Coordinating multiple optimization-based controllers: new opportunities and challenges
|
|
- Caren Bryan
- 5 years ago
- Views:
Transcription
1 Coordinating multiple optimization-based controllers: new opportunities and challenges James B. Rawlings and Brett T. Stewart Department of Chemical and Biological Engineering University of Wisconsin Madison October 15, 2008 Rawlings and Stewart Multiple MPCs: Status and Future 1 / 32
2 Outline 1 Introduction 2 Game theory results 3 Distributed control example 4 Current Challenge: Coupled constraints 5 Conclusions Rawlings and Stewart Multiple MPCs: Status and Future 2 / 32
3 Electrical power distribution Rawlings and Stewart Multiple MPCs: Status and Future 3 / 32
4 Chemical plant integration Material flow Energy flow Rawlings and Stewart Multiple MPCs: Status and Future 4 / 32
5 MPC at the large scale Decentralized Control Most large-scale systems consist of networks of interconnected/interacting subsystems Chemical plants, electrical power grids, water distribution networks,... Rawlings and Stewart Multiple MPCs: Status and Future 5 / 32
6 MPC at the large scale Decentralized Control Most large-scale systems consist of networks of interconnected/interacting subsystems Chemical plants, electrical power grids, water distribution networks,... Traditional approach: Decentralized control Wealth of literature from the early 1970 s on improved decentralized control a Well-known that poor performance may result if the interconnections are not negligible a [Sandell Jr. et al., 1978, Siljak, 1991, Lunze, 1992] Rawlings and Stewart Multiple MPCs: Status and Future 5 / 32
7 MPC at the large scale Centralized Control Steady increase in available computational power has provided the opportunity for centralized control Most practitioners view centralized control of large, networked systems as impractical and unrealistic A divide and conquer strategy is essential for control of large, networked systems [Ho, 2005] Centralized control: A benchmark control framework for comparing and assessing other control formulations Rawlings and Stewart Multiple MPCs: Status and Future 6 / 32
8 Nomenclature: consider two interacting units Objective functions Φ 1 (u 1, u 2 ), Φ 2 (u 1, u 2 ) and Φ(u 1, u 2 ) = w 1 Φ 1 (u 1, u 2 ) + w 2 Φ 2 (u 1, u 2 ) decision variables for units u 1 Ω 1, u 2 Ω 2 Rawlings and Stewart Multiple MPCs: Status and Future 7 / 32
9 Nomenclature: consider two interacting units Objective functions Φ 1 (u 1, u 2 ), Φ 2 (u 1, u 2 ) and Φ(u 1, u 2 ) = w 1 Φ 1 (u 1, u 2 ) + w 2 Φ 2 (u 1, u 2 ) decision variables for units u 1 Ω 1, u 2 Ω 2 Decentralized Control min Φ1 (u 1 ) u 1 Ω 1 min Φ2 (u 2 ) u 2 Ω 2 Rawlings and Stewart Multiple MPCs: Status and Future 7 / 32
10 Nomenclature: consider two interacting units Objective functions Φ 1 (u 1, u 2 ), Φ 2 (u 1, u 2 ) and Φ(u 1, u 2 ) = w 1 Φ 1 (u 1, u 2 ) + w 2 Φ 2 (u 1, u 2 ) decision variables for units u 1 Ω 1, u 2 Ω 2 Decentralized Control Noncooperative Control (Nash equilibrium) min Φ1 (u 1 ) u 1 Ω 1 min Φ 1 (u 1, u 2 ) u 1 Ω 1 min Φ2 (u 2 ) u 2 Ω 2 min Φ 2 (u 1, u 2 ) u 2 Ω 2 Rawlings and Stewart Multiple MPCs: Status and Future 7 / 32
11 Nomenclature: consider two interacting units Objective functions Φ 1 (u 1, u 2 ), Φ 2 (u 1, u 2 ) and Φ(u 1, u 2 ) = w 1 Φ 1 (u 1, u 2 ) + w 2 Φ 2 (u 1, u 2 ) decision variables for units u 1 Ω 1, u 2 Ω 2 Decentralized Control Noncooperative Control (Nash equilibrium) Cooperative Control (Pareto optimal) min Φ1 (u 1 ) u 1 Ω 1 min Φ 1 (u 1, u 2 ) u 1 Ω 1 min Φ(u 1, u 2 ) u 1 Ω 1 min Φ2 (u 2 ) u 2 Ω 2 min Φ 2 (u 1, u 2 ) u 2 Ω 2 min Φ(u 1, u 2 ) u 2 Ω 2 Rawlings and Stewart Multiple MPCs: Status and Future 7 / 32
12 Nomenclature: consider two interacting units Objective functions Φ 1 (u 1, u 2 ), Φ 2 (u 1, u 2 ) and Φ(u 1, u 2 ) = w 1 Φ 1 (u 1, u 2 ) + w 2 Φ 2 (u 1, u 2 ) decision variables for units u 1 Ω 1, u 2 Ω 2 Decentralized Control Noncooperative Control (Nash equilibrium) Cooperative Control (Pareto optimal) Centralized Control (Pareto optimal) min Φ1 (u 1 ) u 1 Ω 1 min Φ 1 (u 1, u 2 ) u 1 Ω 1 min Φ(u 1, u 2 ) u 1 Ω 1 min Φ2 (u 2 ) u 2 Ω 2 min Φ 2 (u 1, u 2 ) u 2 Ω 2 min Φ(u 1, u 2 ) u 2 Ω 2 min Φ(u 1, u 2 ) u 1,u 2 Ω 1 Ω 2 Rawlings and Stewart Multiple MPCs: Status and Future 7 / 32
13 Noninteracting systems 2 1 b Φ 2 (u) n, d, p u 2 0 a -1 Φ 1 (u) u 1 Rawlings and Stewart Multiple MPCs: Status and Future 8 / 32
14 Weakly interacting systems 0.5 Φ 2 (u) 0 b p n, d -0.5 u 2-1 a Φ 1 (u) u 1 Rawlings and Stewart Multiple MPCs: Status and Future 9 / 32
15 Moderately interacting systems Φ 1 (u) 1 Φ 2 (u) a u b p d n u 1 Rawlings and Stewart Multiple MPCs: Status and Future 10 / 32
16 Strongly interacting (conflicting) systems Φ 1 (u) Φ 2 (u) p a u 2 0 b d u 1 Rawlings and Stewart Multiple MPCs: Status and Future 11 / 32
17 Strongly interacting (conflicting) systems n u Φ 2 (u) Φ 1 (u) u 1 Rawlings and Stewart Multiple MPCs: Status and Future 12 / 32
18 Geometry of cooperative vs. noncooperative MPC 10 p n 5 a u 2 0 Φ 1 (u) 0 Φ 2 (u) 1 b u 1 Rawlings and Stewart Multiple MPCs: Status and Future 13 / 32
19 Geometry of cooperative vs. noncooperative MPC 10 p n 5 a u 2 0 Φ 1 (u) 1 Φ 2 (u) 0 b u 1 Rawlings and Stewart Multiple MPCs: Status and Future 13 / 32
20 Two reactors with separation and recycle D, x Ad, x Bd MPC 3 MPC 1 MPC 2 F purge F 0, x A0 F 1, x A1 H r Hm F m, x Am, x Bm H b Q F r, x Ar, x Br A B B C A B B C F b, x Ab, x Bb, T Rawlings and Stewart Multiple MPCs: Status and Future 14 / 32
21 Two reactors with separation and recycle H m Time setpoint Cent Ncoop Coop (1 iterate) H b Time setpoint Cent Ncoop Coop (1 iterate) F 1 0 D Time Cent Ncoop Coop (1 iterate) Time Cent Ncoop Coop (1 iterate) Rawlings and Stewart Multiple MPCs: Status and Future 15 / 32
22 Two reactors with separation and recycle Performance comparison Cost ( 10 2 ) Performance loss Centralized MPC Decentralized MPC Noncooperative MPC Cooperative MPC (1 iterate) % Cooperative MPC (10 iterates) % Rawlings and Stewart Multiple MPCs: Status and Future 16 / 32
23 Two reactors with separation and recycle Zero-offset control in the presence of non-zero mean disturbances and plant-model mismatch Rawlings and Stewart Multiple MPCs: Status and Future 17 / 32
24 Two reactors with separation and recycle Zero-offset control in the presence of non-zero mean disturbances and plant-model mismatch Several formulations possible For simplicity, integrating disturbances assumed to be local Under mild assumptions, zero-offset control in the distributed MPC framework can be established Disturbance models that give zero-offset performance under decentralized MPC also give zero-offset performance in the FC-MPC framework Rawlings and Stewart Multiple MPCs: Status and Future 17 / 32
25 Two reactors with separation and recycle d k D, x Ad, x Bd MPC 3 MPC 1 MPC 2 F purge F 0, x A0 F 1, x A1 H r Hm F m, x Am, x Bm H b Q F r, x Ar, x Br A B B C A B B C F b, x Ab, x Bb, T Rawlings and Stewart Multiple MPCs: Status and Future 18 / 32
26 Two reactors with separation and recycle H m Disturbance affects the system from time = 30 to time = Time setpoint Cent Decent Coop (1 iterate) H b Time setpoint Cent Decent Coop (1 iterate) F 1 0 D Time Time Rawlings and Cent Coop (1 iterate) Multiple MPCs: Status and Future Cent Coop (1 iterate)
27 Two reactors with separation and recycle Performance comparison Cost ( 10 2 ) Performance loss Centralized MPC Decentralized MPC - Noncooperative MPC - Cooperative MPC (1 iterate) % Cooperative MPC (10 iterates) % Rawlings and Stewart Multiple MPCs: Status and Future 20 / 32
28 Current Challenge: Coupled Constraints D, x Ad, x Bd MPC 3 MPC 1 MPC 2 F purge F 0, x A0 F 1, x A1 H b H r A B B C Hm F r, x Ar, x Br Q 1 Q 2 A B B C F m, x Am, x Bm Q 3 F b, x Ab, x Bb, T Steam distribution between MPC controllers Q 1 + Q 2 + Q 3 Q T Rawlings and Stewart Multiple MPCs: Status and Future 21 / 32
29 Geometry of Coupled Constraints Q1 + Q 2 Q T Feasible region cannot be separated into Cartesian product of subspaces Υ = (Ω 1 Ω M ) Υ Rawlings and Stewart Multiple MPCs: Status and Future 22 / 32
30 Geometry of Coupled Constraints Coupled constraints give suboptimal points of attraction u 2 u 1 Φ(u 1,u 2 ) u 0 Rawlings and Stewart Multiple MPCs: Status and Future 23 / 32
31 Geometry of Coupled Constraints Coupled constraints give suboptimal points of attraction u 2 u 1 Φ(u 1,u 2 ) u u 0 Rawlings and Stewart Multiple MPCs: Status and Future 23 / 32
32 Geometry of Resource Manager ˆΩ 1 û 1 û 2 ˆΩ 2 Υ Managing constraints feasibly Different inner box constraints Different points of attraction û ˆΩ Rawlings and Stewart Multiple MPCs: Status and Future 24 / 32
33 Resource Manager Resource Manager Problem min ˆΩ M i s.t. { } w i min Φ(u 1,..., u i,..., u M ), u i ˆΩ i ˆΩ = (ˆΩ 1 ˆΩ M ) Υ ˆΩ i local decoupled subspace Υ coupled feasible region Resource manager finds optimal decoupled subspace to pass to each subsystem Rawlings and Stewart Multiple MPCs: Status and Future 25 / 32
34 Resource Manager Example Model A 1 = A 2 = 0.5I R 1 = R 2 = 2I B 11 = B 22 = I B 12 = B 21 = 0.5I Q 1 = 2I Q 2 = I C 11 = C 22 = [I I ] Constraints u 1 0 u 2 0 u 1 + u 2 1 Rawlings and Stewart Multiple MPCs: Status and Future 26 / 32
35 Resource Manager Example Centralized Cooperative Coop. w/rm 1.5 u s u s1 Rawlings and Stewart Multiple MPCs: Status and Future 27 / 32
36 Resource Manager Example y Time Centralized Cooperative Coop. w/rm u Time Rawlings and Stewart Multiple MPCs: Status and Future 28 / 32
37 Resource Manager Example Performance comparison Cost Performance loss Centralized MPC Cooperative MPC (1 iterate) % Coop. + Resource Manager (1 iterate) % Rawlings and Stewart Multiple MPCs: Status and Future 29 / 32
38 Conclusions Distributed MPC can be split into two types based on game theory Noncooperative MPC is unreliable and can produce closed-loop instability Cooperative MPC gives nominal closed-loop stability for any number of iterations Rawlings and Stewart Multiple MPCs: Status and Future 30 / 32
39 Conclusions Distributed MPC can be split into two types based on game theory Noncooperative MPC is unreliable and can produce closed-loop instability Cooperative MPC gives nominal closed-loop stability for any number of iterations A local state estimator can be used with the distributed state regulator Distributed target calculation can be used instead of a centralized target calculation for large-scale systems Rawlings and Stewart Multiple MPCs: Status and Future 30 / 32
40 Conclusions Distributed MPC can be split into two types based on game theory Noncooperative MPC is unreliable and can produce closed-loop instability Cooperative MPC gives nominal closed-loop stability for any number of iterations A local state estimator can be used with the distributed state regulator Distributed target calculation can be used instead of a centralized target calculation for large-scale systems Coupled constraints can be included in target calculation with use of resource manager Rawlings and Stewart Multiple MPCs: Status and Future 30 / 32
41 Acknowledgments Support from the U.S. National Science Foundation through grant CTS Collaboration with and support from Aspentech, Eastman, ExxonMobil and Shell Global Solutions. Rawlings and Stewart Multiple MPCs: Status and Future 31 / 32
42 Further Reading I Y.-C. Ho. On Centralized Optimal Control. IEEE Trans. Auto. Cont., 50(4): , J. Lunze. Feedback Control of Large Scale Systems. Prentice-Hall, London, U.K., N. R. Sandell Jr., P. Varaiya, M. Athans, and M. Safonov. Survey of decentralized control methods for larger scale systems. IEEE Trans. Auto. Cont., 23(2): , D. D. Siljak. Decentralized Control of Complex Systems. Academic Press, London, ISBN Rawlings and Stewart Multiple MPCs: Status and Future 32 / 32
Coordinating multiple optimization-based controllers: new opportunities and challenges
Coordinating multiple optimization-based controllers: new opportunities and challenges James B. Rawlings and Brett T. Stewart Department of Chemical and Biological Engineering University of Wisconsin Madison
More informationCooperation-based optimization of industrial supply chains
Cooperation-based optimization of industrial supply chains James B. Rawlings, Brett T. Stewart, Kaushik Subramanian and Christos T. Maravelias Department of Chemical and Biological Engineering May 9 2,
More informationOptimal dynamic operation of chemical processes: Assessment of the last 20 years and current research opportunities
Optimal dynamic operation of chemical processes: Assessment of the last 2 years and current research opportunities James B. Rawlings Department of Chemical and Biological Engineering April 3, 2 Department
More informationAn overview of distributed model predictive control (MPC)
An overview of distributed model predictive control (MPC) James B. Rawlings Department of Chemical and Biological Engineering August 28, 2011 IFAC Workshop: Hierarchical and Distributed Model Predictive
More informationDistributed model predictive control of large-scale systems
Distributed model predictive control of large-scale systems James B Rawlings 1, Aswin N Venkat 1 and Stephen J Wright 2 1 Department of Chemical and Biological Engineering 2 Department of Computer Sciences
More informationControlling Large-Scale Systems with Distributed Model Predictive Control
Controlling Large-Scale Systems with Distributed Model Predictive Control James B. Rawlings Department of Chemical and Biological Engineering November 8, 2010 Annual AIChE Meeting Salt Lake City, UT Rawlings
More informationOutline. Model Predictive Control: Current Status and Future Challenges. Separation of the control problem. Separation of the control problem
Otline Model Predictive Control: Crrent Stats and Ftre Challenges James B. Rawlings Department of Chemical and Biological Engineering University of Wisconsin Madison UCLA Control Symposim May, 6 Overview
More informationOnline monitoring of MPC disturbance models using closed-loop data
Online monitoring of MPC disturbance models using closed-loop data Brian J. Odelson and James B. Rawlings Department of Chemical Engineering University of Wisconsin-Madison Online Optimization Based Identification
More informationOptimal dynamic operation of chemical processes: current opportunities
Optimal dynamic operation of chemical processes: current opportunities James B. Rawlings Department of Chemical and Biological Engineering August 6, 29 Westhollow Research Center Shell Rawlings Current
More informationOptimizing Economic Performance using Model Predictive Control
Optimizing Economic Performance using Model Predictive Control James B. Rawlings Department of Chemical and Biological Engineering Second Workshop on Computational Issues in Nonlinear Control Monterey,
More informationPostface to Model Predictive Control: Theory and Design
Postface to Model Predictive Control: Theory and Design J. B. Rawlings and D. Q. Mayne August 19, 2012 The goal of this postface is to point out and comment upon recent MPC papers and issues pertaining
More informationOn the Inherent Robustness of Suboptimal Model Predictive Control
On the Inherent Robustness of Suboptimal Model Predictive Control James B. Rawlings, Gabriele Pannocchia, Stephen J. Wright, and Cuyler N. Bates Department of Chemical and Biological Engineering and Computer
More informationCiência e Natura ISSN: Universidade Federal de Santa Maria Brasil
Ciência e Natura ISSN: 0100-8307 cienciaenaturarevista@gmail.com Universidade Federal de Santa Maria Brasil Iranmanesh, Hamidreza; Afshar, Ahmad Application of Cooperative Distributed Predictive Control
More informationOn the Inherent Robustness of Suboptimal Model Predictive Control
On the Inherent Robustness of Suboptimal Model Predictive Control James B. Rawlings, Gabriele Pannocchia, Stephen J. Wright, and Cuyler N. Bates Department of Chemical & Biological Engineering Computer
More informationARTICLE IN PRESS Systems & Control Letters ( )
Systems & Control Letters Contents lists available at ScienceDirect Systems & Control Letters journal homepage: wwwelseviercom/locate/sysconle Cooperative distributed model predictive control Brett T Stewart
More informationResource-aware Quasi-decentralized Control of Nonlinear Plants Over Communication Networks
29 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June -2, 29 WeA5.5 Resource-aware Quasi-decentralized Control of Nonlinear Plants Over Communication Networks Yulei Sun and Nael
More informationDecentralized and distributed control
Decentralized and distributed control Constrained distributed control for discrete-time systems M. Farina 1 G. Ferrari Trecate 2 1 Dipartimento di Elettronica e Informazione (DEI) Politecnico di Milano,
More informationNonlinear Stochastic Modeling and State Estimation of Weakly Observable Systems: Application to Industrial Polymerization Processes
Nonlinear Stochastic Modeling and State Estimation of Weakly Observable Systems: Application to Industrial Polymerization Processes Fernando V. Lima, James B. Rawlings and Tyler A. Soderstrom Department
More informationINTEGRATION OF CONTROL THEORY AND SCHEDULING METHODS FOR SUPPLY CHAIN MANAGEMENT. Kaushik Subramanian
INTEGRATION OF CONTROL THEORY AND SCHEDULING METHODS FOR SUPPLY CHAIN MANAGEMENT by Kaushik Subramanian A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy
More informationDynamic Decomposition for Monitoring and Decision Making in Electric Power Systems
Dynamic Decomposition for Monitoring and Decision Making in Electric Power Systems Contributed Talk at NetSci 2007 May 20, 2007 Le Xie (lx@ece.cmu.edu) Advisor: Marija Ilic Outline Motivation Problem Statement
More informationCooperative distributed MPC for tracking
Milano (Italy) August 28 - September 2, 211 Cooperative distributed MPC for tracking A. Ferramosca D. Limon J.B. Rawlings E.F. Camacho Departamento de Ingeniería de Sistemas y Automática, Universidad de
More informationProcess Unit Control System Design
Process Unit Control System Design 1. Introduction 2. Influence of process design 3. Control degrees of freedom 4. Selection of control system variables 5. Process safety Introduction Control system requirements»
More informationASSESSMENT OF DECENTRALIZED MODEL PREDICTIVE CONTROL TECHNIQUES FOR POWER NETWORKS
ASSESSMENT OF DECENTRALIZED MODEL PREDICTIVE CONTROL TECHNIQUES FOR POWER NETWORKS Armand Damoiseaux Andrej Jokic Mircea Lazar University of Technology University of Technology University of Technology
More informationDesign of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process
Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process D.Angeline Vijula #, Dr.N.Devarajan * # Electronics and Instrumentation Engineering Sri Ramakrishna
More informationOptimal H Control Design under Model Information Limitations and State Measurement Constraints
Optimal H Control Design under Model Information Limitations and State Measurement Constraints F. Farokhi, H. Sandberg, and K. H. Johansson ACCESS Linnaeus Center, School of Electrical Engineering, KTH-Royal
More informationIn search of the unreachable setpoint
In search of the unreachable setpoint Adventures with Prof. Sten Bay Jørgensen James B. Rawlings Department of Chemical and Biological Engineering June 19, 2009 Seminar Honoring Prof. Sten Bay Jørgensen
More informationCoordinating Multiple Model Predictive Controllers for Water Reservoir Networks Operation
19th International Congress on Modelling and Simulation, Perth, Australia, 12 16 December 2011 http://mssanz.org.au/modsim2011 Coordinating Multiple Model Predictive Controllers for Water Reservoir Networks
More informationCourse on Model Predictive Control Part II Linear MPC design
Course on Model Predictive Control Part II Linear MPC design Gabriele Pannocchia Department of Chemical Engineering, University of Pisa, Italy Email: g.pannocchia@diccism.unipi.it Facoltà di Ingegneria,
More informationModel Predictive Control for Distributed Systems: Coordination Strategies & Structure
The 26th Chinese Process Control Conference, 2015 Model Predictive Control for Distributed Systems: Coordination Strategies & Structure Yi ZHENG, Shaoyuan LI School of Electronic Information and Electrical
More informationIntroduction to Model Predictive Control. Dipartimento di Elettronica e Informazione
Introduction to Model Predictive Control Riccardo Scattolini Riccardo Scattolini Dipartimento di Elettronica e Informazione Finite horizon optimal control 2 Consider the system At time k we want to compute
More informationStable Hierarchical Model Predictive Control Using an Inner Loop Reference Model
Stable Hierarchical Model Predictive Control Using an Inner Loop Reference Model Chris Vermillion Amor Menezes Ilya Kolmanovsky Altaeros Energies, Cambridge, MA 02140 (e-mail: chris.vermillion@altaerosenergies.com)
More informationSimulation of Quadruple Tank Process for Liquid Level Control
Simulation of Quadruple Tank Process for Liquid Level Control Ritika Thusoo 1, Sakshi Bangia 2 1 M.Tech Student, Electronics Engg, Department, YMCA University of Science and Technology, Faridabad 2 Assistant
More informationOpen/Closed Loop Bifurcation Analysis and Dynamic Simulation for Identification and Model Based Control of Polymerization Reactors
European Symposium on Computer Arded Aided Process Engineering 15 L. Puigjaner and A. Espuña (Editors) 2005 Elsevier Science B.V. All rights reserved. Open/Closed Loop Bifurcation Analysis and Dynamic
More informationNonlinear Model Predictive Control Tools (NMPC Tools)
Nonlinear Model Predictive Control Tools (NMPC Tools) Rishi Amrit, James B. Rawlings April 5, 2008 1 Formulation We consider a control system composed of three parts([2]). Estimator Target calculator Regulator
More informationA tutorial overview on theory and design of offset-free MPC algorithms
A tutorial overview on theory and design of offset-free MPC algorithms Gabriele Pannocchia Dept. of Civil and Industrial Engineering University of Pisa November 24, 2015 Introduction to offset-free MPC
More informationUniversity of Science and Technology, Sudan Department of Chemical Engineering.
ISO 91:28 Certified Volume 3, Issue 6, November 214 Design and Decoupling of Control System for a Continuous Stirred Tank Reactor (CSTR) Georgeous, N.B *1 and Gasmalseed, G.A, Abdalla, B.K (1-2) University
More informationMS-E2133 Systems Analysis Laboratory II Assignment 2 Control of thermal power plant
MS-E2133 Systems Analysis Laboratory II Assignment 2 Control of thermal power plant How to control the thermal power plant in order to ensure the stable operation of the plant? In the assignment Production
More informationCOMPUTATIONAL DELAY IN NONLINEAR MODEL PREDICTIVE CONTROL. Rolf Findeisen Frank Allgöwer
COMPUTATIONAL DELAY IN NONLINEAR MODEL PREDICTIVE CONTROL Rolf Findeisen Frank Allgöwer Institute for Systems Theory in Engineering, University of Stuttgart, 70550 Stuttgart, Germany, findeise,allgower
More informationMULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO. PROCESSES. A Preliminary Study
MULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO PROCESSES. A Preliminary Study Eduardo J. Adam 1,2*, Carlos J. Valsecchi 2 1 Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) (Universidad
More informationModel Predictive Control
Model Predictive Control Davide Manca Lecture 6 of Dynamics and Control of Chemical Processes Master Degree in Chemical Engineering Davide Manca Dynamics and Control of Chemical Processes Master Degree
More informationDESIGN OF AN ON-LINE TITRATOR FOR NONLINEAR ph CONTROL
DESIGN OF AN ON-LINE TITRATOR FOR NONLINEAR CONTROL Alex D. Kalafatis Liuping Wang William R. Cluett AspenTech, Toronto, Canada School of Electrical & Computer Engineering, RMIT University, Melbourne,
More informationCourse on Model Predictive Control Part III Stability and robustness
Course on Model Predictive Control Part III Stability and robustness Gabriele Pannocchia Department of Chemical Engineering, University of Pisa, Italy Email: g.pannocchia@diccism.unipi.it Facoltà di Ingegneria,
More informationGiulio Betti, Marcello Farina and Riccardo Scattolini
1 Dipartimento di Elettronica e Informazione, Politecnico di Milano Rapporto Tecnico 2012.29 An MPC algorithm for offset-free tracking of constant reference signals Giulio Betti, Marcello Farina and Riccardo
More informationQuis custodiet ipsos custodes?
Quis custodiet ipsos custodes? James B. Rawlings, Megan Zagrobelny, Luo Ji Dept. of Chemical and Biological Engineering, Univ. of Wisconsin-Madison, WI, USA IFAC Conference on Nonlinear Model Predictive
More informationMultiobjective optimization for automatic tuning of robust Model Based Predictive Controllers
Proceedings of the 7th World Congress The International Federation of Automatic Control Multiobjective optimization for automatic tuning of robust Model Based Predictive Controllers P.Vega*, M. Francisco*
More informationEconomic and Distributed Model Predictive Control: Recent Developments in Optimization-Based Control
SICE Journal of Control, Measurement, and System Integration, Vol. 10, No. 2, pp. 039 052, March 2017 Economic and Distributed Model Predictive Control: Recent Developments in Optimization-Based Control
More informationBatch-to-batch strategies for cooling crystallization
Batch-to-batch strategies for cooling crystallization Marco Forgione 1, Ali Mesbah 1, Xavier Bombois 1, Paul Van den Hof 2 1 Delft University of echnology Delft Center for Systems and Control 2 Eindhoven
More informationRESEARCH ARTICLE. Assessment of Non-Centralized Model Predictive Control Techniques for Electrical Power Networks
International Journal of Control Vol. 00, No. 00, Month 200x, 1 19 RESEARCH ARTICLE Assessment of Non-Centralized Model Predictive Control Techniques for Electrical Power Networks [Names and affiliations
More informationImproved Crude Oil Processing Using Second-Order Volterra Models and Nonlinear Model Predictive Control
8 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA June -3, 8 ThA3. Improved Crude Oil Processing Using Second-Order Volterra Models and Nonlinear Model Predictive Control T.
More informationEconomic Model Predictive Control Historical Perspective and Recent Developments and Industrial Examples
1 Economic Model Predictive Control Historical Perspective and Recent Developments and Industrial Examples Public Trial Lecture Candidate: Vinicius de Oliveira Department of Chemical Engineering, Faculty
More informationDistributed and Real-time Predictive Control
Distributed and Real-time Predictive Control Melanie Zeilinger Christian Conte (ETH) Alexander Domahidi (ETH) Ye Pu (EPFL) Colin Jones (EPFL) Challenges in modern control systems Power system: - Frequency
More informationSimple criteria for controller performance monitoring
Simple criteria for controller performance monitoring Kostas Tsakalis (ASU) and Sachi Dash (Honeywell) Introduction/motivation Performance monitoring philosophy Simple criteria based on small-gain arguments
More informationSufficient Statistics in Decentralized Decision-Making Problems
Sufficient Statistics in Decentralized Decision-Making Problems Ashutosh Nayyar University of Southern California Feb 8, 05 / 46 Decentralized Systems Transportation Networks Communication Networks Networked
More informationNonlinearControlofpHSystemforChangeOverTitrationCurve
D. SWATI et al., Nonlinear Control of ph System for Change Over Titration Curve, Chem. Biochem. Eng. Q. 19 (4) 341 349 (2005) 341 NonlinearControlofpHSystemforChangeOverTitrationCurve D. Swati, V. S. R.
More informationOptimal Decentralized Control of Coupled Subsystems With Control Sharing
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 58, NO. 9, SEPTEMBER 2013 2377 Optimal Decentralized Control of Coupled Subsystems With Control Sharing Aditya Mahajan, Member, IEEE Abstract Subsystems that
More informationIndex Accumulation, 53 Accuracy: numerical integration, sensor, 383, Adaptive tuning: expert system, 528 gain scheduling, 518, 529, 709,
Accumulation, 53 Accuracy: numerical integration, 83-84 sensor, 383, 772-773 Adaptive tuning: expert system, 528 gain scheduling, 518, 529, 709, 715 input conversion, 519 reasons for, 512-517 relay auto-tuning,
More informationDecentralized and distributed control
Decentralized and distributed control Centralized control for constrained discrete-time systems M. Farina 1 G. Ferrari Trecate 2 1 Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) Politecnico
More informationVerteilte modellprädiktive Regelung intelligenter Stromnetze
Verteilte modellprädiktive Regelung intelligenter Stromnetze Institut für Mathematik Technische Universität Ilmenau in Zusammenarbeit mit Philipp Braun, Lars Grüne (U Bayreuth) und Christopher M. Kellett,
More informationGlocal Control for Network Systems via Hierarchical State-Space Expansion
Glocal Control for Network Systems via Hierarchical State-Space Expansion Hampei Sasahara, Takayuki Ishizaki, Tomonori Sadamoto, Jun-ichi Imura, Henrik Sandberg 2, and Karl Henrik Johansson 2 Abstract
More informationCONTROLLER PERFORMANCE ASSESSMENT IN SET POINT TRACKING AND REGULATORY CONTROL
ADCHEM 2, Pisa Italy June 14-16 th 2 CONTROLLER PERFORMANCE ASSESSMENT IN SET POINT TRACKING AND REGULATORY CONTROL N.F. Thornhill *, S.L. Shah + and B. Huang + * Department of Electronic and Electrical
More informationConstrained Output Feedback Control of a Multivariable Polymerization Reactor
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 8, NO. 1, JANUARY 2000 87 Constrained Output Feedback Control of a Multivariable Polymerization Reactor Michael J. Kurtz, Guang.-Yan Zhu, and Michael
More informationAnalyzing Control Problems and Improving Control Loop Performance
OptiControls Inc. Houston, TX Ph: 713-459-6291 www.opticontrols.com info@opticontrols.com Analyzing Control s and Improving Control Loop Performance -by Jacques F. Smuts Page: 1 Presenter Principal Consultant
More informationFloor Control (kn) Time (sec) Floor 5. Displacement (mm) Time (sec) Floor 5.
DECENTRALIZED ROBUST H CONTROL OF MECHANICAL STRUCTURES. Introduction L. Bakule and J. Böhm Institute of Information Theory and Automation Academy of Sciences of the Czech Republic The results contributed
More informationUNIVERSITY OF CALIFORNIA. Los Angeles. Distributed Model Predictive Control of Nonlinear. and Two-Time-Scale Process Networks
UNIVERSITY OF CALIFORNIA Los Angeles Distributed Model Predictive Control of Nonlinear and Two-Time-Scale Process Networks A dissertation submitted in partial satisfaction of the requirements for the degree
More informationDecentralized and distributed control
Decentralized and distributed control Models of large-scale systems M. Farina 1 G. Ferrari Trecate 2 1 Dipartimento di Elettronica e Informazione (DEI) Politecnico di Milano, Italy farina@elet.polimi.it
More informationDynamic Real-Time Optimization: Linking Off-line Planning with On-line Optimization
Dynamic Real-Time Optimization: Linking Off-line Planning with On-line Optimization L. T. Biegler and V. Zavala Chemical Engineering Department Carnegie Mellon University Pittsburgh, PA 15213 April 12,
More informationLectures 25 & 26: Consensus and vehicular formation problems
EE 8235: Lectures 25 & 26 Lectures 25 & 26: Consensus and vehicular formation problems Consensus Make subsystems (agents, nodes) reach agreement Distributed decision making Vehicular formations How does
More informationRobust Actuator Fault Detection and Isolation in a Multi-Area Interconnected Power System
Proceedings of the World Congress on Engineering 2011 Vol II, July 6-8, 2011, London, U.K. Robust Actuator Fault Detection and Isolation in a Multi-Area Interconnected Power System Istemihan Genc, and
More informationROBUST DECENTRALIZED CONTROL OF LARGE SCALE SYSTEMS Lubom r Bakule Institute of Information Theory and Automation Academy of Sciences of the Czech Rep
ROBUST DECENTRALIZED CONTROL OF LARGE SCALE SYSTEMS Lubom r Bakule Institute of Information Theory and Automation Academy of Sciences of the Czech Republic Summary In this paper, a new methodology is proposed
More informationDECENTRALIZED CONTROL DESIGN USING LMI MODEL REDUCTION
Journal of ELECTRICAL ENGINEERING, VOL. 58, NO. 6, 2007, 307 312 DECENTRALIZED CONTROL DESIGN USING LMI MODEL REDUCTION Szabolcs Dorák Danica Rosinová Decentralized control design approach based on partial
More informationDistributed Model Predictive Control: A Tutorial Review
Distributed Model Predictive Control: A Tutorial Review Panagiotis D. Christofides, Riccardo Scattolini, David Muñoz de la Peña and Jinfeng Liu Abstract In this paper, we provide a tutorial review of recent
More informationMultiple Model Based Adaptive Control for Shell and Tube Heat Exchanger Process
Multiple Model Based Adaptive Control for Shell and Tube Heat Exchanger Process R. Manikandan Assistant Professor, Department of Electronics and Instrumentation Engineering, Annamalai University, Annamalai
More informationDiscussion on: Measurable signal decoupling with dynamic feedforward compensation and unknown-input observation for systems with direct feedthrough
Discussion on: Measurable signal decoupling with dynamic feedforward compensation and unknown-input observation for systems with direct feedthrough H.L. Trentelman 1 The geometric approach In the last
More informationSelf-Tuning Control for Synchronous Machine Stabilization
http://dx.doi.org/.5755/j.eee.2.4.2773 ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN 392-25, VOL. 2, NO. 4, 25 Self-Tuning Control for Synchronous Machine Stabilization Jozef Ritonja Faculty of Electrical Engineering
More informationTHERE are a class of complex large-scale systems which
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL, NO, MAY 99 Networked Coordination-Based Distributed Model Predictive Control for Large-Scale System Yi Zheng, Shaoyuan Li, and Hai Qiu Abstract A class
More informationDecentralized LQG Control of Systems with a Broadcast Architecture
Decentralized LQG Control of Systems with a Broadcast Architecture Laurent Lessard 1,2 IEEE Conference on Decision and Control, pp. 6241 6246, 212 Abstract In this paper, we consider dynamical subsystems
More informationControl of MIMO processes. 1. Introduction. Control of MIMO processes. Control of Multiple-Input, Multiple Output (MIMO) Processes
Control of MIMO processes Control of Multiple-Input, Multiple Output (MIMO) Processes Statistical Process Control Feedforward and ratio control Cascade control Split range and selective control Control
More informationDECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER
th June 4. Vol. 64 No. 5-4 JATIT & LLS. All rights reserved. ISSN: 99-8645 www.jatit.org E-ISSN: 87-395 DECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER
More informationSemi-decentralized Strategies in Structural Vibration Control
Semi-decentralized Strategies in Structural Vibration Control By: Josep M. Rossell Francisco Palacios Control, Dynamics and Applications (CoDAlab) Department of Applied Mathematics III Universitat Politècnica
More informationMulti-Loop Control. Department of Chemical Engineering,
Interaction ti Analysis and Multi-Loop Control Sachin C. Patawardhan Department of Chemical Engineering, I.I.T. Bombay Outline Motivation Interactions in Multi-loop control Loop pairing using Relative
More informationPROPORTIONAL-Integral-Derivative (PID) controllers
Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process R.Vinodha S. Abraham Lincoln and J. Prakash Abstract Multi-loop (De-centralized) Proportional-Integral- Derivative
More informationDistributed Receding Horizon Control of Cost Coupled Systems
Distributed Receding Horizon Control of Cost Coupled Systems William B. Dunbar Abstract This paper considers the problem of distributed control of dynamically decoupled systems that are subject to decoupled
More informationDISTURBANCE OBSERVER BASED CONTROL: CONCEPTS, METHODS AND CHALLENGES
DISTURBANCE OBSERVER BASED CONTROL: CONCEPTS, METHODS AND CHALLENGES Wen-Hua Chen Professor in Autonomous Vehicles Department of Aeronautical and Automotive Engineering Loughborough University 1 Outline
More informationSTATE ESTIMATION IN COORDINATED CONTROL WITH A NON-STANDARD INFORMATION ARCHITECTURE. Jun Yan, Keunmo Kang, and Robert Bitmead
STATE ESTIMATION IN COORDINATED CONTROL WITH A NON-STANDARD INFORMATION ARCHITECTURE Jun Yan, Keunmo Kang, and Robert Bitmead Department of Mechanical & Aerospace Engineering University of California San
More informationClosed-loop Formulation for Nonlinear Dynamic Real-time Optimization
Preprint, 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems Closed-loop Formulation for Nonlinear Dynamic Real-time Optimization Mohammad Zamry Jamaludin Christopher
More informationTime scale separation and the link between open-loop and closed-loop dynamics
Time scale separation and the link between open-loop and closed-loop dynamics Antonio Araújo a Michael Baldea b Sigurd Skogestad a,1 Prodromos Daoutidis b a Department of Chemical Engineering Norwegian
More informationA Riccati-Genetic Algorithms Approach To Fixed-Structure Controller Synthesis
A Riccati-Genetic Algorithms Approach To Fixed-Structure Controller Synthesis A Farag and H Werner Technical University Hamburg-Harburg, Institute of Control Engineering afarag@tu-harburgde, hwerner@tu-harburgde
More informationFeedback Control CONTROL THEORY FUNDAMENTALS. Feedback Control: A History. Feedback Control: A History (contd.) Anuradha Annaswamy
Feedback Control CONTROL THEORY FUNDAMENTALS Actuator Sensor + Anuradha Annaswamy Active adaptive Control Laboratory Massachusetts Institute of Technology must follow with» Speed» Accuracy Feeback: Measure
More informationDecentralized Control Design for Interconnected Systems Based on A Centralized Reference Controller
Decentralized Control Desin for Interconnected Systems Based on A Centralized Reference Controller Javad Lavaei and Amir G Ahdam Department of Electrical and Computer Enineerin, Concordia University Montréal,
More informationDynamic Operability for the Calculation of Transient Output Constraints for Non-Square Linear Model Predictive Controllers
Dynamic Operability for the Calculation of Transient Output Constraints for Non-Square Linear Model Predictive Controllers Fernando V. Lima and Christos Georgakis* Department of Chemical and Biological
More informationState Estimation of Linear and Nonlinear Dynamic Systems
State Estimation of Linear and Nonlinear Dynamic Systems Part III: Nonlinear Systems: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) James B. Rawlings and Fernando V. Lima Department of
More informationDissipativity. Outline. Motivation. Dissipative Systems. M. Sami Fadali EBME Dept., UNR
Dissipativity M. Sami Fadali EBME Dept., UNR 1 Outline Differential storage functions. QSR Dissipativity. Algebraic conditions for dissipativity. Stability of dissipative systems. Feedback Interconnections
More informationA METHOD OF ADAPTATION BETWEEN STEEPEST- DESCENT AND NEWTON S ALGORITHM FOR MULTI- CHANNEL ACTIVE CONTROL OF TONAL NOISE AND VIBRATION
A METHOD OF ADAPTATION BETWEEN STEEPEST- DESCENT AND NEWTON S ALGORITHM FOR MULTI- CHANNEL ACTIVE CONTROL OF TONAL NOISE AND VIBRATION Jordan Cheer and Stephen Daley Institute of Sound and Vibration Research,
More informationFirst Prev Next Last Go Back Full Screen Close Quit. Game Theory. Giorgio Fagiolo
Game Theory Giorgio Fagiolo giorgio.fagiolo@univr.it https://mail.sssup.it/ fagiolo/welcome.html Academic Year 2005-2006 University of Verona Summary 1. Why Game Theory? 2. Cooperative vs. Noncooperative
More informationEstimating Disturbance Covariances From Data For Improved Control Performance
Estimating Disturbance Covariances From Data For Improved Control Performance by Brian J. Odelson A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY
More informationLQG/LTR ROBUST CONTROL SYSTEM DESIGN FOR A LOW-PRESSURE FEEDWATER HEATER TRAIN. G. V. Murphy J. M. Bailey The University of Tennessee, Knoxville"
LQG/LTR ROBUST CONTROL SYSTEM DESIGN FOR A LOW-PRESSURE FEEDWATER HEATER TRAIN G. V. Murphy J. M. Bailey The University of Tennessee, Knoxville" CONF-900464 3 DE90 006144 Abstract This paper uses the linear
More informationAN OPTIMIZATION-BASED APPROACH FOR QUASI-NONINTERACTING CONTROL. Jose M. Araujo, Alexandre C. Castro and Eduardo T. F. Santos
ICIC Express Letters ICIC International c 2008 ISSN 1881-803X Volume 2, Number 4, December 2008 pp. 395 399 AN OPTIMIZATION-BASED APPROACH FOR QUASI-NONINTERACTING CONTROL Jose M. Araujo, Alexandre C.
More informationCBE495 LECTURE IV MODEL PREDICTIVE CONTROL
What is Model Predictive Control (MPC)? CBE495 LECTURE IV MODEL PREDICTIVE CONTROL Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University * Some parts are from
More informationStochastic Tube MPC with State Estimation
Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems MTNS 2010 5 9 July, 2010 Budapest, Hungary Stochastic Tube MPC with State Estimation Mark Cannon, Qifeng Cheng,
More information3.1 Overview 3.2 Process and control-loop interactions
3. Multivariable 3.1 Overview 3.2 and control-loop interactions 3.2.1 Interaction analysis 3.2.2 Closed-loop stability 3.3 Decoupling control 3.3.1 Basic design principle 3.3.2 Complete decoupling 3.3.3
More information