Control Design of Lower Limb Rehabilitation Robot Based on Gait Data

Abstract

In order to make the assisted rehabilitation training of the lower limb rehabilitation robot (LLRR)more consistent with human motor characteristics, and to provide more safe and effective rehabilitation training for patients with lower limb motor dysfunction, a control strategy of the LLRR based on human gait data and radial basis function (RBF) neural network is proposed. Firstly, the gait data of healthy human body acquired by 3D motion capture system is taken as the expected input of the system. Secondly, the RBF neural network adaptive controller generates the torque to drive the joint motion of the LLRR, so that the motion trajectory of the robot can track the desired trajectory. Then, the modified model based on feedforward control is used to compensate and correct the input torque of the robot, so as to achieve real-time correction and better tracking effect. Finally, the simulation results based on experimental data demonstrate the feasibility and effectiveness of the proposed method.