State Estimation and Fault Diagnosis under Imperfect Measurements

Description

The objective of this book is to present the up-to-date research developments and novel methodologies on state estimation and fault diagnosis (FD) techniques for a class of complex systems subject to closed-loop control, nonlinearities, and stochastic phenomena. It covers state estimation design methodologies and FD unit design methodologies including framework of optimal filter and FD unit design, robust filter and FD unit design, stability, and performance analysis for the considered systems subject to various kinds of complex factors. Features: Reviews latest research results on the state estimation and fault diagnosis issues. Presents comprehensive framework constituted for systems under imperfect measurements. Includes quantitative performance analyses to solve problems in practical situations. Provides simulation examples extracted from practical engineering scenarios. Discusses proper and novel techniques such as the Carleman approximation and completing the square method is employed to solve the mathematical problems. This book aims at Graduate students, Professionals and Researchers in Control Science and Application, Stochastic Process, Fault Diagnosis, and Instrumentation and Measurement. Cover Half Title Title Page Copyright Page Dedication Contents List of Figures List of Tables Preface Author's Biography Acknowledgment Symbols 1. Introduction 1.1. Challenges with Imperfect Measurements 1.1.1. Measurement Noises 1.1.2. Nonlinear Measurements 1.1.3. Communication Constraints 1.2. Analysis and Synthesis of Imperfect Measurements 1.2.1. Measurement Noises 1.2.2. Nonlinear Measurement 1.2.3. Communication Constraints 1.2.3.1. Transmission Delay 1.2.3.2. Missing Measurement 1.2.3.3. Signal Quantization 1.2.3.4. Sensor Gain Degradation 1.2.3.5. Event-Triggered Transmission 1.2.3.6. Sensor Saturation 1.2.3.7. Integral Measurement 1.3. Outline of This Book 2. Optimal Filtering for Networked Systems with Stochastic Sensor Gain Degradation 2.1. Problem Formulation and Preliminaries 2.2. Optimal Filter Design 2.3. Simulation Example 2.4. Conclusions 3. Recursive Filtering over Sensor Networks with Stochastic Sensor Gain Degradation 3.1. Problem Formulation and Preliminaries 3.2. Main Results 3.2.1. Filter Design 3.2.2. Boundedness 3.2.3. Monotonicity 3.3. Numerical Example 3.4. Conclusions 4. H∞ Filtering for Nonlinear Systems with Stochastic Sensor Saturations and Markov Time Delays 4.1. Problem Formulation 4.2. Main Results 4.3. Simulation Examples 4.3.1. Example A 4.3.2. Example B 4.4. Conclusion 5. Observer Design for Systems with Unknown Inputs and Missing Measurements 5.1. Problem Formulation 5.2. Observer Design 5.3. Boundedness Analysis 5.4. Illustrative Examples 5.5. Conclusions 6. Filtering and Fault Detection for Nonlinear Systems with Polynomial Approximation 6.1. Problem Formulation 6.1.1. Polynomial Approximation of Nonlinear Functions 6.1.2. The Polynomial Nonlinear Systems 6.1.3. The Filter and the Fault Detection Problems 6.2. Polynomial Filter Design 6.3. Fault Detection 6.4. Illustrative Example 6.5. Conclusion 7. Event-Triggered Filtering and Fault Estimation for Nonlinear Systems with Stochastic Sensor Saturations 7.1. Problem Formulation 7.2. Filter Design 7.3. Boundedness Analysis 7.4. Fault Estimation 7.5. Illustrations 7.6. Conclusions 8. Finite-Horizon Quantized H∞ Filter Design for Time-Varying Systems under Event-Triggered Transmissions 8.1. Problem Formulation 8.2. Filter Design 8.3. An Illustrative Example 8.4. Conclusion 9. Observer-Based Fault Diagnosis Schemes under Closed-Loop Control 9.1. Unknown-Input-Observer Method 9.2. Luenberger-Observer-Based and Robust-Observer-Based Methods 9.3. A Simulation Example 9.4. Conclusion 10. State Estimation and Fault Reconstruction with Integral Measurements under Partially Decoupled Disturbances 10.1. Problem Formulation 10.2. Filter Design 10.3. Parameter Calculation 10.4. Illustrative Example 10.5. Conclusion 11. Conclusion and Further Work Bibliography Index