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

Zahir AhmidaAbdelfettah CharefVictor Becerra — 2005

International Journal of Applied Mathematics and Computer Science

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...

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