Parameter Estimation and Adaptive Control for Nonlinear Servo Systems

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.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. 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.

• Part I: Modeling and control for servo systems with friction compensation1. Neural network-based adaptive funnel sliding mode control for servo mechanisms with friction compensation2. Robust adaptive tracking control for a servo mechanism with continuous friction compensationPart II: Modeling and control for servo systems with a dead-zone3. Neural-network-based adaptive funnel control for servo mechanisms with unknown dead-zonePart III: Parameter estimation for servo systems with unknown parameters4. Finite time parameter estimation-based adaptive predefined performance control for servo mechanisms5. Adaptive optimal parameter estimation and control of servo mechanisms: theory and experimentsPart IV: Disturbance rejection and control for servo systems6. USDE-based sliding mode control for servo mechanisms with unknown system dynamics7. Unknown input observer-based robust adaptive funnel motion control for nonlinear servo mechanisms8. Extended-state-observer based funnel control for nonlinear servo mechanisms with prescribed tracking performancePart V: Prescribed performance control for servo systems9. Adaptive predefined performance sliding mode control of motor driving systems with disturbances10. Approximation-free control for nonlinear helicopters with unknown dynamicsPart VI: Adaptive asymptotic tracking control for servo systems11. RISE-based asymptotic prescribed performance tracking control of nonlinear servo mechanisms12. Funnel tracking control for nonlinear servo drive systems with unknown disturbances13. Asymptotic tracking control for nonaffine systems with disturbances