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    Optimisation- vs. non-optimisation- based energy-maximising control for wave energy converters: A case study


    Faedo, Nicolás and García-Violini, Demián and Peña-Sanchez, Yerai and Ringwood, John (2020) Optimisation- vs. non-optimisation- based energy-maximising control for wave energy converters: A case study. In: 2020 European Control Conference (ECC), 12-15 May 2020, Saint Petersburg, Russia.

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    Abstract

    Energy-maximising control of wave energy converters can be separated into two different classes: optimisation and non-optimisation based strategies. While optimisationbased controllers can outperform non-optimisation based strategies, the computational requirements associated with numerical optimisation routines, and the high control forces required under optimal conditions, can render these energymaximising control laws unsuitable for realistic scenarios. Nonoptimisation-based controllers present an alternative solution, where linear time-invariant systems are used to approximate the so-called impedance-matching condition. These strategies are often simple to implement but suffer from performance degradation when motion constraints are considered. This paper aims to present a critical comparison between both families of controllers, highlighting the strengths and weaknesses of each approach. We present simulation results for a state-of-theart CorPower-like device under polychromatic (irregular) wave excitation, for both (motion) unconstrained and constrained scenarios.

    Item Type: Conference or Workshop Item (Paper)
    Additional Information: Funding: This material is based upon works supported by Science Foundation Ireland under Grant no. 13/IA/1886.
    Keywords: Optimisation; non-optimisation; based energy-maximising control; wave energy converters; case study;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Centre for Ocean Energy Research
    Item ID: 14329
    Depositing User: Professor John Ringwood
    Date Deposited: 13 Apr 2021 14:44
    Refereed: Yes
    Funders: Science Foundation Ireland (SFI)
    URI:

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