Control de Robots Móviles con Incertidumbres Dinámicas usando Redes de Base Radial

Abstract

This paper presents an adaptive trajectory tracking control for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The control system design is done considering uncertain dynamic parameters in the dynamic model of the robot. The uncertainty in the dynamics model is learned by a RBF neural network in an adaptive feedback loop, adjusting the weight and the radial basis functions. The proposed RBF-NN scheme is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to back propagate the control errors through the model (or network model) to adjust the neurocontroller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results show the practical feasibility and performance of the proposed approach to mobile robots.