Distributed Model Predictive Control for Plant-Wide Systems

A comprehensive examination of DMPC theory and its technological applications A comprehensive examination of DMPC theory and its technological applications from basic through to advanced level A systematic introduction to DMPC technology providing classic DMPC coordination strategies, analysis of their performance, and design methods for both unconstraint and constraint systems Includes the system partition methods, coordination strategies, the performance analysis and how to design stabilized DMPC under different coordination strategies Presents useful theories and technologies which can be used in many different industrial fields, such as the metallurgical process and high speed transport, helping readers to grasp the procedure of using the DMPC Reflects  the authors combined research in the area, providing a wealth of and current and background information INDICE: Preface xi .About the Authors xv .Acknowledgement xvii .List of Figures xix .List of Tables xxiii .1 Introduction 1 .1.1 Plant–Wide System 1 .1.2 Control System Structure of the Plant–Wide System 3 .1.2.1 Centralized Control 4 .1.2.2 Decentralized Control and Hierarchical Coordinated Decentralized Control 5 .1.2.3 Distributed Control 6 .1.3 Predictive Control 8 .1.3.1 What is Predictive Control 8 .1.3.2 Advantage of Predictive Control 9 .1.4 Distributed Predictive Control 9 .1.4.1 Why Distributed Predictive Control 9 .1.4.2 What is Distributed Predictive Control 10 .1.4.3 Advantage of Distributed Predictive Control 10 .1.4.4 Classification of DMPC 11 .1.5 About this Book 13 .Part I FOUNDATION .2 Model Predictive Control 19 .2.1 Introduction 19 .2.2 Dynamic Matrix Control 20 .2.2.1 Step Response Model 20 .2.2.2 Prediction 21 .2.2.3 Optimization 22 .2.2.4 Feedback Correction 23 .2.2.5 DMC with Constraint 24 .2.3 Predictive Control with the State Space Model 26 .2.3.1 System Model 27 .2.3.2 Performance Index 28 .2.3.3 Prediction 28 .2.3.4 Closed–Loop Solution 30 .2.3.5 State Space MPC with Constraint 31 .2.4 Dual Mode Predictive Control 33 .2.4.1 Invariant Region 33 .2.4.2 MPC Formulation 34 .2.4.3 Algorithms 35 .2.4.4 Feasibility and Stability 36 .2.5 Conclusion 37 .3 Control Structure of Distributed MPC 39 .3.1 Introduction 39 .3.2 Centralized MPC 40 .3.3 Single–Layer Distributed MPC 41 .3.4 Hierarchical Distributed MPC 42 .3.5 Example of the Hierarchical DMPC Structure 43 .3.6 Conclusion 45 .4 Structure Model and System Decomposition 47 .4.1 Introduction 47 .4.2 System Mathematic Model 48 .4.3 Structure Model and Structure Controllability 50 .4.3.1 Structure Model 50 .4.3.2 Function of the Structure Model in System Decomposition 51 .4.3.3 Input Output Accessibility 53 .4.3.4 General Rank of the Structure Matrix 56 .4.3.5 Structure Controllability 56 .4.4 Related Gain Array Decomposition 58 .4.4.1 RGA Definition 59 .4.4.2 RGA Interpretation 60 .4.4.3 Pairing Rules 61 .4.5 Conclusion 63 .Part II UNCONSTRAINED DISTRIBUTED PREDICTIVE CONTROL .5 Local Cost Optimization–based Distributed Model Predictive Control 67 .5.1 Introduction 67 .5.2 Local Cost Optimization–based Distributed Predictive Control 68 .5.2.1 Problem Description 68 .5.2.2 DMPC Formulation 69 .5.2.3 Closed–loop Solution 72 .5.2.4 Stability Analysis 79 .5.2.5 Simulation Results 79 .5.3 Distributed MPC Strategy Based on Nash Optimality 82 .5.3.1 Formulation 83 .5.3.2 Algorithm 86 .5.3.3 Computational Convergence for Linear Systems 86 .5.3.4 Nominal Stability of Distributed Model Predictive Control System 88 .5.3.5 Performance Analysis with Single–step Horizon Control Under Communication Failure 89 .5.3.6 Simulation Results 94 .5.4 Conclusion 99 .Appendix 99 .Appendix A. QP problem transformation 99 .Appendix B. Proof of Theorem 5.1 100 .6 Cooperative Distributed Predictive Control 103 .6.1 Introduction 103 .6.2 Noniterative Cooperative DMPC 104 .6.2.1 System Description 104 .6.2.2 Formulation 104 .6.2.3 Closed–Form Solution 107 .6.2.4 Stability and Performance Analysis 109 .6.2.5 Example 113 .6.3 Distributed Predictive Control based on Pareto Optimality 114 .6.3.1 Formulation 118 .6.3.2 Algorithm 119 .6.3.3 The DMPC Algorithm Based on Plant–Wide Optimality 119 .6.3.4 The Convergence Analysis of the Algorithm 121 .6.4 Simulation 121 .6.5 Conclusions 123 .7 Networked Distributed Predictive Control with Information Structure Constraints 125 .7.1 Introduction 125 .7.2 Noniterative Networked DMPC 126 .7.2.1 Problem Description 126 .7.2.2 DMPC Formulation 127 .7.2.3 Closed–Form Solution 132 .7.2.4 Stability Analysis 135 .7.2.5 Analysis of Performance 135 .7.2.6 Numerical Validation 137 .7.3 Networked DMPC with Iterative Algorithm 144 .7.3.1 Problem Description 144 .7.3.2 DMPC Formulation 145 .7.3.3 Networked MPC Algorithm 147 .7.3.4 Convergence and Optimality Analysis for Networked 150 .7.3.5 Nominal Stability Analysis for Distributed Control Systems 152 .7.3.6 Simulation Study 153 .7.4 Conclusion 159 .Appendix 159 .Appendix A. Proof of Lemma 7.1 159 .Appendix B. Proof of Lemma 7.2 160 .Appendix C. Proof of Lemma 7.3 160 .Appendix D. Proof of Theorem 7.1 161 .Appendix E. Proof of Theorem 7.2 161 .Appendix F. Derivation of the QP problem (7.52) 164 .Part III CONSTRAINT DISTRIBUTED PREDICTIVE CONTROL .8 Local Cost Optimization Based Distributed Predictive Control with Constraints 169 .8.1 Introduction 169 .8.2 Problem Description 170 .8.3 Stabilizing Dual Mode Noncooperative DMPC with Input Constraints 171 .8.3.1 Formulation 171 .8.3.2 Algorithm Design for Resolving Each Subsystem–based Predictive Control 176 .8.4 Analysis 177 .8.4.1 Recursive Feasibility of Each Subsystem–based Predictive Control 177 .8.4.2 Stability Analysis of Entire Closed–loop System 183 .8.5 Example 184 .8.5.1 The System 184 .8.5.2 Performance Comparison with the Centralized MPC 185 .8.6 Conclusion 187 .9 Cooperative Distributed Predictive Control with Constraints 189 .9.1 Introduction 189 .9.2 System Description 190 .9.3 Stabilizing Cooperative DMPC with Input Constraints 191 .9.3.1 Formulation 191 .9.3.2 Constraint C–DMPC Algorithm 193 .9.4 Analysis 194 .9.4.1 Feasibility 194 .9.4.2 Stability 199 .9.5 Simulation 201 .9.6 Conclusion 208 .10 Networked Distributed Predictive Control with Inputs and Information Structure Constraints 209 .10.1 Introduction 209 .10.2 Problem Description 210 .10.3 Constrained N–DMPC 212 .10.3.1 Formulation 212 .10.3.2 Algorithm Design for Resolving Each Subsystem–based Predictive Control 218 .10.4 Analysis 219 .10.4.1 Feasibility 219 .10.4.2 Stability 225 .10.5 Formulations Under Other Coordination Strategies 227 .10.5.1 Local Cost Optimization Based DMPC 227 .10.5.2 Cooperative DMPC 228 .10.6 Simulation Results 229 .10.6.1 The System 229 .10.6.2 Performance of Closed–loop System under the N–DMPC 230 .10.6.3 Performance Comparison with the Centralized MPC and the Local Cost Optimization based MPC 231 .10.7 Conclusions 236 .Part IV APPLICATION .11 Hot–Rolled Strip Laminar Cooling Process with Distributed Predictive Control 239 .11.1 Introduction 239 .11.2 Laminar Cooling of Hot–rolled Strip 240 .11.2.1 Description 240 .11.2.2 Thermodynamic Model 241 .11.2.3 Problem Statement 242 .11.3 Control Strategy of HSLC 244 .11.3.1 State Space Model of Subsystems 244 .11.3.2 Design of Extended Kalman Filter 247 .11.3.3 Predictor 247 .11.3.4 Local MPC Formulation 248 .11.3.5 Iterative Algorithm 249 .11.4 Numerical Experiment 251 .11.4.1 Validation of Designed Model 251 .11.4.2 Convergence of EKF 252 .11.4.3 Performance of DMPC Comparing with Centralized MPC 252 .11.4.4 Advantages of the Proposed DMPC Framework Comparing with the Existing Method 253 .11.5 Experimental Results 256 .11.6 Conclusion 258 .12 High–Speed Train Control with Distributed Predictive Control 263 .12.1 Introduction 263 .12.2 System Description 264 .12.3 N–DMPC for High–Speed Trains 264 .12.3.1 Three Types of Force 264 .12.3.2 The Force Analysis of EMUs 266 .12.3.3 Model of CRH2 267 .12.3.4 Performance Index 271 .12.3.5 Optimization Problem 272 .12.4 Simulation Results 272 .12.4.1 Parameters of CRH2 273 .12.4.2 Simulation Matrix 273 .12.4.3 Results and Some Comments 274 .12.5 Conclusion 278 .13 Operation Optimization of Multitype Cooling Source System Based on DMPC 279 .13.1 Introduction 279 .13.2 Structure of Joint Cooling System 279 .13.3 Control Strategy of Joint Cooling System 280 .13.3.1 Economic Optimization Strategy 281 .13.3.2 Design of Distributed Model Predictive Control in Multitype Cold Source System 283 .13.4 Results and Analysis of Simulation 286 .13.5 Conclusion 292 .References 293 .Index 299

• ISBN: 978-1-118-92156-2
• Editorial: Wiley–Blackwell
• Encuadernacion: Cartoné
• Páginas: 330
• Fecha Publicación: 22/09/2015
• Nº Volúmenes: 1
• Idioma: Inglés