报告题目：Disturbance-observer-based Neural Sliding Mode Repetitive Learning Control of Hydraulic Rehabilitation Exoskeleton Knee Joint with Input Saturation
Yong Yang received the B.S. degree of mechatronic engineering in 2011 and M.S. degree of mechanical engineering in 2013, and the Ph.D. degree of control science and engineering in Southwest Jiaotong University in 2017, respectively. He is currently an associate professor with the School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China. His current research interests include robotics, exoskeleton systems, learning control, adaptive control, and mechatronics system design.
Rehabilitation exoskeleton is a wearable robot for recovery training of stroke patients. It is a complex human-robot interaction system with highly nonlinearities, such as modeling uncertainties, unknown human-robot interactive force, input constraints, and external disturbances. This work focuses on trajectory tracking control of a rehabilitation exoskeleton knee joint which is driven by a hydraulic actuator with input saturation. A radial basis function neural network (RBF-NN) sliding mode repetitive learning control strategy is presented for the exoskeleton knee joint, where the RBF-NN is combined with a sliding mode surface to compensate for the modeling uncertainties and the controller difference as well as enhanced the robustness of the system. Incorporating with a nonlinear observer, a repetitive learning scheme is constructed to estimate the unknown external disturbances and learn the periodic human-robot interactive force caused by repetitive recovery training. Utilizing the Lyapunov approach, the stability of the closed-loop control system and the observer are guaranteed. Comparative simulation results verify the effectiveness of the proposed control scheme.