Autonomous mobile robots navigation using RBF neural compensator

This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. 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. The analysis of the RBF-NN approximation error on the control errors is included. Finally, the performance of the control system is verified through experiments.

Research highlights
► Adaptive trajectory tracking of mobile robots.
► Combines a feedback linearization based on a nominal model and a RBF-NN compensation.
► Robot with uncertain dynamic parameters, two controllers are implemented separately.
► Uncertainty in the nominal dynamics model is compensated by a RBF-NN.
► Control system performance is verified through experiments