Fuzzy-sliding mode control with the self tuning fuzzy inference based on genetic algorithm

Abstract

This paper shows a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a polishing robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method, and it is guaranteed that the selected solution become the global optimal solution by optimizing Akaike's information criterion expressing the quality of the inference rules. Also, trajectory tracking simulation shows that the optimal fuzzy inference rules by the genetic algorithm are automatically selected and the trajectory control result is almost similar to the result which uses the inference rules to be selected through the trial and error method by an expert. Therefore, although a designer is a non-expert of robot systems, the fuzzy-sliding mode controller can be designed by the proposed self tuning fuzzy inference method based on the genetic algorithm.