[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Vaccari et al., 2019 - Google Patents

A performance monitoring algorithm for sustained optimal operation with economic MPC

Vaccari et al., 2019

View PDF
Document ID
9302911848747427768
Author
Vaccari M
Pannocchia G
Publication year
Publication venue
2019 18th European Control Conference (ECC)

External Links

Snippet

This paper addresses the problem of performance monitoring for Economic Model Predictive Control (EMPC) in the presence of plant parameter changes. In order to cope with plant- model mismatch, we adopt a recently developed offset-free EMPC algorithm which requires …
Continue reading at arpi.unipi.it (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
    • G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/32—Automatic controllers electric with inputs from more than one sensing element; with outputs to more than one correcting element

Similar Documents

Publication Publication Date Title
Biegler et al. Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization
Sun et al. Performance monitoring of model-predictive controllers via model residual assessment
CN101652730B (en) Apparatus and method for automated closed-loop identification of an industrial process in a process control system
US20170206469A1 (en) Model adaption and online learning for unstable environments
Bobal et al. Adaptive predictive control of time-delay systems
Chachuat et al. Process optimization via constraints adaptation
Larsson et al. Experimental evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer
Larsson et al. An application-oriented approach to dual control with excitation for closed-loop identification
de Avila Ferreira et al. Real-time optimization of uncertain process systems via modifier adaptation and gaussian processes
Calderón et al. Koopman operator-based model predictive control with recursive online update
Zhou et al. An integrated robust iterative learning control strategy for batch processes based on 2D system
Terzi et al. Learning multi-step prediction models for receding horizon control
Wang et al. Autocovariance-based plant-model mismatch estimation for linear model predictive control
Aliskan Adaptive model predictive control for Wiener nonlinear systems
Chen et al. An inexact sensitivity updating scheme for fast nonlinear model predictive control based on a curvature-like measure of nonlinearity
Vaccari et al. Implementation of an economic MPC with robustly optimal steady-state behavior
Chan et al. PID based nonlinear processes control model uncertainty improvement by using Gaussian process model
Hassanpour et al. Artificial neural network based model predictive control: implementing achievable set‐points
Vaccari et al. A performance monitoring algorithm for sustained optimal operation with economic MPC
Darup et al. A novel event-triggered robust MPC scheme for linear systems
Simkoff et al. Plant-model mismatch evaluation for unconstrained MPC with state estimation
Ławryńczuk Neural networks in model predictive control
Maiworm et al. Online gaussian process learningbased model predictive control with stability guarantees
Kaneko et al. Automatic database monitoring for process control systems
Adhau et al. Fast Reinforcement Learning Based MPC based on NLP Sensitivities