JP7014330B1 - Controls, control methods, and programs - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically adjust control parameters required for model predictive control while realizing model predictive control on an edge device. SOLUTION: This is a control device for making a controlled amount of a controlled object follow a target value, and has a model parameter estimation unit for calculating an estimated value of a model parameter of a plant response function, the plant response function, and the present. A correction target deviation calculation unit that calculates a correction target deviation that corrects the target deviation based on the predicted value of the control amount after the elapse of the look-ahead length based on the change amount of the past manipulated variable, the change amount of the manipulated variable, and the current It has an operation amount calculation unit that calculates a new operation amount by adding the operation amount, and the operation amount calculation unit sequentially repeats the calculation of the new operation amount at predetermined control cycles. The calculation of the estimated value of the model parameter by the model parameter estimation unit and the calculation of the control parameter by the control parameter calculation unit are sequentially repeated at predetermined adjustment cycles. [Selection diagram] Fig. 1

Description

The present invention relates to control devices, control methods, and programs.

Control devices such as temperature control devices, PLCs (Programmable Logic Controllers) and DCSs (Distributed Control Systems), and control devices mounted on personal computers and embedded control devices are widely used in industry.

In addition, PID (Proportional-Integral-Differential) control, model prediction control, internal model control, LQG (Linear-Quadratic-Gaussian) control, etc. Various control methods such as H2 control and H∞ control are known.

Model predictive control is a method for obtaining a desired response by sequentially performing optimization calculations using a controlled state space model or a future time response model, and is widely used in the industry (for example, non-). Patent Document 1). For example, as an industrial application of a standard model predictive control that executes a numerical optimization algorithm online, an application to control of an air conditioning system or the like is known (for example, Patent Document 1).

In addition, a control device has been proposed that determines a new manipulated variable based on a corrected target deviation, which is the difference between the predicted value of the controlled variable according to the change in the past manipulated variable up to the present and the target value. (For example, Patent Document 2 and Patent Document 3). A design support device has also been proposed that displays on a graph an area in which a control logical formula showing the relationship between the current value of the target deviation and the amount of change in the manipulated variable is established (for example, Patent Document 4).

Further, as a method for identifying parameters for a linear prediction model, a sequential least squares method, that is, an RLS (Recursive Least Squares) method, a Kalman filter method, or the like is known (for example, Non-Patent Document 2 and Non-Patent Document 3). ).

Further, a technique for determining PID control parameters by a self-tuning method is known (for example, Non-Patent Document 4 and Non-Patent Document 5).

JP-A-2015-108499 International Publication No. 2016/092872 Japanese Unexamined Patent Publication No. 2020-21411 International Publication No. 2015/060149

Yang M. Matieyovsky, "Optimal Control under Model Predictive Control Constraints", Tokyo Denki University Press, 2005 Shuichi Adachi, "System Identification for Control by MATLAB", Tokyo Denki University Press, 1996 Shuichi Adachi, "Identification of Advanced Systems for Control by MATLAB", Tokyo Denki University Press, 2004 Toru Yamamoto, Kenzo Fujii, "Design of Self-Tuning Control System for Practical Use", IEEJ Journal D, Vol. 123, No. 9 (2003) Toru Yamamoto, "New Development of Self-Tuning Method, Generalized Minimum Distributed Control and PID Control", Measurement and Control, Vol. 40, No. 10 (2001)

The techniques described in Non-Patent Documents 4 and 5 are promising as techniques capable of realizing parameter tuning on an edge device (for example, a control device such as a PLC) having relatively few computational resources. There are the following issues.

(1) In PID control, since there are only three degrees of freedom, it may not be possible to match an ideal response.

(2) When using generalized minimum distributed control, it is necessary to give a reference model function that fully considers the plant time constant in advance.

(3) Since PID control uses a differential signal, it is vulnerable to noise.

(4) With PID control, it is not possible to sufficiently realize control of a plant having a long dead time or a plant having an adverse response.

On the other hand, the model predictive control has a relatively high degree of freedom in the plant response model, and can be applied to a plant having a long dead time and a plant having an inverse response, which PID control is not good at.

However, in order to determine the control parameters (for example, control gain, etc.) of the model predictive control, prior offline adjustment work (this work is also called engineering) such as model-based simulation and data analysis is required. Moreover, this engineering generally needs to be performed by a person having skills related to it using dedicated software.

In addition, since model predictive control is a control method based on a model, it is necessary to identify the model to be controlled in advance, and it is difficult to perform this model identification and the above engineering on an edge device.

One embodiment of the present invention has been made in view of the above points, and an object thereof is to automatically adjust control parameters required for model predictive control while realizing model predictive control on an edge device.

In order to achieve the above object, the control device according to the embodiment is a control device that outputs an operation amount for a control target and causes the control amount of the control target to follow a target value, and the control amount and the operation. A model parameter estimation unit that calculates an estimated value of the model parameter of the plant response function, which is a function representing the plant response model to be controlled based on the quantity and includes the model parameter, and the above. A control parameter calculation unit that calculates a control parameter including a control gain and a look-ahead length based on a plant response function, and a target deviation calculation unit that calculates a target deviation that is the difference between the target value and the current control amount. , The corrected target deviation corrected by the predicted value of the controlled amount after the elapse of the look-ahead length is calculated based on the plant response function and the amount of change in the manipulated variable in the past up to the present. The correction target deviation calculation unit, the operation change amount calculation unit that calculates the change amount of the operation amount by multiplying the correction target deviation by the control gain, the change amount of the operation amount, and the current operation amount. It has an operation amount calculation unit that calculates a new operation amount by adding the above, and the target deviation calculation unit calculates the target deviation, the correction target deviation calculation unit calculates the correction target deviation, and the above. The operation amount calculation unit sequentially repeats the calculation of the new operation amount at predetermined control cycles, the model parameter estimation unit calculates the estimated value of the model parameter, and the control parameter calculation unit calculates the control. The calculation of parameters is sequentially repeated at predetermined adjustment cycles.

While realizing model predictive control on an edge device, control parameters required for model predictive control can also be automatically adjusted.

It is a figure which shows an example of the whole structure of the control device which concerns on this embodiment. It is a figure for demonstrating an example of the operation of a plant response function. It is a flowchart for demonstrating an example of the calculation process of a plant response function. It is a flowchart for demonstrating an example of the estimation process of a model parameter. It is a figure for demonstrating an example of the operation of the look-ahead response correction part. It is a figure for demonstrating an example of operation of operation change amount calculation. It is a flowchart for demonstrating an example of the calculation process of a control parameter. It is a figure for demonstrating an example of the calculation outline of a look-ahead length. It is a flowchart for demonstrating an example of the calculation detail of a look-ahead length. It is a figure (the 1) for demonstrating an example of the operation of a limit cycle controller. It is a figure (the 2) for demonstrating an example of the operation of a limit cycle controller. It is a figure which shows an example of the hardware composition of the control device which concerns on this embodiment. It is a figure for demonstrating an Example. It is a figure (the 1) for demonstrating the model parameter estimation result in an Example. It is a figure (the 1) for demonstrating the control parameter calculation result in an Example. It is a figure (the 1) for demonstrating the control response and the operation amount in an Example. It is a figure (the 2) for demonstrating the model parameter estimation result in an Example. It is a figure (the 2) for demonstrating the control parameter calculation result in an Example. It is a figure (the 2) for demonstrating the control response and the operation amount in an Example.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In the following, the operation for making the control amount follow the target value by model prediction control when the target value is given to the plant with a long dead time or the plant having the reverse response, which is not good at PID control. A control device 10 that calculates a quantity, estimates model parameters for model update, and calculates control parameters required for model predictive control will be described. The control device 10 according to the present embodiment is, for example, an edge device (PLC, DCS, etc.) having scarce computational resources as compared with a PC (personal computer) or the like.

The control device 10 according to the present embodiment has an arbitrary target value r, a control amount y indicating the state of the controlled plant 20, and an operation amount for the controlled plant 20 based on a plant response model of the controlled plant 20. In addition to calculating u, model parameters are estimated and control parameters are calculated. Then, the control device 10 measures the control amount y of the controlled plant 20 according to the operation amount u, calculates the next operation amount u based on the target value r, the control amount y, the plant response model, and the like. , Estimate new model parameters and calculate new control parameters. As described above, the control device 10 according to the present embodiment performs online calculation of the operation amount u for making the control amount y follow the target value r, estimation of the model parameter of the plant response model, and calculation of the control parameter. It is repeatedly executed during execution (that is, during control of the controlled plant 20). That is, the control device 10 according to the present embodiment realizes model prediction control without requiring an online optimization calculation each time, and automatically adjusts the control parameters required for the model prediction control.

As a result, the control device 10 according to the present embodiment can realize high control performance by model predictive control even with relatively scarce computational resources, and can eliminate the work required for adjusting control parameters. Further, by eliminating the need for control parameter adjustment work (for example, engineering work related to model prediction control such as offline data analysis and simulation), the time and cost required for it can be reduced.

In addition, after the operation of the controlled plant 20 starts, the control parameters are automatically adjusted while estimating the model parameters that follow the seasonal changes and aging deterioration of the plant characteristics, so that the control performance is stable. Can be maintained.

Further, it is possible to realize model predictive control that does not require a pre-adjusted reference model function and can be applied to a plant (a plant having a long dead time or a plant having an inverse response) that is not good at PID control.

The controlled amount y includes, for example, the temperature of the controlled plant 20, and the target value r includes, for example, a set temperature. However, the controlled amount y and the target value r are not limited to the temperature and the set temperature, and an arbitrary controlled amount in the controlled plant 20 and a target target value of the controlled amount can be used.

<Overall configuration of control device 10>
First, the overall configuration of the control device 10 according to the present embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of the overall configuration of the control device 10 according to the present embodiment.

As shown in FIG. 1, the control device 10 according to the present embodiment includes a model parameter estimation unit 101, a measurement unit 102, a difference unit 103, an operation amount update unit 104, a control parameter calculation unit 105, and a first unit. It has a timer 106, a second timer 107, a limit cycle controller 108, and a switch 109. These are realized, for example, by a process in which one or more programs installed in the control device 10 are executed by a processor such as a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit).

The measuring unit 102 measures (observes) the controlled variable y of the controlled plant 20 for each control cycle T _c . Then, the measurement unit 102 outputs the latest value of the measured controlled variable y as the current controlled variable y ₀ . The controlled variable y of the controlled plant 20 is determined according to the manipulated variable u and the disturbance v. Examples of the disturbance v include a decrease or increase in the outside air temperature when the control amount y is a temperature. Further, as described above, the controlled target plant 20 is assumed to be a plant having a long dead time or a plant having an adverse response.

Further, the measurement unit 102 acquires (observes) the operation amount u output from the operation amount update unit 104, and outputs the latest value of the acquired operation amount _u as the operation amount current value u0.

The difference device 103 outputs the difference (deviation) between the target value r and the control amount current value y ₀ as the target deviation e ₀ . The target deviation e ₀ (t) at time t is calculated by e ₀ (t) = r (t) − y ₀ (t). In this embodiment, the target value is constant, that is, r (t) = constant.

The operation amount update unit 104 outputs the operation amount u for the controlled plant 20 for each control cycle T _c . Here, the operation amount update unit 104 includes a look-ahead response correction unit 111, an operation change amount calculation unit 112, and an adder 113.

The look-ahead response correction unit 111 has a plant response function {S _θ (t)}, a target deviation e ₀ (t), and an operation change amount time series {du, which is time series data of the change amount du of the past operation amount u. Based on (t)} and the look-ahead length T _p , the corrected target deviation e ^* (t) corrected for the target deviation e ₀ (t) is calculated.

The operation change amount calculation unit 112 calculates the operation change amount du (t) based on the correction target deviation e ^* (t) and the control gain k _I. The operation change amount calculation unit 112 calculates and outputs the operation change amount du (t) in the order of, for example, du (t-3T _c ), du (t-2T _c ), and du (tT _c ). The operation change amount du is an amount in which the operation amount u changes for each control cycle T _c .

The adder 113 calculates a new operation amount u by adding the operation amount current value u ₀ output from the measurement unit 102 and the operation change amount du output from the operation change amount calculation unit 112. Then, the adder 113 outputs this new manipulated variable u to the controlled plant 20. The new manipulated variable u is calculated by u (t) = u ₀ + du (t) = u (t−T _c ) + du (t).

The model parameter estimation unit 101 inputs the control amount current value y ₀ (t) and the operation amount current value u ₀ (t) for each adjustment cycle T _t , and the model of the plant response function {S _θ (t)}. The estimated value θ _est of the parameter is calculated and output. The plant response function {S _θ (t)} is a function including the model parameter θ, and is a plant response model of the controlled plant 20. Hereinafter, the model parameter θ set in the plant response function {S _θ (t)} is also referred to as “model parameter setting value θ”. When calculating the estimated value θ _est of the model parameter, the initial value θ ₀ of the model parameter may be input.

The control parameter calculation unit 105 inputs the plant response function {S _θ (t)} and the adjustment coefficients α and β for each adjustment cycle T _t , and sets the control gain k _I and the look-ahead length T _p as control parameters. It is calculated and output to the operation change amount calculation unit 112 and the look-ahead response correction unit 111, respectively.

The first timer 106 operates the measurement unit 102 and the operation amount update unit 104 for each control cycle T _c . The control cycle T _c is a cycle for controlling the controlled plant 20, and its value is set in advance.

The second timer 107 operates the model parameter estimation unit 101 and the control parameter calculation unit 105 every adjustment cycle T _t . The adjustment cycle T _t is a cycle for estimating model parameters and calculating control parameters, and its value is set in advance. However, it is assumed that T _t ≧ T _c . In addition, it is preferable to set T _t = nT _c as an arbitrary natural number determined in advance. By making the adjustment cycle T _t longer than the control cycle T _c , the amount of calculation of model parameters and control parameters can be reduced, and even when the time constant of the controlled plant 20 is long, the plant response model can be used. It is possible to improve the identification accuracy (that is, the identification accuracy of the model parameters).

The limit cycle controller 108 is a controller different from the manipulated variable update unit 104, and is a controller that controls the controlled plant 20 by the limit cycle method. The limit cycle controller 108 inputs the target deviation e ₀ and outputs the manipulated variable u.

The switch 109 switches which of the operation amount output from the operation amount update unit 104 and the operation amount output from the limit cycle controller 108 is output to the controlled plant 20. In the following, the operation amount output from the operation amount update unit 104 is also referred to as _ua , and the operation amount output from the limit cycle controller 108 is also referred to as _ub . At this time, the switch 109 outputs either the operation amount u _{a or the operation amount u b to} the controlled plant 20 as the operation amount u. Switching between the operation amount u _a and the operation amount u _b to be output to the controlled plant 20 as the operation amount u may be performed by, for example, a user operation, or automatically after a predetermined period has elapsed. It may be done automatically, or it may be done automatically when the model parameters are sufficiently updated. The timing at which the model parameters are sufficiently updated is, for example, the timing at which the model parameters have converged, that is, the timing at which the amount of change before and after the update when updating the model parameter set values becomes a predetermined value or less. Is. As a result, the control device 10 according to the present embodiment outputs, for example, the operation amount for the controlled plant 20 by the limit cycle controller 108 in the initial stage, updates the model parameter and calculates the control parameter, and performs the model parameter. It becomes possible to switch to model predictive control at the timing when is sufficiently updated.

The control device 10 according to the present embodiment has, for example, a display unit that sequentially displays model parameters and control parameters (control gain, look-ahead length) calculated for each adjustment cycle T _t on a display device such as a display. It may have a display control unit that sequentially displays these model parameters and control parameters on a display device of a terminal such as a PC connected by a communication network. As a result, the user or administrator of the control device 10 (hereinafter, also referred to as “user or the like”) can know the trend of the model parameter and the control parameter, and the identification condition of the plant response model and the adjustment condition of the control parameter can be obtained. Will be able to be confirmed. It also makes it possible to detect signs of sudden fluctuations in the plant response model.

<Operation of plant response function {S _θ (t)}>
Next, the operation of the plant response function {S _θ (t)} will be described with reference to FIG. FIG. 2 is a diagram for explaining an example of the operation of the plant response function {S _θ (t)}.

As shown in FIG. 2, in the plant response function {S _θ (t)}, when the model parameter setting value θ and the time t are input, the unit step response S at the time t after the lapse of time t from the initial time 0 Output _θ (t). The unit step response is a response when the operation amount u is a unit step input (that is, the output of the plant response model of the controlled plant 20).

<Calculation of plant response function {S _θ (t)}>
Next, the process of calculating the unit step response S _θ (t) of the plant response function {S _θ (t)} will be described with reference to FIG. FIG. 3 is a flowchart for explaining an example of the calculation process of the plant response function {S _θ (t)}. In FIG. 3, it is assumed that the model parameter setting value θ and the time t are input.

Here, in the present embodiment, as the calculation model of the plant response function S _θ (t), the autoregressive of the past N points is used for the controlled variable y, and the moving average of the current value and the past M points is used for the manipulated variable u. The case where the ARMA (autoregressive moving average) model is adopted will be described. Note that N and M are set in advance by, for example, a user or the like. However, this is only an example, and other models such as the ARMAX model may be adopted.

At this time, the model parameter set value θ is set.

とする。モデルパラメータ設定値θの各要素は、以下の数２に示すＡＲＭＡモデルの係数である。

And. Each element of the model parameter setting value θ is a coefficient of the ARMA model shown in Equation 2 below.

ここで、ｋはインデックスである。ただし、これは一例であって、例えば、過去Ｌ点（Ｌは予め設定された１以上の整数）の外乱ｖを考慮したモデルが用いられてもよい。

Here, k is an index. However, this is only an example, and for example, a model considering the disturbance v of the past L point (L is a preset integer of 1 or more) may be used.

Step S11: The operation amount update unit 104 initializes the time τ and the state vector φ (0), where the variable representing the time is τ and the state vector at the index k is φ (k). Further, the operation amount update unit 104 initializes the index k to k = 0.

Here, the state vector φ (k) is

と表される。

It is expressed as.

For example, when considering the disturbance v at the past L point, the state vector φ (k) has L disturbances v (k) as elements in addition to y (k) and u (k). It can be expanded to a vector.

The operation amount update unit 104 is initialized to, for example, τ = 0, and at the same time,

と初期化する。すなわち、φ（０）はｕ（ｋ）に相当する要素のみ１、他の要素は０に初期化される。これは、単位ステップ応答を模擬する際の初期値として設定していることを意味する。

And initialize. That is, φ (0) is initialized to 1 only for the element corresponding to u (k), and 0 for the other elements. This means that it is set as the initial value when simulating the unit step response.

Step S12: Next, the manipulated variable update unit 104 calculates the controlled variable predicted value y (k) based on the model parameter set value θ and the state vector φ (k). The manipulated variable update unit 104 calculates the controlled variable predicted value y (k) by, for example, y (k) = φ (k) ^Τθ . Here, Τ represents transpose.

Step S13: Next, the manipulated variable update unit 104 updates the state vector φ (k + 1) at the next index (that is, k + 1) based on the controlled variable predicted value y (k) and the state vector φ (k). do. The operation amount update unit 104 updates the state vector φ (k + 1) by, for example, as follows.

ここで、ｙ（ｋ）には上記のステップＳ１２で計算した制御量予測値を設定し、ｕ（ｋ＋１）には１を設定する。また、ｙ（ｋ－１），・・・，ｙ（ｋ－Ｎ＋１）及びｕ（ｋ），・・・，ｕ（ｋ－Ｍ＋１）には、状態ベクトルφ（ｋ）と同じ値を設定する（すなわち、状態ベクトルφ（ｋ）に含まれるｙ（ｋ－１），・・・，ｙ（ｋ－Ｎ＋１）及びｕ（ｋ），・・・，ｕ（ｋ－Ｍ＋１）をそれぞれ設定する。）。

Here, the control amount predicted value calculated in step S12 above is set in y (k), and 1 is set in u (k + 1). Further, the same values as the state vector φ (k) are set in y (k-1), ..., Y (k-N + 1) and u (k), ..., U (k-M + 1). (That is, y (k-1), ..., Y (k-N + 1) and u (k), ..., U (k-M + 1) included in the state vector φ (k) are set, respectively. ).

Step S14: Next, the manipulated variable update unit 104 updates the time τ to τ + ΔT and the index k to k + 1. Here, ΔT represents the time width of one step of the ARMA model. The time step ΔT of the plant response function can be changed according to the adjustment cycle T _t and the control cycle T _c . For example, it is assumed that T _t = 4 [sec] and T _c = 1 [sec]. At this time, since the model parameters are updated by the ARMA model (or ARMAX model) based on the sampling of 4 [sec] cycles, the calculation is performed with 4 times the sampling when viewed in the control cycle 1 [set]. .. Therefore, for example, when it is desired to predict t = 40 [sec] ahead in the control cycle unit, the time step ΔT may be set to 4 because the adjustment cycle corresponds to the calculation for 10 samples. In this way, when expressed as T _t = nT _c , ΔT = n may be set. As a result, the control amount predicted value is calculated by performing time correction for n times.

Step S15: Next, the operation amount update unit 104 determines whether or not τ ≧ t. Then, when it is not determined that τ ≧ t (NO in step S15), the manipulated variable update unit 104 returns to step S12. As a result, steps S12 to S14 are repeatedly executed until τ ≧ t.

On the other hand, when it is determined that τ ≧ t (YES in step S15), the operation amount update unit 104 ends the process. As a result, the finally calculated y (k) is obtained as the unit step response S _θ (t) (that is, S _θ (t) = y (k) is the step response function {S _θ (t)}. Calculated as a unit step response at time t).

<Estimation of model parameter θ>
Next, a process of estimating the model parameter θ of the plant response function {S _θ (t)} (that is, a process of calculating the model parameter estimated value θ _est ) will be described with reference to FIG. FIG. 4 is a flowchart for explaining an example of the estimation process of the model parameter θ. Note that FIG. 4 describes a case where the model parameter estimated value θ _est at a certain index k is calculated assuming that the control amount current value y ₀ and the operation amount current value u ₀ are input. Note that this index k is a value independent of the index k used in the calculation of the plant response function in FIG. 3, and represents a run-time index of the model parameter estimation process.

Step S21: The model parameter estimation unit 101 determines whether or not to initialize the model parameter estimated value θ _est (k) and the covariance matrix P (k). Here, as a case where it is determined to be initialized, for example, when the model parameter estimated value θ _est is calculated for the first time (that is, when the model parameter estimated value θ _est is calculated for the first time), an initialization instruction is given by the user or the like. For example.

When it is determined to initialize the model parameter estimated value θ _est (k) and the covariance matrix P (k) (YES in step S21), the model parameter estimation unit 101 proceeds to step S22. On the other hand, if it is not determined to initialize the model parameter estimated value θ _est (k) and the covariance matrix P (k) (NO in step S21), the model parameter estimation unit 101 proceeds to step S23.

Step S22: When it is determined in step S21 above that the model parameter estimated value θ _est (k) and the covariance matrix P (k) are initialized, the model parameter estimation unit 101 determines that the model parameter estimated value θ _est (0) = θ ₀ . And P (0) = I, and further, the control amount is initialized to the previous value y _-1 ← y ₀ , and is initialized to k ← 0. Here, as θ ₀ , the model parameter setting value θ already set in the plant response function {S _θ (t)} may be used, or the initial value assumed in advance by the user or the like may be used. However, a fixed initial value such as a vector in which all elements are 0 may be used. Further, I may be an identity matrix or an arbitrary predetermined matrix.

Step S23: The model parameter estimation unit 101 updates the state vector φ (k) based on the state vector φ (k-1), the operation amount current value u ₀ , and the control amount previous value y _-1 . That is, the model parameter estimation unit 101 updates the state vector φ (k) as follows.

ここで、ｙ（ｋ－１）にはｙ_－１を設定し、ｕ（ｋ）にはｕ_０を設定する。また、ｙ（ｋ－２），・・・，ｙ（ｋ－Ｎ）及びｕ（ｋ－１），・・・，ｕ（ｋ－Ｍ）には、状態ベクトルφ（ｋ－１）と同じ値を設定する（すなわち、状態ベクトルφ（ｋ－１）に含まれるｙ（ｋ－２），・・・，ｙ（ｋ－Ｎ）及びｕ（ｋ－１），・・・，ｕ（ｋ－Ｍ）をそれぞれ設定する。）。更に、モデルパラメータ推定部１０１は、制御量前回値ｙ_－１←ｙ_０と更新する。

Here, y (k-1) is set to y _-1 , and u (k) is set to u ₀ . Further, y (k-2), ..., y (k-N) and u (k-1), ..., U (k-M) are the same as the state vector φ (k-1). Set the value (that is, y (k-2), ..., y (k-N) and u (k-1), ..., U (k) included in the state vector φ (k-1). -M) is set respectively.). Further, the model parameter estimation unit 101 updates the control amount previous value y _-1 ← y ₀ .

For example, when considering the disturbance v at the past L point, as described above, the state vector φ (k) is added to y (k) and u (k), and L disturbances v (k) are added. It may be expanded to a state vector having the above as an element, and the covariance matrix and the model parameters may be estimated for the disturbance.

Step S24: Next, the model parameter estimation unit 101 makes a prediction error ε (k) based on the model parameter estimation value θ _est (k-1), the state vector φ (k), and the control amount current value y ₀ . calculate. The model parameter estimated value θ _est (k-1) is an estimated value of the model parameter θ estimated last time (that is, at the time of k-1).

The model parameter estimation unit 101 calculates the prediction error ε (k) by, for example, as follows.

y (k) = y ₀
ε (k) = y (k) -φ (k) ^Τ θ _est (k-1)
Step S25: Next, the model parameter estimation unit 101 updates the covariance matrix P (k). The model parameter estimation unit 101 updates the covariance matrix P (k) as follows, for example.

ここで、Ｐ（ｋ－１）は前回（つまり、ｋ－１のとき）得られた共分散行列である。また、λは０＜λ≦１を取る忘却係数であり、予め設定された値である。忘却係数λは過去データを忘却するための係数であり、０に近いほど急激に過去データの影響が減少し、１に近いほど過去データの影響が保持される。

Here, P (k-1) is the covariance matrix obtained last time (that is, at the time of k-1). Further, λ is a forgetting coefficient that takes 0 <λ ≦ 1, and is a preset value. The forgetting coefficient λ is a coefficient for forgetting past data, and the closer it is to 0, the sharper the influence of the past data decreases, and the closer it is to 1, the more the influence of the past data is retained.

Step S26: Next, the model parameter estimation unit 101 determines whether or not to update the model parameter estimated value θ _est . Here, as a case where it is determined to update the model parameter estimated value θ _est , for example, when the model parameter estimated value θ _est is not updated until a predetermined period has elapsed from the time of initialization in step S22 above. , When an update instruction is given by a user or the like.

When it is determined that the model parameter estimated value θ _est is to be updated (YES in step S26), the model parameter estimation unit 101 proceeds to step S27. On the other hand, if it is not determined to update the model parameter estimated value θ _est (NO in step S26), the model parameter estimation unit 101 ends without executing step S27.

Step S27: When it is determined in step S26 above that the model parameter estimated value θ _est is to be updated, the model parameter estimation unit 101 updates the model parameter estimated value θ _est (k). The model parameter estimation unit 101 updates the model parameter estimation value θ _est (k) as follows, for example.

ステップＳ２８：そして、モデルパラメータ推定部１０１は、モデルパラメータ推定処理の実行時インデックスｋをｋ＋１に更新する。

Step S28: Then, the model parameter estimation unit 101 updates the run-time index k of the model parameter estimation process to k + 1.

<Operation of look-ahead response correction unit 111>
Next, the operation of the look-ahead response correction unit 111 will be described with reference to FIG. FIG. 5 is a diagram for explaining an example of the operation of the look-ahead response correction unit 111.

As shown in FIG. 5, the look-ahead response correction unit 111 is input with the plant response function {S _θ (t)}, the target deviation e ₀ (t), and the operation change amount time series {du (t)}. Then, the value predicted that the control amount changes after the lapse of Tp from the current time t due to the past operation change amount _{is calculated as the look-ahead response correction value y n (} t). The look-ahead length _Tp is calculated by the control parameter calculation unit 105.

Then, the look-ahead response correction unit 111 outputs a correction target deviation e ^* (t) obtained by correcting the target deviation e ₀ (t) with the look-ahead response correction value y _n (t). Here, the correction target deviation e ^* (t) is calculated by e ^* (t) = r (t)-(y ₀ (t) + y _n (t)) = e ₀ (t) -y _n (t). Will be done. The look-ahead response correction value y _n (t) can be calculated, for example, by the method described in Patent Document 2 or Patent Document 3 described above.

<Operation of operation change amount calculation unit 112>
Next, the operation of the operation change amount calculation unit 112 will be described with reference to FIG. FIG. 6 is a diagram for explaining an example of the operation of the operation change amount calculation unit 112.

As shown in FIG. 6, when the correction target deviation e ^* (t) is input, the operation change amount calculation unit 112 is calculated by the control parameter calculation unit 105 with respect to the correction target deviation e ^* (t). The operation change amount du (t) is output by multiplying the control gain k _I. That is, the operation change amount du (t) is calculated by du (t) ₌ kI × e ^* (t).

However, when the result of multiplying the correction target deviation e ^* (t) by the control gain k _I exceeds the upper limit value du _max , the operation change amount calculation unit 112 sets du _max as the operation change amount du (t). .. Similarly, when the result of multiplying the correction target deviation e ^* (t) by the control gain k _I is less than the lower limit value du _min , the operation change amount calculation unit 112 sets du _min as the operation change amount du (t). do. This makes it possible to provide a limiter for the upper and lower limit ranges. Note that du _max and du _min are set each time so that the manipulated variable u _a after the current manipulated variable u ₀ and the manipulated variable du are added by the adder 113 does not deviate from the predetermined upper and lower limit ranges. May be good.

<Calculation of control parameters>
Next, the process of calculating the control gain k _I and the look-ahead length T _p as control parameters will be described with reference to FIG. 7. FIG. 7 is a flowchart for explaining an example of the calculation process of the control parameter. In FIG. 7, it is assumed that the plant response function {S _θ (t)} and the adjustment coefficients α and β are input.

Step S31: The control parameter calculation unit 105 calculates the look-ahead length T _p based on the plant response function {S _θ (t)} and the adjustment coefficient β. The control parameter calculation unit 105 uses the plant response function {S _θ (t)} to adjust the adjustment coefficient β with respect to the plant response function value S _θ (T _max ) at the final time T _max preset as a sufficiently long value. The time point at which the plant response function value becomes equal to the value multiplied by is defined as the look-ahead length _Tp time point. That is, the control parameter calculation unit 105 calculates the look-ahead length T _p by the following.

Find T _p , where S _θ (T _p ) = β × S _θ (T _max )
Here, β is preferably 0 <β ≦ 1. If the response of the closed loop is slow, the speed of the response can be adjusted by setting the adjustment coefficient β to a smaller value.

Further, the control parameter calculation unit 105 calculates the gain g _p at the time of the look-ahead length T _p by the following.

g _p = S _θ (T _p )
In this way, the control gain g _p is calculated based on the look-ahead length T _p . This makes it possible to respond faster, for example, even when the dead time and time constant are long.

It should be noted that T _max may be a value sufficient for the plant response function to converge, or may be a value determined from a memory limitation or the like. Alternatively, the maximum value of the period that can be predicted by the look-ahead response correction unit 111 may be T _max .

Step S32: Then, the control parameter calculation unit 105 calculates the control gain k _I based on the gain _gp at the look-ahead length T _p and the adjustment coefficient α. The control parameter calculation unit 105 calculates the control gain k _I as follows.

すなわち、先読み長Ｔ_ｐ時点でのゲインｇ_ｐに非負の微小値εを加えた値の逆数に対して調整係数αを乗じた値を制御ゲインｋ_Ｉとする。ここで、αは、０＜α≦１であることが好ましい。また、εはゲインが大きくなりすぎるのを回避するための微小な非負の値である。なお、閉ループの応答でのオーバーシュートが多い場合には、調整係数αをより小さい値とすることでオーバーシュートの多さを調整することができる。

That is, the value obtained by multiplying the reciprocal of the value obtained by adding the non-negative minute value ε to the gain g _p at the time of the look-ahead length T _p by the adjustment coefficient α is defined as the control gain k _I. Here, α is preferably 0 <α ≦ 1. Also, ε is a small non-negative value to avoid the gain becoming too large. When there are many overshoots in the response of the closed loop, the number of overshoots can be adjusted by setting the adjustment coefficient α to a smaller value.

<Summary of calculation of look-ahead length _Tp >
Next, the outline of the calculation of the look-ahead length _Tp in step S31 will be described with reference to FIG. FIG. 8 is a diagram for explaining an example of the calculation outline of the look-ahead length _Tp .

As shown in FIG. 8, the value of the plant response function S _θ (t) gradually changes from time 0 to T _max , but at a certain time T _p , S _θ (T _p ) = β × S _θ ( T _max ) holds. Therefore, such time _Tp is set as the look-ahead length. As a result, the behavior of the plant at the time of the look-ahead length _Tp is linked to the behavior of the final plant, so that it is possible to suppress unnecessary changes in the amount of operation, and it is possible to suppress the occurrence of overshoot and undershoot. can. In addition, it is possible to determine the look-ahead length that avoids the dead time and the initial period of the response in which the adverse response occurs.

In PID control known as a prior art, it is necessary to increase the differential gain according to the amount of wasted time, and there is a drawback that it is vulnerable to noise. For example, in the control rule by the step response method of Ziegler-Nichols, k _p = 1.2 / (RL), TI = 2L, TD = _0.5L , and _R = _{gp / T p are} used. Since the differential time _TD is long in proportion to the waste time L and the differential intensity is large, the control becomes vulnerable to noise and impulse-like disturbance as a result. On the other hand, in the present embodiment, since the differential signal of the observed value is not used, robust control with respect to noise and wasted time can be realized.

<Details of calculation of look-ahead length _Tp >
Next, the calculation details of the look-ahead length _Tp in step S31 will be described with reference to FIG. FIG. 9 is a flowchart for explaining an example of the calculation details of the look-ahead length _Tp .

Step S41: First, the control parameter calculation unit 105 sets T ₁ as a variable for searching the look-ahead length T _p , and sets T ₁ ← 0.

Step S42: Next, the control parameter calculation unit 105 determines whether or not T ₁ ≧ T _max .

When it is determined that T ₁ ≧ T _max (YES in step S42), the control parameter calculation unit 105 proceeds to step S46. On the other hand, if it is not determined that T ₁ ≧ T _max (NO in step S42), the control parameter calculation unit 105 proceeds to step S43.

Step S43: The control parameter calculation unit 105 determines whether or not S _θ (T ₁ ) ≧ β × S _θ (T _max ) is satisfied.

When it is determined that S _θ (T ₁ ) ≧ β × S _θ (T _max ) is satisfied (YES in step S43), the control parameter calculation unit 105 proceeds to step S45. On the other hand, if it is not determined that S _θ (T ₁ ) ≧ β × S _θ (T _max ) is satisfied (NO in step S43), the control parameter calculation unit 105 proceeds to step S44.

Step S44: The control parameter calculation unit 105 updates T ₁ ← T ₁ + ΔT, and returns to step S42. As a result, step S44 is repeatedly executed until either T ₁ ≧ T _max or S _θ (T ₁ ) ≧ β × S _θ (T _max ) is satisfied.

Step S45: When it is determined that S _θ (T ₁ ) ≧ β × S _θ (T _max ) is satisfied, the control parameter calculation unit 105 sets T _p ← T ₁ . That is, the control parameter calculation unit 105 sets T ₁ satisfying S _θ (T ₁ ) ≧ β × S _θ (T _max ) as the look-ahead length T _p .

Step S46: If it is not determined that T ₁ ≧ T _max , the control parameter calculation unit 105 sets T _p ← γ × T _max . Here, 0 <γ <1. That is, when T ₁ satisfying S _θ (T ₁ ) ≧ β × S _θ (T _max ) cannot be searched, the value obtained by multiplying T _max by γ is defined as the look-ahead length T _p .

<Operation of limit cycle controller 108>
Next, the operation of the limit cycle controller 108 will be described with reference to FIGS. 10 and 11. 10 and 11 are diagrams (No. 1) for explaining an example of the operation of the limit cycle controller 108.

As shown in FIG. 10, when the target deviation e ₀ (t) is input, the limit cycle controller 108 uses a predetermined value MVH as the operation amount ub ( _t ) when e ₀ (t) ≧ 0. When e ₀ (t) <0, the predetermined value MVL is output as the operation amount ub ( _t ). In the example shown in FIG. 10, it is assumed that the controlled plant 20 operates normally (that is, the increase in the manipulated variable operates in the direction of the increase in the controlled variable), and MVH> MVL, but the controlled object. When the plant 20 operates in the reverse direction, the sign of the condition determination is inverted (that is, when e ₀ (t) ≤ 0, MVH is output as an operation amount ub ( _t ), and when e ₀ (t)> 0. Needs to output MVL as an operation amount ub ( _t )).

As shown in FIG. 11, by using the limit cycle controller 108 for closed loop control, it is possible to cause vibration (limit cycle) of a fixed cycle.

<Hardware configuration of control device 10>
Next, the hardware configuration of the control device 10 according to the present embodiment will be described with reference to FIG. FIG. 12 is a diagram showing an example of the hardware configuration of the control device 10 according to the present embodiment.

As shown in FIG. 12, the control device 10 according to the present embodiment includes an input device 201, a display device 202, an external I / F 203, a communication I / F 204, a processor 205, and a memory device 206. Each of these hardware is communicably connected by bus 207.

The input device 201 is, for example, a touch panel, various buttons, or the like. The display device 202 is, for example, a display panel or the like. The control device 10 does not have to have at least one of the input device 201 and the display device 202.

The external I / F 203 is an interface with an external device such as a recording medium 203a. Examples of the recording medium 203a include an SD memory card (Secure Digital memory card) and a USB (Universal Serial Bus) memory card.

The communication I / F 204 is an interface for connecting the control device 10 to the communication network. The processor 205 is, for example, various arithmetic units such as a CPU and an MPU. The memory device 206 is, for example, various storage devices such as SSD (Solid State Drive), RAM (Random Access Memory), ROM (Read Only Memory), and flash memory.

The hardware configuration shown in FIG. 12 is an example, and the control device 10 may have another hardware configuration. For example, the control device 10 may have various hardware other than the illustrated hardware.

<Example>
Next, an embodiment will be described. In this embodiment, a case where the controlled plant 20 is controlled by the control device 10 according to the above embodiment will be described.

FIG. 13 shows the entire control system including the control device 10 and the controlled plant 20 in the present embodiment. As shown in FIG. 13, a heater and a thermocouple are attached to a metal block, the heater is connected to the AC power supply and the DO module of the controller 10 (PLC), and the thermocouple is connected to the AI module of the controller 10. Has been done. At this time, it is assumed that the control amount is the temperature of the metal block detected from the thermocouple, and the operation amount is the duty ratio of PWM modulation by the DO module of the control device 10 (ON time / PWM cycle ratio [%]).

Further, the true plant response of the controlled plant 20 uses ΔT as a time mesh.

と表されるものとする。ここで、δ（ｔ）はノイズを表す。

It shall be expressed as. Here, δ (t) represents noise.

On the other hand, the plant response function is the following quadratic ARMA model having the model parameter θ.

よって、状態ベクトルは、

Therefore, the state vector is

となり、モデルパラメータ推定値は、

And the model parameter estimate is

となり、モデルパラメータの真値は、

And the true value of the model parameter is

となる。また、制御量推定値は、ｙ_ｅｓｔ（ｋ）＝φ（ｋ）^Τθ（ｋ－１）で計算される。

Will be. Further, the control amount estimated value is calculated by _yest (k) = φ (k) ^Τ θ (k-1).

Different values are set for the first timer 106 and the second timer 107, and the control cycle T _c is set to 5 [sec] and the adjustment cycle T _t is set to 20 [sec]. That is, the adjustment cycle T _t is four times the control cycle T _c , and the model parameter estimation unit 101 and the control parameter calculation unit 105 operate once every four control cycles. Therefore, the time step ΔT of the plant response function is 4. In this way, by suppressing the frequency of adjustment, the calculation load of the control device 10 (PLC) is reduced.

Further, the upper limit of the operation amount was 90 and the lower limit was 0. The adjustment coefficients were α = 0.9 and β = 0.3. The forgetting coefficient was λ = 0.95. The limit cycle parameters were MVH = 60 and MVL = 40.

At this time, an example of control operation when the limit cycle controller 108 is not used will be described. The model parameter estimation result when the control by the operation amount update unit 104 is started from time 0 and controlled until time 1000 is shown in FIG. 14, the control parameter calculation result is shown in FIG. 15, and the control response and the operation amount are shown in FIG. 16, respectively.

As shown in FIG. 14, the initial value of the estimated value of the model parameter θ is 0, but the value changes from 0 at the start of control, and the value is close to the final value at about time 50. As shown in FIG. 15, the control parameter changes greatly at time 0, and the control gain k _I and the look-ahead length T _p become large values, but immediately change to low values. As shown in FIG. 16, when looking at the control response, although a relatively large vibration occurs near time 0, the control amount y immediately converges to the target value r. Further, each time the target value r changes from 51 → 49 → 51 → 49 → 51, the overshoot gradually decreases, and the followability is further improved. This is because the model parameter estimation unit 101 updates the model and the control parameter calculation unit 105 calculates the control parameters, so that adjustments are automatically performed during control and the control performance is improved.

As described above, the present embodiment is related to this embodiment without using prior information for the initial values of model parameters and control parameters, or specifying a normative model (reference model function) such as generalized distributed control. The control device 10 can automatically tune the control parameters and automatically improve the control performance during the control operation while controlling the controlled plant 20 from the complete initial state.

Next, an example of control operation when the limit cycle controller 108 is used will be described. Control is performed by the limit cycle controller 108 from time 0 to time 50, then control by the operation amount update unit 104 is started from time 50, and the model parameter estimation result when controlled until time 1000 is shown in FIG. 17, control parameter calculation. The results are shown in FIG. 18, and the control response and the operation amount are shown in FIG. 19, respectively.

As shown in FIG. 17, the initial value of the estimated value of the model parameter θ is 0, but the value changes from 0 at the start of control, and the value is close to the final value at about time 50. As shown in FIG. 18, the control parameter calculation unit 105 calculates the control parameter from the time 50 when the control by the limit cycle controller 108 ends. Compared with FIG. 15, the control parameters calculated by the control parameter calculation unit 105 are relatively stable, and the model parameters estimated at the time of limit cycle control (that is, at the time of control from time 0 to time 50) are used. It can be seen that stabilization has been achieved. As shown in FIG. 19, it can be seen that the fluctuation range of the control amount y and the operation amount u near time 0 is reduced, and the influence on the plant can be relatively suppressed. Further, in the response after switching from the control by the limit cycle controller 108 to the control by the operation amount update unit 104, every time the target value r changes from 51 → 49 → 51 → 49 → 51, as in FIG. Overshoot gradually decreases, and followability is further improved.

As described above, by using the limit cycle controller 108, it is possible to stabilize the model parameters and control parameters at a relatively early stage, and it is possible to suppress the initial fluctuations. In addition, for example, when the model parameter can be estimated in advance by the preliminary examination, the value can be set as the initial value.

The present invention is not limited to the above-described embodiment disclosed specifically, and various modifications and modifications, combinations with known techniques, and the like are possible without departing from the scope of claims.

10 Control device 20 Controlled plant 101 Model parameter estimation unit 102 Measurement unit 103 Difference device 104 Operation amount update unit 105 Control parameter calculation unit 106 First timer 107 Second timer 108 Limit cycle controller 109 Switcher 111 Look-ahead response correction Unit 112 Operation change amount calculation unit 113 Adder 201 Input device 202 Display device 203 External I / F
203a Recording medium 204 Communication I / F
205 Processor 206 Memory Device 207 Bus

Claims (12)

A control device that outputs an operation amount for a controlled object and causes the controlled amount of the controlled object to follow a target value.
Based on the control amount and the operation amount, the estimated value of the model parameter of the plant response function, which is a function representing the plant response model to be controlled and includes the model parameter, is calculated. Model parameter estimation unit and
A control parameter calculation unit that calculates control parameters including control gain and look-ahead length based on the plant response function.
A target deviation calculation unit that calculates a target deviation, which is the difference between the target value and the current control amount,
Correction to calculate the correction target deviation by correcting the target deviation by the predicted value of the control amount after the elapse of the look-ahead length based on the plant response function and the amount of change in the operation amount in the past up to the present. Target deviation calculation unit and
An operation change amount calculation unit that calculates the change amount of the operation amount by multiplying the correction target deviation by the control gain.
An operation amount calculation unit that calculates a new operation amount by adding the change amount of the operation amount and the current operation amount.
Have,
The calculation of the target deviation by the target deviation calculation unit, the calculation of the correction target deviation by the correction target deviation calculation unit, and the calculation of the new operation amount by the operation amount calculation unit are sequentially performed at predetermined control cycles. The model parameter estimation unit calculates the estimated value of the model parameter and the control parameter calculation unit calculates the control parameter at each predetermined adjustment cycle , which is a cycle longer than the control cycle . A control device that repeats sequentially. The control device according to claim 1, wherein the model parameter estimation unit updates the model parameters included in the plant response function with the estimated values of the model parameters at each adjustment cycle. The adjustment cycle is a value obtained by multiplying the control cycle by a predetermined natural number n.
The correction target deviation calculation unit is
The control device according to claim 1 or 2, wherein the time correction for the natural number n times is performed to calculate the predicted value of the control amount. A limit cycle control unit that outputs an operation amount by a limit cycle based on the target deviation,
It has a switching unit for switching which of the operation amount output from the limit cycle control unit and the operation amount calculated by the operation amount calculation unit is output to the control target.
The model parameter estimation unit is
The control device according to any one of claims 1 to 3, which calculates an estimated value of the model parameter based on the control amount and the operation amount output to the control target. The switching unit is
In a predetermined initial period, the operation amount output from the limit cycle control unit is output to the control target, and after the initial period elapses, the operation amount calculated by the operation amount calculation unit is output to the control target. , The control device according to claim 4. The control parameter calculation unit is
The time width until the pre-reading length matches the value of the plant response function at the time of the pre-reading length and the value obtained by multiplying the plant response function value at the final time of the controlled object by a predetermined adjustment coefficient β. The control device according to any one of claims 1 to 5, which is calculated as described above. The control parameter calculation unit is
Any of claims 1 to 6, wherein the control gain is calculated as a value obtained by multiplying the reciprocal of the value obtained by adding a predetermined non-negative value to the plant response function value at the time of the look-ahead length by a predetermined adjustment coefficient α. The control device according to paragraph 1. The plant response function is an ARMA model or an ARMAX model.
The model parameter estimation unit is
The control device according to any one of claims 1 to 7, wherein the estimated value is calculated using the coefficient of the ARMA model or the ARMAX model as the model parameter. The model parameter estimation unit is
In any one of claims 1 to 8, the estimated value of the model parameter is calculated by estimating the covariance matrix and the model parameter based on the sequential least squares method including the oblivion element. The control device described. A display in which at least one of the estimated value of the model parameter estimated by the model parameter estimation unit and the control parameter calculated by the control parameter calculation unit is sequentially output to a predetermined display device for each adjustment cycle. The control device according to any one of claims 1 to 9, which has a control unit. A control device that outputs the manipulated variable for the controlled object and causes the controlled variable of the controlled object to follow the target value.
Based on the control amount and the operation amount, the estimated value of the model parameter of the plant response function, which is a function representing the plant response model to be controlled and includes the model parameter, is calculated. Model parameter estimation procedure and
A control parameter calculation procedure for calculating a control parameter including a control gain and a look-ahead length based on the plant response function, and a control parameter calculation procedure.
A target deviation calculation procedure for calculating a target deviation, which is the difference between the target value and the current control amount, and
Correction to calculate the correction target deviation by correcting the target deviation by the predicted value of the control amount after the elapse of the look-ahead length based on the plant response function and the amount of change in the operation amount in the past up to the present. Target deviation calculation procedure and
An operation change amount calculation procedure for calculating the change amount of the operation amount by multiplying the correction target deviation by the control gain, and
An operation amount calculation procedure for calculating a new operation amount by adding the change amount of the operation amount and the current operation amount, and
And run
The calculation of the target deviation by the target deviation calculation procedure, the calculation of the correction target deviation by the correction target deviation calculation procedure, and the calculation of the new operation amount by the operation amount calculation procedure are sequentially performed at predetermined control cycles. The calculation of the estimated value of the model parameter by the model parameter estimation procedure and the calculation of the control parameter by the control parameter calculation procedure are performed every predetermined adjustment cycle , which is a cycle longer than the control cycle . A control method that repeats sequentially. A control device that outputs the manipulated variable for the controlled object and causes the controlled variable of the controlled object to follow the target value.
Based on the control amount and the operation amount, the estimated value of the model parameter of the plant response function, which is a function representing the plant response model to be controlled and includes the model parameter, is calculated. Model parameter estimation procedure and
A control parameter calculation procedure for calculating a control parameter including a control gain and a look-ahead length based on the plant response function, and a control parameter calculation procedure.
A target deviation calculation procedure for calculating a target deviation, which is the difference between the target value and the current control amount, and
Correction to calculate the correction target deviation by correcting the target deviation by the predicted value of the control amount after the elapse of the look-ahead length based on the plant response function and the amount of change in the operation amount in the past up to the present. Target deviation calculation procedure and
An operation change amount calculation procedure for calculating the change amount of the operation amount by multiplying the correction target deviation by the control gain, and
An operation amount calculation procedure for calculating a new operation amount by adding the change amount of the operation amount and the current operation amount, and
To execute,
The calculation of the target deviation by the target deviation calculation procedure, the calculation of the correction target deviation by the correction target deviation calculation procedure, and the calculation of the new operation amount by the operation amount calculation procedure are sequentially performed at predetermined control cycles. The calculation of the estimated value of the model parameter by the model parameter estimation procedure and the calculation of the control parameter by the control parameter calculation procedure are performed every predetermined adjustment cycle , which is a cycle longer than the control cycle . A program that repeats sequentially.

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