## 2 Related Works  
The control system of a wind turbine comprises two simultaneous controllers: the control of the generation torque and the pitch control. Control of generation torque is a well-known problem with solutions generally accepted by industry and academia [14], so this section will deal only with pitch control. Wind turbines usually operate in four regions depending on wind speed. Pitch controllers are key in Region III, which is limited by the rated speed and the cut-out value. The goal is to limit the rotor speed and power by changing the angle of attack of the blades of the rotor [16]. The most common controller is the PID. Several configurations are possible, with only PI terms [17] or with PID [18]. Scheduled gains are also a usual solution [19; 20], as well as the nonlinear PIDs [21]. More advanced schemes, such as Model Predictive Control [22; 23], have been proposed. An interesting alternative is the use of the Fractional Order PID (FOPID) [24]. A FOPID can enhance the performance of a PID since two additional parameters can be tuned. Its use in pitch control for wind turbines is reported in [25]. However, floating offshore wind turbines (FOWTs) are highly nonlinear systems. There are hydrodynamic and aerodynamic interactions with a set of coupled elements such as the rotor, power transmission, platform dynamics, mooring lines, and tower. These interactions must be taken into account to design a control strategy [26]. If not, then the FWT motions can produce instabilities, resulting in large pitch motions and structural bending moments [27]. Reducing the gains of the controller can mitigate this problem, at the cost of worse controller performance, which is not desired from an industrial perspective. Furthermore, its use in floating wind turbines can introduce resonant motions associated with the controller-motion interaction [28]. An alternative is to design a controller based on a model [29; 30; 31]. However, these models assume some simplifications and numerical approximations, which, together with uncertainty in the actual parameters of the system, have prevented the adoption of advanced controllers in commercial offshore wind turbines. Another problem is the limited attention that large wind turbines have received in the literature, either in control or in modeling, and works on turbines from 5 MW on are very limited [32; 33]. An alternative approach is intelligent control, which may be capable of enhancing the performance of the system even if the model is not available or is too complex to apply conventional control techniques [34]. Although some other schemes have been proposed [34; 35], fuzzy logic presents good performances in wind energy due to its ability to be adapted and its capability to deal with uncertainties [36]. For instance, [37] proposes a control system for a 3.5 kW wind turbine based on three fuzzy logic controllers; however, the implementation of the control system is complex, and the wind turbine is placed onshore and comparatively small. In another study, [38] presents a fuzzy-based controller for an offshore turbine closer to industrial models. The turbine has a power of 5 MW and is simulated using an NREL (National Renewable Energy Laboratory) model, although no details about the specific model or the foundations are disclosed. The proposed controller includes a fuzzy logic element combined with a conventional PI, which requires a complex design and tuning before the fuzzy inference can be designed. The overall control system is compared to an NREL reference controller. Type 2 fuzzy systems have also been proposed, offering additional degrees of freedom to tune the controller [39]. Finally, intelligent techniques can be used to obtain an expert system that, based on human experience, can obtain a control solution that is potentially adapted to the specifications of any wind turbine [40].