Three-Dimensional Path-Following Control of an Autonomous Underwater Vehicle Based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Three-Dimensional Path-Following Control of an Autonomous Underwater Vehicle Based on Deep Reinforcement Learning

Published:2022-12-01 Issue:4 Volume:29 Page:36-44
ISSN:2083-7429
Container-title:Polish Maritime Research
language:en
Short-container-title:

Author:

Liang Zhenyu¹²^ORCID,Qu Xingru¹^ORCID,Zhang Zhao²,Chen Cong²

Affiliation:

1. School of Mechanical and Electronic Engineering , Dalian Minzu University , Dalian , China

2. School of Naval Architecture and Ocean Engineering , Dalian Maritime University , Dalian , China

Abstract

Abstract In this article, a deep reinforcement learning based three-dimensional path following control approach is proposed for an underactuated autonomous underwater vehicle (AUV). To be specific, kinematic control laws are employed by using the three-dimensional line-of-sight guidance and dynamic control laws are employed by using the twin delayed deep deterministic policy gradient algorithm (TD3), contributing to the surge velocity, pitch angle and heading angle control of an underactuated AUV. In order to solve the chattering of controllers, the action filter and the punishment function are built respectively, which can make control signals stable. Simulations are carried out to evaluate the performance of the proposed control approach. And results show that the AUV can complete the control mission successfully.

Publisher

Walter de Gruyter GmbH

Subject

Mechanical Engineering,Ocean Engineering

Link

https://www.sciendo.com/pdf/10.2478/pomr-2022-0042

Reference24 articles.

1. 1. I. Stenius et al., “A System for Autonomous Seaweed Farm Inspection with an Underwater Robot,” Sensors, vol. 22, no. 13, Jul 2022, doi: 10.3390/s22135064.926977835808560

2. 2. L. Rowinski and M. Kaczmarczyk, “Evaluation of Effectiveness of Waterjet Propulsor for a Small Underwater Vehicle,” Polish Marit. Res., vol. 28, no. 4, 2022, doi: 10.2478/pomr-2021-0047.

3. 3. H. Choukri and L. Z. Qidan, “Path Following Control of Fully Actuated Autonomous Underwater Vehicle Based on LADRC,” Polish Marit. Res., vol. 25, no. 4, 2018, doi: 10.2478/pomr-2018-0130.

4. 4. L. Li, Z. Pei, J. Jin, and Y. Dai, “Control of Unmanned Surface Vehicle along the Desired Trajectory Using Improved Line of Sight and Estimated Sideslip Angle,” Polish Marit. Res., vol. 28, no. 2, 2021, doi: 10.2478/pomr-2021-0017.

5. 5. E. Vidal, N. Palomeras, M. Carreras, “Online 3D Underwater Exploration and Coverage,” in IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), Rectory Univ Porto, Porto, Portugal, 2018.10.1109/AUV.2018.8729736

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