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Multi-Agent Systems: Platoon Control and Non-Fragile Quantized Consensus [Kõva köide]

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(Nanyang Technological University, Singapore), (National University of Singapore, Singapore), (National University of Singapore, Singapore), (University of Science and Technology Beijing, China.)
  • Formaat: Hardback, 222 pages, kõrgus x laius: 234x156 mm, kaal: 476 g, 20 Tables, black and white; 64 Illustrations, black and white
  • Sari: Automation and Control Engineering
  • Ilmumisaeg: 27-Jun-2019
  • Kirjastus: CRC Press
  • ISBN-10: 0367254328
  • ISBN-13: 9780367254322
Teised raamatud teemal:
Multi-Agent Systems: Platoon Control and Non-Fragile Quantized ConsensusSuurem pilt
  • Formaat: Hardback, 222 pages, kõrgus x laius: 234x156 mm, kaal: 476 g, 20 Tables, black and white; 64 Illustrations, black and white
  • Sari: Automation and Control Engineering
  • Ilmumisaeg: 27-Jun-2019
  • Kirjastus: CRC Press
  • ISBN-10: 0367254328
  • ISBN-13: 9780367254322
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367254322.html
Märksõnad:

Multi-Agent Systems: Platoon Control and Non-Fragile Quantized Consensus aims to present recent research results in designing platoon control and non-fragile quantized consensus for multi-agent systems. The main feature of this book is that distributed adaptive sliding mode control (SMC) algorithms are proposed to guarantee strong string stability based on modified constant time headway (MCTH) policy. The MCTH policy is used to remove the unrealistic assumption in the most existing literature that initial spacing, velocity and acceleration errors are zero. This monograph investigates the platoon control issue by combining SMC technique with neural network and fuzzy logic system approximation methods.

Preface ix
Authors xiii
1 Introduction
1(8)
1.1 Platoon Control Problem
1(4)
1.1.1 Spacing Policies
2(1)
1.1.2 Actuator Nonlinearities
3(2)
1.1.3 Communications/Sensing Restricted Applications
5(1)
1.2 Non-Fragile Quantized Consensus
5(2)
1.2.1 Quantized Consensus
6(1)
1.2.2 Non-Fragile Control Problems
6(1)
1.3 Preview of
Chapters
7(2)
2 Preliminaries
9(8)
2.1 Notations
9(1)
2.2 Acronyms
10(1)
2.3 String Stability Theory
11(1)
2.4 Basic Algebraic Graph Theory
11(2)
2.5 H∞ Performance Index
13(1)
2.6 Some Other Definitions and Lemmas
14(3)
3 String Stability of Vehicle Platoons with External Disturbances
17(20)
3.1 Introduction
17(2)
3.2 Model Description and Problem Formulation
19(1)
3.2.1 Vehicle Dynamics
19(1)
3.2.2 Control Objective
20(1)
3.3 Design of Distributed Adaptive Integral Sliding Mode Control
20(9)
3.3.1 Zero Initial Spacing Error Case
20(8)
3.3.2 Non-zero Initial Spacing Error Case
28(1)
3.4 Numerical Examples
29(2)
3.5 Conclusion
31(6)
4 String Stability of Vehicle Platoons with Nonlinear Acceleration Uncertainties
37(20)
4.1 Introduction
37(2)
4.2 Vehicle Platoon and Problem Formulation
39(1)
4.2.1 Vehicle Platoon
39(1)
4.2.2 Problem Formulation
40(1)
4.3 Distributed Adaptive Integral Sliding Mode Control Strategy
40(10)
4.3.1 Control Strategy 1: TCTH Control Law
40(7)
4.3.2 Control Strategy 2: MCTH Control Law
47(3)
4.4 Simulation Study and Performance Results
50(6)
4.4.1 Example 1 (Numerical Example)
50(1)
4.4.2 Example 2 (Practical Example)
51(5)
4.5 Conclusion
56(1)
5 CNN-Based Adaptive Control for Vehicle Platoon with Input Saturation
57(22)
5.1 Introduction
57(2)
5.2 Vehicle-Following Platoon Model and Preliminaries
59(3)
5.2.1 Vehicle-Following Platoon Description
59(2)
5.2.2 Chebyshev Neural Network
61(1)
5.3 Distributed Adaptive NN Control Design and Stability Analysis
62(7)
5.3.1 Control Scheme I: TCTH Policy
62(6)
5.3.2 Control Strategy H: MCTH Control Law
68(1)
5.4 Simulation Study and Performance Results
69(9)
5.4.1 Example 1 (Numerical Example)
69(3)
5.4.2 Example 2 (Practical Example)
72(6)
5.5 Conclusion
78(1)
6 Adaptive Fuzzy Fault-Tolerant Control for Multiple High Speed Trains
79(20)
6.1 Introduction
79(2)
6.2 High Speed Train Dynamics and Preliminaries
81(4)
6.2.1 Model Description of High Speed Train Dynamics
81(2)
6.2.2 Fuzzy Logic Systems
83(1)
6.2.3 Problem Formulation
84(1)
6.3 Pi-Based Sliding Mode and Coupled Sliding Mode
85(1)
6.4 Adaptive Fuzzy Control Design and Stability Analysis
85(6)
6.4.1 Controller Design for Fault-Free Case
86(3)
6.4.2 Fault-Tolerant Controller Design with Actuator Faults
89(2)
6.5 Simulation Study and Performance Results
91(2)
6.5.1 Simulation Results of Theorem 6.1
92(1)
6.5.2 Simulation Results of Theorem 6.2
93(1)
6.6 Conclusion
93(6)
7 Collision Avoidance for Vehicle Platoon with Input Deadzone
99(14)
7.1 Introduction
99(1)
7.2 Vehicular Platoon Model and Preliminaries
100(3)
7.2.1 Vehicular Platoon Description
100(2)
7.2.2 Radial Basis Function Neural Network
102(1)
7.2.3 Problem Formulation
102(1)
7.3 Distributed Adaptive NN Control Design
103(4)
7.4 Simulation Study
107(3)
7.5 Conclusion
110(3)
8 Neuro-Adaptive Quantized PID-Based SMC of Vehicular Platoon with Deadzone
113(22)
8.1 Introduction
113(3)
8.2 Vehicle-Following Platoon Model and Preliminaries
116(4)
8.2.1 Vehicle-Following Platoon Description
116(2)
8.2.2 Nonlinear Actuator Decomposition
118(1)
8.2.3 Radial Basis Function Neural Network
119(1)
8.3 Neuro-Adaptive Quantized PIDSMC Design and Strong String Stability Analysis
120(7)
8.3.1 MCTH Policy and Control Problem
120(1)
8.3.2 PDD-Based Sliding Mode Control Design
121(1)
8.3.3 Stability Analysis
122(5)
8.4 Simulation Study
127(5)
8.5 Conclusion
132(3)
9 Low-Complexity Control of Vehicular Platoon with Asymmetric Saturation
135(14)
9.1 Introduction
135(2)
9.2 Vehicular Platoon Description
137(2)
9.3 Adaptive PIDSMC Design and Strong String Stability Analysis
139(3)
9.3.1 Control Problem
139(1)
9.3.2 PID-Based Sliding Mode Control Design
139(3)
9.4 Simulation Results
142(1)
9.5 Conclusion
143(6)
10 Non-Fragile Quantized Consensus for Multi-Agent Systems Based on Incidence Matrix
149(22)
10.1 Introduction
149(2)
10.2 Uniform Quantizer and Logarithmic Quantizer
151(1)
10.3 Problem Formulation
152(4)
10.4 Non-Fragile Quantized Controller Design
156(6)
10.4.1 Non-Fragile Control with Uniform Quantization
156(5)
10.4.2 Non-Fragile Control with Logarithmic Quantization
161(1)
10.5 Numerical Example
162(2)
10.6 Conclusion
164(7)
11 Non-Fragile H∞ Consensus for Multi-Agent Systems with Interval-Bounded Variations
171(12)
11.1 Introduction
171(1)
11.2 Problem Formulation
172(3)
11.3 Non-Fragile H∞ Consensus for Multi-Agent Systems
175(3)
11.4 Numerical Example
178(1)
11.5 Conclusion
179(4)
12 Quantized Consensus for Multi-Agent Systems with Quantization Mismatch
183(20)
12.1 Introduction
183(2)
12.2 Quantized H∞ Consensus for General Linear Dynamics
185(8)
12.3 Quantized H∞ Consensus for Lipschitz Nonlinearity
193(3)
12.4 Numerical Examples
196(4)
12.4.1 Example 1
196(1)
12.4.2 Example 2
197(3)
12.5 Conclusion
200(3)
Bibliography 203(18)
Index 221
Xiang-Gui Guo is an Associate Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.

Jian-Liang Wang is an Associate Professor with the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore.

Fang Liao is a Research Scientist with Temasek Laboratories, National University of Singapore, Singapore. Rodney Swee Huat Teo is a Principal Member of Technical Staff of the DSO National Laboratories, Singapore, and a Senior Research Scientist with Temasek Laboratories, National University of Singapore.