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

Zahir Ahmida; Abdelfettah Charef; Victor Becerra

International Journal of Applied Mathematics and Computer Science (2005)

  • Volume: 15, Issue: 3, page 369-381
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Ahmida, Zahir, Charef, Abdelfettah, and Becerra, Victor. "Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks." International Journal of Applied Mathematics and Computer Science 15.3 (2005): 369-381. <http://eudml.org/doc/207751>.

@article{Ahmida2005,
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. 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.},
author = {Ahmida, Zahir, Charef, Abdelfettah, Becerra, Victor},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {neural networks; feedforward control; predictive control; optimal control; radial basis functions; nonlinear systems},
language = {eng},
number = {3},
pages = {369-381},
title = {Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks},
url = {http://eudml.org/doc/207751},
volume = {15},
year = {2005},
}

TY - JOUR
AU - Ahmida, Zahir
AU - Charef, Abdelfettah
AU - Becerra, Victor
TI - Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks
JO - International Journal of Applied Mathematics and Computer Science
PY - 2005
VL - 15
IS - 3
SP - 369
EP - 381
AB - 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.
LA - eng
KW - neural networks; feedforward control; predictive control; optimal control; radial basis functions; nonlinear systems
UR - http://eudml.org/doc/207751
ER -

References

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