Fuzzy model reference learning control in surgical robots

Abstract

This paper proposes to apply the fuzzy model reference learning control method based on the guide control to the collaborative human-machine dragging process of the surgical robot. Variable damping coefficient adjustment parameter rules for fuzzy conductance controllers are trained by an offline learning mechanism in order to achieve the best human-machine interaction control effect. The surgical risk caused by the doctor's misoperation is lowered. Through the simulation experiment results, the speed curve tracking error is reduced by 70%. Trajectory curve tracking error is reduced by 57%. The both results prove that the strategy can reduce the error of the surgical robot and ensure the safety of robotic surgical operation.