Movatterモバイル変換


[0]ホーム

URL:


Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation,member institutions, and all contributors.Donate
arxiv logo>eess> arXiv:2308.09326
arXiv logo
Cornell University Logo

Electrical Engineering and Systems Science > Systems and Control

arXiv:2308.09326 (eess)
[Submitted on 18 Aug 2023]

Title:Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet

View PDF
Abstract:Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in three-dimensional space is a challenging but practical problem. To address this problem, this paper develops a novel consensus based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.
Comments:This paper is accepted by IEEE Transactions on Cybernetics
Subjects:Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as:arXiv:2308.09326 [eess.SY]
 (orarXiv:2308.09326v1 [eess.SY] for this version)
 https://doi.org/10.48550/arXiv.2308.09326
arXiv-issued DOI via DataCite
Related DOI:https://doi.org/10.1109/TCYB.2023.3301737
DOI(s) linking to related resources

Submission history

From: Simon X. Yang [view email]
[v1] Fri, 18 Aug 2023 06:04:12 UTC (11,121 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
Current browse context:
eess.SY
Change to browse by:
export BibTeX citation

Bookmark

BibSonomy logoReddit logo

Bibliographic and Citation Tools

Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
scite Smart Citations(What are Smart Citations?)

Code, Data and Media Associated with this Article

CatalyzeX Code Finder for Papers(What is CatalyzeX?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)

Demos

Hugging Face Spaces(What is Spaces?)

Recommenders and Search Tools

Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.

Which authors of this paper are endorsers? |Disable MathJax (What is MathJax?)

[8]ページ先頭

©2009-2025 Movatter.jp