@misc{Ahmida_Zahir_Stabilising,
 author={Ahmida, Zahir and Charef, Abdelfettah and Becerra, Victor M.},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={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.},
 abstract={The state feedback stabilising regulator is computed online 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.},
 type={artykuł},
 title={Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks},
 keywords={nonlinear systems, optimal control, radial basis functions, neural networks, predictive control, feedforward control},
}