Physics-Data-Driven Economic Model Predictive Control for Wave Energy Converters

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A physics-data-driven economic model predictive control (EMPC) is proposed in this article for effective energy harvesting in wave energy converters (WECs). By combining artificial intelligence techniques, this article develops a new method that applies a data-driven model upon physical WEC models to address the challenges associated with the nonlinearity and uncertainty in WEC physical models. By collecting the error data between the actual system and the physical model, a deep Koopman operator is applied to transform the nonlinear and uncertain parts of the actual system into a linear model, which is then embedded into the physical model to establish a physical-data-driven model. By iteratively optimizing the physical data-driven model, EMPC generates the optimal control sequence for the WEC. Theoretical analysis is conducted to prove that the physical-data-driven EMPC algorithm ensures that the Lyapunov function converges to the neighborhood of the optimal steady state. Simulation results show that the proposed physical-data-driven model achieves faster convergence and higher accuracy during training compared to data-driven models. This improves the system’s control and optimization performance under EMPC, demonstrating the effectiveness of the proposed algorithm.