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

• Autorzy: Zahir Ahmida , Abdelfettah Charef , Victor M. Becerra
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

neural networks; feedforward control; predictive control; optimal control; radial basis functions; nonlinear systems

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2005
• Tom: 15
• Numer: 3
• Strony: 369-381

Daty

wydano

2005

otrzymano

2004-11-06

poprawiono

2005-04-22

Twórcy

autor

Zahir Ahmida

• Department of Electrical Engineering, University of Skikda, Al-Hadaik Rd., P.B.: 26, Skikda 21000, Algeria

autor

Abdelfettah Charef

• Department of Electronics, Mentouri University, Zerzara, Ain-Bey Rd., Constantine 25000, Algeria

autor

Victor M. Becerra

• Department of Cybernetics, University of Reading, Whiteknights, Reading RG6 6AY, U.K.

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