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 …