Abstract
This paper describes the application of a nonlinear adaptive constrained model-based predictive control scheme to the distributed collector field of a solar power plant at the Plataforma Solar de Almeria (Spain). This methodology exploits the intrinsic nonlinear modelling capabilities of nonlinear state-space neural networks and their online training by means of an unscented Kalman filter. Tests on the ACUREX field illustrate the great engineering potential of the proposed control strategy.
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