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 …

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

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 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 …

Nowadays, a lot of research effort is invested in systems which extract energy from
resources provided daily by nature. Renewable energy gives a possibility to control energy …

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 …

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 …

A non-linear adaptive constrained model-based predictive control scheme with steady-state
offset compensation is applied to a Distributed Solar Collector field. The strategy is based on …