Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks
International Journal of Applied Mathematics and Computer Science, Tome 15 (2005) no. 3, p. 369.

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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.
Classification : 93D15
Mots-clés : neural networks, feedforward control, predictive control, optimal control, radial basis functions, nonlinear systems 
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     author = {Zahir Ahmida and Abdelfettah Charef and Victor Becerra},
     title = {Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks},
     journal = {International Journal of Applied Mathematics and Computer Science},
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Zahir Ahmida; Abdelfettah Charef; Victor Becerra. Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks. International Journal of Applied Mathematics and Computer Science, Tome 15 (2005) no. 3, p. 369. https://geodesic-test.mathdoc.fr/item/IJAMCS_2005__15_3_207751/