期刊
IEEE LATIN AMERICA TRANSACTIONS
卷 9, 期 5, 页码 593-602出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TLA.2011.6030965
关键词
subspace; adaptive inverse control; system identification; RBF neural nets; mobile robot control; Lyapunov theory
In the present paper, it will be reported original results concerning the application of Neural Networks (NN) in mobile robot in trajectory tracking control. This work combines a feedback linearization based on a nominal model and an NN adaptive dynamic compensation. In mobile robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematic controller and an inverse dynamic controller. The uncertainty in the nominal dynamic 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 learning laws were deduced by Lyapunov stability analysis. Finally, the performance of the control system is verified through experiments.
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