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.

In the context of systems and control, incomplete informationrefers to a dynamical system in which knowledge about the systemstates is limited due to the difficulties in modelling complexityin a quantitative way. The well-known types of incompleteinformation include parameter uncertainties and norm-boundednonlinearities. Recently, in response to the development of networktechnologies, the phenomenon of randomly occurring incompleteinformation has become more and more prevalent.Filtering, Control and Fault Detection with RandomlyOccurring Incomplete Information reflects the state-of-the-artof the research area for handling randomly occurring incompleteinformation from three interrelated aspects of control, filteringand fault detection. Recent advances in networked control systemsand distributed filtering over sensor networks are covered, andapplication potential in mobile robotics is also considered. Thereader will benefit from the introduction of new concepts, newmodels and new methodologies with practical significance in controlengineering and signal processing.Key Features:* Establishes a unified framework for filtering, control andfault detection problem for various discrete-time nonlinearstochastic systems with randomly occurring incompleteinformation* Investigates several new concepts for randomly occurringphenomena and proposes a new system model to better describenetwork-induced problems* Demonstrates how newly developed techniques can handle emergingmathematical and computational challenges* Contains the latest research resultsFiltering, Control and Fault Detection with RandomlyOccurring Incomplete Information provides a unified yet neatframework for control/filtering/fault-detection with randomlyoccurring incomplete information. It is a comprehensivetextbook for graduate students and is also a useful practicalresearch reference for engineers dealing with control, filteringand fault detection problems for networked systems.

Preface xiAcknowledgments xiiiList of Abbreviations xvList of Notations xvii1 Introduction 11.1 Background, Motivations, and Research Problems 21.2 Outline 72 Variance-Constrained Finite-Horizon Filtering and Control withSaturations 112.1 Problem Formulation for Finite-Horizon Filter Design 122.2 Analysis of H infinity and Covariance Performances 142.3 Robust Finite-Horizon Filter Design 192.4 Robust H infinity Finite-Horizon Control with Sensor andActuator Saturations 222.5 Illustrative Examples 302.6 Summary 363 Filtering and Control with Stochastic Delays and MissingMeasurements 413.1 Problem Formulation for Robust Filter Design 423.2 Robust H infinity Filtering Performance Analysis 453.3 Robust H infinity Filter Design 503.4 Robust H infinity Fuzzy Control 533.5 Illustrative Examples 593.6 Summary 724 Filtering and Control for Systems with Repeated ScalarNonlinearities 734.1 Problem Formulation for Filter Design 744.2 Filtering Performance Analysis 784.3 Filter Design 804.4 Observer-Based H infinity Control with Multiple Packet Losses834.5 Illustrative Examples 894.6 Summary 995 Filtering and Fault Detection for Markov Systems with VaryingNonlinearities 1015.1 Problem Formulation for Robust H infinity Filter Design1025.2 Performance Analysis of Robust H infinity Filter 1055.3 Design of Robust H infinity Filters 1095.4 Fault Detection with Sensor Saturations and Randomly VaryingNonlinearities 1155.5 Illustrative Examples 1225.6 Summary 1386 Quantized Fault Detection with Mixed Time-Delays and PacketDropouts 1396.1 Problem Formulation for Fault Detection Filter Design1406.2 Main Results 1436.3 Fuzzy-Model-Based Robust Fault Detection 1506.4 Illustrative Examples 1586.5 Summary 1707 Distributed Filtering over Sensor Networks with Saturations1717.1 Problem Formulation 1717.2 Main Results 1767.3 An Illustrative Example 1827.4 Summary 1878 Distributed Filtering with Quantization Errors: TheFinite-Horizon Case 1898.1 Problem Formulation 1898.2 Main Results 1948.3 An Illustrative Example 1988.4 Summary 2039 Distributed Filtering for Markov Jump Nonlinear Time-DelaySystems 2059.1 Problem Formulation 2059.2 Main Results 2119.3 An Illustrative Example 2209.4 Summary 22310 A New Finite-Horizon H infinity Filtering Approach to MobileRobot Localization 22710.1 Mobile Robot Kinematics and Absolute Measurement 22710.2 A Stochastic H infinity Filter Design 23210.3 Simulation Results 24210.4 Summary 24511 Conclusions and Future Work 24711.1 Conclusions 24711.2 Contributions 24911.3 Future Work 250References 253Index 261