Robot dynamic trajectory tracking control algorithm based on steady-state closed-loop learning

Abstract

In order to improve the stability control ability of flexible lower limb exoskeleton robot, a dynamic trajectory tracking control algorithm of flexible lower limb exoskeleton robot based on steady-state closed-loop learning is proposed. Gyroscope and rangefinder are used as information sensors of flexible lower limb exoskeleton robot to collect position information of flexible lower limb exoskeleton robot, fuse collected positioning information of flexible lower limb exoskeleton robot, fuse physical information and measure parameters of flexible lower limb exoskeleton robot by using dynamic information measurement method, and obtain mapping feature set of Cartesian space according to trajectory components of flexible lower limb exoskeleton robot. The pose information of the flexible lower limb exoskeleton robot is obtained through the forward kinematics, and the information is enhanced according to the spatial position information. The steady-state closed-loop learning method is adopted to realize the adaptive learning of the robot dynamic trajectory tracking control. The simulation results show that this method is adaptive to the dynamic trajectory tracking control of the robot, and the positioning control ability of the robot is strong.