Analysis and Optimization of Servo Motor Control Strategy for Minimally Invasive Surgical Robot

Abstract

The servo motor control system is of great significance to the performance of the redundant master manipulator of minimally invasive surgical robot. In this paper, an integrated controller is built to meet the requirement of multi-axis application. Model adaptive self-learning method is employed to obtain the manipulator gravity compensation torque, which is essential to alleviate operation fatigue. Different servo control strategies are compared. A novel control strategy of the double close loops switching strategy combined with static friction compensation is proposed and evaluated by experiments, which achieves active disturbance rejection and fast dynamic response. Besides, fuzzy control is also introduced to regulate the proportion integral (PI) regulator parameter of current loop inside the position close loop. The effectiveness of Fuzzy-PI control strategy is validated by MATLAB/SIMULINK simulation.