Performance analysis and synthesis for discrete-time stochastic systems with network-enhanced complexities

268 Pages·2019·17.626 MB·English

by  Ding, Derui, Wang, Zidong, Wei, Guoliang

Performance Analysis
and Synthesis for
Discrete-Time Stochastic
Systems with
Network-Enhanced
Complexities

Performance Analysis
and Synthesis for
Discrete-Time Stochastic
Systems with
Network-Enhanced
Complexities
Derui Ding
Zidong Wang
Guoliang Wei
CRC Press
Taylor & Francis Group
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Library of Congress Cataloging-in-Publication Data
Names: Ding, Derui, author. | Wang, Zidong, 1966- author. | Wei, Guoliang,
author.
Title: Performance analysis and synthesis for discrete-time stochastic
systems with network-enhanced complexities / Derui Ding, Zidong Wang and
Guoliang Wei.
Description: First edition. | Boca Raton, FL : CRC Press/Taylor & Francis
Group, 2019. | Includes bibliographical references and index.
Identifiers: LCCN 2018025090| ISBN 9781138610019 (hardback : acid-free paper)
| ISBN 9780429465901 (ebook)
Subjects: LCSH: Automatic control--Mathematics. | Stochastic processes. |
Discrete-time systems.
Classification: LCC TJ213 .D54135 2019 | DDC 629.801/176--dc23
LC record available at https://lccn.loc.gov/2018025090
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This book is dedicated to the Dream Dynasty,
consisting of a group of bright people who have been to
Brunel University London to enjoy memorable
research with challenging network-enhanced
complexities through performance analysis and
synthesis in a discrete (success-driven) yet stochastic
(laziness-perturbed) way ......

Contents
Preface xi
Acknowledgements xv
Symbols xvii
1 Introduction 1
1.1 Discrete-Time Stochastic Systems . . . . . . . . . . . . . . . 1
1.2 Network-Enhanced Complexities . . . . . . . . . . . . . . . . 4
1.3 Performance Analysis and Engineering Design Synthesis . . . 7
1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Finite-Horizon Control with Randomly Occurring
∞
H
Nonlinearities and Fading Measurements 19
2.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 20
2.2 Performance Analysis . . . . . . . . . . . . . . . . . . . 22
∞
H
2.3 Controller Design . . . . . . . . . . . . . . . . . . . . . . 30
∞
H
2.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 32
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3 Finite-Horizon Consensus Control for Multi-Agent
∞
H
Systems with Missing Measurements 37
3.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 38
3.2 Consensus Performance Analysis . . . . . . . . . . . . . . . 41
3.3 Controller Design . . . . . . . . . . . . . . . . . . . . . . 47
∞
H
3.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 51
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4 Finite-Horizon Distributed State Estimation with
∞
H
Stochastic Parameters through Sensor Networks 59
4.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 60
4.2 Performance Analysis . . . . . . . . . . . . . . . . . . . 63
∞
H
4.3 Distributed Filter Design . . . . . . . . . . . . . . . . . . . . 69
4.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 75
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
vii
viii Contents
5 Finite-Horizon Dissipative Control for State-Saturated
Discrete Time-Varying Systems with MissingMeasurements 81
5.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 81
5.2 Dissipative Control for Full State Saturation Case . . . . . . 84
5.3 Dissipative Control for Partial State Saturation Case . . . . 94
5.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 98
5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6 Finite-Horizon Filtering for State-Saturated Discrete
∞
H
Time-Varying Systems with Packet Dropouts 103
6.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 103
6.2 Filtering for Full State Saturation Case . . . . . . . . . 106
∞
H
6.3 Filtering for Partial State Saturation Case . . . . . . . . 112
∞
H
6.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 116
6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
7 Finite-Horizon Envelope-Constrained Filtering with
∞
H
Fading Measurements 123
7.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 124
7.2 Performance Analysis . . . . . . . . . . . . . . . . . . . 127
∞
H
7.3 Envelope Constraint Analysis . . . . . . . . . . . . . . . . . . 131
7.4 Envelope-Constrained Filter Design . . . . . . . . . . . . 136
∞
H
7.5 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 138
7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
8 Distributed Filtering under Uniform Quantizations and
Deception Attacks through Sensor Networks 143
8.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 144
8.2 Distributed Filter Design . . . . . . . . . . . . . . . . . . . . 146
8.3 Boundedness Analysis . . . . . . . . . . . . . . . . . . . . . . 154
8.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 159
8.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
9 Event-Triggered Distributed State Estimation with
∞
H
Packet Dropouts through Sensor Networks 163
9.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 164
9.2 Performance Analysis . . . . . . . . . . . . . . . . . . . 167
∞
H
9.3 Estimator Design . . . . . . . . . . . . . . . . . . . . . . 171
∞
H
9.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 173
9.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
10 Event-TriggeredConsensusControlforMulti-AgentSystems
in the Framework of Input-to-State Stability in Probability 179
10.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 180
10.2 Analysis of Input-to-State Stability in Probability . . . . . . 183
10.3 Event-TriggeredConsensus Control for Multi-Agent Systems 188
Contents ix
10.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 195
10.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
11 Event-TriggeredSecurityControlforDiscrete-TimeStochastic
Systems Subject to Cyber-Attacks 201
11.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . 202
11.2 Security Performance Analysis . . . . . . . . . . . . . . . . . 205
11.3 Security Controller Design . . . . . . . . . . . . . . . . . . . 208
11.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 212
11.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
12 Event-TriggeredConsensusControlforMulti-AgentSystems
Subject to Cyber-Attacks in the Framework of Observers 215
12.1 Modeling and Problem Formulation . . . . . . . . . . . . . . 216
12.2 Consensus Analysis . . . . . . . . . . . . . . . . . . . . . . . 222
12.3 Consensus Controller Design . . . . . . . . . . . . . . . . . . 226
12.4 Simulation Examples . . . . . . . . . . . . . . . . . . . . . . 229
12.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Bibliography 235
Index 249