Robust Data-driven Estimation of Wave Excitation Force for Wave Energy Converters

Shi, Shuo; Patton, Ron J.; Liu, Yanhua

doi:10.1016/j.ifacol.2020.12.1231

Cited by 1 publication

(1 citation statement)

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“…The authors of [16] presented a novel data-driven approach for updating reactive control parameters based on a Multifidelity Gaussian process model for a WEC device. The machine learning applications for WEC control are focused on wave force forecasting [14], wave excitation force estimation [17], complex wave hydrodynamic approximation [18], direct controller optimization using a reinforcement learning algorithm [15,19], etc. However, the use of machine learning techniques to describe overall WEC dynamics and to study corresponding control methods is relatively underdeveloped in the literature.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Model Predictive Control for Wave Energy Converters Using Gaussian Process

Liu

Shi²,

Zhang

et al. 2022

Symmetry

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The energy harvested by an ocean wave energy converter (WEC) can be enhanced by a well-designed wave-by-wave control strategy. One of such superior control methods is model predictive control (MPC), which is a nonlinear constrained optimization control strategy. A limitation of the classical MPC algorithm is its requirement of an accurate WEC dynamic model for real-time implementation. This article overcomes this challenge by proposing a data-driven MPC scheme for wave energy converters. The data-based WEC model is developed by a Gaussian process (encompassing mean predictions and symmetric uncertainties) for a more accurate description of nonlinear and unmodeled system dynamics. A cross-entropy solver for data-driven MPC is employed for rapid, high-performance results, which samples trajectories from Gaussian distributions based on the concept of the symmetry principle. The proposed strategy is verified numerically by simulations which demonstrate its superior performance over a classical complex-conjugate controller.

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