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 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 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 …

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 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 …

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 …

The optimal control of complex thermal energy systems is a challenge due to their dynamic
behavior and constantly changing boundary conditions. To maximize the energy efficiency …

Renewable energies are gaining space in the energy generation panorama, thanks to
technological advances and policy support. To take profit of these energies in an optimal …

Solar thermal plants have high nonlinearities and non-manipulated energy source which
make their control task a very challenging work. Linear controllers cannot cope with …

Using neural networks to capture complex dynamics of highly nonlinear systems is a
promising feature for advanced control applications. Recently it has been shown that ReLU …