Parameter Estimation and Adaptive Control for Nonlinear Servo Systems. Emerging Methodologies and Applications in Modelling, Identification and Control

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

Authors

Shubo Wang 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 Professor, Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China. 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 Professor, School of Automation, Beijing Institute of Technology, Beijing, China. 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.