Parameter Estimation and Adaptive Control for Nonlinear Servo Systems

Parameter Estimation and Adaptive Control for Nonlinear Servo Systems presents the latest advances in observer-based control design, focusing on adaptive control for nonlinear systems such as adaptive neural network control, adaptive parameter estimation, and system identification. This book offers an array of new real-world applications in the field. Written by eminent scientists in the field of control theory, this book covers the latest advances in observer-based control design. It provides fundamentals, algorithms, and it discusses key applications in the fields of power systems, robotics and mechatronics, flight and automotive systems. - Presents a clear and concise introduction to the latest advances in parameter estimation and adaptive control with several concise applications for servo systems - Covers a wide range of applications usually not found in similar books, such as power systems, robotics, mechatronics, aeronautics, and industrial systems - Contains worked examples which make it ideal for advanced courses as well as for researchers starting to work in the field, particularly suitable for engineers wishing to enter the field quickly and efficiently

Shubo Wang received his M.S. degree in control science and engineering from the School of Information Science and Engineering, Central South University, Hunan, China, 2011; and Ph.D. degree in control science and engineering from the Beijing Institute of Technology, Beijing, China, in 2017. Since 2017, He has been with the School of Automation, Qingdao University, where he became an associate professor in 2019. He has co-authored one monograph and more than 40 international journal and conference papers. His current research interests include adaptive control, parameter estimation, neural network, servo system, robotic, nonlinear control and applications for robotics and motor driving systems.