Human-Robot Interaction for Surgical Robot Based on Fuzzy ModelReference Learning Control

In order to solve the instability problem in human-robot interaction in the robot-assisted surgery environment and the difficulty in modeling the personal factors of the doctor, a variable admittance human-robot cooperative control method is proposed based on fuzzy model reference learning. Firstly, the natural movement characteristics of human arm are taken as the reference model for fuzzy learning control, and the variable damping coefficient adjustment parameter rules are trained for the fuzzy admittance controller by an offline learning mechanism. Then a variable admittance control method is constructed based on variable damping parameter adjustment, taking the doctor's dragging force on the robot and the speed of the robot as the input, to output the desired speed of the robot. The offline training experimental results show that after offline training 10 times, the maximum error of the human-machine cooperation velocity is less than 17 mm/s, and the maximum error of the human-machine cooperation trajectory is less than 15 mm. Therefore, the proposed method has both a better tracking speed and a better precision than fuzzy control based on fixed admittance parameters.