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