Distributed Model Predictive Control for Plant-Wide Systems

 
 
John Wiley & Sons Inc (Verlag)
  • 1. Auflage
  • |
  • erschienen am 24. Juli 2015
  • |
  • 328 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-118-92158-6 (ISBN)
 
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
weitere Ausgaben werden ermittelt
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • About the Authors
  • Acknowledgement
  • List of Figures
  • List of Tables
  • Chapter 1 Introduction
  • 1.1 Plant-Wide System
  • 1.2 Control System Structure of the Plant-Wide System
  • 1.2.1 Centralized Control
  • 1.2.2 Decentralized Control and Hierarchical Coordinated Decentralized Control
  • 1.2.3 Distributed Control
  • 1.3 Predictive Control
  • 1.3.1 What is Predictive Control
  • 1.3.2 Advantage of Predictive Control
  • 1.4 Distributed Predictive Control
  • 1.4.1 Why Distributed Predictive Control
  • 1.4.2 What is Distributed Predictive Control
  • 1.4.3 Advantage of Distributed Predictive Control
  • 1.4.4 Classification of DMPC
  • 1.5 About this Book
  • Part I Foundation
  • Chapter 2 Model Predictive Control
  • 2.1 Introduction
  • 2.2 Dynamic Matrix Control
  • 2.2.1 Step Response Model
  • 2.2.2 Prediction
  • 2.2.3 Optimization
  • 2.2.4 Feedback Correction
  • 2.2.5 DMC with Constraint
  • 2.3 Predictive Control with the State Space Model
  • 2.3.1 System Model
  • 2.3.2 Performance Index
  • 2.3.3 Prediction
  • 2.3.4 Closed-Loop Solution
  • 2.3.5 State Space MPC with Constraint
  • 2.4 Dual Mode Predictive Control
  • 2.4.1 Invariant Region
  • 2.4.2 MPC Formulation
  • 2.4.3 Algorithms
  • 2.4.4 Feasibility and Stability
  • 2.5 Conclusion
  • Chapter 3 Control Structure of Distributed MPC
  • 3.1 Introduction
  • 3.2 Centralized MPC
  • 3.3 Single-Layer Distributed MPC
  • 3.4 Hierarchical Distributed MPC
  • 3.5 Example of the Hierarchical DMPC Structure
  • 3.6 Conclusion
  • Chapter 4 Structure Model and System Decomposition
  • 4.1 Introduction
  • 4.2 System Mathematic Model
  • 4.3 Structure Model and Structure Controllability
  • 4.3.1 Structure Model
  • 4.3.2 Function of the Structure Model in System Decomposition
  • 4.3.3 Input-Output Accessibility
  • 4.3.4 General Rank of the Structure Matrix
  • 4.3.5 Structure Controllability
  • 4.4 Related Gain Array Decomposition
  • 4.4.1 RGA Definition
  • 4.4.2 RGA Interpretation
  • 4.4.3 Pairing Rules
  • 4.5 Conclusion
  • Part II Unconstrained Distributed Predictive Control
  • Chapter 5 Local Cost Optimization-based Distributed Model Predictive Control
  • 5.1 Introduction
  • 5.2 Local Cost Optimization-based Distributed Predictive Contro
  • 5.2.1 Problem Description
  • 5.2.2 DMPC Formulation
  • 5.2.3 Closed-loop Solution
  • 5.2.4 Stability Analysis
  • 5.2.5 Simulation Results
  • 5.3 Distributed MPC Strategy Based on Nash Optimality
  • 5.3.1 Formulation
  • 5.3.2 Algorithm
  • 5.3.3 Computational Convergence for Linear Systems
  • 5.3.4 Nominal Stability of Distributed Model Predictive Control System
  • 5.3.5 Performance Analysis with Single-step Horizon Control Under Communication Failure
  • 5.3.6 Simulation Results
  • 5.4 Conclusion
  • Appendix
  • Appendix A. QP problem transformation
  • Appendix B. Proof of Theorem 5.1
  • Chapter 6 Cooperative Distributed Predictive Control
  • 6.1 Introduction
  • 6.2 Noniterative Cooperative DMPC
  • 6.2.1 System Description
  • 6.2.2 Formulation
  • 6.2.3 Closed-Form Solution
  • 6.2.4 Stability and Performance Analysis
  • 6.2.5 Example
  • 6.3 Distributed Predictive Control based on Pareto Optimality
  • 6.3.1 Formulation
  • 6.3.2 Algorithm
  • 6.3.3 The DMPC Algorithm Based on Plant-Wide Optimality
  • 6.3.4 The Convergence Analysis of the Algorithm
  • 6.4 Simulation
  • 6.5 Conclusions
  • Chapter 7 Networked Distributed Predictive Control with Information Structure Constraints
  • 7.1 Introduction
  • 7.2 Noniterative Networked DMPC
  • 7.2.1 Problem Description
  • 7.2.2 DMPC Formulation
  • 7.2.3 Closed-Form Solution
  • 7.2.4 Stability Analysis
  • 7.2.5 Analysis of Performance
  • 7.2.6 Numerical Validation
  • 7.3 Networked DMPC with Iterative Algorithm
  • 7.3.1 Problem Description
  • 7.3.2 DMPC Formulation
  • 7.3.3 Networked MPC Algorithm
  • 7.3.4 Convergence and Optimality Analysis for Networked
  • 7.3.5 Nominal Stability Analysis for Distributed Control Systems
  • 7.3.6 Simulation Study
  • 7.4 Conclusion
  • Appendix
  • Appendix A. Proof of Lemma 7.1
  • Appendix B. Proof of Lemma 7.2
  • Appendix C. Proof of Lemma 7.3
  • Appendix D. Proof of Theorem 7.1
  • Appendix E. Proof of Theorem 7.2
  • Appendix F. Derivation of the QP problem (7.52)
  • Part III Constraint Distributed Predictive Control
  • Chapter 8 Local Cost Optimization Based Distributed Predictive Control with Constraints
  • 8.1 Introduction
  • 8.2 Problem Description
  • 8.3 Stabilizing Dual Mode Noncooperative DMPC with Input Constraints
  • 8.3.1 Formulation
  • 8.3.2 Algorithm Design for Resolving Each Subsystem-based Predictive Control
  • 8.4 Analysis
  • 8.4.1 Recursive Feasibility of Each Subsystem-based Predictive Control
  • 8.4.2 Stability Analysis of Entire Closed-loop System
  • 8.5 Example
  • 8.5.1 The System
  • 8.5.2 Performance Comparison with the Centralized MPC
  • 8.6 Conclusion
  • Chapter 9 Cooperative Distributed Predictive Control with Constraints
  • 9.1 Introduction
  • 9.2 System Description
  • 9.3 Stabilizing Cooperative DMPC with Input Constraints
  • 9.3.1 Formulation
  • 9.3.2 Constraint C-DMPC Algorithm
  • 9.4 Analysis
  • 9.4.1 Feasibility
  • 9.4.2 Stability
  • 9.5 Simulation
  • 9.6 Conclusion
  • Chapter 10 Networked Distributed Predictive Control with Inputs and Information Structure Constraints
  • 10.1 Introduction
  • 10.2 Problem Description
  • 10.3 Constrained N-DMPC
  • 10.3.1 Formulation
  • 10.3.2 Algorithm Design for Resolving Each Subsystem-based Predictive Control
  • 10.4 Analysis
  • 10.4.1 Feasibility
  • 10.4.2 Stability
  • 10.5 Formulations Under Other Coordination Strategies
  • 10.5.1 Local Cost Optimization Based DMPC
  • 10.5.2 Cooperative DMPC
  • 10.6 Simulation Results
  • 10.6.1 The System
  • 10.6.2 Performance of Closed-loop System under the N-DMPC
  • 10.6.3 Performance Comparison with the Centralized MPC and the Local Cost Optimization based MPC
  • 10.7 Conclusions
  • Part IV Application
  • Chapter 11 Hot-Rolled Strip Laminar Cooling Process with Distributed Predictive Control
  • 11.1 Introduction
  • 11.2 Laminar Cooling of Hot-rolled Strip
  • 11.2.1 Description
  • 11.2.2 Thermodynamic Model
  • 11.2.3 Problem Statement
  • 11.3 Control Strategy of HSLC
  • 11.3.1 State Space Model of Subsystems
  • 11.3.2 Design of Extended Kalman Filter
  • 11.3.3 Predictor
  • 11.3.4 Local MPC Formulation
  • 11.3.5 Iterative Algorithm
  • 11.4 Numerical Experiment
  • 11.4.1 Validation of Designed Model
  • 11.4.2 Convergence of EKF
  • 11.4.3 Performance of DMPC Comparing with Centralized MPC
  • 11.4.4 Advantages of the Proposed DMPC Framework Comparing with the Existing Method
  • 11.5 Experimental Results
  • 11.6 Conclusion
  • Chapter 12 High-Speed Train Control with Distributed Predictive Control
  • 12.1 Introduction
  • 12.2 System Description
  • 12.3 N-DMPC for High-Speed Trains
  • 12.3.1 Three Types of Force
  • 12.3.2 The Force Analysis of EMUs
  • 12.3.3 Model of CRH2
  • 12.3.4 Performance Index
  • 12.3.5 Optimization Problem
  • 12.4 Simulation Results
  • 12.4.1 Parameters of CRH2
  • 12.4.2 Simulation Matrix
  • 12.4.3 Results and Some Comments
  • 12.5 Conclusion
  • Chapter 13 Operation Optimization of Multitype Cooling Source System Based on DMPC
  • 13.1 Introduction
  • 13.2 Structure of Joint Cooling System
  • 13.3 Control Strategy of Joint Cooling System
  • 13.3.1 Economic Optimization Strategy
  • 13.3.2 Design of Distributed Model Predictive Control in Multitype Cold Source System
  • 13.4 Results and Analysis of Simulation
  • 13.5 Conclusion
  • References
  • Index
  • EULA

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