内容简介：　　　

　　In 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. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent.
　　Filtering, Control and Fault Detection with Randomly Occurring Incomplete Information reflects the state-of-the-art of the research area for handling randomly occurring incomplete information from three interrelated aspects of control, filtering and fault detection. Recent advances in networked control systems and distributed filtering over sensor networks are covered, and application potential in mobile robotics is also considered. The reader will benefit from the introduction of new concepts, new models and new methodologies with practical significance in control engineering and signal processing.
　　Key Features:
　　• Establishes a unified framework for filtering, control and fault detection problem for various discrete-time nonlinear stochastic systems with randomly occurring incomplete information.
　　• Investigates several new concepts for randomly occurring phenomena and proposes a new system model to better describe network-induced problems.
　　• Demonstrates how newly developed techniques can handle emerging mathematical and computational challenges.
　　• Contains the latest research results.
　　Filtering, Control and Fault Detection with Randomly Occurring Incomplete Information provides a unified yet neat framework for control/filtering/fault-detection with randomly occurring incomplete information. It is a comprehensive textbook for graduate students and is also a useful practical research reference for engineers dealing with control, filtering and fault detection problems for networked systems.

英文目录：
Preface
Acknowledgments
List of Abbreviations
List of Notations
1 Introduction
　　1.1 Background, Motivations, and Research Problems
　　1.2 Outline
2 Variance-Constrained Finite-Horizon Filtering and Control with Saturations
　　2.1 Problem Formulation for Finite-Horizon Filter Design
　　2.2 Analysis of H∞ and Covariance Performances
　　2.3 Robust Finite-Horizon Filter Design
　　2.4 Robust H∞ Finite-Horizon Control with Sensor and Actuator Saturations
　　2.5 Illustrative Examples
　　2.6 Summary
3 Filtering and Control with Stochastic Delays and Missing Measurements
　　3.1 Problem Formulation for Robust Filter Design
　　3.2 Robust H∞ Filtering Performance Analysis
　　3.3 Robust H∞ Filter Design
　　3.4 Robust H∞ Fuzzy Control
　　3.5 Illustrative Examples
　　3.6 Summary
4 Filtering and Control for Systems with Repeated Scalar Nonlinearities
　　4.1 Problem Formulation for Filter Design
　　4.2 Filtering Performance Analysis
　　4.3 Filter Design
　　4.4 Observer-Based H∞ Control with Multiple Packet Losses
　　4.5 Illustrative Examples
　　4.6 Summary
5 Filtering and Fault Detection for Markov Systems with Varying Nonlinearities
　　5.1 Problem Formulation for Robust H∞ Filter Design
　　5.2 Performance Analysis of Robust H∞ Filter
　　5.3 Design of Robust H∞ Filters
　　5.4 Fault Detection with Sensor Saturations and Randomly Varying Nonlinearities
　　5.5 Illustrative Examples
　　5.6 Summary
6 Quantized Fault Detection with Mixed Time-Delays and Packet Dropouts
　　6.1 Problem Formulation for Fault Detection Filter Design
　　6.2 Main Results
　　6.3 Fuzzy-Model-Based Robust Fault Detection
　　6.4 Illustrative Examples
　　6.5 Summary
7 Distributed Filtering over Sensor Networks with Saturations
　　7.1 Problem Formulation
　　7.2 Main Results
　　7.3 An Illustrative Example
　　7.4 Summary
8 Distributed Filtering with Quantization Errors: The Finite-Horizon Case
　　8.1 Problem Formulation
　　8.2 Main Results
　　8.3 An Illustrative Example
　　8.4 Summary
9 Distributed Filtering for Markov Jump Nonlinear Time-Delay Systems
　　9.1 Problem Formulation
　　9.2 Main Results
　　9.3 An Illustrative Example
　　9.4 Summary
10 A New Finite-Horizon H∞ Filtering Approach to Mobile Robot Localization
　　10.1 Mobile Robot Kinematics and Absolute Measurement
　　10.2 A Stochastic H∞ Filter Design
　　10.3 Simulation Results
　　10.4 Summary
11 Conclusions and Future Work
　　11.1 Conclusions
　　11.2 Contributions
　　11.3 Future Work
References
Index