Networked control systems : theory and applications

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Berlin : Springer, 2008

Physical Description:

xviii, 344 p. : ill. ; 25 cm.

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9781848002142

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Networked control systems (NCS) confer advantages of cost reduction, system diagnosis and flexibility, minimizing wiring and simplifying the addition and replacement of individual elements; efficient data sharing makes taking globally intelligent control decisions easier with NCS.

The applications of NCS range from the large scale of factory automation and plant monitoring to the smaller networks of computers in modern cars, places and autonomous robots.

Networked Control Systems presents recent results in stability and robustness analysis and new developments related to networked fuzzy and optimal control. Many chapters contain case-studies, experimental, simulation or other application-related work showing how the theories put forward can be implemented.

The state-of-the art research reported in this volume by an international team of contributors makes it an essential reference for researchers and postgraduate students in control, electrical, computer and mechanical engineering and computer science.

Author Notes

A list of the professional achievements of the Professor Derong Liu includes:

Fellow of the IEEE (Institute of Electrical and Electronics Engineers), since 2005

AdCom Member (elected), IEEE Computational Intelligence Society, 2006-2009

Editor, IEEE Computational Intelligence Society Electronic Letter, 2004-present

Letters Editor, IEEE Trans. on Neural Networks, 2006-present

Associate Editor, Automatica, 2006-present

Associate Editor, IEEE Computational Intelligence Magazine, 2006-present

Associate Editor, IEEE Trans. on Neural Networks, 2004-2006

Associate Editor, IEEE Trans. on Signal Processing, 2001-2003

Associate Editor, IEEE Trans. on Circuits and Systems-I, 1997-1999

Member, Conference Editorial Board, IEEE Control Systems Society, 1995-2000

General Chair, IEEE International Conference on Networking, Sensing
and Control, Sanya, China, 2008

General Chair, 4th International Symposium on Neural Networks,Nanjing, China, 2007

Program Chair, International Joint Conference on Neural Networks, Hong Kong, 2008

Program Chair, IEEE International Symposium on Approximate Dynamic Programming and
Reinforcement Learning, Honolulu, Hawaii, 2007

Program Chair, 21st IEEE International Symposium on IntelligentControl, Munich, Germany, 2006

Program Chair, IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, FL, 2006

University Scholar, University of Illinois, 2006-2009 CAREER Award, National Science Foundation, 1999

Harvey N. Davis Distinguished Teaching Award, Stevens Institute of Technology, 1997

Michael J. Birck Fellowship, University of Notre Dame, 1990

Listed in Who's Who in America

Listed in Who's Who in Science and Engineering

Rachana A. Gupta and Mo-Yuen ChowFei-Yue WangMohammad Tabbara and Dragan Nešić and Andrew R. TeelGuo-Ping LiuDong Yue and Qing-Long Han and James LamGeorge Nikolakopoulos and Athanasia Panousopoulou and Anthony TzesDedong Yang and Huaguang ZhangFuchun Sun and Fengge WuYangQuan ChenWei Li and Xiaofan WangWensheng Yu and Long Wang

List of Contributors	p. xvii
1 Overview of Networked Control Systems	p. 1
1.1 Introduction	p. 1
1.1.1 Advantages and Applications of Control over Network	p. 2
1.1.2 Brief History of Research Field of NCS	p. 4
1.2 NCS Categories and NCS Components	p. 5
1.2.1 NCS Components	p. 8
1.2.2 Information Acquisition in a Network	p. 8
1.2.3 Control of Actuators over a Network	p. 9
1.2.4 Communication	p. 9
1.3 NCS Challenges and Solutions	p. 10
1.3.1 Integration of Components and Distribution of Intelligence	p. 13
1.4 A Case Study for NCS-iSpace	p. 14
1.5 Conclusions	p. 20
References	p. 21
2 Overview of Agent-based Control and Management for NCS	p. 25
2.1 Introduction	p. 25
2.2 From Electricity to Connectivity: Why Agent-based Control and Management for Networked Systems	p. 26
2.3 Hosting Mechanism and System Architecture for ABC	p. 28
2.4 Design Principle for Networked Control Systems: Local Simple, Remote Complex (LSRC)	p. 33
2.5 Modular Construction and Learning Algorithms of Neuro-fuzzy Networks for LSRC Implementation	p. 35
2.6 Issues in Software, Middleware, and Hardware Platforms	p. 46
2.7 Real-world Applications	p. 50
2.8 Concluding Remarks and Future Work	p. 51
References	p. 53
3 Networked Control Systems: Emulation-based Design	p. 57
3.1 Introduction	p. 57
3.2 Overview of Emulation-based NCS Design	p. 60
3.2.1 Principles of Emulation-based NCS Design	p. 60
3.2.2 Results in Perspective	p. 61
3.3 Modeling Networked Control Systems and Scheduling Protocols	p. 65
3.3.1 Scheduling and a Hybrid System Model for NCS	p. 68
3.3.2 NCS Scheduling Protocol Properties	p. 70
3.3.3 Lyapunov UGES and a.s. UGES Scheduling Protocols	p. 71
3.3.4 PE T Scheduling Protocols	p. 73
3.3.5 a.s. Covering Protocols	p. 76
3.3.6 Slotted p Persistent CSMA	p. 79
3.3.7 CSMA with Random Waits	p. 80
3.4 NCS Stability	p. 81
3.4.1 L p Stability of NCS with Lyapunov UGES Protocols	p. 82
3.4.2 L p Stability of NCS with PE T Protocols	p. 83
3.4.3 L p Stability of NCS with Random Protocols	p. 84
3.4.4 L p Stability of NCS with a.s. Lyapunov Protocols	p. 85
3.5 Case Studies and Comparisons	p. 86
3.5.1 Comparison of Analytical Inter-transmission Bounds	p. 87
3.5.2 Comparison of Numerical Inter-transmission Bounds (p 0 = 0)	p. 89
3.5.3 Comparison of Numerical Inter-transmission Bounds (p 0 > 0)	p. 91
3.6 Conclusions	p. 93
References	p. 94
4 Analysis and Design of Networked Predictive Control Systems	p. 95
4.1 Introduction	p. 95
4.2 Networked Predictive Control	p. 97
4.2.1 Design of the Control Prediction Generator	p. 97
4.2.2 Design of the Network Delay Compensator	p. 101
4.2.3 Algorithm of Networked Predictive Control	p. 102
4.3 Stability of Networked Predictive Control Systems	p. 102
4.3.1 Fixed Network Transmission Delay	p. 102
4.3.2 Random Network Communication Time Delay	p. 103
4.4 Simulation of Networked Predictive Control Systems	p. 106
4.4.1 Estimation of Network Transmission Delay	p. 106
4.4.2 Off-line Simulation	p. 106
4.4.3 Real-time Simulation	p. 107
4.5 Implementation of Networked Predictive Control Systems	p. 111
4.5.1 Software of Networked Control Systems	p. 111
4.5.2 Networked Control System Test Rig	p. 114
4.5.3 Practical Experiments	p. 115
4.6 Conclusions	p. 118
References	p. 118
5 Robust H ∞ Control and Filtering of Networked Control Systems	p. 121
5.1 Introduction	p. 121
5.2 Robust H ∞ Control of NCS	p. 123
5.2.1 System Description and Preliminaries	p. 123
5.2.2 H ∞ Performance Analysis	p. 125
5.2.3 Robust H ∞ Controller Design	p. 132
5.2.4 Numerical Examples	p. 134
5.3 Robust H ∞ Filter Design of NCS	p. 136
5.3.1 Modeling a Network-based Filter	p. 136
5.3.2 H ∞ Performance Analysis of Filtering-error System	p. 139
5.3.3 H ∞ Filter Design	p. 142
5.3.4 Numerical Examples	p. 144
5.4 Definition of ¿ ij	p. 147
5.5 Conclusions	p. 149
References	p. 150
6 Switched Feedback Control for Wireless Networked Systems	p. 153
6.1 Introduction	p. 153
6.2 Mathematical Modeling of NCS as a Switched System	p. 155
6.3 Optimal Output Feedback Control	p. 157
6.3.1 Gain Tuning of Output Feedback Parameter	p. 158
6.3.2 Stability Investigation: Numerical Results	p. 160
6.4 Experimental and Simulation Results	p. 162
6.4.1 Switched Feedback Control Over GPRS	p. 162
6.4.2 Switched Feedback Control Over IEEE 802.11b	p. 169
6.4.3 Switched Optimal Feedback Control Over IEEE 802.11b in MANETs	p. 185
6.5 Conclusions	p. 193
References	p. 193
7 Networked Control for T-S Fuzzy Systems with Time Delay	p. 197
7.1 Introduction	p. 197
7.2 Guaranteed Cost Networked Control for T-S Fuzzy Systems with Time Delay	p. 199
7.3 Simulation Results	p. 214
7.4 Robust H ∞ Networked Control for T-S Fuzzy Systems with Time Delay	p. 220
7.5 Simulation Results	p. 228
7.6 Conclusions	p. 231
References	p. 231
8 A Discrete-time Jump Fuzzy System Approach to NCS Design	p. 233
8.1 Introduction	p. 233
8.1.1 Fundamental Issues in NCS	p. 234
8.1.2 Previous Work	p. 234
8.2 Modeling NCS	p. 235
8.2.1 Markov Characteristics of NCS	p. 236
8.2.2 Discrete-time Jump Fuzzy System	p. 237
8.3 State-feedback Controller Design	p. 238
8.3.1 The Closed-loop Model of an NCS	p. 238
8.3.2 Guaranteed Cost Controller Design	p. 239
8.3.3 Homotopy Algorithm	p. 245
8.4 Output Feedback Controller Synthesis of an NCS	p. 246
8.4.1 Fuzzy Observer Design	p. 246
8.4.2 Output Feedback Controller Design	p. 247
8.4.3 Simulation Example	p. 249
8.5 Neuro-fuzzy Controller Design	p. 253
8.5.1 Neuro-fuzzy Predictor	p. 255
8.5.2 Fuzzy Controller	p. 256
8.6 Conclusions	p. 256
References	p. 257
9 Networked Boundary Control of Damped Wave Equations	p. 261
9.1 Introduction	p. 261
9.2 A Brief Introduction to the Smith Predictor	p. 262
9.3 Boundary Control of Damped Wave Equations with Large Delays	p. 263
9.4 Stability and Robustness Analysis	p. 265
9.5 Fractional Order Case - Problem Formulation	p. 268
9.6 Fractional Order Case - Robustness of Boundary Stabilization	p. 270
9.7 Fractional Order Case - Compensation of Large Delays in Boundary Measurement	p. 271
9.8 Conclusions	p. 272
References	p. 272
10 Coordination of Multi-agent Systems Using Adaptive Velocity Strategy	p. 275
10.1 Introduction	p. 275
10.2 The Constant Speed Vicsek Model	p. 277
10.3 The Adaptive Velocity Model	p. 278
10.4 Simulations and Discussions	p. 281
10.5 Conclusions	p. 288
References	p. 290
11 Design of Robust Strictly Positive Real Transfer Functions	p. 293
11.1 Introduction	p. 293
11.2 Definitions and Notation	p. 294
11.3 Some Properties of SPR (WSPR) Regions	p. 295
11.4 Characterization of SPR (WSPR) Regions	p. 302
11.5 Robust SPR Synthesis: Intersection of WSPR Regions	p. 307
11.6 Applications to Robust SPR Synthesis for Low-order Systems	p. 310
11.6.1 The Third-order SPR Synthesis	p. 312
11.6.2 The Fourth-order SPR Synthesis	p. 316
11.7 Robust SPR Synthesis for Polynomial Segment of Arbitrary Order	p. 324
11.7.1 Main Results	p. 324
11.7.2 Design Procedure and Some Examples	p. 330
11.7.3 Appendix: Proof of Lemma 11.17	p. 332
11.8 Conclusions	p. 337
References	p. 338
Index	p. 343

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