Control of a Point Absorber using Reinforcement Learning
Anderlini, E; Forehand, D; Stansell, P; et al.Xiao, Q; Abusara, M
Date: 1 June 2016
Article
Journal
IEEE Transactions on Sustainable Energy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
This work presents the application of reinforcement
learning for the optimal resistive control of a point absorber.
The model-free Q-learning algorithm is selected in order to
maximise energy absorption in each sea state. Step changes are
made to the controller damping, observing the associated penalty,
for excessive motions, or ...
This work presents the application of reinforcement
learning for the optimal resistive control of a point absorber.
The model-free Q-learning algorithm is selected in order to
maximise energy absorption in each sea state. Step changes are
made to the controller damping, observing the associated penalty,
for excessive motions, or reward, i.e. gain in associated power.
Due to the general periodicity of gravity waves, the absorbed
power is averaged over a time horizon lasting several wave
periods. The performance of the algorithm is assessed through
the numerical simulation of a point absorber subject to motions
in heave in both regular and irregular waves. The algorithm is
found to converge towards the optimal controller damping in
each sea state. Additionally, the model-free approach ensures the
algorithm can adapt to changes to the device hydrodynamics over
time and is unbiased by modelling errors.
Engineering
Faculty of Environment, Science and Economy
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