Dual adaptive ANN controllers based on wiener models for controlling stable nonlinear systems

Authors:
D. Sbarbaro
Affiliations:
Department of Electrical Engineering, Universidad de Concepción, Chile
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

Citing 2
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Original Contribution: The gamma model-A new neural model for temporal processing

Neural Networks
Brief paper: Dual adaptive control of nonlinear stochastic systems using neural networks

Automatica (Journal of IFAC)

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Abstract

This paper presents two nonlinear adaptive predictive algorithms based on Artificial Neural Network (ANN) and a Wiener structure for controlling asymptotically stable nonlinear plants. The first algorithm is based on the minimization of a cost function taking into account the future tracking error and the Certainty Equivalence (CE) principle, under which the estimated parameters are used as if they were the true parameters. In order to improve the performance of the adaptive algorithm, we propose to use a cost function, considering not only the future tracking error, but also the effect of the control signal over the estimated parameters. A simulated chemical reactor example illustrates the performance and feasibility of both approaches.