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Constraints Implementation in the Application of Reinforcement Learning to the Reactive Control of a Point Absorber


Citation Anderlini, E., Forehand, D.I.M., Bannon, E. and Abusara, M. Constraints Implementation in the Application of Reinforcement Learning to the Reactive Control of a Point Absorber, ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, 2017. https://dx.doi.org/10.1115/OMAE2017-61294.
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Author(s) Anderlini, E., Forehand, D.I.M., Bannon, E. and Abusara, M.
Project partner(s) University of Edinburgh, Wave Energy Scotland, University of Exeter
Publisher ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering
DOI https://dx.doi.org/10.1115/OMAE2017-61294
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Abstract Least-squares policy iteration, a reinforcement learning algorithm, is applied to the reactive control of a wave energy converter for the first time. Simulations of a linear point absorber are used for this analysis. The focus of this study is on the implementation of displacement constraints. The use of a penalty term is effective in teaching the controller to avoid the selection of combinations of the damping and stiffness coefficients that would result in excessive displacements in particular sea states. However, the controller can learn that the actions are bad only after trying them, as shown by the simulations. For this reason, a lower-level control scheme is proposed, which changes the sign of the controller force based on the magnitude of the float displacement and sign of its velocity. Its effectiveness is proven in both regular and irregular waves, although greater care is required for the determination of soft constraints
Associated Project(s) ETI-MA2003: Industrial Doctorate Centre for Offshore Renewable Energy (IDCORE)
Associated Dataset(s) EDC0000134: Tidal Energy Site Environmental Conditions Measured During the ReDAPT Tidal Project 2011-2014
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