a dead time, an indication of a priority and a Specification of a time constant contains. Process control configuration system according to claim 11, where the specification of the gain is a Number includes. Process control configuration system according to claim 11 or 12, wherein the indication of the dead time comprises a number. Process control configuration system after a of the preceding claims, the answer information for each of the at least some of the control and auxiliary quantities at least one indication a reinforcement, an indication of a dead time, an indication of a priority and a Specification of a time constant contains. Process control configuration system after a of the preceding claims, the answer information for each of the at least some of the control and auxiliary quantities contain information which are related to a step response. The process control configuration system of any one of the preceding claims, wherein the response information for each of the at least some of the control and auxiliary quantities includes data associated with a Pulse response related. Process control configuration system after a of the preceding claims, the answer information for each of the at least some of the control and auxiliary quantities contain information which are related to a rising response. Process control system for controlling a process Including: a multiple input / multiple output controller, which is designed while each cycle of operation of the process control system multiple control outputs which are configured to base the process on measured multiple entries from the process and based on Control a set of target values assigned to the multiple-input / multi-output controller while Each operational cycle of the process control system provided become; and an optimizer designed to handle the sentence Target values for use by the multi-input / multi-output controller while to form each operation cycle of the process control system; at the optimizer is a linear or quadratic programming Including optimizer is an objective function, and the optimizer is designed to do the Goal function to minimize or maximize and at the same time one Set of tax variables within predetermined setpoint limits, a set of auxiliary variables within a set of predetermined auxiliary size limits, and a sentence of manipulated variables within to hold a set of predetermined manipulated variable limits, and, if not a solution consists of allowing at least one of the setpoint limits to be violated becomes. A process control system according to claim 18, wherein the optimizer is designed to store a set of priorities, which correspond to the set of control variables, and where the optimizer uses the priorities from the sentence, to determine the at least one of the control setpoint limits that violates shall be. Process control system according to claim 18 or 19, where the optimizer is designed to, if not, to enable that at least one of the setpoint limits and the auxiliary size limits violated becomes. Process control system according to one of claims 18 to 20, where the optimizer is designed to be a first set of priorities to store, which correspond to the set of control variables, and a second Set of priorities, which correspond to the set of auxiliary quantities, and where the optimizer has the priorities from the first sentence and the priorities used from the second sentence to the at least one of the control setpoint limits and the auxiliary size limits to be determined, who are to be injured. A process control system for controlling a process, comprising: a response matrix defining a response of each of a set of control and auxiliary quantities to a change in each of a set of manipulated variables, wherein a number of control and auxiliary quantities in the set of control and auxiliary quantities Auxiliary quantities is equal to a first number in which a number of manipulated variables in the set of manipulated variables is equal to a second number; a linear or quadratic optimizer configured to: produce a set of target setpoint values, the target setpoint values defining an optimum operating point based on a set of predictive values of control and auxiliary variables of the process and based on a set of current values of manipulated variables of the process, wherein a number of predictive values of control and auxiliary quantities in the set of predictive values of control and auxiliary quantities equals the first number, wherein a number of current values of manipulated variables in the set of current values of manipulated variables is equal to the second number; use a set of predictive control variables and auxiliary quantities, a set of predictive variable sizes, and the response matrix to produce a set of target values for a predetermined subset of a set of control and auxiliary quantities comprising a number of predictive control variables and auxiliary variables in the set of predictive control variables and -sizes is equal to the first number at which a number of control and auxiliary quantities in the predetermined subset of the set of control and auxiliary quantities is different from the first number; wherein the linear or quadratic optimizer is configured to produce the set of target setpoint values that maximize or minimize an objective function while maintaining each of the control variables at their predetermined setpoints and each of the auxiliary quantities and manipulated variables within predetermined constraints; wherein the optimizer is configured to produce the set of target setpoint values that maximize or minimize the objective function while each of the control quantities are within predetermined setpoint limits and each of the auxiliary quantities and manipulated variables are kept within constraints if there is no solution holding each of the control variables at predetermined setpoints and each of the auxiliary variables and manipulated variables within predetermined constraints; wherein the optimizer is configured to establish the set of target setpoint values that maximize or minimize the objective function while maintaining each of the auxiliary parameters within predetermined constraints and the manipulated variables within predetermined constraints while one or more of the control variables are allowed to violate predetermined setpoint limits based on priorities associated with the control quantities when there is no solution holding each of the control quantities within predetermined setpoint limits and each of the auxiliary quantities and manipulated variables within predetermined constraints; a multiple input / multi-output controller configured to: establish the set of predictive control variables and auxiliary quantities and the set of predictive variable sizes, and the set of target values for the predetermined subset of the set of pilot and auxiliary quantities with measurements of the predetermined subset of the set of control and auxiliary quantities to produce a set of drive control signals for controlling the manipulated variables of the process, wherein a number of drive control signals in the set of manipulated variables is equal to the second number. A process control system according to claim 22, wherein the optimizer is designed to set the set of target setpoint values which maximize or minimize the objective function while the Manipulated variables within predetermined compulsory limits are kept while one or more of the Control variables predetermined Setpoint limits, and the auxiliary variables predetermined Compulsory limits based on priorities, which are the control variables and assigned to the auxiliary quantities are allowed to hurt, if not a solution which consists of each of the auxiliary sizes and Manipulated variables within holds predetermined compulsory limits. Process control system according to claim 22 or 23, wherein the optimizer is configured to set the set of target size values which maximize or minimize the objective function while the manipulated variables are within predetermined compulsory limits are kept while one or more of the Control variables predetermined Setpoint limits, and the auxiliary variables predetermined Compulsory limits based on priorities, which are the control variables and assigned to the auxiliary quantities are allowed to hurt, if not a solution which consists of each of the control variables within predetermined setpoint limits and the manipulated variables within a predetermined Forbearance holds. Method for controlling a process with several Manipulated variables and many control and auxiliary sizes, the through changes influenced by the manipulated variables can be where the number of manipulated variables is numerically different from the many and auxiliary sizes differ, the method comprising: a subset of the many tax and to select auxiliary sizes that when performing the process control to be used in selecting the Subset includes selecting one of the control or auxiliary quantities that is most severe one of the manipulated variables responds; a Control matrix using the selected subset of the many the tax and auxiliary sizes and the produce several manipulated variables; one Controller from the control matrix with the selected subset of the many the tax and auxiliary sizes as Generate inputs and the multiple manipulated variables as outputs; a Perform process optimization, by selecting a process operating point to be an objective function dependent on of the several manipulated variables and to minimize the many control and auxiliary sizes or to maximize, the process operating point by a set of target values for the selected one Subset of the many control and auxiliary variables is defined; on Multi-input / multi-output control method using to perform a controller that from the control matrix was generated to a set of manipulated variable values from the target sizes for the selected subset the many tax and auxiliary sizes and Measured values of the selected Subset of the many control and auxiliary quantities; and the formed set of manipulated variable values to use for controlling the process. The method of claim 25, wherein selecting a the tax or auxiliary quantities as the strongest responsive to one of the manipulated variables selecting one of the tax or Auxiliary sizes based at least on a cross-correlation analysis. A method according to claim 25 or 26, wherein the Choose one of the control or auxiliary quantities than the the strongest responsive to one of the manipulated variables selecting one of the tax or Auxiliary sizes based includes at least a heuristic process. Method according to one of claims 25 to 27, wherein the Choose one of the control or auxiliary quantities as the strongest responsive to one of the manipulated variables selecting based on one of the control or auxiliary quantities at least on priorities includes, which are assigned to the control and auxiliary variables. Method for controlling a process that has multiple Manipulated variables and has many control and auxiliary sizes, by changes influenced by the manipulated variables can be where the number of manipulated variables is numerically different from the many and auxiliary sizes differ, the method comprising: a subset of the many tax and to select auxiliary sizes that when performing the process control should be used in which a number of tax and auxiliary quantities in the Subset is less than a number of manipulated variables at the multiple manipulated variables; a Control matrix using the selected subset of the many the tax and auxiliary sizes and the produce several manipulated variables; a controller from the control matrix with the selected subset of many of the control and auxiliary sizes as Generate inputs and the multiple manipulated variables as outputs; a Perform process optimization, by selecting a process operating point to be an objective function dependent on of the several manipulated variables and to minimize the many control and auxiliary sizes or to maximize, the process operating point by a set of target values for the selected one Subset of the many control and auxiliary variables is defined; on Multi-input / multi-output control method using to perform a controller that from the control matrix was generated to a set of manipulated variable values from the target sizes for the selected subset the many tax and auxiliary sizes and Measured values of the selected Form subset of the many control and auxiliary quantities; and the trained set of manipulated variable values to use for controlling the process. Process control element designed to as part of a process control program implemented on a processor to be used to create several control and auxiliary parameters of a Control process using many control parameters, wherein the process control element comprises: a computer readable Medium; a functional block on the computer readable Stored medium and is designed to run on the processor, around during Each control scan period is a multiple input / multiple output control of the process, the functional block comprising: a Target function, which is an optimization criterion based on the multiple Defines control and auxiliary parameters in which the objective function an optimization criterion based on a first number of Defined control and auxiliary parameters; an optimization program, which uses the objective function to during each Steuerabtastperiode a set of optimal target values for the control and auxiliary parameters in which the optimizer is a linear or contains quadratic programming routine; a control matrix, the one predetermined subset of the plurality of control and auxiliary parameters relates to the many control parameters in which a number of control and auxiliary parameters at the predetermined subset is equal to the first number, where a number of setting parameters is equal to the first number in the many setting parameters; and on Multiple Input / Multiple Output Control Program During Each Steuerabtastperiode a control signal for each of the many control parameters using the control matrix and target values for the subset which generates several control and auxiliary quantities, in which the control signals are intended to be the subset of several of the control and auxiliary parameters to those for the subset of tax and auxiliary parameters optimal Zielwer te to drive at the multi-input / multi-output control program is a model prediction control program includes. A process control element configured to be used as part of a process control program implemented on a processor to control a plurality of control and auxiliary parameters of a process using a plurality of control parameters, the process control element comprising: a computer readable medium; a functional block stored on the computer-readable medium and adapted to run on the processor to effect multiple input / multiple output control of the process during each control scan period, the functional block comprising: an objective function having an optimization criterion based on the plurality of control criteria; and auxiliary parameters defined; an optimizer that uses the objective function to establish a set of optimal target values for the control and auxiliary parameters during each control scan period; a control matrix relating a predetermined subset of the plurality of control and auxiliary parameters to the plurality of control parameters; and a multiple input / multiple output control program that generates, during each control sampling period, a control signal for each of the plurality of control parameters using the control matrix and the target values for the subset of the plurality of control and auxiliary quantities at which the control signals are determined to be the subset of the plurality to drive the control and auxiliary parameters to the optimum target values for the subset of control and auxiliary parameters; wherein the functional block includes memory for storing a set of control parameter setpoints and a set of auxiliary and setpoint parameters, and wherein the optimizer is configured to obtain the set of optimal target values for the set parameters that yield the control parameters the control parameter setpoints, the assist and control parameters are within the assist and control parameter limits, and the objective function is minimized or maximized; wherein the memory has also stored a set of control parameter setpoint limits and the optimizer is configured to establish the set of optimal target values for the adjustment parameters that maximize or minimize the objective function, while each of the control parameters within the control parameter setpoint limits and each of the auxiliary parameters and set parameters within the control parameters Auxiliary and Stellparametergrenzen is maintained if there is no solution that holds the control parameters on the control parameter setpoints and the auxiliary and control parameters within the auxiliary and Stellparametergrenzen; wherein the memory has also stored a set of priority values for the control parameters and the optimizer is configured to produce the set of target setting parameters that maximize or minimize the objective function while maintaining each of the control parameters within the control parameter setpoint limits while one or more of the control parameters violate the control parameter setpoint limits based on the priority information for the control parameters if there is no solution holding each of the control parameters within the control parameter setpoint limits and each of the auxiliary parameters and set parameters within the auxiliary and setpoint parameter limits; wherein the memory has also stored a set of priority values for the auxiliary parameters and the optimizer is adapted to establish the set of target setting parameters that maximize or minimize the objective function while at least one of the auxiliary parameters is allowed to violate the auxiliary parameter limits based on the auxiliary parameter priority information and the control parameters are allowed to violate the control parameter setpoint limits based on the priority information for the control parameters if there is no solution holding each of the control parameters within the control parameter setpoint limits and each of the auxiliary parameters and set parameters within the auxiliary and setpoint parameter limits. Method for carrying out the control of a process with a first number of control and auxiliary quantities, that of a second Number of manipulated variables controlled which method comprises: a step response matrix determining a response of each of the control and auxiliary quantities to changes in each of the manipulated variables; a Subset of the control and auxiliary quantities, the subset being the same as or a smaller number of control and auxiliary sizes as the manipulated variables, at to choose the subset selecting one of the strongest responsive to one of the manipulated variables Includes control or auxiliary quantities; a square control matrix from the responses within the response matrix for the selected To produce a subset of the control and auxiliary variables and the manipulated variables; and while every sampling of the process a reading of each of the chosen To obtain a subset of the control and auxiliary quantities; one optimal operating target value for each of the selected Calculate subset of the control and auxiliary quantities; on To run multi-input / multi-output control program, that the target values for each of the selected Subset of the control and auxiliary quantities, the measured values of the selected subset the tax and auxiliary sizes and the control matrix uses a set of control parameter signals to create; and the Stellparametersignale for controlling the Process to use. The method of claim 32, wherein selecting a the strongest responsive to one of the manipulated variables Tax or auxiliary sizes that Choose based on one of the control or auxiliary quantities at least on a cross-correlation analysis. The method of claim 32 or 33, wherein selecting one of the strongest on one of the Stell size responsive control or auxiliary quantities comprises selecting one of the control or auxiliary quantities based at least on a heuristic method. Method according to one of claims 32 to 34, wherein the Choose one of the strongest responsive to one of the manipulated variables Tax or auxiliary sizes that Choose based on one of the control or auxiliary quantities at least on priorities includes, which are assigned to the control and auxiliary variables.