Control of a Wave Energy Converter Using Model-Free Deep Reinforcement Learning

This paper presents a model-free control approach using deep reinforcement learning(DRL) which aims at optimizing the performance of wave energy converter(WEC). Several researchers commonly utilize a linear WEC model, neglecting the inherent non-linear static friction of the WEC system. This oversight may result in a decline in control performance. Consequently, static friction is deliberately included as a modeling error in the control framework to compare the resilience between model-based reactive control (RC) and DRL method against disturbances. To further address the challenge of real-time wave information being difficult to acquire in practical applications of the DRL method, a novel DRL controller is proposed with no wave information. The simulations are conducted to compare the proposed DRL controller with the RC approach based on a nonlinear WEC system in MATLAB/Simulink. It is shown that when considering modeling errors of static friction force, the DRL-based controller can outperform reactive control in both regular and irregular wave conditions - by 20.0% and 6.2%. Preliminary experimental validation of the DRL controller has been obtained through wave tank testing in regular wave conditions, showing satisfactory performance and consistency with simulation results. This is the first successful practice of running the RL algorithm on a practical controller and proves the feasibility of the proposed approach.