Self-tuning, load-mitigating feedback control of a 3-DOF point absorber
A simple, self-tuning multi-objective controller is demonstrated in simulation for a 3-DOF (surge, heave, pitch) point absorber. In previous work, the proposed control architecture has been shown to be effective in experiments for a variety of device archetypes for the single objective of the maximization of electrical power capture: here this architecture is extended to reduce device loading as well. In particular, PTO actuation forces and the minimization of fatigue damage (determined from the sum of wave-exerted and PTO forces) are considered as additional objectives for the self-tuning controller. Because the power surface is consistently fairly flat in the vicinity of control parameters that maximize power capture in contrasting sea-states (i.e., WECs are often broad banded), it is found to be generally possible to mitigate either fatigue damage or PTO load. However, PTO load is found to contradict with fatigue damage in some sea-states, limiting the efficacy of control objectives that attempt to mitigate both simultaneously. Additionally, coupling between the surge and pitch DOFs also limits the extent to which fatigue damage can be mitigated for both DOFs in some sea-states. Because control objectives can be considered a function of the sea-state (e.g., load mitigation may not be a concern until the sea is sufficiently large) a simple transition strategy is proposed and demonstrated. This transition strategy is found to be effective with some caveats: firstly, it cannot circumvent the aforementioned objective contradictions. Secondly, the thresholds at which objective transitions occur are somewhat exceeded: in this respect they cannot be considered as constraints and must be selected more conservatively. Finally, selection of well-performing transition parameters can be a function of sea-state. While a simple selection procedure is proposed, it is non-optimal, and a more robust selection procedure is suggested for future work.
Copyright (c) 2022 Dominic D Forbush, Giorgio Bacelli, Ryan G Coe
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