Adaptive Wave Energy Converter Excitation Force Predictor in the Support Vector Machine Framework

The performance of Wave Energy Converters (WECs) depends on the capability of the control system to effectively predict the force of excitation, caused by the dynamics of sea waves acting on the system. This is of particular importance in the case of advanced control policies, as for constrained and predictive control algorithms, that makes explicit use of the predicted dynamics of controlled system and related disturbances acting on it for developing the control law. This paper proposes a prediction algorithm, developed within the Support Vector Machine framework, able to provide an effective prediction of the excitation forces acting on WECs. The proposed data-driven algorithm can be designed by off-line training but, due to the unpredictable long-term dynamics variability of sea conditions, pre-trained data-driven algorithms cannot effectively consider such a varying conditions. To overcome this limit, the proposed approach is featured by the capability to adapt the prediction to unknown dynamics by learning from on-line measured or estimated data. This feature also allows to limit the computational complexity of the algorithm while its prediction capabilities are adapted to time-varying sea state conditions evaluated in real-time. The proposed approach is tested on simulated data generated from a high-fidelity WEC simulator.