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Symmetry
Volume 14
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10.3390/sym14071284
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Article

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

by
Yanhua Liu
1,
Shuo Shi
2,
Zhenbin Zhang
1,3,*,
Zhenfeng Di
4 and
Oluleke Babayomi
1
1
School of Electrical Engineering, Shandong University, Jinan 250061, China
2
State Grid Shandong Electric Power Research Institute, Jinan 250001, China
3
Shenzhen Research Institute, Shandong University, Shenzhen 518057, China
4
CRRC Shandong Wind Power Corporation Limited, Jinan 250104, China
*
Author to whom correspondence should be addressed.
Symmetry 2022, 14(7), 1284; https://doi.org/10.3390/sym14071284
Submission received: 30 April 2022 / Revised: 10 June 2022 / Accepted: 16 June 2022 / Published: 21 June 2022
(This article belongs to the Section F: Engineering and Materials)
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Abstract

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.
Keywords: wave energy converters; data-driven; model predictive control; Gaussian process; complex-conjugate control wave energy converters; data-driven; model predictive control; Gaussian process; complex-conjugate control

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MDPI and ACS Style

Liu, Y.; Shi, S.; Zhang, Z.; Di, Z.; Babayomi, O. Data-Driven Model Predictive Control for Wave Energy Converters Using Gaussian Process. Symmetry 2022, 14, 1284. https://doi.org/10.3390/sym14071284

AMA Style

Liu Y, Shi S, Zhang Z, Di Z, Babayomi O. Data-Driven Model Predictive Control for Wave Energy Converters Using Gaussian Process. Symmetry. 2022; 14(7):1284. https://doi.org/10.3390/sym14071284

Chicago/Turabian Style

Liu, Yanhua, Shuo Shi, Zhenbin Zhang, Zhenfeng Di, and Oluleke Babayomi. 2022. "Data-Driven Model Predictive Control for Wave Energy Converters Using Gaussian Process" Symmetry 14, no. 7: 1284. https://doi.org/10.3390/sym14071284

APA Style

Liu, Y., Shi, S., Zhang, Z., Di, Z., & Babayomi, O. (2022). Data-Driven Model Predictive Control for Wave Energy Converters Using Gaussian Process. Symmetry, 14(7), 1284. https://doi.org/10.3390/sym14071284

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