Observer-Based Unknown Input Estimator of Wave Excitation Force for a Wave Energy Converter
Observer-Based Unknown Input Estimator of Wave Excitation Force for a Wave Energy Converter
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DOI：
10.1109/tcst.2019.2944329
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发表时间：
2020-11
影响因子：
4.8
通讯作者：
Mustafa Abdelrahman;R. Patton
中科院分区：
文献类型：
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作者：
Mustafa Abdelrahman;R. Patton
几种能量最大化控制方法的点吸收器波能转换器（PAWEC）系统需要的知识的波激振力（WEF），这是不可测量的PAWEC操作期间。已经提出了许多WEF估计的基础上使用卡尔曼滤波器（KF），扩展KF（EKF），或滚动时域估计的随机PAWEC建模。另外，一个确定性的WEF估计提出了快速未知输入估计（FUIE）的概念的基础上。WEF被估计为一个未知的输入，避免了表示其动态的要求。所提出的基于模糊的未知输入估计器（OBUIE）继承了FUIE估计快速变化信号的能力，这在考虑不规则波条件时很重要。与以前的方法不同，OBUIE是基于PAWEC模型设计的，包括非线性粘性阻力。它已被证明，非线性粘性阻力是必不可少的准确PAWEC模型描述，在能量最大化控制的作用。所提出的估计器的性能进行评估的PAWEC转换效率在一个单一的自由度PAWEC设备在规则和不规则波操作。仿真结果得到使用MATLAB评估估计器在不同的控制方法和参数不确定性。
Several energy maximization control approaches for point-absorber wave-energy converter (PAWEC) systems require knowledge of the wave excitation force (WEF) that is not measurable during the PAWEC operation. Many WEF estimators have been proposed based on stochastic PAWEC modeling using the Kalman filter (KF), extended KF (EKF), or receding-horizon estimation. Alternatively, a deterministic WEF estimator is proposed here based on the fast unknown input estimation (FUIE) concept. The WEF is estimated as an unknown input obviating the requirement to represent its dynamics. The proposed observer-based unknown input estimator (OBUIE) inherits the capability of estimating fast-changing signals from the FUIE, which is important when considering irregular wave conditions. Unlike preceding methods, the OBUIE is designed based on a PAWEC model, including the nonlinear viscous drag force. It has been shown that the nonlinear viscous drag force is essential for accurate PAWEC model description, within the energy maximization control role. The performance of the proposed estimator is evaluated in terms of PAWEC conversion efficiency in a single degree-of-freedom PAWEC device operating in regular and irregular waves. Simulation results are obtained using MATLAB to evaluate the estimator under different control methods and subject to parametric uncertainty.