Title :
Nonlinear neural model-based predictive control of a solar plant
Author :
Arahal, M.R. ; Berenguel, M. ; Camacho, E.F.
Author_Institution :
Dept. de Ing. de Sist. y Autom., Univ. de Sevilla, Sevilla, Spain
Abstract :
This paper presents a neural model-based predictive control (MFC) scheme for nonlinear systems. A neural network is used to predict future outputs of the system, or more specifically, to predict the free response. A linear model is used to obtain the forced response of the system, providing an efficient and easy-implementable MFC algorithm to cope with nonlinear systems subject to disturbances. The control scheme has been applied to the distributed collector field of a solar power plant. Results are shown in the paper.
Keywords :
neurocontrollers; nonlinear control systems; predictive control; solar power stations; MPC algorithm; distributed collector; forced response; free response; linear model; neural network; nonlinear neural model-based predictive control; nonlinear systems; solar power plant; Neural networks; Prediction algorithms; Predictive models; Solar radiation; Temperature; Temperature control; Training; Solar power plant; model predictive control; neural networks;
Conference_Titel :
Control Conference (ECC), 1997 European
Conference_Location :
Brussels
Print_ISBN :
978-3-9524269-0-6
