State Estimation and Fault Diagnosis under Imperfect Measurements

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.

1. Introduction1.1 Challenges with Imperfect Measurements1.2 Analysis and Synthesis of Imperfect Measurements1.3 Outline of This Book 2. Optimal Filtering for Networked Systems with Stochastic Sensor Gain Degradation2.1 Problem Formulation and Preliminaries2.2 Optimal Filter Design2.3 Simulation Example2.4 Conclusions 3. Recursive Filtering over Sensor Networks with Stochastic Sensor Gain Degradation3.1 Problem Formulation and Preliminaries3.2 Main Results3.3 Numerical Example3.4 Conclusions 4. H8 Filtering for Nonlinear Systems with Stochastic Sensor Saturations and Markov Time-Delays4.1 Problem Formulation4.2 Main Results4.3 Simulation Examples4.4 Conclusion 5. Observer Design for Systems with Unknown Inputs and Missing Measurements5.1 Problem Formulation5.2 Observer Design5.3 Boundedness Analysis5.4 Illustrative Examples5.5 Conclusions 6. Filtering and Fault Detection for Nonlinear Systems with Polynomial Approximation6.1 Problem Formulation6.2 Polynomial Filter Design6.3 Fault Detection6.4 Illustrative Example6.5 Conclusion 7. Event-triggered Filtering and Fault Estimation for Nonlinear Systems with Stochastic Sensor Saturations7.1 Problem Formulation7.2 Filter Design7.3 Boundedness Analysis7.4 Fault Estimation7.5 Illustrations7.6 Conclusions 8. Finite-horizon Quantized H8 Filter Design for Time-Varying Systems under Event-Triggered Transmissions8.1 Problem Formulation8.2 Filter Design8.3 An Illustrative Example8.4 Conclusion 9. Observer-Based Fault Diagnosis Schemes under Closed-loop Control9.1 Unknown-input-observer method9.2 Luenberger-observer-based and robust-observer-based method9.3 A Simulation Example9.4 Conclusion 10. State Estimation and Fault Reconstruction with Integral Measurements under Partially Decoupled Disturbances10.1 Problem Formulation10.2 Filter Design10.3 Parameter Calculation10.4 Illustrative Example10.5 Conclusion 11. Conclusion and Further Work

Yang Liu received the B.Sc. degree and the Ph.D. degree in the Department of Automation at Tsinghua University, Beijing, China, in 2010 and 2016, respectively. From August 2016 to September 2018, he worked as a Postdoctoral Researcher in the College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China. Then he worked the same department as a lecturer and was promoted to an Associate Professor in January 2020. In April 2021, he joined the Department of Aeronautical and Automotive Engineering, Loughborough University, U.K., as a Research Associate. He has published over 30 papers in refereed international journals. His research interests include optimal filtering, complex systems, neural networks as well as fault detection and diagnosis. He is a very active reviewer for several international journals. He was an outstanding reviewer for IEEE Transactions on Automatic Control in 2013, for the journal Neurocomputing in 2016, and for Journal of the Franklin Institute in 2016. He was a guest editor for Systems Science and Control Engineering in 2020. Zidong Wang is Professor of Dynamical Systems and Computing at Brunel University London, West London, United Kingdom. He was born in 1966 in Yangzhou, Jiangsu, China. He received the BSc degree in Mathematics in 1986 from Suzhou University, Suzhou, the MSc degree in Applied Mathematics in 1990 and the PhD degree in Electrical and Computer Engineering in 1994, both from Nanjing University of Science and Technology, Nanjing. He was appointed as Lecturer in 1990 and Associate Professor in 1994 at Nanjing University of Science and Technology. From January 1997 to December 1998, he was an Alexander von Humboldt research fellow with the Control Engineering Laboratory, Ruhr-University Bochum, Germany. From January 1999 to February 2001, he was a Lecturer with the Department of Mathematics, University of Kaiserslautern, Germany. From March 2001 to July 2002, he was a University Senior Research Fellow with the School of Mathematical and Information Sciences, Coventry University, U.K. In August 2002, he joined the Department of Computer Science, Brunel University London, U.K., as a Lecturer, and was then promoted to a Reader in September 2003 and to a Chair Professor in July 2007. Professor Wang's research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published more than 600 papers in refereed international journals. He was awarded the Humboldt research fellowship in 1996 from Alexander von Humboldt Foundation, the JSPS Research Fellowship in 1998 from Japan Society for the Promotion of Science, and the William Mong Visiting Research Fellowship in 2002 from the University of Hong Kong. He was a recipient of the State Natural Science Award from the State Council of China in 2014 and the Outstanding Science and Technology Development Awards (once in 2005 and twice in 1997) from the National Education Committee of China. Professor Wang is currently serving or has served as the Editor-in-Chief for International Journal of Systems Science, the Editor-in-Chief for Neurocomputing, Executive Editor for Systems Science and Control Engineering, Subject Editor for Journal of The Franklin Institute, an Associate Editor for IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man, and Cybernetics - Systems, Asian Journal of Control, Science China Information Sciences, IEEE/CAA Journal of Automatica Sinica, Control Theory and Technology, an Action Editor for Neural Networks, an Editorial Board Member for Information Fusion, IET Control Theory & Applications, Complexity, International Journal of Systems Science, Neurocomputing, International Journal of General Systems, Studies in Autonomic, Data-driven and Industrial Computing, and a member of the Conference Editorial Board for the IEEE Control Systems Society. He served as an Associate Editor for IEEE Transactions on Neural Networks, IEEE Transactions on Systems, Man, and Cybernetics - Part C, IEEE Transactions on Signal Processing, Circuits, Systems & Signal Processing, and an Editorial Board Member for International Journal of Computer Mathematics. Professor Wang is a Member of the Academia Europaea (section of Physics and Engineering Sciences), a Fellow of the IEEE (for contributions to networked control and complex networks), a Fellow of the Chinese Association of Automation, a Member of the IEEE Press Editorial Board, a Member of the EPSRC Peer Review College of the UK, a Fellow of the Royal Statistical Society, a member of program committee for many international conferences, and a very active reviewer for many international journals. He was nominated an appreciated reviewer for IEEE Transactions on Signal Processing in 2006-2008 and 2011, an appreciated reviewer for IEEE Transactions on Intelligent Transportation Systems in 2008, an outstanding re