As a non-causal optimal control problem, the performance of wave energy converter (WEC) control relies on the accuracy of the future incoming wave prediction. However, the inevitable prediction errors can degrade WEC performance dramatically especially when a long prediction horizon is needed by a WEC non-causal optimal controller. This paper proposes a novel non-causal linear optimal control with adaptive sliding mode observer (NLOC+ASMO) scheme, which can effectively mitigate the control performance degradation caused by wave prediction errors. This advantage is achieved by embedding the following enabling techniques into the scheme: (i) an adaptive sliding mode observer (ASMO) to estimate current excitation force in real-time with explicitly formulated boundary of estimation error, (ii) an auto-regressive (AR) model to predict the incoming excitation force with explicitly formulated boundary of prediction error using a set of latest historical data of ASMO estimations from (i), and (iii) a compensator to compensate for both the estimation error and the prediction error of excitation force. Moreover, the proposed NLOC+ASMO scheme does not cause heavy computational load enabling its real-time implementation on standard computational hardware, which is especially critical for the control of WECs with complicated dynamics. The proposed NLOC+ASMO framework is generic and can be applied to a wide range of WECs, and in this paper we demonstrate the efficacy by using a multi-float and multi-motion WEC called M4 as a case study, whose control problem is more challenging than the widely studied point absorbers. Simulation results show the effectiveness of the proposed control scheme in a wide range of sea states, and it is also found that the controller is not sensitive to change of ASMO parameters.

中文翻译：

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多体波能量转换器的自适应滑模观测器的非因果线性最优控制

作为非因果最优控制问题，波能转换器（WEC）的控制性能依赖于未来入射波预测的准确性。但是，不可避免的预测错误会极大地降低WEC性能，尤其是当WEC非因果最优控制器需要较长的预测范围时。提出了一种新的具有自适应滑模观测器（NLOC + ASMO）的非因果线性最优控制方法，该方法可以有效地缓解由于波浪预测误差而引起的控制性能下降。通过将以下启用技术嵌入到方案中，可以实现这一优势：（i）自适应滑模观察器（ASMO），可使用明确制定的估算误差边界实时估算当前激励力，（ii）一种自动回归（AR）模型，它使用来自（i）的ASMO估计值的最新历史数据集，用明确制定的预测误差边界来预测进入的激振力，以及（iii）补偿器，以补偿估计误差和激振力的预测误差。此外，提出的NLOC + ASMO方案不会造成繁重的计算负担，从而使其能够在标准计算硬件上实时实现，这对于控制具有复杂动态特性的WEC尤其重要。提出的NLOC + ASMO框架是通用的，可以应用于各种WEC，在本文中，我们通过使用称为M4的多浮点多运动WEC作为案例研究来证明其有效性，该案例的控制问题更多。比广泛研究的点吸收器更具挑战性。