Deep Reinforcement Learning Based Controller for Active Heave Compensation
Abstract
Heave compensation is an essential part in various offshore operations. It is used in various applications, which include on-loading or off-loading systems, offshore drilling, landing helicopter on oscillating structures, and deploying and retrieving manned submersibles. In this paper, a reinforcement learning (RL) based controller is proposed for active heave compensation using a deep deterministic policy gradient (DDPG) algorithm. A DDPG algorithm which is a model-free, online reinforcement learning method, is adopted to capture the experience of the agent during the training trials. The simulation results demonstrate up to 10 % better heave compensation performance of RL controller as compared to a tuned Proportional-Derivative Control. The performance of the proposed method is compared with respect to heave compensation, offset tracking, disturbance rejection, and noise attenuation.
Cite
@article{arxiv.2104.05599,
title = {Deep Reinforcement Learning Based Controller for Active Heave Compensation},
author = {Shrenik Zinage and Abhilash Somayajula},
journal= {arXiv preprint arXiv:2104.05599},
year = {2021}
}
Comments
Accepted to the 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS 2021)