Fault Diagnosis and Fault-tolerant Control of Nonlinear Systems With Higher System Input Powers

This book provides recent theoretical results and applications of the fault diagnosis and fault-tolerant control for the nonlinear systems with higher input powers. Combining adaptive control technique, fuzzy logic systems, neural networks, Observer-based methods with the other control technique or approaches, this book investigates the fault diagnosis and fault-tolerant control problem of the nonlinear systems with higher input powers and proposes the corresponding diagnostic mechanisms, Fault estimation algorithm and fault-tolerant control schemes. This book intends to provide the readers a good understanding on fault diagnosis and fault-tolerant control of the nonlinear systems with higher input powers based on adaptive control technology. This book can serve as a reference for the main research issues and results on nonlinear systems with higher input powers for researchers devoting to various areas of fault-tolerant control theory, as well as a material for graduate and undergraduate students interested in nonlinear system control, especially fault diagnosis and fault-tolerant control, and their applications. Some prerequisites for reading this book include nonlinear system theory, matrix theory, mathematics, basic graph theory, and so on.

Dr. Qikun Shen received the B.Sc. degree in computer science and applications from the Chinese University of Mining and Technology, Xuzhou, China, in 1996, the M.Sc. degree in computer science and applications from Yangzhou University, Yangzhou, China, in 2007, and the Ph.D. degree in control theorem and control applications from the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2015.

Dr. Shen is currently Professor with the College of Information Engineering, Yangzhou University. His current research interests include distributed control, consensus control, fault-tolerant control, adaptive control, fuzzy control, neural networks-based control, and intelligent control.