NEURAL NETWORK CONTROL OF PNEUMATIC ARTIFICIAL MUSCLE MANIPULATOR FOR KNEE REHABILITATION

Abstract

An interesting alternative to electric actuators for medical purposes, particularly promising for rehabilitation, is a pneumatic artificial muscle (PAM) actuator because of its muscle–like properties such as tunable stiffness, high strength to weight ratio, structure flexibility, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks. However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion. Then it is not easy to realize the performance of transient response of PAM manipulator due to the changes in the physical condition of patients as well as the various treatment methods. In this study, an intelligent control algorithm using neural network for one degree of freedom manipulator is proposed for knee rehabilitation. The experiments are carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm is demonstrated through experiments with two conditions of patient and three kinds of treatment methods.