Title: Reconfigurable exoskeleton: enhanced rehabilitation and control efficiency

Authors: Jiayi Wen; Peiyi Zhu; Chengcheng Liu

Addresses: College of Electrical Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, 224000, China ' College of Electrical and Automation Engineering, Changshu Institute of Technology, Suzhou, Jiangsu, 215556, China ' College of Electrical and Automation Engineering, Changshu Institute of Technology, Suzhou, Jiangsu, 215556, China

Abstract: Our work encompasses the modelling and simulation of dynamic inertia and centre of mass for a three-degree-of-freedom upper limb exoskeleton designed for hemiparetic rehabilitation. The exoskeleton can dynamically update its model based on the attached functional modules, supporting extended rehabilitation periods and promoting self-care for patients. To address the issue of the exoskeleton significantly affecting its inherent state, we propose a dynamic model that promptly updates inertia and centre of mass parameters. An adaptive controller using an RBF neural network is developed based on this model. Trajectory tracking simulation conducted in MATLAB show that the proposed model outperforms the traditional controller, offering a notable 17.46% improvement in control accuracy and a substantial 36.6% reduction in energy consumption. This research contributes to the advancement of rehabilitation exoskeletons, offering potential benefits in enhancing rehabilitation outcomes for patients with hemiplegia.

Keywords: upper limb exoskeleton; state update adaptive control; multimodal control; RBF neural network.

DOI: 10.1504/IJSPM.2023.136479

International Journal of Simulation and Process Modelling, 2023 Vol.20 No.2, pp.144 - 157

Received: 30 Jun 2023
Accepted: 07 Oct 2023
Published online: 02 Feb 2024 *

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