Multi-Objective Model-Predictive Control for Dielectric Elastomer Wave Harvesters

TL;DR

This paper presents a multi-objective model-predictive control approach for dielectric elastomer wave energy harvesters, balancing energy extraction and device damage, with an adaptive weighting heuristic demonstrated through simulation.

Contribution

It introduces an adaptive weighting heuristic for MPC that effectively balances energy harvesting and damage mitigation in stochastic sea conditions.

Findings

Adaptive heuristic limits damage accumulation.

Maintains or improves energy yield compared to fixed weights.

Effective in stochastic wave simulations.

Abstract

This contribution deals with multi-objective model-predictive control (MPC) of a wave energy converter (WEC) device concept, which can harvest energy from sea waves using a dielectric elastomer generator (DEG) power take-off system. We aim to maximise the extracted energy through control while minimising the accumulated damage to the DEG. With reference to system operation in stochastic waves, we first generate ground truth solutions by solving an optimal control problem, and we analyse the MPC performance to determine a prediction horizon that trades off accuracy and efficiency for computation. Fixed weights in the MPC scheme can produce unpredictable costs for variable sea condition, meaning the average rate of cost accumulation can vary vastly. To steer this cost growth, we propose a heuristic to adapt the algorithm by changing the weighting of the cost functions using for fulfilling the long-time goal of accumulating a small enough damage in a fixed time. A simulated case-study is presented in order to evaluate the performance of the proposed MPC framework and the weight-adaptation algorithm. The proposed heuristic proves to be able to limit the amount of accumulated damage while remaining close to (or even improving) the energy yield obtained with a comparable fixed-weight MPC.