Adaptive fuzzy sliding mode and indirect radial-basis-function neural network controller for trajectory tracking control of a car-like robot

The ever-growing use of various vehicles for transportation, on the one hand, and the statistics of soaring road accidents resulting from human error, on the other hand, reminds us of the necessity to conduct more extensive research on the design, manufacturing and control of driver-less intelligent vehicles. For the automatic control of an autonomous vehicle, we need its dynamic model, which, due to the existing uncertainties, the unmodeled dynamics and the performed simplifications, is impossible to determine exactly. Add to this, the external disturbances that exist on the movement path. In this paper, two adaptive controllers have been proposed for tracking the trajectory of a car-like robot. The first controller includes an indirect radial-basis-function neural network whose parameters are updated online via gradient descent. The second controller is adaptively updated online by means of fuzzy logic. The proposed controller includes a nonlinear robust section that uses the sliding mode method and a fuzzy logic section that updates some of the nonlinear control parameters online. The fuzzy logic system has been designed to deal with the chattering problem in the controller of a car-like robot. In both controllers, the parameters have been determined by means of genetic algorithm. The obtained results indicate that even with the consideration of un-ideal effects such as uncertainties and external disturbances, the proposed controller has been able to perform successfully.