Anderlini, E;
Forehand, DIM;
Bannon, E;
Abusara, M;
(2017)
Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration.
IEEE Transactions on Sustainable Energy
, 8
(4)
pp. 1618-1628.
10.1109/TSTE.2017.2696060.
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Abstract
An algorithm has been developed for the resistive control of a nonlinear model of a wave energy converter using least-squares policy iteration, which incorporates function approximation, with tabular and radial basis functions being used as features. With this method, the controller learns the optimal power take-off damping coefficient in each sea state for the maximization of the mean generated power. The performance of the algorithm is assessed against two online reinforcement learning schemes: Q-learning and SARSA. In both regular and irregular waves, least-squares policy iteration outperforms the other strategies, especially when starting from unfavorable conditions for learning. Similar performance is observed for both basis functions, with a smaller number of radial basis functions underfitting the Q-function. The shorter learning time is fundamental for a practical application on a real wave energy converter. Furthermore, this paper shows that least-squares policy iteration is able to maximize the energy absorption of a wave energy converter despite strongly nonlinear effects due to its model-free nature, which removes the influence of modeling errors. Additionally, the floater geometry has been changed during a simulation to show that reinforcement learning control is able to adapt to variations in the system dynamics.
Type: | Article |
---|---|
Title: | Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/TSTE.2017.2696060 |
Publisher version: | http://doi.org/10.1109/TSTE.2017.2696060 |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Science & Technology, Technology, GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY, Energy & Fuels, Engineering, Electrical & Electronic, Science & Technology - Other Topics, Engineering, Function approximation, radial basis function (RBF), reinforcement learning (RL), resistive control, wave energy converter (WEC), PREDICTIVE CONTROL, LATCHING CONTROL, DEVICE |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
URI: | https://discovery.ucl.ac.uk/id/eprint/10027953 |
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