上肢康复机器人模糊自适应交互控制研究.

An adaptive interactive control system based on fuzzy rules for upper limb rehabilitation robots is proposed to address the insufficient or excessive training intensity during active rehabilitation exercises for stroke patients due to their individual differences. According to the difference of muscle strength of patients with different conditions, a fuzzy adaptive impedance controller is designed, which adjusts the damping and stiffness coefficients adaptively with fuzzy inference based on human‑machine interaction forces and system errors, altering the training intensity to achieve on‑demand assistive control for rehabilitation robots. Additionally, to ensure accurate tracking of the motion trajectory during rehabilitation training, a GA-FuzzyPID controller is designed to optimize the fuzzy rule membership functions and rule base according to an improved genetic algorithm, thereby reducing the trajectory tracking error of rehabilitation robots. Finally, trajectory tracking and adaptive impedance controlling simulation experiments are conducted for the system based on Matlab/Simulink. The results show that in the trajectory tracking experiment, the trajectory error of GA-FuzzyPID controller is reduced by 55. 9% and 34. 0% respectively compared with PID controller and FuzzyPID controller, which effectively improves the trajectory tracking accuracy. Compared with the fixed impedance method, the adaptive impedance control experiment verifies the effectiveness and feasibility of the proposed adaptive interactive control system. [ABSTRACT FROM AUTHOR]