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Dual Adaptive ANN Controllers Based on Wiener Models for Controlling Stable Nonlinear Systems

  • D. Sbarbaro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

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.

Keywords

Model Predictive Control Posterior Density Predictive Controller Parameter Estimation Algorithm Wiener Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • D. Sbarbaro
    • 1
  1. 1.Department of Electrical EngineeringUniversidad de ConcepciónChile

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