Adaptive robust fuzzy control and implementation for path tracking of a mobile robot

In this paper, an adaptive robust fuzzy control scheme with a genetic algorithm (GA) is proposed to solve the path tracking problem of a wheeled mobile robot. The presented controller consists of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot dynamics with uncertainties. A genetic algorithm is employed in the fuzzy inference to optimize the fuzzy rules of FBFN. The robust term with adaptive update rules is designed to suppress the external disturbances, hence it makes the system insensitive to the noises and disturbances of the environment. The robot dynamics including the actuator dynamics is considered. The stability and the convergence of the tracking errors are guaranteed by using the Lyapunov stability theory. The validity and robustness of the proposed control scheme are demonstrated through computer simulations and experiments with a wheeled mobile robot.