Adaptive Prescribed-Time Formation Control of Surface Vehicles Using Dynamic Surface Method

Ping Wang*, Chengpu Yu, Maolong Lv

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The adaptive prescribed-time (PT) formation control is addressed in this paper for multiple unmanned surface vehicles (USVs) with parameter uncertainties and external disturbances. The novelty lies in proposing a novel dynamic surface control (DSC)-based PT forma tion algorithm. Specifically, to effectively compensate for filter errors and facilitate PT convergence, a new nonlinear filter (NLF) with an adaptive parameter estimator and a piece-wise function is first con structed within the DSC framework. Subsequently, combined with the adaptive technology, a unified PT control scheme is provided. It can ensure the achievement of expected formation pattern within a prede f ined time, while guaranteeing that formation errors converge to a user defined set. More importantly, the proposed control framework not only tackles the complexity explosion caused by conventional backstepping but also reduces the constraints on filter design parameters. Finally, the presented scheme’s validity is confirmed through simulation implementation.

Original languageEnglish
Title of host publicationAdvances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 3
EditorsLiang Yan, Haibin Duan, Yimin Deng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages361-371
Number of pages11
ISBN (Print)9789819622078
DOIs
Publication statusPublished - 2025
EventInternational Conference on Guidance, Navigation and Control, ICGNC 2024 - Changsha, China
Duration: 9 Aug 202411 Aug 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1339 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Guidance, Navigation and Control, ICGNC 2024
Country/TerritoryChina
CityChangsha
Period9/08/2411/08/24

Keywords

  • Adaptive Prescribed-Time Formation Control
  • Dynamic Surface Control
  • Nonlinear Filter
  • Unmanned Surface Vehicles

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