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Parameter Estimation and Adaptive Control for Nonlinear Servo Systems

  • 1st Edition - January 16, 2024
  • Latest edition
  • Authors: Shubo Wang, Jing Na, Xuemei Ren
  • Language: English

Parameter Estimation and Adaptive Control for Nonlinear Servo Systems presents the latest advances in observer-based control design, focusing on adaptive control for nonlinear… Read more

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Description

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.

Key features

  • 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

Readership

Graduate and undergraduate students, engineers, and researchers in the fields of control theory, nonlinear systems, power systems, robotics, mechatronics, automotive, flight systems, industrial systems

Table of contents

Part I: Modeling and control for servo systems with friction compensation


1. Neural network-based adaptive funnel sliding mode control for servo mechanisms with friction compensation

2. Robust adaptive tracking control for a servo mechanism with continuous friction compensation

Part II: Modeling and control for servo systems with a dead-zone


3. Neural-network-based adaptive funnel control for servo mechanisms with unknown dead-zone

Part III: Parameter estimation for servo systems with unknown parameters


4. Finite time parameter estimation-based adaptive predefined performance control for servo mechanisms

5. Adaptive optimal parameter estimation and control of servo mechanisms: theory and experiments

Part IV: Disturbance rejection and control for servo systems


6. USDE-based sliding mode control for servo mechanisms with unknown system dynamics

7. Unknown input observer-based robust adaptive funnel motion control for nonlinear servo mechanisms

8. Extended-state-observer based funnel control for nonlinear servo mechanisms with prescribed tracking performance

Part V: Prescribed performance control for servo systems


9. Adaptive predefined performance sliding mode control of motor driving systems with disturbances

10. Approximation-free control for nonlinear helicopters with unknown dynamics

Part VI: Adaptive asymptotic tracking control for servo systems


11. RISE-based asymptotic prescribed performance tracking control of nonlinear servo mechanisms

12. Funnel tracking control for nonlinear servo drive systems with unknown disturbances

13. Asymptotic tracking control for nonaffine systems with disturbances

Product details

  • Edition: 1
  • Latest edition
  • Published: January 16, 2024
  • Language: English

About the authors

SW

Shubo Wang

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.

Affiliations and expertise
Professor, Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Chenggong District, Kunming City, Yunnan Province, China

JN

Jing Na

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-lès-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.
Affiliations and expertise
Professor, Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China

XR

Xuemei Ren

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.
Affiliations and expertise
Professor, School of Automation, Beijing Institute of Technology, Beijing, China

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