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. © Springer-Verlag Berlin Heidelberg 2006.