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

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Ahmida Z., Charef A., Becerra V. M.

International Journal of Applied Mathematics and Computer Science

2005

tom 15

nr 3

369-381

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.

Linear-wavelet networks

100%

Galvão R. K. H., Becerra V. M., Calado J. M. F., Silva P. M.

International Journal of Applied Mathematics and Computer Science

2004

tom 14

nr 2

221-232

This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term. The introduction of the linear term is aimed at providing a more parsimonious interpolation in high-dimensional spaces when the modelling samples are sparse. A constructive procedure for building such structures, termed linear-wavelet networks, is described. For illustration, the proposed procedure is employed in the framework of dynamic system identification. In an example involving a simulated fermentation process, it is shown that a linear-wavelet network yields a smaller approximation error when compared with a wavelet network with the same number of regressors. The proposed technique is also applied to the identification of a pressure plant from experimental data. In this case, the results show that the introduction of wavelets considerably improves the prediction ability of a linear model. Standard errors on the estimated model coefficients are also calculated to assess the numerical conditioning of the identification process.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Becerra V. M.

1 Ahmida Z.

1 Calado J. M. F.

1 Charef A.

1 Galvão R. K. H.

1 Silva P. M.

1 2005

1 2004

Wyniki wyszukiwania

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

Linear-wavelet networks