Neural Adaptive Tracking Control for Wheeled Mobile Robots

In this study, an adaptive control method is proposed by combining the tracking controller with neural torque controller for wheeled mobile robots. The kinematics and dynamics control laws are developed, and stability of control system is ensured by standard Lyapunov theory. For the trajectory tracking control of the wheeled mobile robot (WMR), we design an efficient neural network control that can manage the unmodeled extra disturbances and unstructured dynamics model of WMR. Especially, we have chosen radial basis function (RBF) neural networks to approximate the nonlinear function. By tuning the neural network weights, ensure the small tracking errors when WMR travels with the bounded input signals. Simulations illustrate the soundness of the proposed control theme for the desired tracking trajectory by Matlab-simulation.