Wind turbine (WT) pitch control is a challenging issue due to the non-linearities of the wind device and its
complex dynamics, the coupling of the variables and the uncertainty of the environment. Reinforcement learn-
ing (RL) based control arises as a promising technique to address these problems. However, its applicability
is still limited due to the slowness of the learning process. To help alleviate this drawback, in this work we
present a hybrid RL-based control that combines a RL-based controller with a proportional–integral–derivative
(PID) regulator, and a learning observer. The PID is beneficial during the first training episodes as the RL based
control does not have any experience to learn from. The learning observer oversees the learning process by
adjusting the exploration rate and the exploration window in order to reduce the oscillations during the training
and improve convergence. Simulation experiments on a small real WT show how the learning significantly
improves with this control architecture, speeding up the learning convergence up to 37%, and increasing the
efficiency of the intelligent control strategy. The best hybrid controller reduces the error of the output power
by around 41% regarding a PID regulator. Moreover, the proposed intelligent hybrid control configuration has
proved more efficient than a fuzzy controller and a neuro-control strategy.
