Modelling the free response of a solar plant for predictive control

This paper demonstrates the identification of a nonlinear plant using neura l networks for predictive control. The problem of neural identification is tackled using a static (non-recurrent) neural network in an autoregressive configuration (NARX). The selection of a set of input variables, a set of i nput/output vectors for training, and a neural structure, is discussed. In particular, an algorithm is proposed to obtain the number of past values of the measured variables needed to feed the network. The neural model is the n used within a model-based predictive control (MBPC) framework. The MBPC s cheme uses the prediction of the output of the system calculated as the sum of the free response (obtained using the nonlinear neural model) and the f orced response (obtained Linearizing around the current operating point) to optimize a performance index. The on-line adaptation of the model and othe r issues are discussed. The control scheme has been applied and tested in a solar power plant. (C) 1998 Elsevier Science Ltd. All rights reserved.