Description
Book SynopsisIn the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modelling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities.
Table of ContentsPreface xi
Acknowledgments xiii
List of Abbreviations xv
List of Notations xvii
1 Introduction 1
1.1 Background, Motivations, and Research Problems 2
1.2 Outline 7
2 Variance-Constrained Finite-Horizon Filtering and Control with Saturations 11
2.1 Problem Formulation for Finite-Horizon Filter Design 12
2.2 Analysis of H∞ and Covariance Performances 14
2.3 Robust Finite-Horizon Filter Design 19
2.4 Robust H∞ Finite-Horizon Control with Sensor and Actuator Saturations 22
2.5 Illustrative Examples 30
2.6 Summary 36
3 Filtering and Control with Stochastic Delays and Missing Measurements 41
3.1 Problem Formulation for Robust Filter Design 42
3.2 Robust H∞ Filtering Performance Analysis 45
3.3 Robust H∞ Filter Design 50
3.4 Robust H∞ Fuzzy Control 53
3.5 Illustrative Examples 59
3.6 Summary 72
4 Filtering and Control for Systems with Repeated Scalar Nonlinearities 73
4.1 Problem Formulation for Filter Design 74
4.2 Filtering Performance Analysis 78
4.3 Filter Design 80
4.4 Observer-Based H∞ Control with Multiple Packet Losses 83
4.5 Illustrative Examples 89
4.6 Summary 99
5 Filtering and Fault Detection for Markov Systems with Varying Nonlinearities 101
5.1 Problem Formulation for Robust H∞ Filter Design 102
5.2 Performance Analysis of Robust H∞ Filter 105
5.3 Design of Robust H∞ Filters 109
5.4 Fault Detection with Sensor Saturations and Randomly Varying Nonlinearities 115
5.5 Illustrative Examples 122
5.6 Summary 138
6 Quantized Fault Detection with Mixed Time-Delays and Packet Dropouts 139
6.1 Problem Formulation for Fault Detection Filter Design 140
6.2 Main Results 143
6.3 Fuzzy-Model-Based Robust Fault Detection 150
6.4 Illustrative Examples 158
6.5 Summary 170
7 Distributed Filtering over Sensor Networks with Saturations 171
7.1 Problem Formulation 171
7.2 Main Results 176
7.3 An Illustrative Example 182
7.4 Summary 187
8 Distributed Filtering with Quantization Errors: The Finite-Horizon Case 189
8.1 Problem Formulation 189
8.2 Main Results 194
8.3 An Illustrative Example 198
8.4 Summary 203
9 Distributed Filtering for Markov Jump Nonlinear Time-Delay Systems 205
9.1 Problem Formulation 205
9.2 Main Results 211
9.3 An Illustrative Example 220
9.4 Summary 223
10 A New Finite-Horizon H∞ Filtering Approach to Mobile Robot Localization 227
10.1 Mobile Robot Kinematics and Absolute Measurement 227
10.2 A Stochastic H∞ Filter Design 232
10.3 Simulation Results 242
10.4 Summary 245
11 Conclusions and Future Work 247
11.1 Conclusions 247
11.2 Contributions 249
11.3 Future Work 250
References 253
Index 261
