Discrete-Time Adaptive Iterative Learning Control

1. Front Matter

   Pages i-x

   PDF

2. Introduction

   • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

   Pages 1-17

3. Model-Based Discrete-Time Adaptive ILC

   1. Front Matter

      Pages 19-19

      PDF

   2. Discrete-Time Adaptive ILC for Nonlinear Parametric Systems

      • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

      Pages 21-48

   3. Data-Weighted Discrete-Time Adaptive ILC

      • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

      Pages 49-68

   4. Nonlinearity Estimator-Based Discrete-Time Adaptive ILC

      • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

      Pages 69-82

   5. Neural Network-Based Discrete-Time Adaptive ILC

      • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

      Pages 83-94

   6. Distributed Discrete-Time Adaptive ILC for Multi-Agent Systems

      • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

      Pages 95-123

4. Data-Driven Discrete-Time Adaptive ILC

   1. Front Matter

      Pages 125-125

      PDF

   2. Data-Driven DAILC for Nonlinear Nonaffine Systems

      • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

      Pages 127-147

   3. Multi-Input Enhanced Data-Driven Discrete-Time Adaptive ILC

      • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

      Pages 149-163

   4. Data-Driven Discrete-Time Adaptive ILC for Terminal Tracking

      • Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang

      Pages 165-206

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.

• Iterative Learning Control
• Adaptive Iterative Learning Control
• Terminal Iterative Learning Control
• Discrete-Time Systems
• Linear Parameterized Systems
• Nonlinear Parameterized Systems
• Nonlinear Affine Systems
• Nonlinear and Non-affine Systems
• SISO Systems
• MIMO Systems
• System Uncertainties

“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)

• College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China

  Ronghu Chi, Na Lin, Huimin Zhang

• College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China

  Ruikun Zhang

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.

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