报告题目：Iterative Learning Control for Robot Manipulators with Non-Repetitive Reference Trajectory, Iteration Varying Trial Lengths, and Asymmetric Output Constraints

报 告 人：Xu Jin，Associate Professor with the Department of Mechanical Engineering, University of Kentucky, Lexington, KY, USA.

照    片：

报告时间：2020.9.24上午9:00-11:00

报告地点: Zoom会议ID: 964 7401 4553, 密码: Xidian

邀 请 人：何超，李俊民

报告人简介: Xu Jin received the Bachelor of Engineering (BEng) degree in electrical and computer engineering (First Hons.) from the National University of Singapore, Singapore, the Master of Applied Science (MASc) degree in electrical and computer engineering from the University of Toronto, Toronto, ON, Canada, the Master of Science (MS) degree in mathematics from the Georgia Institute of Technology, Atlanta, GA, USA, and the Doctor of Philosophy (PhD) degree in aerospace engineering from the Georgia Institute of Technology, Atlanta, GA, USA.

Dr. Jin is currently an Assistant Professor with the Department of Mechanical Engineering, University of Kentucky, Lexington, KY, USA. He has authored/co-authored over 35 journal papers in top-tier journals of the field, and dozens of conference papers. He has been a reviewer for over 20 journals in areas of control systems and cybernetics. His current research interests include adaptive and iterative learning control, fault-tolerant control, nonlinear systems control, stochastic system control, with applications to intelligent vehicles, robot manipulators, and multiagent systems.

报告摘要: In this talk, we discuss a novel iterative learning control (ILC) scheme for non-repetitive reference trajectories tracking problems of robot manipulators over an iteration domain with varying trial lengths, subject to asymmetric constraint requirements on joint angles. To address iteration varying trial lengths, unlike the existing approaches based on the contraction mapping analysis, a new structure of ILC laws has been presented in this work, using analysis based on composite energy functions. A novel universal barrier function is proposed to deal with joint angle constraints. We show that under the proposed novel ILC scheme, beyond a small initial time interval in each iteration, the joint angle tracking error is uniformly converging to zero over the iteration domain, and the joint velocity tracking error is asymptotically converging to zero in the sense of certain L2 norm. In the end, a simulation example on a two-degree-of-freedom robot manipulator is presented to demonstrate the efficacy of the proposed scheme.