Globally stable adaptive tracking control of a wheeled mobile robot using RBF neural network as feedforward compensator

In this paper, a new adaptive position tracking control strategy is proposed for a class of wheeled mobile robot systems where radial basis function (RBF) neural network (NN) is used to model the uncertainty. The so-called feedforward compensation scheme is developed where only the information of the reference position is employed as the NN input. The main advantage is that the global stability of closed-loop systems can be guaranteed and the NN approximation domain can be determined based on the reference signal a prior, which is different from the conventional adaptive neural network control (ANNC) schemes where only the semi-globally stable result can be obtained and no method is provided to determined NN approximation domain. Finally, a simulation is given to verify the effectiveness of the proposed control scheme.