Adaptive fuzzy logic controller of visual servoing robot system by membership optimization using genetic algorithms

Abstract

In this paper, fuzzy logic control systems (FLC) and genetic algorithm (GA) are integrated for adaptive fuzzy logic controller to control visual servoing robot. Genetic algorithms are employed as an adaptive method for optimizing the internal parameters of fuzzy membership functions. The overall optimization of membership functions is done by selection of randomly generated parameters. Fitness function plays a crucial role in parameters selection .A proposed visual servoing simulator is used to verify the effectiveness of the proposed manner to control position-based visual servoing robot manipulator.