Control of a New Cycling Rehabilitation Robot Based on Fuzzy PID

Abstract

For completing the standardized rehabilitation training of the affected limb based on the lower limb rehabilitation training robot system, it is designed that a lower limb rehabilitation robot control system based on fuzzy PID control method to realize stable and accurate control of the rehabilitation training process. First of all, this paper analyzes the ranges of motion of human hip and knees, and a single degree of freedom lower limb rehabilitation training robot system based on the belt transmission principle is designed. Then the fuzzy PID control is analyzed. And the Matlab is used to simulate the passive rehabilitation mode of the lower limb rehabilitation training robot system under different control strategies. The designed rehabilitation training test shows that the control system of the lower limb rehabilitation robot based on the fuzzy PID control method has a small speed overshoot, high control accuracy and smoother operation.

Adaptive sliding mode control strategy based on disturbance observer and neural network for lower limb rehabilitative robot

“…It is almost impossible to formulate an accurate mathematical model for nonlinear, time-varying, human-robot interacting systems [12][13][14]. In order to achieve better performance of controller, a lot of advanced control strategies have been employed to the passive control of lower limb rehabilitation robots including fuzzy control [15], sliding mode control [16], adaptive controller [17], neural network control [18][19][20].…”

A Study on the Response of the Rehabilitation Lower Device using Sliding Mode Controller

“…Setting up the dynamic equation for each link is an essential factor to calculate and simulate the motion of the joints, from which it is possible to calculate the design of the mechanical structure and the controller so that it is suitable for research models. To establish the dynamic equation of the device [13][14], we conduct a dynamic analysis of the joints. The diagram of the dynamic analysis of the research model is shown in Figure 2.…”