Reinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditions.
approximate dynamic programming (ADP)
autonomous ships
dynamic positioning (DP)
model-based adaptive control
optimal control
reinforcement learning
system identification
trajectory tracking
Journal
Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350
Informations de publication
Date de publication:
2020
2020
Historique:
received:
06
11
2019
accepted:
20
02
2020
entrez:
27
1
2021
pubmed:
28
1
2021
medline:
28
1
2021
Statut:
epublish
Résumé
We present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing feedback policy in order to follow a desired trajectory under the influence of environmental forces. The method's efficiency is evaluated via simulations and sea trials, with the unmanned surface vehicle (USV)
Identifiants
pubmed: 33501200
doi: 10.3389/frobt.2020.00032
pmc: PMC7806118
doi:
Types de publication
Journal Article
Langues
eng
Pagination
32Informations de copyright
Copyright © 2020 Martinsen, Lekkas, Gros, Glomsrud and Pedersen.
Références
Neural Comput. 2000 Jan;12(1):219-45
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Nature. 2016 Jan 28;529(7587):484-9
pubmed: 26819042