In general, the modelling of wind turbines is a challenging task, since they are complex dynamic systems, whose aerodynamics\r\nare nonlinear and unsteady. Accurate models should contain many degrees of freedom, and their control algorithm design must\r\naccount for these complexities. However, these algorithms must capture the most important turbine dynamics without being\r\ntoo complex and unwieldy, mainly when they have to be implemented in real-time applications. The first contribution of this\r\nwork consists of providing an application example of the design and testing through simulations, of a data-driven fuzzy wind\r\nturbine control. In particular, the strategy is based on fuzzy modelling and identification approaches to model-based control\r\ndesign. Fuzzy modelling and identification can represent an alternative for developing experimental models of complex systems,\r\ndirectly derived directly from measured input-output data without detailed system assumptions. Regarding the controller design,\r\nthis paper suggests again a fuzzy control approach for the adjustment of both the wind turbine blade pitch angle and the generator\r\ntorque. The effectiveness of the proposed strategies is assessed on the data sequences acquired from the considered wind turbine\r\nbenchmark. Several experiments provide the evidence of the advantages of the proposed regulator with respect to different control\r\nmethods.
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