Path Following With Finite-Time Convergence for Unmanned Surface Vehicle

A path-following control scheme is proposed using finite-time convergence line-of-sight (FCL) to achieve rapid convergence of position errors for an unmanned surface vehicle (USV). FCL combines classical line-of-sight (LOS) guidance with finite-time stability theory to ensure that the position of th...

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Veröffentlicht in:IEEE journal of oceanic engineering Jg. 50; H. 2; S. 1153 - 1164
Hauptverfasser: Huang, Yanwei, Lai, Guozhen, Lin, Feng, Shi, Xiaocheng, Li, Dongfang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Autonomous vehicles
Control systems
Convergence
Convergence speed
Error functions
hyperbolic characteristics
Hyperbolic functions
Line of sight
line-of-sight (LOS) guidance law
Line-of-sight propagation
Neural networks
path following
Position errors
Stability criteria
Surface vehicles
Surges
Systems stability
Target tracking
Tracking
Tracking errors
Trajectory planning
unmanned surface vehicle (USV)
Unmanned vehicles
Upper bounds
Vehicle dynamics
ISSN:0364-9059, 1558-1691
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Zusammenfassung:A path-following control scheme is proposed using finite-time convergence line-of-sight (FCL) to achieve rapid convergence of position errors for an unmanned surface vehicle (USV). FCL combines classical line-of-sight (LOS) guidance with finite-time stability theory to ensure that the position of the USV converges to the reference path within a predetermined finite time. Specifically, a USV model is developed to analyze target tracking. Moreover, a novel cross-tracking error function is designed with hyperbolic characteristics for FCL through an analysis of the relationship between convergence speed and cross-tracking error function. Further, the path-following system stability with FCL and finite-time controllers is analyzed to indicate the smaller value of the upper bound of the system convergence time by comparisons. Finally, simulations and experiments are performed to verify that FCL has a faster convergence speed than the LOS with error sign function (LS) and the linear cross-tracking error function (LLCF), and a wider algorithm parameter tuning range compared to LS.
Bibliographie:ObjectType-Article-1
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ISSN:0364-9059
1558-1691
DOI:10.1109/JOE.2024.3483328