Variable Gain Design in Stochastic Iterative Learning Control

This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control.

Provides first comprehensive investigation of multiple improved stochastic iterative learning control via gain design Proposes four mainstream gain design strategies for stochastic ILC to achieve multiple performance demands Establishes a systematic design and analysis framework for stochastic ILC with in-depth technical discussions

Autorentext

Dong Shen received the B.S. degree in mathematics from the School of Mathematics, Shandong University, Jinan, China, in 2005, and the Ph.D. degree in mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing, China, in 2010. From 2010 to 2012, he was a postdoctoral fellow with the Institute of Automation, CAS. From 2012 to 2019, he was with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing. In 2016 and 2019, he was a visiting scholar with the National University of Singapore, Singapore, and RMIT University, Melbourne, VIC, Australia, respectively. Since 2020, he has been a professor at the School of Mathematics, Renmin University of China, Beijing. His research interests include iterative learning control, stochastic optimization, stochastic systems, and distributed artificial intelligence.

Inhalt

Introduction.- Preliminary Results.- Decreasing Gain Design.- Adaptive Gain Design.- Event triggering Gain Design.- Optimal Gain Design.- Conclusions and Open Problems.- References.