Optimal control strategies represent a widespread solution to increase the extracted energy of a Wave Energy Converter (WEC). The aim is to bring the WEC into resonance enhancing the produced power without compromising its reliability and durability. Most of the control algorithms proposed in literature require for the knowledge of the Wave Excitation Force (WEF) generated from the incoming wave field. In practice, WEFs are unknown, and an estimate must be used. This paper investigates the WEF estimation of a non-linear WEC. A model-based and a model-free approach are proposed. First, a Kalman Filter (KF) is implemented considering the WEC linear model and the WEF modelled as an unknown state to be estimated. Second, a feedforward Neural Network (NN) is applied to map the WEC dynamics to the WEF by training the network through a supervised learning algorithm. Both methods are tested for a wide range of irregular sea-states showing promising results in terms of estimation accuracy. Sensitivity and robustness analyses are performed to investigate the estimation error in presence of un-modelled phenomena, model errors and measurement noise.

中文翻译：

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实时波激励力估计：在ISWEC设备上的应用

最佳控制策略代表了一种增加波能转换器（WEC）提取能量的广泛解决方案。目的是使WEC产生共振，从而在不损害其可靠性和耐用性的情况下提高产生的功率。文献中提出的大多数控制算法都需要了解从入射波场产生的波激发力（WEF）。在实践中，WEF未知，必须使用估计值。本文研究了非线性WEC的WEF估计。提出了一种基于模型和无模型的方法。首先，考虑WEC线性模型和将WEF建模为待估计的未知状态来实现卡尔曼滤波器（KF）。第二，前馈神经网络（NN）用于通过监督学习算法训练网络，从而将WEC动力学映射到WEF。两种方法都针对各种不规则海况进行了测试，这些海图在估计精度方面显示出可喜的结果。进行灵敏度和鲁棒性分析以调查存在未建模现象，模型误差和测量噪声的情况下的估计误差。