Design of Front Feed PID Control System for the Limb Rehabilitation Robot based on BP Neural Network

Abstract

Lower limb rehabilitation robot can provide rehabilitation training therapy for patients with lower limb hemiplegia and poor flexibility caused by stroke or accident. Realizing the precise control of rehabilitation robot can improve the effect of rehabilitation training. The control system is one of the key modules of the lower limb rehabilitation robot, and its performance will have a direct impact on the effect of rehabilitation training. Artificial neural network has the ability of self-learning and self-adaptation.Front feed control has the ability to improve the steady-state accuracy and response speed of the system. The integrated application of neural network and front feed control in the control system can optimize and improve the response speed, accuracy and follow-through of the overall control system. Therefore, the intelligence of control system of lower limb rehabilitation robot device is improved and the effectiveness of rehabilitation training is improved. This paper proposes a front feed PID control system based on neural network. Through step signal and gait tracking simulation experiments, the tracking effects of traditional PID, neural network PID and neural network front feed PID are compared. According to the simulation experiment, the neural network front feed PID can effectively improve the response speed and tracking effect of the system.