分布式控制.pdf

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Doctoral Training Programme Ghent University 1 Doctoral School of the Faculty of Automatic Control and Computer Engineering Department of Electrical energy, Systems and Automation University of Ghent Technical University ‘Gh. Asachi’ of Iasi Department of Automatic Control and Applied Informatics DISTRIBUTED MPC FOR DYNAMICALLY COUPLED SYSTEMS PhD Student: Eng. Anca MAXIM Coordinators: Prof. dr. eng. Robin DE KEYSER & Prof. dr. eng. Corneliu LAZAR Co-supervisors: Dr. Eng. Clara M. IONESCU Assist. Prof. dr. eng. Constantin F. CARUNTU

Outline 2  Introduction  State of art  Method Implementation Theoretical description Matlab implementation Process information Simulation Results  Possible improvements  Non-cooperative DMPC  Conclusions

Introduction 3 Large Scale System= Complex Process consisting in Independent but Interacting subsystems. Control demands: • performance • efficiency • interactions eradication Control approaches: • centralized •decentralized •distributed

Introduction 4 DMPC Up-to date & in expansion Research topic Proof: -9 DMPC-related papers at 19th IFAC World Congress 2014 -presence in literature: ACC, ACODS, Journal of Process Control, AICHE Journal, International Journal of Electrical Power & Energy Systems - 4th IFAC Nonlinear Model Predictive Control Conference 2012 -Workshops: 52nd CDC 2013, May 2010- Lund University 

State of Art 5 DMPC Lyapunov – based DMPC Game-Theory based DMPC Cooperative/Non Cooperative DMPC •J.M. Maestre, D. Munoz de la Pena, E.F. Camacho, Distributed model predictive control based on a cooperative game, Optim. Control Appl. Meth 32, 2011, Pages 153-176 • Each agent plays a cooperative game • three options for control input: - Shifted trajectory - The altruist trajectory - The selfish trajectory • closed-loop stability via terminal constraint

Outline 6  Introduction  State of art  Method Implementation Theoretical description Matlab implementation Process information Simulation Results  Possible improvements  Non-cooperative DMPC  Conclusions

Method Implementation Theoretical Description 7 DMPC algorithm •each agent i receives its corresponding partial state measurement •Communication: Controller 1 sends and controller 2 sends • Selfish Optimization: •Altruist Optimization: Agent 1: • Communication: Agent 1 sends and receives • Cost function evaluation: 9 input combinations • Communication: Sharing time => all 9 cost functions evaluations • Optimal solutions: min • Receding horizon principle ()ixt11,NKx22,NKx12,ssUU12*11112*22221()argmin((),(),())()argmin((),(),())sUsUUtJxtUtUtUtJxtUtUt2*21112()argmin((),(),())wUUtJxtUtUt*12,wUU*21,wUU*1111*2222,,,,swswUUUUUUUU12,ddUU12JJJ

Method Implementation Matlab approach 8 State Prediction : Model recursion Matrix form 1111111221111111221111111221111111221111222111111111221111221(1)(0)(0)(0)(2)(1)(1)(1) (0)(0)(0)(1)(1) = (0)(0)(0)(1)(1)(XxUUxAxBuBuxAxBuBuAAxBuBuBuBuAxABuABuBuBuxN)1111111111212111111111212111211111111111(1)00(0)00(1)0(1)0(0)()(1)NNNXUxBuBAxABBuABBAxxNABABBuNA1222212112112122(0)(1)(1)NNUuuABABBuN222211222XxUU

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