Neural network adaptive trajectory tracking controller for mobile robots

FRANCISCO ROSSOMANDO; CARLOS SORIA; RICARDO CARELLI; DANIEL PATIÑO

Río Gallegos, Santa Cruz

Simposio; Reunión de Procesamiento de la Información y Control, RPIC´07; 2007

Universidad Nacional de la Patagonia Austral

This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a NN adaptive dynamics 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 NN approximation error on the control errors is included. Finally, the performance of the control system is verified through experiments.