Safety-Critical Trajectory Tracking Control with Safety-Enhanced Reinforcement Learning for Autonomous Underwater Vehicle

Safety-Critical Trajectory Tracking Control with Safety-Enhanced Reinforcement Learning for Autonomous Underwater Vehicle

This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). The proposed approach addresses the substantial challenge posed by model uncertainty, which may hinder the...

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Bibliographic Details
Main Authors: Tianli Li, Jiaming Tao, Yu Hu, Shiyu Chen, Yue Wei, Bo Zhang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Drones
Subjects:
CBF
CLF
AUV
safety-critical control
Online Access:https://www.mdpi.com/2504-446X/9/1/65
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