Deep Reinforcement Learning Based Controller for Active Heave Compensation
نویسندگان
چکیده
Heave compensation is an essential part in various offshore operations. It used applications, which include on-loading or offloading systems, drilling, landing helicopter on oscillating structures, and deploying retrieving manned submersibles. In this paper, a reinforcement learning (RL) based controller proposed for active heave using deep deterministic policy gradient (DDPG) lgorithm. A DDPG algorithm model-free, online method, adopted to capture the experience of agent during training trials. The simulation results demonstrate up 10 % better performance RL as compared tuned Proportional-Derivative Control. method with respect compensation, offset tracking, disturbance rejection, noise attenuation.
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ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2021
ISSN: ['2405-8963', '2405-8971']
DOI: https://doi.org/10.1016/j.ifacol.2021.10.088