Adaptive neural output regulation control of a solar power plant



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, being further improved by means of an on-line learning strategy using the Lyapunov and nonlinear observation theories.
The regulation problem is solved by an iterative strategy, formulated as an eigenvalue assignment problem, ensuring the convergence of the regulation equations.
The strategy was tested on a distributed collector field of a solar power plant (Plataforma Solar de Almería, Spain).
Experimental results, collected on the solar power plant, show the effectiveness of the proposed approach.


Recurrent neural networks, output regulation, discrete-time nonlinear adaptive control, on-line learning, solar poer plants


computational intelligence


Control Engineering Practice, October 2010

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Year 2011 : 1 citations

 M. Pasamontesa, J.D. Álvareza, J.L. Guzmána, J.M. Lemos, and M. Berenguela, “A switching control strategy applied to a solar collector field”, IFAC Control Engineering Practice, article in press, 2011.