Predictive Learning Control for Unknown Nonaffine Nonlinear Systems (eBook)
Theory and Applications
eBook Download:
PDF
2023
|
1st ed. 2023
IX, 206 Seiten
Springer Nature Singapore (Verlag)
978-981-19-8857-8 (ISBN)
IX, 206 Seiten
Springer Nature Singapore (Verlag)
978-981-19-8857-8 (ISBN)
This book investigates both theory and various applications of predictive learning control (PLC) which is an advanced technology for complex nonlinear systems. To avoid the difficult modeling problem for complex nonlinear systems, this book begins with the design and theoretical analysis of PLC method without using mechanism model information of the system, and then a series of PLC methods is designed that can cope with system constraints, varying trial lengths, unknown time delay, and available and unavailable system states sequentially. Applications of the PLC on both railway and urban road transportation systems are also studied. The book is intended for researchers, engineers, and graduate students who are interested in predictive control, learning control, intelligent transportation systems and related fields.
Qiongxia Yu received the B.E. and M.E. degrees from Henan Polytechnic University, Jiaozuo, China, in 2009 and 2012, respectively, and the Ph.D. degree from Beijing Jiaotong University, Beijing, China, in 2017. She is currently Lecturer in Henan Polytechnic University. Dr. Yu has published over 20 peer-reviewed journals and conference proceedings. She was awarded the first prize in Natural Science Academic Award of Henan Province in 2021. Her current research interests include learning control, data-driven control, predictive control, and their applications in transportation systems.
Ting Lei received the B.E. degree from Zhengzhou University, Zhengzhou, China, in 2012, and the Ph.D. degree from Beijing Jiaotong University, Beijing, China, in 2020. He is currently Lecturer in Zhengzhou University of Light Industry. Dr. Lei has published 9 peer-reviewed journals and conference proceedings. His current research interests include data-driven control, model free adaptive control, predictive control, and their applications in urban transportation systems.
Fengchen Tian received the B.E. degree in Electrical engineering and automation from Wanjiang University of Technology, Ma'anshan, China, in 2019, where he is currently pursuing the M.E. degree in School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, China. His research interests include data-driven control and learning control.
Zhongsheng Hou received the B.E. and M.E. degrees from Jilin University of Technology, Changchun, China, in 1983 and 1988, respectively, and the Ph.D. degree from Northeastern University, Shenyang, China, in 1994. From 1997 to 2018, he was with Beijing Jiaotong University, Beijing, China, where he was Distinguished Professor and Head of Department of Automatic Control. Currently, he is Chair Professor at Qingdao University, Qingdao, China. Prof. Hou is Founding Director of the Technical Committee on Data Driven Control, Learning and Optimization (DDCLO), Chinese Association of Automation. He is International Federation of Automatic Control Technical Committee Member of both adaptive and learning systems and transportation systems. He is Fellow of both IEEE and Chinese Association of Automation (CAA). Dr. Hou has authored over 400 peer-reviewed journals and conference proceedings and two monographs. His research interests include data-driven control, learning control, and intelligent transportation systems.
Xuhui Bu received the B.E. and M.E. degrees from Henan Polytechnic University, Jiaozuo, China, in 2004 and 2007, respectively, and the Ph.D. degree from Beijing Jiaotong University, Beijing, China, in 2011. He is currently Professor in Henan Polytechnic University. Dr. Bu has authored over 90 peer-reviewed journals and conference proceedings. He was supported by Program for Central Plains Top Young Talents of Henan Province in 2019. His current research interests are mainly related to data-driven control, iterative learning control, traffic control, and networked system control.
This book investigates both theory and various applications of predictive learning control (PLC) which is an advanced technology for complex nonlinear systems. To avoid the difficult modeling problem for complex nonlinear systems, this book begins with the design and theoretical analysis of PLC method without using mechanism model information of the system, and then a series of PLC methods is designed that can cope with system constraints, varying trial lengths, unknown time delay, and available and unavailable system states sequentially. Applications of the PLC on both railway and urban road transportation systems are also studied. The book is intended for researchers, engineers, and graduate students who are interested in predictive control, learning control, intelligent transportation systems and related fields.
| Erscheint lt. Verlag | 17.2.2023 |
|---|---|
| Reihe/Serie | Intelligent Control and Learning Systems | Intelligent Control and Learning Systems |
| Zusatzinfo | IX, 206 p. 77 illus., 75 illus. in color. |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik ► Analysis | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Naturwissenschaften | |
| Technik ► Bauwesen | |
| Technik ► Elektrotechnik / Energietechnik | |
| Schlagworte | Discrete-time systems • High-speed Railway • High-Speed Train • Iterative Learning Control • MIMO Systems • Nonlinear and Nonaffine Systems • Predictive control • SISO Systems • System Constraints |
| ISBN-10 | 981-19-8857-9 / 9811988579 |
| ISBN-13 | 978-981-19-8857-8 / 9789811988578 |
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