Adaptive neural network control of a wheeled mobile robot violating the pure nonholonomic constraint

In this paper, adaptive neural network control is presented for a wheeled mobile robot violating the pure nonholonomic constraints. The nonholonomic constraint of the vehicle is assumed to be violated by an unknown slippage. Under a restricted assumption of the slippage, the proposed controller is constructed at the dynamical level using backstepping. The neural network (NN) controller deals with the unmodelled dynamics in the robot and eliminates the need for the error prone process in obtaining the LIP form of the system dynamics. In addition, the time-consuming offline training process for the NN is avoided. All the system states are shown to be able to track the desired trajectory. Simulation results are given to show the effectiveness of the proposed controller.