Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks

Ahmida, Zahir ; Charef, Abdelfettah ; Becerra, Victor

International Journal of Applied Mathematics and Computer Science, Tome 15 (2005), p. 369-381 / Harvested from The Polish Digital Mathematics Library

Résumé

A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed on-line by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear predictive control strategy with guaranteed stability. The feedforward neuro-controller has been designed using the concept of inverse mapping. The proposed control scheme is demonstrated on a simulated single-link robotic manipulator.

Détail

Publié le : 2005-01-01

Zbl 1169.93389

EUDML-ID : urn:eudml:doc:207751

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