Adaptive sliding mode control with RBF neural networks for omni-directional mobile robot

Abstract

In this paper, we consider the adaptive sliding mode control with radial basis function neural networks for the Omni-directional mobile robot. This is a holonomic robot that can operate easily in small and narrow spaces, due to the ability of flexible rotational and translational moving, simultaneously and independently. This robot is a MIMO nonlinear system. We design the sliding mode control (SMC) to ensure the trajectory tracking problem for a mobile robot. Therein, the radial basis function (RBF) neural networks are trained and used to approximate the adaptive SMC control law. In addition, the parameters of the neural networks are updated during the operation by using the gradient descent algorithm. Furthermore, we show the asymptotical convergence of the system state with the proposed control strategy. Finally, the simulation is conducted to verify the effectiveness of the proposed control system under disturbances and system uncertainties. These results demonstrate that the proposed algorithm is feasible to control the robot as well as control the nonlinear systems.