Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control

Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control

Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based...

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Bibliographic Details
Main Authors: Gaosheng Luo, Dong Zhang, Wei Feng, Zhe Jiang, Xingchen Liu
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied Sciences
Subjects:
ROV
linear active disturbance rejection control
deep reinforcement learning
deep deterministic policy gradient (DDPG) algorithm
resilience to disturbance
Online Access:https://www.mdpi.com/2076-3417/15/8/4443
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