Research on gravity compensation control of BPNN upper limb rehabilitation robot based on particle swarm optimization

Author:

Pang Zaixiang1,Deng Xiaomeng1ORCID,Gong Linan2,Guo Danqiu3,Wang Nan4,Li Ye5

Affiliation:

1. School of Computer Science and Engineering Changchun University of Technology Changchun China

2. School of Mechanical and Electrical Engineering Changchun Polytechnic Changchun China

3. School of Artificial Intelligence The Tourism College of Changchun University Changchun China

4. Jilin Weimiao Medical Technology Co., Ltd Changchun China

5. School of Mechatronic Engineering Changchun University of Technology Changchun China

Abstract

AbstractA four‐degree‐of‐freedom upper limb exoskeleton rehabilitation robot system with a gravity compensation device is constructed. The objective is to address the rehabilitation training needs of patients with upper limb motor dysfunction. A BP neural network adaptive control method based on particle swarm optimization is proposed. First, the degrees of freedom of the human body are analyzed, and a Lagrange method is employed to construct a dynamic model. Second, a particle swarm optimization back propagation neural network adaptive control algorithm based on particle swarm optimization is presented. Subsequently, the range of motion of the upper limbs is analyzed with reference to muscle anatomy and a three‐dimensional motion capture system. And the robot structure design is analyzed in detail. Finally, simulation experiments were conducted, and the results demonstrated that the proposed method exhibited high effectiveness and accuracy.

Publisher

Institution of Engineering and Technology (IET)

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