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 ofsoaring road accidents resulting from human error, on the other hand, reminds us of the necessity toconduct more extensive research on the design, manufacturing and control of driver-less intelligentvehicles. For the automatic control of an autonomous vehicle, we need its dynamic model, which,due to the existing uncertainties, the un-modeled dynamics and the performed simplications, isimpossible to determine exactly. Add to this, the external disturbances that exist on the movementpath. In this paper, two adaptive controllers have been proposed for tracking the trajectory of acar-like robot. The rst controller includes an indirect radial-basis-function neural network whoseparameters are updated online via gradient descent. The second controller is adaptively updatedonline by means of fuzzy logic. The proposed controller includes a nonlinear robust section thatuses the sliding mode method and a fuzzy logic section that updates some of the nonlinear controlparameters online. The fuzzy logic system has been designed to deal with the chattering problem inthe controller of car-like robot. In both controllers, the parameters have been determined by means ofgenetic algorithm. The obtained results indicate that even with the consideration of un-ideal effectssuch as uncertainties and external disturbances, the proposed controller has been able to performsuccessfully.