Fuzzy-sliding mode control of automatic polishing robot system with the self tuning fuzzy inference based on genetic algorithm

Abstract

Proposes a self tuning fuzzy inference method based on genetic algorithms for the fuzzy-sliding mode control of a 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 becomes the global optimal solution by optimizing Akaike's information criterion. A trajectory tracking experiment with the automatic polishing robot system shows that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.