Control of Lower Limb Rehabilitation Exoskeleton Robot Based on CPG Neural Network

In view of the difficulties in modeling, large external interference, weak adaptability and other problems in the control strategy adopted by the lower limb exoskeleton robot for medical rehabilitation at the present stage. This paper applies the bionic control method based on CPG to the exoskeleton control of lower limb rehabilitation. By adopting Dynamic Hebbian learning algorithm to improve the Hopf oscillator. And build a CPG oscillator network, realize the gait signal study, and eventually to improve lower limb exoskeleton robot movement performance and enhance its adaptability. Through the patient's wear-wearing test. It is proved that CPG bionic control exoskeleton can be matched with the control signal of the limb produced by the human body in the case of the human body motion cycle, and it can effectively control the exoskeleton of the lower extremity and to perform rehabilitation exercises.