NEURAL IDENTIFICATION APPLIED TO PREDICTIVE CONTROL OF A SOLAR PLANT

This paper presents an application of the general identification metho dology to obtain neural predictors for use in a nonlinear predictive c ontrol scheme derived from the generalized predictive controller (GPC) structure. Every step of the design procedure is illustrated using a real-life problem: the identification of a solar power plant for predi ctive control. Different solutions are discussed for the problems that arise in the application of the methodology. Experimental results are given, showing the performance of the predictor and the controller. ( C) 1998 Elsevier Science Ltd. All rights reserved.