Parameter Estimation and Adaptive Control for Nonlinear Servo Systems

1st Edition - January 16, 2024
Imprint: Academic Press
Authors: Shubo Wang, Jing Na, Xuemei Ren
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 5 5 7 4 - 1
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 5 5 7 5 - 8

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

Purchase options

World Book Day Celebration!

Save up to 30% on books and eBooks!

Shop now

Institutional subscription on ScienceDirect

Request a sales quote

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.

Cover image
Title page
Table of Contents
Copyright
Acknowledgment
Introduction
1. Introduction
2. Prologue
3. Objective of the book
4. Book outline
Part I: Modeling and control for servo systems with friction compensation
Chapter 1: Neural network-based adaptive funnel sliding mode control for servo mechanisms with friction compensation
Abstract
1.1. Introduction
1.2. Problem formulation
1.3. Controller design
1.4. Stability analysis
1.5. Experimental results
1.6. Conclusion
References
Chapter 2: Robust adaptive tracking control for a servo mechanism with continuous friction compensation
Abstract
2.1. Introduction
2.2. Problem formulation
2.3. Controller design
2.4. Experimental results
2.5. Conclusion
References
Part II: Modeling and control for servo systems with a dead-zone
Chapter 3: Neural-network-based adaptive funnel control for servo mechanisms with unknown dead-zone
Abstract
3.1. Introduction
3.2. Problem statement
3.3. Control design and stability analysis
3.4. Experiment results
3.5. Conclusion
References
Part III: Parameter estimation for servo systems with unknown parameters
Chapter 4: Finite time parameter estimation-based adaptive predefined performance control for servo mechanisms
Abstract
4.1. Introduction
4.2. Problem formulation
4.3. Adaptive funnel controller design
4.4. Online parameter estimation
4.5. Stability analysis
4.6. Simulation results
4.7. Experiment results
4.8. Conclusion
References
Chapter 5: Adaptive optimal parameter estimation and control of servo mechanisms: theory and experiments
Abstract
5.1. Introduction
5.2. Dynamic model and problem formulation
5.3. Adaptive optimal parameter estimation
5.4. Adaptive sliding mode controller design
5.5. Stability analysis
5.6. Simulation results
5.7. Experiments
5.8. Conclusion
References
Part IV: Disturbance rejection and control for servo systems
Chapter 6: USDE-based sliding mode control for servo mechanisms with unknown system dynamics
Abstract
6.1. Introduction
6.2. Problem formulation
6.3. Unknown system dynamics estimator (USDE) design
6.4. Controller design
6.5. Simulation
6.6. Experimental results
6.7. Conclusion
References
Chapter 7: Unknown input observer-based robust adaptive funnel motion control for nonlinear servo mechanisms
Abstract
7.1. Introduction
7.2. Problem formulation
7.3. Controller design
7.4. Unknown input observer design
7.5. Stability analysis
7.6. Experimental studies
7.7. Conclusion
References
Chapter 8: Extended-state-observer based funnel control for nonlinear servo mechanisms with prescribed tracking performance
Abstract
8.1. Introduction
8.2. Dynamic model and problem formulation
8.3. Extended state observer design
8.4. Funnel control and funnel variable
8.5. Controller design and stability analysis
8.6. Simulation results
8.7. Experimental validation
8.8. Conclusion
References
Part V: Prescribed performance control for servo systems
Chapter 9: Adaptive predefined performance sliding mode control of motor driving systems with disturbances
Abstract
9.1. Introduction
9.2. Problem formulation
9.3. Control design and analysis
9.4. Experiments
9.5. Conclusion
References
Chapter 10: Approximation-free control for nonlinear helicopters with unknown dynamics
Abstract
10.1. Introduction
10.2. Preliminaries
10.3. Problem formulation
10.4. Approximation-free control design
10.5. Simulations
10.6. Conclusion
References
Part VI: Adaptive asymptotic tracking control for servo systems
Chapter 11: RISE-based asymptotic prescribed performance tracking control of nonlinear servo mechanisms
Abstract
11.1. Introduction
11.2. Problem formulation and preliminaries
11.3. Controller design
11.4. Stability analysis
11.5. Experimental results
11.6. Conclusion
References
Chapter 12: Funnel tracking control for nonlinear servo drive systems with unknown disturbances
Abstract
12.1. Introduction
12.2. System model
12.3. Control development
12.4. Convergence analysis
12.5. Practical implementation
12.6. Experiment verification
12.7. Conclusion
References
Chapter 13: Asymptotic tracking control for nonaffine systems with disturbances
Abstract
13.1. Introduction
13.2. Problem formulation
13.3. Control design
13.4. Experiment
13.5. Conclusion
References
Index

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

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

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

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health