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. Qiang Chen received the B.S. degree in measure 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. Since 2012, he has been with the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China, where he was a Professor in 2022. He has published over 100 peer-reviewed papers in journals and conference proceedings. He has been authorized more than 60 invention patents, 13 of which were transferred. His research interests include adaptive control and iterative learning control with application to motion control systems.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.

"This book is interesting for both researchers and practitioners which can learn and understand nonlinear adaptive control designs. It is also useful for academics, engineers and graduate students in the fields of electrical engineering, control systems, mechanical engineering, applied mathematics and computer science. It may be used as a reference book on adaptive control for servo systems for students with some background in control engineering." --zbMATH

• I. Introduction 1II. Modeling and control of uncertain systems with friction 21. Friction dynamics and modeling2. Adaptive control for servo systems with LuGrefriction model 3. Robust tracking control for two-inertia systems with friction compensation4. Adaptive prescribed performance control with continuous friction model5. 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 saturation13.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 hysteresis18.Adaptive parameter estimation and suspension control with continuous hysteresis model Appendix A. Constants and Conversion Factors Appendix B. Introduction to MATLAB

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. Qiang Chen received the B.S. degree in measure 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. Since 2012, he has been with the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China, where he was a Professor in 2022. He has published over 100 peer-reviewed papers in journals and conference proceedings. He has been authorized more than 60 invention patents, 13 of which were transferred. His research interests include adaptive control and iterative learning control with application to motion control systems.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.

"This book is interesting for both researchers and practitioners which can learn and understand nonlinear adaptive control designs. It is also useful for academics, engineers and graduate students in the fields of electrical engineering, control systems, mechanical engineering, applied mathematics and computer science. It may be used as a reference book on adaptive control for servo systems for students with some background in control engineering." --zbMATH

• I. Introduction 1II. Modeling and control of uncertain systems with friction 21. Friction dynamics and modeling2. Adaptive control for servo systems with LuGrefriction model 3. Robust tracking control for two-inertia systems with friction compensation4. Adaptive prescribed performance control with continuous friction model5. 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 saturation13.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 hysteresis18.Adaptive parameter estimation and suspension control with continuous hysteresis model Appendix A. Constants and Conversion Factors Appendix B. Introduction to MATLAB

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. Qiang Chen received the B.S. degree in measure 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. Since 2012, he has been with the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China, where he was a Professor in 2022. He has published over 100 peer-reviewed papers in journals and conference proceedings. He has been authorized more than 60 invention patents, 13 of which were transferred. His research interests include adaptive control and iterative learning control with application to motion control systems.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.

"This book is interesting for both researchers and practitioners which can learn and understand nonlinear adaptive control designs. It is also useful for academics, engineers and graduate students in the fields of electrical engineering, control systems, mechanical engineering, applied mathematics and computer science. It may be used as a reference book on adaptive control for servo systems for students with some background in control engineering." --zbMATH

• I. Introduction 1II. Modeling and control of uncertain systems with friction 21. Friction dynamics and modeling2. Adaptive control for servo systems with LuGrefriction model 3. Robust tracking control for two-inertia systems with friction compensation4. Adaptive prescribed performance control with continuous friction model5. 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 saturation13.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 hysteresis18.Adaptive parameter estimation and suspension control with continuous hysteresis model Appendix A. Constants and Conversion Factors Appendix B. Introduction to MATLAB