# 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

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topAhmida, 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 -

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