An intelligent fuzzy controller based on genetic algorithms

In view of many applications, in recent years, there has been increasing interest in robot´s control. Two intelligent controllers based on fuzzy logic and neural network are developed to trace the desired trajectory for a robot. A variety of evolutionary algorithms, have been proposed to approximately solve problems of common engineering applications. Increasingly common applications involve automatic learning of nonlinear mappings that govern the behavior of control systems. In many cases where robot control is of primary concern, the systems used to demonstrate the effectiveness of evolutionary algorithms often do not represent practical robotic systems. In this paper, genetic algorithms (GA) are the evolutionary strategy of interest. This procedure and the manner in which fuzzy controllers are codified into chromosomes is described. It is applied to learn fuzzy control rules for a practical autonomous vehicle steering control problem, namely, path tracking. GA handles the simultaneous evolution of membership functions and rule bases for the fuzzy path tracker. Simulation results show that the proposed fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance.