Adaptive Identification and Control of Uncertain Systems with Nonsmooth Dynamics reports some of the latest research on modeling, identification and adaptive control for systems with nonsmooth dynamics (e.g., backlash, dead zone, friction, saturation, etc). The authors present recent research results for the modelling and control designs of uncertain systems with nonsmooth dynamics, such as friction, dead-zone, saturation and hysteresis, etc., with particular applications in servo systems. The book is organized into 19 chapters, distributed in five parts concerning the four types of nonsmooth characteristics, namely friction, dead-zone, saturation and hysteresis, respectively. Practical experiments are also included to validate and exemplify the proposed approaches.
This valuable resource can help both researchers and practitioners to learn and understand nonlinear adaptive control designs. Academics, engineers and graduate students in the fields of electrical engineering, control systems, mechanical engineering, applied mathematics and computer science can benefit from the book. It can be also used as a reference book on adaptive control for servo systems for students with some background in control engineering.
- Explains the latest research outputs on modeling, identification and adaptive control for systems with nonsmooth dynamics
- Provides practical application and experimental results for robotic systems, and servo motors
Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.
I. Introduction 1
II. Modeling and control of uncertain systems with friction 2 1. Friction dynamics and modeling 2. Adaptive control for servo systems with LuGrefriction model 3. Robust tracking control for two-inertia systems with friction compensation 4. Adaptive prescribed performance control with continuous friction model 5. Composite adaptive control with discontinuous piecewise parametric friction model
III. Modeling and control of uncertain systems with input dead zone 6. Dead zone dynamics and modeling 7. Adaptive Robust Finite-Time Neural Control of Uncertain PMSM Servo System with Nonlinear Dead Zone 8. Adaptive dynamic surface control for strict-feedback systems with nonlinear dead zone 9. Adaptive prescribed performance control for strict-feedback systems with nonlinear dead zone 10.A modified dynamic surface control for pure-feedback systems with nonlinear dead zone
IV. Modeling and control of uncertain systems with saturation 11.Saturation dynamics and modeling 12.ESO based adaptive sliding mode control for systems with input saturation 13.Nonsingular terminal sliding mode funnel control for systems with unknown input saturation 14.Adaptive neural dynamic surface sliding mode control for uncertain systems with saturation
V. Modeling and control of uncertain systems with hysteresis 15.Hysteresis dynamics and modeling 16.Adaptive parameter estimation and model inverse control for uncertain systems with backlash 17.Parameter identification and control for Hammerstein systems with hysteresis 18.Adaptive parameter estimation and suspension control with continuous hysteresis model
Appendix A. Constants and Conversion Factors Appendix B. Introduction to MATLAB
Jing Na received the B.S. and Ph.D. degrees from the School of Automation, Beijing Institute of Technology, Beijing, China, in 2004 and 2010, respectively. He is currently a Marie Curie Intra-European Fellow, hosted at the Department of Mechanical Engineering, University of Bristol, UK. Since 2010, he has been with the Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, where he was promoted to be a Professor in 2013. From 2011 to 2013, he was a Postdoctoral Fellow with the ITER Organization, Cadarache, France. He was a Visiting Student in the Universitat Politecnica de Catalunya (UPC), Spain, in 2008 and a joint-training PhD student in the University of Bristol, UK in 2009.
He is the member of Technical Committee on intelligent control, IEEE Control System Society, the programme Chair of International Conference on Modelling, Identification and Control (ICMIC 2017), Kunming, China. He is serving as an Associate Editors of International Journal of Modelling, Identification and Control, and IPC member of more than 10 international conferences. Jing Na profehas published more than 90 refereed journal articles and conference papers. He has awarded 'Best Application paper' in the 3rd IFAC International Conference on Intelligent Control and Automation Science (IFAC ICONS 2013). His current research interests include intelligent control, adaptive parameter estimation, adaptive optimal control, nonlinear neural control and applications to robotics, servo mechanisms, automotive engines, powertrain and vehicle suspension systems.
Qiang Chen. Associate professor in the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China. He received the B.S. degree in measurement and control technology and instrumentation from Hebei Agricultural University, Baoding, China, in 2006 and the Ph.D. degree in control science and engineering from Beijing Institute of Technology, Beijing, China, in 2012. His research interests include neural networks, sliding mode control and adaptive learning control with applications to motion control systems.
Professor, School of Automation, Beijing Institute of Technology, Beijing, China