Deep Reinforcement Learning Based Controller for Active Heave Compensation

TL;DR

This paper introduces a deep reinforcement learning controller for active heave compensation in offshore operations, demonstrating improved performance over traditional control methods through simulation results.

Contribution

It proposes a novel RL-based control approach using DDPG for active heave compensation, showing enhanced effectiveness in simulation compared to conventional methods.

Findings

Up to 10% better heave compensation performance.

Effective disturbance rejection and noise attenuation.

Superior offset tracking capabilities.

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.