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 …

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 …

A feedback linearisation control scheme is proposed an implemented on a real solar power
plant. This structure is based on a non-linear control methodology combined with a recurrent …

This paper presents experimental results concerning the control of a distributed solar
collector field, where the main objective concerns the regulation of the outlet oil temperature …

This paper demonstrates the identification of a nonlinear plant using neural networks for
predictive control. The problem of neural identification is tackled using a static (non …

In this paper the modelling capabilities of a recurrent neural network and the effectiveness
and stability of the output regulation control theory are combined. The control structure …

This paper presents the application of a nonlinear controller, using a predictive control
strategy, to the distributed collector field of a solar power plant at the Plataforma Solar de …

This work proposes an indirect adaptive nonlinear control scheme based on a recurrent
neural network and the output regulation theory. The neural model is first trained off-line …

This paper deals with the identification of a nonlinear plant by means of a neural network
(NN) modelling approximation. The problem of neural identification is tackled using a static …

This paper presents an application of the general identification methodology to obtain neural
predictors for use in a nonlinear predictive control scheme derived from the generalized …