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This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
Chapter 1: Introduction.- Part 1: Model-based Discrete-time Adaptive ILC.- Chapter 2: Discrete-time Adaptive ILC for Linear Parametric Systems.- Chapter 3: Discrete-time Adaptive ILC for Higher order Parametric Systems.- Chapter 4: Data-weighted Discrete-time Adaptive ILC Chapter.- 5: Discrete-time Adaptive ILC for Nonparametric Nonlinear Systems Part 2: Data-driven Discrete-time Adaptive ILC Chapter.- 6: Neural Network based Discrete-time Adaptive ILC.- Chapter 7: Data-driven Discrete-time Adaptive ILC for Nonaffined Nonlinear Systems.- Chapter 8: Multi-input Enhanced Data-driven Discrete-time Adaptive ILC.- Chapter 9: High-order Internal Model based Data-driven Terminal Adaptive ILC.- Chapter 10: Conclusions Appendices
Ronghu Chi received the Ph.D. degree from Beijing Jiaotong
University, Beijing China, in 2007. He was Visiting Scholar with
Nanyang Technological University, Singapore, from 2011 to 2012 and
Visiting Professor with University of Alberta, Edmonton, AB,
Canada, from 2014 to 2015. In 2007, he joined Qingdao University of
Science and Technology, Qingdao, China, and is currently a full
professor in the School of Automation and Electronic Engineering.
He served as various positions in international conferences and was
an invited guest editor of International Journal of Automation and
Computing. He has also served as a council member of a Shandong
Institute of Automation and the deputy director of the technical
committee of Data-driven Control, Learning and Optimization
Professional Committee, etc. He was awarded the “Taishan
scholarship” in 2016. His current research interests include
iterative learning control, data-driven control, intelligent
transportation systems and so on. He has published over 100 papers
in important international journals and conference proceedings.
Na Lin received the M.Sc. degree in automatic control from the
Qingdao University of Science and Technology, Qingdao, China, in
2017, where she is currently pursuing the Ph.D. degree in automatic
control with the Institute of Artificial Intelligence and Control,
School of Automation and Electronic Engineering. Her current
research interests include data-driven control and iterative
learning control.
Huimin Zhang received the bachelor's degree in electrical
engineering and automation from University of Jinan, Jinan,
Shandong, China, in 2017. She is currently pursuing the doctoral
degree with the Institute of Artificial Intelligence and Control,
School of Automation and Electronic Engineering, Qingdao University
of Science and Technology, Qingdao, China. Her research interests
include data-driven control, iterative learning control, networked
system control, and data quantization methods.
Ruikun Zhang received his B.S. and M.S. degrees in applied
mathematics from Qingdao University, Qingdao, China, in 2003 and
Zhejiang University, Hangzhou, China, in 2006, respectively. In
2006, he joined Qingdao University of Science and Technology and is
currently an associate professor in the school of mathematics and
physics. In 2016, he received the Ph.D. degree from Beijing
Jiaotong University, Beijing China. His research interests include
iterative learning control, robust control, and adaptive control.
“This book should be viewed as a (relatively) small handbook of discrete-time adaptive iterative learning control (DAILC) in its (very) contemporary version. … The book can serve both as a handbook but also a textbook for graduate and postgraduate researchers.” (Vladimir Răsvan, zbMATH 1491.93002, 2022)
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