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 and provides fundamentals, algorithms, and key applications in the fields of power systems, robotics and mechatronics, flight, and automotive systems.
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Shubo Wang received his PhD in Control Science and Engineering from the Beijing Institute of Technology, Beijing, China, in 2017. From 2017 to 2024, he was with the School of Automation at Qingdao University, Qingdao, China, where he was promoted to Full Professor in 2023. Since 2024, he has been with the Faculty of Mechanical and Electrical Engineering at Kunming University of Science and Technology, Kunming, China.
He has coauthored one monograph and more than 70 international journal and conference papers. His current research interests include adaptive control, parameter estimation, neural networks, servo systems, robotics, nonlinear control, and their applications in robotic and motor drive 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.
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