Compared with PI, Fuzzy-PI and PSO-PI Controllers of Robotic Grinding Force Servo System

Abstract

By grinding process, when an industrial robot is used to finish a curved surface, both feed movement and contact force must controlled at the similar time in order that the grinding tool would machine the work-piece at  the  right  position  in  right  posture with  required  force. A passive wrist system is advanced, in this paper, to conform the shape of the machining propeller by altering its posture along with the surface. The proportional-integral (PI) controller, due to its simplicity, robustness, and affordable price, is extremely often used in practical applications, but it is effective for linear systems, as well as, the challenging task is to find its optimal gains. If the processes involved higher order and time delay systems, many intelligent controllers were appeared. In this paper, to cope with nonlinearities, improve the controller parameters and at the same time modeling uncertainties of grinding marine propeller surface, a PI torque controller is proposed such that its optimal gains are derived via a modern systems based on fuzzy logic theory and particle swarm optimization algorithm which are used to solve various engineering problems. Grinding force is controlled under Fuzzy-PI controller which is being assembled and compared with a PSO-PI controller to obtain which controller that provides grinding with higher quality. The compared controllers have been optimized together with the parameters of the Two-Phase Hybrid Stepping Motor. The suggested fuzzy rule function and PSO algorithm improve the response of the controlled system and searches a high-quality solution impressively. Simulation and comparison results are presented and that the proposed control systems are coping well with nonlinearities and uncertainties while find PI control parameter set effectively, the PSO-PI controller has a better control performance with improved step response for robotic grinding force servo system. These control methods was simulated using MATLAB/SIMULINK.