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Disturbance-observer-based Neural Sliding Mode Repetitive Learning Control of Hydraulic Rehabilitation Exoskeleton Knee Joint with Input Saturation

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Abstract

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 paper 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.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Electronic Information, Xihua University, 999 Jinzhou Road, Jinniu District, Chengdu, 610039, China

    Yong Yang, Xiu-Cheng Dong, Zu-Quan Wu & Xia Liu

  2. School of Electrical Engineering, Southwest Jiaotong University, 111 North 2nd Ring Road, Jinniu District, Chengdu, 611756, China

    De-Qing Huang

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  1. Yong Yang
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Correspondence to De-Qing Huang.

Additional information

Yong Yang received his B.S. degree of mechatronic engineering in 2011, an M.S. degree of mechanical engineering in 2013, and a 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.

Xiu-Cheng Dong is a professor with the School of Electrical Engineering and Electronic Information in Xihua University, China. In 2000, he was a visiting scholar at Redriver College, Canada. In 2006, he was a senior visiting scholar at Yamaguchi University, Japan. Prof. Dong’s research interests broadly involve the areas of intelligent control, modeling of nonlinear system, machine vision, and virtual reality.

Zu-Quan Wu received his Ph.D. degree from the School of Information Science and Technology, Southwest Jiaotong University, Sichuan, China. He is currently with the School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China. His current research interests include sensor, 2D-material, and conducting polymer composites.

Xia Liu received her Ph.D. degree from the University of Electronic Science and Technology of China in 2011. In 2009- 2010, she was a visiting scholar in the Department of Electrical and Computer Engineering, University of Alberta, Canada. In 2013-2014, she was a visiting scholar in the Department of Mechanical Engineering, the University of Adelaide, Australia. Since 2012, She has been with the School of Electrical Engineering and Electronic Information in Xihua University, China, and now a professor. Her research interests include nonlinear system and control, especially control of robotic teleoperation systems.

De-Qing Huang received his B.S. and Ph.D. degrees with a major of applied mathematics from the Mathematical College, Sichuan University, Chengdu, China, in 2002 and 2007, respectively. He attended the Department of Electrical and Computer Engineering (ECE), National University of Singapore (NUS), Singapore, in 2006, where he received a second Ph.D. degree with a major in control engineering in 2011. From January 2010 to February 2013, he was a Research Fellow in the Department of Electrical and Computer Engineering of NUS. From March 2013 to January 2016, he was a Research Associate with the Department of Aeronautics, Imperial College London, London, U.K. In January 2016, he joined the Department of Electronic and Information Engineering, Southwest Jiaotong University, Chengdu, China as a professor and department head. His research interests lie in the areas of modern control theory, artificial intelligence, and fault diagnosis as well as robotics.

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This work was supported in part by the National Natural Science Foundation of China under Grant 62003278, 61773323, 11872069, and 61973257.

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Yang, Y., Dong, XC., Wu, ZQ. et al. Disturbance-observer-based Neural Sliding Mode Repetitive Learning Control of Hydraulic Rehabilitation Exoskeleton Knee Joint with Input Saturation. Int. J. Control Autom. Syst. 20, 4026–4036 (2022). https://doi.org/10.1007/s12555-021-0540-z

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