UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration

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. Green open access

[thumbnail of 07911321.pdf]
Preview
Text
07911321.pdf - Published version

Download (1MB) | Preview

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
Downloads since deposit
77Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item