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Iterative Learning Stabilization and Fault-Tolerant Control for Batch Processes 2020 ed. [Kõva köide]

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  • Formaat: Hardback, 323 pages, kõrgus x laius: 235x155 mm, kaal: 670 g, 58 Illustrations, color; 8 Illustrations, black and white; X, 323 p. 66 illus., 58 illus. in color., 1 Hardback
  • Ilmumisaeg: 26-Mar-2019
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811357897
  • ISBN-13: 9789811357893
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Iterative Learning Stabilization and Fault-Tolerant Control for Batch Processes 2020 ed.Suurem pilt
  • Formaat: Hardback, 323 pages, kõrgus x laius: 235x155 mm, kaal: 670 g, 58 Illustrations, color; 8 Illustrations, black and white; X, 323 p. 66 illus., 58 illus. in color., 1 Hardback
  • Ilmumisaeg: 26-Mar-2019
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811357897
  • ISBN-13: 9789811357893
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811357893.html
This book is based on the authors’ research on the stabilization and fault-tolerant control of batch processes, which are flourishing topics in the field of control system engineering. It introduces iterative learning control for linear/nonlinear single/multi-phase batch processes; iterative learning optimal guaranteed cost control; delay-dependent iterative learning control; and iterative learning fault-tolerant control for linear/nonlinear single/multi-phase batch processes. Providing important insights and useful methods and practical algorithms that can potentially be applied in batch process control and optimization, it is a valuable resource for researchers, scientists, and engineers in the field of process system engineering and control engineering.

Arvustused

The book is a useful and timely treatise of carefully selected topics of batch control. It could serve well as a useful reference material for the control engineering community. (Witold Pedrycz, zbMATH 1428.93006, 2020)

1 Introduction
1(18)
1.1 Batch Process Control
1(1)
1.2 Current Status of Batch Process Control
2(4)
1.3 The Main Content of This Book
6(2)
References
8(11)
2 Iterative Learning Control of Linear Batch Processes
19(46)
2.1 Introduction
19(1)
2.2 2D Roesser Model-Based Iterative Learning Control for Batch Processes with Uncertainties and Interval Time-Varying Delays
20(11)
2.2.1 Problem Description
20(1)
2.2.2 Equivalent 2D System Representation
21(3)
2.2.3 Robust Stability and Control of the 2D System
24(7)
2.3 2D-FM Model-Based Iterative Learning Control for Batch Processes with Uncertainties and Interval Time-Varying Delays
31(15)
2.3.1 Equivalent 2D System Representation
32(2)
2.3.2 Robust 2D Controller Design and System Structure
34(11)
2.3.3 Design Algorithms
45(1)
2.4 Delay-Range-Dependent Robust 2D Output Feedback Iterative Learning Control for Batch Processes with State Delay and Uncertainties Based on 2D-FM Model
46(9)
2.4.1 Equivalent 2D System Representation
46(1)
2.4.2 Robust Stability and Control of a 2D-FM System
47(8)
2.5 Case Study
55(7)
2.5.1 Design Case 1
55(5)
2.5.2 Design Case 2
60(2)
2.6 Conclusion
62(1)
References
62(3)
3 Iterative Learning Control of Nonlinear Batch Processes
65(32)
3.1 Introduction
65(1)
3.2 2D T-S Fuzzy Model-Based Iterative Learning Control for Nonlinear Batch Processes
66(13)
3.2.1 Establishment of the 2D T-S Fuzzy Model
66(1)
3.2.2 Robust Fuzzy Iterative Learning Controller Design
67(12)
3.3 2D T-S Fuzzy Model-Based Iterative Learning Control for Batch Processes with Interval Time-Varying Delays
79(5)
3.3.1 Establishment of the 2D T-S Fuzzy Model
79(2)
3.3.2 Fuzzy Iterative Learning Controller Design
81(3)
3.4 Case Study
84(10)
3.4.1 Design Case 3
84(6)
3.4.2 Design Case 4
90(4)
3.5 Conclusion
94(1)
References
94(3)
4 Iterative Learning Control of Multi-phase Batch Processes
97(34)
4.1 Introduction
97(1)
4.2 Iterative Learning Control for Multi-phase Batch Processes
98(12)
4.2.1 Problem Description
98(2)
4.2.2 Hybrid 2D ILC Design (Without Disturbance)
100(4)
4.2.3 Robust 2D ILC Design (With Disturbance)
104(5)
4.2.4 Performance Optimization
109(1)
4.3 Iterative Learning Control for Multi-phase Batch Processes with Time Delay and Disturbances
110(10)
4.3.1 Problem Statement
110(1)
4.3.2 Robust Hybrid 2D Iterative Learning Control Design Based on Time Delay in a Range
111(9)
4.4 Case Study
120(5)
4.4.1 Design Case 1 (Without Disturbance)
120(1)
4.4.2 Robust Analysis (With Disturbances)
121(3)
4.4.3 Design Case 3
124(1)
4.5 Conclusion
125(2)
References
127(4)
5 Iterative Learning Optimal Guaranteed Cost Control of Batch Processes
131(58)
5.1 Introduction
131(1)
5.2 Guaranteed Cost Control for Batch Processes with Time-Varying Delay
132(18)
5.2.1 Problem Description and 2D System Representation
132(3)
5.2.2 Main Results
135(15)
5.3 Output Feedback-Based Iterative Learning Optimal Guaranteed Cost Control of Batch Processes with State Delay
150(7)
5.3.1 Problem Description and 2D System Representation
150(3)
5.3.2 Iterative Learning Guaranteed Cost Controller Design
153(4)
5.3.3 Design Algorithms
157(1)
5.4 A Suboptimal Guaranteed Cost Control for Multi-phase Batch Processes with Time-Varying Delay
157(9)
5.4.1 Problem Description and 2D System Representation
157(1)
5.4.2 Delay-Range-Dependent Robust Hybrid 2D Iterative Learning Control Design
158(8)
5.5 2D Fuzzy Guaranteed Cost Control Strategy for Nonlinear Batch Processes with Time-Varying Delay
166(8)
5.5.1 Problem Formulation
166(1)
5.5.2 The Design of Fuzzy Iterative Learning Controller with Optimal Control Performance
167(7)
5.6 Case Study
174(12)
5.6.1 Design Case 1
174(2)
5.6.2 Design Case 2
176(3)
5.6.3 Design Case 3
179(4)
5.6.4 Design Case 4
183(3)
5.7 Conclusion
186(1)
References
186(3)
6 Iterative Learning Predictive Control for Batch Processes
189(26)
6.1 Introduction
189(1)
6.2 2D-FM Model-Based Robust Iterative Learning Predictive Control for Batch Processes
190(6)
6.2.1 Problem Description and 2D-FM System Representation
190(1)
6.2.2 Stability Analysis and Controller Design of a 2D System
191(5)
6.3 2D Fuzzy Constrained Predictive Control of Nonlinear Batch Processes
196(6)
6.3.1 Question Description and Modeling
196(1)
6.3.2 Design of 2D T-S Fuzzy Iterative Learning Predictive Controller
197(5)
6.4 2D-FM Model-Based Robust Iterative Learning Predictive Control for Multi-Phase Batch Processes
202(5)
6.4.1 Equivalent 2D System Representation
203(1)
6.4.2 Stability Analysis and Controller Design of a 2D System
204(3)
6.5 Case Study
207(5)
6.5.1 Design Case 1
207(1)
6.5.2 Design Case 2
208(4)
6.6 Conclusion
212(1)
References
213(2)
7 Iterative Learning Fault-Tolerant Control of Linear Batch Processes
215(26)
7.1 Introduction
215(1)
7.2 Robust Delay-Dependent Iterative Learning Fault-Tolerant Control for Batch Processes with State Delay and Actuator Failures
216(9)
7.2.1 Problem Formulation
216(1)
7.2.2 Robust 2D FILRC Design
217(8)
7.3 Delay-Range-Dependent Method for Iterative Learning Fault-Tolerant Guaranteed Cost Control for Batch Processes
225(5)
7.3.1 Equivalent 2D Representation
225(2)
7.3.2 ILRG Design
227(3)
7.3.3 Design Algorithms
230(1)
7.4 Design of Fault-Tolerant Guaranteed Performance Controller for Batch Process Compound Iterative Learning
230(5)
7.4.1 Equivalent Two-Dimensional Description
230(2)
7.4.2 Iterative Learning Reliable Guaranteed Performance Control Law Design
232(2)
7.4.3 Design Algorithm
234(1)
7.5 Case Study
235(2)
7.5.1 Design Case 3
235(2)
7.6 Conclusion
237(1)
References
238(3)
8 Iterative Learning Fault-Tolerant Control of Nonlinear Batch Processes
241(28)
8.1 Introduction
241(1)
8.2 Iterative Learning Fault-Tolerant Control for Batch Processes Based on the T-S Fuzzy Model
242(5)
8.2.1 Equivalent 2D Representation
242(5)
8.3 Design of Fuzzy Iterative Learning Fault-Tolerant Control for Batch Processes with Time-Varying Delays
247(5)
8.3.1 Equivalent 2D Description
247(5)
8.4 Fuzzy Iterative Learning Control-Based Design of Fault-Tolerant Guaranteed Cost Controller for Nonlinear Batch Processes with Time-Varying Delays
252(6)
8.4.1 The Design of Optimal Cost-Guaranteed Controller Based on Fuzzy Iterative Learning Control
252(5)
8.4.2 Design Algorithms
257(1)
8.5 Case Study
258(7)
8.5.1 Design Case 1
258(1)
8.5.2 Design Case 2
259(2)
8.5.3 Design Case 3
261(4)
8.6 Conclusion
265(1)
References
266(3)
9 Iterative Learning Fault-Tolerant Control of Multi-phase Batch Processes
269(36)
9.1 Introduction
269(1)
9.2 Robust Iterative Learning Fault-Tolerant Control for Multi-phase Batch Processes with Uncertainties
270(7)
9.2.1 Traditional Reliable Control (TRC)
270(2)
9.2.2 Iterative Learning Reliable Control (ILRC)
272(5)
9.2.3 Algorithm Design
277(1)
9.3 A Hybrid 2D Fault-Tolerant Controller Design for Multi-phase Batch Processes with Time Delay
277(7)
9.3.1 Equivalent 2D Expression
277(2)
9.3.2 Design of Delay-Range-Dependent Robust Hybrid 2D Iterative Learning Fault-Tolerant Control Law
279(5)
9.4 Delay-Range-Dependent-Based Hybrid Iterative Learning Fault-Tolerant Guaranteed Cost Control for Multi-phase Batch Processes
284(6)
9.4.1 Equivalent 2D Expression
284(2)
9.4.2 Robust H∞ Guaranteed Cost Performance Analysis
286(4)
9.4.3 Performance Optimization
290(1)
9.5 Case Study
290(11)
9.5.1 Design Case 1
290(6)
9.5.2 Design Case 2
296(2)
9.5.3 Design Case 3
298(3)
9.6 Conclusion
301(1)
References
302(3)
10 Further Ideas on Constrained Infinite Horizon Fault-Tolerant Control of Batch Processes
305
10.1 Introduction
305(1)
10.2 Robust Constraint Iterative Learning Predictive Fault-Tolerant Control of Uncertain Batch Processes
306(5)
10.2.1 Equivalent 2D Model
306(5)
10.3 2D Fuzzy-Constrained Fault-Tolerant Predictive Control of Nonlinear Batch Processes
311(3)
10.3.1 Design of Optimal Controller
311(3)
10.4 2D-FM Model-Based Robust Iterative Learning Fault-Tolerant Predictive Control for Multi-phase Batch Processes
314(2)
10.4.1 Equivalent 2D Model
314(1)
10.4.2 Stability Analysis and Controller Design of a 2D System
315(1)
10.5 Case Study
316(5)
10.5.1 Design Case 1
316(3)
10.5.2 Design Case 2
319(2)
10.6 Summary
321(1)
References
322
Limin Wang is currently a professor at the School of Mathematics and Statistics, Hainan Normal University. She is a member of the Fault Diagnosis and Safety Professional Committee of the China Association of Automation. She received her Ph.D. degree in Operations Research and Cybernetics from Dalian University of Technology in 2009. Her current research interests include batch process control, fault-tolerant control and fault diagnosis. She worked as a postdoctoral fellow at the Hong Kong University of Science and Technology, Zhejiang University, and Tsinghua University, researching on advanced control methods, fault diagnosis and fault tolerant control of batch processes and published a series of original results in international journals, such as the Journal of Process Control, AIChE Journal, Industrial & Engineering Chemistry Research, and Control Engineering Practice.

Ridong Zhang received his Ph.D. degree in control science and engineering from Zhejiang University in 2007. From 2007 to 2015, he was a professor at the Institute of Information and Control, Hangzhou Dianzi University. Since 2015, he has been a visiting professor at the Chemical and Biomolecular Engineering Department, the Hong Kong University of Science and Technology. He has published more than 40 journal papers in the fields of process modeling and control. His research interests include process modeling, model predictive control, and nonlinear systems.

Furong Gao received his B.Eng. degree in automation from the China University of Petroleum in 1985 and M. Eng. and Ph.D. degrees in chemical engineering from McGill University, Canada, in 1989 and 1993 respectively. He worked as a senior research engineer at Moldflow International Company Ltd. Since 1995, he has been working at the Hong Kong University of Science and Technology, where he is currently the chair professor in the Department of Chemical and Biomolecular Engineering. His research interests include process monitoring, control and polymer processing.