The real-time implementation of wave energy control leads to non-causality as the wave load that comes in the next few seconds is used to optimize the control command. The present work tackles non-causality through online forecasting of future wave force using artificial intelligence technique. The past free surface elevation is used to forecast the incoming wave load. A feedforward artificial neural network is developed for the forecasting, which learns to establish the intrinsic link between past free surface elevation and future wave force through machine learning algorithm. With the implementation of the developed online wave force prediction algorithm, a real-time discrete control algorithm taking constraint on response amplitude into account is developed and implemented to a bi-oscillator wave energy converter in the present research. The dynamic response and the wave power extraction are simulated using a state-space hydrodynamic model. It is shown that the developed real-time control algorithm enhances the power capture substantially whereas the motion of the system is hardly increased. The prediction error effect on power extraction is investigated. The reduction of power extraction is mainly caused by phase error, whilst the amplitude error has minimal influence. A link between the power capture efficiency and the constraint on control is also identified.

波浪能量控制的实时实施会导致无因果关系，因为接下来几秒钟出现的波浪负载将用于优化控制命令。本工作通过使用人工智能技术在线预测未来波浪力来解决非因果关系。过去的自由表面标高用于预测入射波载荷。开发了前馈人工神经网络进行预测，该网络通过机器学习算法学习建立过去自由表面高度和未来波浪力之间的内在联系。通过实现已开发的在线波浪力预测算法，开发了一种考虑了响应幅度约束的实时离散控制算法，并将其实现到双振荡器波能转换器中。使用状态空间流体动力学模型对动力响应和波浪功率提取进行了仿真。结果表明，所开发的实时控制算法大大增强了功率捕获能力，而系统的运动几乎没有增加。研究了预测误差对功率提取的影响。功率提取的减少主要由相位误差引起，而幅度误差的影响最小。还确定了功率捕获效率和控制约束之间的联系。功率提取的减少主要由相位误差引起，而幅度误差的影响最小。还确定了功率捕获效率和控制约束之间的联系。功率提取的减少主要由相位误差引起，而幅度误差的影响最小。还确定了功率捕获效率和控制约束之间的联系。