| description abstract | Offshore wind energy is a promising option for emission-free power generationbecause its potential can satisfy the entire US energy demand. However, deep offshore wind energy is mostly left untapped due to the high levelized cost of energy (LCOE) of floating offshore wind turbines (FOWTs). To address the challenge, individual blade pitch control (IPC) of each blade is necessary to reduce fatigue due to the nonlinear dynamics involving unbalanced nonstationary wind/wave loading. In this paper, a machine learning control (MLC) method is proposed that utilizes a genetic program to selectively evolve promising control law candidates from simulated FOWT sensor data. The proposed method utilizes unique MLC features including an efficient selection of design load cases (DLCs) to accelerate the evolution, DLC-based elitism to identify promising candidates, and the use of internal state feedback to learn effective controller properties across all the target DLCs. These features are used to reduce evaluation time on the complex nonlinear FOWT model. The feasibility of the method is demonstrated by reducing fatigue and ultimate loading by 41% under the representative DLCs provided by the Aerodynamic Turbines with Load Attenuation Systems (ATLAS) competition hosted by the Advanced Research Projects Agency–Energy (ARPA-E). Unlike the methods founded upon black-box models [e.g., artificial neural networks (ANNs)], the proposed method provides interpretable results; thus, it can contribute to learning important design features to guide the future controller design. For example, the best individual driven during the feasibility study exhibits a nonlinear proportional controller to a sigmoidlike platform pitch signal, which can be a starting point for more advanced IPC design for FOWTs. The proposed method will improve the cost-effectiveness of FOWTs and be applied to similar nonlinear control problems, such as unmanned aerial vehicle (UAV) control. | |
