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Robust Fully Actuated Control for Underactuated USVs via Dynamic Extension and Neural Compensation

  • School of New Energy, Harbin Institute of Technology Weihai
  • School of Information Science and Engineering, Harbin Institute of Technology Weihai
  • School of Astronautics, Harbin Institute of Technology

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

Abstract

This paper presents a novel robust control framework for underactuated unmanned surface vehicles (USVs) by integrating fully actuated system (FAS) theory with dynamic extension. To overcome the inherent relative degree mismatch and lateral underactuation, a dynamic extension strategy is employed to map the nonlinear dynamics into a third-order strict fully actuated standard form. A data-driven robust FAS control law, incorporating radial basis function neural networks (RBF NNs) and a disturbance observer (DOB), is proposed. Rigorous Lyapunov analysis proves that the closed-loop system is semi-globally uniformly ultimately bounded (SGUUB) without exhibiting actuator chattering. Numerical simulations on the Cybership-II model under multi-level disturbances validate the effectiveness of the proposed approach and reveal the physical robustness boundary imposed by Coriolis coupling and actuator constraints.

Original languageEnglish
Title of host publicationProceedings of the 5th Conference on Fully Actuated System Theory and Applications, FASTA 2026
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2091-2096
Number of pages6
ISBN (Electronic)9798319547323
DOIs
StatePublished - 2026
Externally publishedYes
Event5th Conference on Fully Actuated System Theory and Applications, FASTA 2026 - Qinhuangdao, China
Duration: 22 May 202624 May 2026

Publication series

NameProceedings of the 5th Conference on Fully Actuated System Theory and Applications, FASTA 2026

Conference

Conference5th Conference on Fully Actuated System Theory and Applications, FASTA 2026
Country/TerritoryChina
CityQinhuangdao
Period22/05/2624/05/26

Keywords

  • Dynamic Extension
  • Radial Basis Function Neural Networks
  • Trajectory Tracking
  • Unmanned Surface Vehicles

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