Adaptive fuzzy nonlinear sliding-mode controller for a car-like robot

In this paper, a nonlinear controller, which can be updated online by means of fuzzy logic, has been proposed for tracking the trajectory of a car-like robot. The advantage of this control scheme is that it eliminates the effects of model disturbances and uncertainties, which cannot be avoided; and especially when we consider the difficult task of determining the exact kinematic and dynamic models of car-like robots. The proposed approach comprises a robust nonlinear section that uses the sliding mode control and a fuzzy section that can update, online, parameters of the nonlinear controller. The stability and the error convergence of the closed-loop system are verified through the Lyapunov criterion. A fuzzy system is designed to deal with the chattering of the car-like robot. In addition to the gains of the sign function, there are also constant parameters in our controller, which are determined by using a genetic algorithm. To show the effectiveness of the proposed design, simulations are performed by considering un-ideal effects such as uncertainties and external disturbances.