Robust Filtering and Fault Detection for T-S Fuzzy Systems - Hardcover

Wang, Xiao-Lei; Yang, Guang-Hong; Wang, Yu-Long

Hardcover

ISBN 10: 9819658179 ISBN 13: 9789819658176

Publisher: Springer, 2025

Synopsis

This book conducts an in-depth research on robust filtering and fault detection for a class of T-S fuzzy systems. On the basis of the existing research on T-S fuzzy theory, robust filtering theory, and fault diagnosis theory, some new and effective technologies are proposed to solve the problems of robust filtering and fault detection for a class T-S fuzzy systems, while overcoming the shortcomings and limitations of the existing solutions.

This book introduces new design solutions for a class of T-S fuzzy systems to address the existing problems in the research of robust filtering and fault detection, namely 1) two new filtering methods are explored to obtain better filtering results than the existing approaches; 2) a new event-triggered filtering scheme is proposed for T-S fuzzy systems with bounded disturbances, which realizes that the designed observer gains in the absence of event-triggered mechanisms are also applicable to the case with event-triggered mechanisms; 3) two new methods are constructed to deal with the asynchronous problems of premise variables effectively, which overcome the defects and limitations of the existing ones; and 4) an effective fault detection scheme for handling measurement outliers is constructed, which can avoid the occurrence of false alarms.

This book is intended to inspire researchers and engineers, offering deeper insights into robust filtering and fault detection for T-S fuzzy systems, and equipping them with the latest advancements in fuzzy system theory, robust filtering, and fault diagnosis. It also provides valuable theoretical references for engineers tackling practical engineering problems.

About this title

About the Author

Associate Professor Xiao-Lei Wang received the B.S. degree in automation, the M.S., and Ph.D. degrees in control theory and control engineering from Northeastern University, Shenyang, China, in 2014, 2016, and 2020, respectively. He is currently Associate Professor with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His current research interests include fuzzy systems, robust filtering, and fault diagnosis. He is also Member of The Institute of Electrical and Electronic Engineers (IEEE).

Professor Guang-Hong Yang received the B.S. and M.S. degrees in mathematics and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 1983, 1986, and 1994, respectively. He is currently Chair Professor and Dean of the College of Information Science and Engineering, Northeastern University. He has authored or coauthored three monographs and over 400 papers in the international journals. His research interests include fault-tolerant control, fault detection and isolation, cyber–physical systems, and unmanned systems. Dr. Yang has been Highly Cited Researcher selected by Elsevier since 2014 and Highly Cited Researcher selected by Clarivate since 2019. He has been General Chair of the Annual Chinese Control and Decision Conference, since 2011 and is Deputy Editor-in-Chief for the Journal of Control and Decision, Editor for the International Journal of Control, Automation and Systems. He is Chair of the Technical Committee on Control and Decision for Cyber–Physical Systems and Chinese Association of Automation (CAA). He is also Fellow of the IEEE.

Professor Yu-Long Wang received the B.Sc. degree in Computer Science and Technology from Liaocheng University, Liaocheng, China, in 2000, and the M.Sc. and Ph.D. degrees in Control Science and Engineering from Northeastern University, Shenyang, China, in 2006 and 2008, respectively. He was Post-doctoral Research Fellow and Research Fellow with Central Queensland University, North Rockhampton, QLD, Australia, Academic Visitor with the University of Adelaide, Adelaide, SA, Australia, and Professor with Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China. In 2017, he was appointed as Eastern Scholar by the Municipal Commission of Education, Shanghai, China, and joined Shanghai University, Shanghai, China, where he is currently Professor.

From the Back Cover

This book conducts an in-depth research on robust filtering and fault detection for a class of T-S fuzzy systems. On the basis of the existing research on T-S fuzzy theory, robust filtering theory, and fault diagnosis theory, some new and effective technologies are proposed to solve the problems of robust filtering and fault detection for a class T-S fuzzy systems, while overcoming the shortcomings and limitations of the existing solutions.

This book introduces new design solutions for a class of T-S fuzzy systems to address the existing problems in the research of robust filtering and fault detection, namely 1) two new filtering methods are explored to obtain better filtering results than the existing approaches; 2) a new event-triggered filtering scheme is proposed for T-S fuzzy systems with bounded disturbances, which realizes that the designed observer gains in the absence of event-triggered mechanisms are also applicable to the case with event-triggered mechanisms; 3) two new methods are constructed to deal with the asynchronous problems of premise variables effectively, which overcome the defects and limitations of the existing ones; and 4) an effective fault detection scheme for handling measurement outliers is constructed, which can avoid the occurrence of false alarms.

This book is intended to inspire researchers and engineers, offering deeper insights into robust filtering and fault detection for T-S fuzzy systems, and equipping them with the latest advancements in fuzzy system theory, robust filtering, and fault diagnosis. It also provides valuable theoretical references for engineers tackling practical engineering problems.

Edition details

• Publisher: Springer
• Publication date: 2025
• Language: English
• ISBN 10: 9819658179
• ISBN 13: 9789819658176
• Binding: Hardcover
• Number of pages: 159