Jixie chuandong (Apr 2024)
Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network
Abstract
In the rehabilitation training process of lower limb rehabilitation robots, the existence of uncertain factors such as model parameters and environmental interference will affect the accuracy of trajectory tracking of the robot. To solve this problem, an adaptive compensation control based on the radial basis function (RBF) neural network is proposed. This control method can improve the accuracy of mechanical system trajectory tracking. Firstly, a closed chain horizontal lower limb rehabilitation robot structure with four working modes and stable movement is designed. Secondly, the Lagrange method is used to solve the kinetic nominal model, the uncertainty factors such as model parameters and external interference of the rehabilitation device are separated, and the adaptive compensation algorithm based on the RBF neural network is designed for the approximate control. Finally, the Matlab/Simulink environment is used to verify the effectiveness of the control strategy. The results show that, compared with the traditional fuzzy proportional integral derivative (PID) control method, the adaptive compensation algorithm based on the RBF neural network has a faster response speed and better tracking effect in human gait curve trajectory tracking. Moreover, the peak angle errors of the hip joint and the knee joint trajectory tracking are 0.08° and 0.13° respectively, which are much less than the rotation angle of patients' lower limbs in rehabilitation exercise. A single-leg prototype experiment is designed to show that the RBF compensation adaptive controller used in the study can achieve high precision tracking results and meet the safety requirements of patients in rehabilitation training.
