Published January 15, 2021 | Version v1

Nonlinear Energy-Maximizing Optimal Control of Wave Energy Systems: A Moment-Based Approach

Description

Linear dynamics are virtually always assumed when designing optimal controllers for wave energy converters (WECs), motivated by both their simplicity and computational convenience. Nevertheless, unlike traditional tracking control applications, the assumptions under which the linearization of WEC models is performed are challenged by the energy-maximizing controller itself, which intrinsically enhances device motion to maximize power extraction from incoming ocean waves. \GSIn this article, we present a moment-based energy-maximizing control strategy for WECs subject to nonlinear dynamics. We develop a framework under which the objective function (and system variables) can be mapped to a finite-dimensional tractable nonlinear program, which can be efficiently solved using state-of-the-art nonlinear programming solvers. Moreover, we show that the objective function belongs to a class of generalized convex functions when mapped to the moment domain, guaranteeing the existence of a global energy-maximizing solution and giving explicit conditions for when a local solution is, effectively, a global maximizer. The performance of the strategy is demonstrated through a case study, where we consider (state and input-constrained) energy maximization for a state-of-the-art CorPower-like WEC, subject to different hydrodynamic nonlinearities.

Notes

2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. N. Faedo, G. Scarciotti, A. Astolfi, and J. V. Ringwood, "Nonlinear Energy-Maximizing Optimal Control of Wave Energy Systems: A Moment-Based Approach," IEEE Transactions on Control Systems Technology, doi: 10.1109/TCST.2020.3047229.

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Funding

European Commission
KIOS CoE - KIOS Research and Innovation Centre of Excellence 739551