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E-grāmata: Distributed Model Predictive Control for Plant-Wide Systems

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  • Formāts: EPUB+DRM
  • Izdošanas datums: 02-May-2017
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118921593
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Distributed Model Predictive Control for Plant-Wide SystemsPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 02-May-2017
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118921593
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DISTRIBUTED MODEL PREDICTIVE CONTROL FOR PLANT-WIDE SYSTEMS

In this book, experienced researchers gave a thorough explanation of distributed model predictive control (DMPC): its basic concepts, technologies, and implementation in plant-wide systems. Known for its error tolerance, high flexibility, and good dynamic performance, DMPC is a popular topic in the control field and is widely applied in many industries.

To efficiently design DMPC systems, readers will be introduced to several categories of coordinated DMPCs, which are suitable for different control requirements, such as network connectivity, error tolerance, performance of entire closed-loop systems, and calculation of speed. Various real-life industrial applications, theoretical results, and algorithms are provided to illustrate key concepts and methods, as well as to provide solutions to optimize the global performance of plant-wide systems.





Features system partition methods, coordination strategies, performance analysis, and how to design stabilized DMPC under different coordination strategies. Presents useful theories and technologies that can be used in many different industrial fields, examples include metallurgical processes and high-speed transport. Reflects the authors extensive research in the area, providing a wealth of current and contextual information.

Distributed Model Predictive Control for Plant-Wide Systems is an excellent resource for researchers in control theory for large-scale industrial processes. Advanced students of DMPC and control engineers will also find this as a comprehensive reference text.
Preface xi
About the Authors xv
Acknowledgement xvii
List of Figures xix
List of Tables xxiii
1 Introduction
1(18)
1.1 Plant-Wide System
1(2)
1.2 Control System Structure of the Plant-Wide System
3(5)
1.2.1 Centralized Control
4(1)
1.2.2 Decentralized Control and Hierarchical Coordinated Decentralized Control
5(1)
1.2.3 Distributed Control
6(2)
1.3 Predictive Control
8(1)
1.3.1 What is Predictive Control
8(1)
1.3.2 Advantage of Predictive Control
9(1)
1.4 Distributed Predictive Control
9(4)
1.4.1 Why Distributed Predictive Control
9(1)
1.4.2 What is Distributed Predictive Control
10(1)
1.4.3 Advantage of Distributed Predictive Control
10(1)
1.4.4 Classification of DMPC
11(2)
1.5 About this Book
13(6)
Part I Foundation
2 Model Predictive Control
19(20)
2.1 Introduction
19(1)
2.2 Dynamic Matrix Control
20(6)
2.2.1 Step Response Model
20(1)
2.2.2 Prediction
21(1)
2.2.3 Optimization
22(1)
2.2.4 Feedback Correction
23(1)
2.2.5 DMC with Constraint
24(2)
2.3 Predictive Control with the State Space Model
26(7)
2.3.1 System Model
27(1)
2.3.2 Performance Index
28(1)
2.3.3 Prediction
28(2)
2.3.4 Closed-Loop Solution
30(1)
2.3.5 State Space MPC with Constraint
31(2)
2.4 Dual Mode Predictive Control
33(4)
2.4.1 Invariant Region
33(1)
2.4.2 MPC Formulation
34(1)
2.4.3 Algorithms
35(1)
2.4.4 Feasibility and Stability
36(1)
2.5 Conclusion
37(2)
3 Control Structure of Distributed MPC
39(8)
3.1 Introduction
39(1)
3.2 Centralized MPC
40(1)
3.3 Single-Layer Distributed MPC
41(1)
3.4 Hierarchical Distributed MPC
42(1)
3.5 Example of the Hierarchical DMPC Structure
43(2)
3.6 Conclusion
45(2)
4 Structure Model and System Decomposition
47(20)
4.1 Introduction
47(1)
4.2 System Mathematic Model
48(2)
4.3 Structure Model and Structure Controllability
50(8)
4.3.1 Structure Model
50(1)
4.3.2 Function of the Structure Model in System Decomposition
51(2)
4.3.3 Input-Output Accessibility
53(3)
4.3.4 General Rank of the Structure Matrix
56(1)
4.3.5 Structure Controllability
56(2)
4.4 Related Gain Array Decomposition
58(5)
4.4.1 RGA Definition
59(1)
4.4.2 RGA Interpretation
60(1)
4.4.3 Pairing Rules
61(2)
4.5 Conclusion
63(4)
Part II Unconstrained Distributed Predictive Control
5 Local Cost Optimization-based Distributed Model Predictive Control
67(36)
5.1 Introduction
67(1)
5.2 Local Cost Optimization-based Distributed Predictive Control
68(14)
5.2.1 Problem Description
68(1)
5.2.2 DMPC Formulation
69(3)
5.2.3 Closed-loop Solution
72(7)
5.2.4 Stability Analysis
79(1)
5.2.5 Simulation Results
79(3)
5.3 Distributed MPC Strategy Based on Nash Optimality
82(17)
5.3.1 Formulation
83(3)
5.3.2 Algorithm
86(1)
5.3.3 Computational Convergence for Linear Systems
86(2)
5.3.4 Nominal Stability of Distributed Model Predictive Control System
88(1)
5.3.5 Performance Analysis with Single-step Horizon Control Under Communication Failure
89(5)
5.3.6 Simulation Results
94(5)
5.4 Conclusion
99(1)
Appendix A. QP problem transformation
99(1)
Appendix B. Proof of Theorem 5.1
100(3)
6 Cooperative Distributed Predictive Control
103(22)
6.1 Introduction
103(1)
6.2 Noniterative Cooperative DMPC
104(10)
6.2.1 System Description
104(1)
6.2.2 Formulation
104(3)
6.2.3 Closed-Form Solution
107(2)
6.2.4 Stability and Performance Analysis
109(4)
6.2.5 Example
113(1)
6.3 Distributed Predictive Control based on Pareto Optimality
114(7)
6.3.1 Formulation
118(1)
6.3.2 Algorithm
119(1)
6.3.3 The DMPC Algorithm Based on Plant-Wide Optimality
119(2)
6.3.4 The Convergence Analysis of the Algorithm
121(1)
6.4 Simulation
121(2)
6.5 Conclusions
123(2)
7 Networked Distributed Predictive Control with Information Structure Constraints
125(44)
7.1 Introduction
125(1)
7.2 Noniterative Networked DMPC
126(18)
7.2.1 Problem Description
126(1)
7.2.2 DMPC Formulation
127(5)
7.2.3 Closed-Form Solution
132(3)
7.2.4 Stability Analysis
135(1)
7.2.5 Analysis of Performance
135(2)
7.2.6 Numerical Validation
137(7)
7.3 Networked DMPC with Iterative Algorithm
144(15)
7.3.1 Problem Description
144(1)
7.3.2 DMPC Formulation
145(2)
7.3.3 Networked MPC Algorithm
147(3)
7.3.4 Convergence and Optimality Analysis for Networked
150(2)
7.3.5 Nominal Stability Analysis for Distributed Control Systems
152(1)
7.3.6 Simulation Study
153(6)
7.4 Conclusion
159(1)
Appendix A. Proof of Lemma 7.1
159(1)
Appendix B. Proof of Lemma 7.2
160(1)
Appendix C. Proof of Lemma 7.3
160(1)
Appendix D. Proof of Theorem 7.1
161(1)
Appendix E. Proof of Theorem 7.2
161(3)
Appendix F. Derivation of the QP problem (7.52)
164(5)
Part III Constraint Distributed Predictive Control
8 Local Cost Optimization Based Distributed Predictive Control with Constraints
169(20)
8.1 Introduction
169(1)
8.2 Problem Description
170(1)
8.3 Stabilizing Dual Mode Noncooperative DMPC with Input Constraints
171(6)
8.3.1 Formulation
171(5)
8.3.2 Algorithm Design for Resolving Each Subsystem-based Predictive Control
176(1)
8.4 Analysis
177(7)
8.4.1 Recursive Feasibility of Each Subsystem-based Predictive Control
177(6)
8.4.2 Stability Analysis of Entire Closed-loop System
183(1)
8.5 Example
184(3)
8.5.1 The System
184(1)
8.5.2 Performance Comparison with the Centralized MPC
185(2)
8.6 Conclusion
187(2)
9 Cooperative Distributed Predictive Control with Constraints
189(20)
9.1 Introduction
189(1)
9.2 System Description
190(1)
9.3 Stabilizing Cooperative DMPC with Input Constraints
191(3)
9.3.1 Formulation
191(2)
9.3.2 Constraint C-DMPC Algorithm
193(1)
9.4 Analysis
194(7)
9.4.1 Feasibility
194(5)
9.4.2 Stability
199(2)
9.5 Simulation
201(7)
9.6 Conclusion
208(1)
10 Networked Distributed Predictive Control with Inputs and Information Structure Constraints
209(30)
10.1 Introduction
209(1)
10.2 Problem Description
210(2)
10.3 Constrained N-DMPC
212(7)
10.3.1 Formulation
212(6)
10.3.2 Algorithm Design for Resolving Each Subsystem-based Predictive Control
218(1)
10.4 Analysis
219(8)
10.4.1 Feasibility
219(6)
10.4.2 Stability
225(2)
10.5 Formulations Under Other Coordination Strategies
227(2)
10.5.1 Local Cost Optimization Based DMPC
227(1)
10.5.2 Cooperative DMPC
228(1)
10.6 Simulation Results
229(7)
10.6.1 The System
229(1)
10.6.2 Performance of Closed-loop System under the N-DMPC
230(1)
10.6.3 Performance Comparison with the Centralized MPC and the Local Cost Optimization based MPC
231(5)
10.7 Conclusions
236(3)
Part IV Application
11 Hot-Rolled Strip Laminar Cooling Process with Distributed Predictive Control
239(24)
11.1 Introduction
239(1)
11.2 Laminar Cooling of Hot-rolled Strip
240(4)
11.2.1 Description
240(1)
11.2.2 Thermodynamic Model
241(1)
11.2.3 Problem Statement
242(2)
11.3 Control Strategy of HSLC
244(7)
11.3.1 State Space Model of Subsystems
244(3)
11.3.2 Design of Extended Kalman Filter
247(1)
11.3.3 Predictor
247(1)
11.3.4 Local MPC Formulation
248(1)
11.3.5 Iterative Algorithm
249(2)
11.4 Numerical Experiment
251(5)
11.4.1 Validation of Designed Model
251(1)
11.4.2 Convergence of EKF
252(1)
11.4.3 Performance of DMPC Comparing with Centralized MPC
252(1)
11.4.4 Advantages of the Proposed DMPC Framework Comparing with the Existing Method
253(3)
11.5 Experimental Results
256(2)
11.6 Conclusion
258(5)
12 High-Speed Train Control with Distributed Predictive Control
263(16)
12.1 Introduction
263(1)
12.2 System Description
264(1)
12.3 N-DMPC for High-Speed Trains
264(8)
12.3.1 Three Types of Force
264(2)
12.3.2 The Force Analysis of EMUs
266(1)
12.3.3 Model of CRH2
267(4)
12.3.4 Performance Index
271(1)
12.3.5 Optimization Problem
272(1)
12.4 Simulation Results
272(6)
12.4.1 Parameters of CRH2
273(1)
12.4.2 Simulation Matrix
273(1)
12.4.3 Results and Some Comments
274(4)
12.5 Conclusion
278(1)
13 Operation Optimization of Multitype Cooling Source System Based on DMPC
279(14)
13.1 Introduction
279(1)
13.2 Structure of Joint Cooling System
279(1)
13.3 Control Strategy of Joint Cooling System
280(6)
13.3.1 Economic Optimization Strategy
281(2)
13.3.2 Design of Distributed Model Predictive Control in Multitype Cold Source System
283(3)
13.4 Results and Analysis of Simulation
286(6)
13.5 Conclusion
292(1)
References 293(6)
Index 299
SHAOYUAN LI Shanghai Jiao Tong University, China

YI ZHENG Shanghai Jiao Tong University, China
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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