Parameter Estimation and Adaptive Control for Nonlinear Servo Systems (Emerging Methodologies and Applications in Modelling, Identification and Control) - Softcover

Wang, Shubo; Na, Jing; Ren, Xuemei

9780443155741: Parameter Estimation and Adaptive Control for Nonlinear Servo Systems (Emerging Methodologies and Applications in Modelling, Identification and Control)

Softcover

ISBN 10: 0443155747 ISBN 13: 9780443155741

Publisher: Academic Press, 2024

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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.

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

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About the Authors

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.

Jing Na received his B.Eng. and Ph.D. degrees from the School of Automation, Beijing Institute of Technology, Beijing, China, in 2004 and 2010, respectively. He was a Monaco/ITER Postdoctoral Fellow at the ITER Organization, Saint-Paul-l�s-Durance, France, and also a Marie Curie Intra-European Fellow with the Department of Mechanical Engineering, University of Bristol, U.K. Since 2010, he has been with the Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, where he became a professor in 2013. He has co-authored one monograph and more than 100 international journal and conference papers. His current research interests include intelligent control, adaptive parameter estimation, nonlinear control.

Xuemei Ren received her B.S. degree from Shandong University, Shandong, China, in 1989, and M.S. and Ph.D. degrees in control engineering from the Beijing University of Aeronautics and Astronautics, Beijing, China, in 1992 and 1995, respectively. She worked at the School of Automation, Beijing Institute of Technology as a professor from 2002. She has published more than 100 academic papers. Her research interests include nonlinear systems, intelligent control, neural network control, adaptive control, multi- drive servo systems and time delay systems.

From the Back Cover

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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