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

• Published in: Electronics letters Vol. 60; no. 15
• Main Authors: Pang, Zaixiang, Deng, Xiaomeng, Gong, Linan, Guo, Danqiu, Wang, Nan, Li, Ye
• Format: Journal Article
• Language: English
• Published: Wiley 01.08.2024
• Subjects: adaptive control; artificial intelligence; backpropagation; control engineering; data acquisition; medical robotics; neural nets; particle swarm optimisation
• DOI: 10.1049/ell2.13283
• ISSN: 0013-5194

A 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. This paper presents the construction of a four‐degree‐of‐freedom upper extremity exoskeleton rehabilitation robotic system. The objective is to address the rehabilitation training needs of patients with upper limb motor dysfunction. The shoulder joint was designed with a gravity compensation device with the objective of enabling zero‐gravity interaction between the robot and the patient. The elbow joint was designed with an auxiliary ratchet to counteract the interference caused by the weight of the robot. By combining Particle Swarm Optimization and Back Propagation Neural Network, an improved BPNN control algorithm is proposed. This results in enhanced control accuracy and performance of the robot, facilitating more effective patient training.