Skip to main content
CenXiv.org
This website is in trial operation, support us!
We gratefully acknowledge support from all contributors.
Contribute
Donate
cenxiv logo > eess > arXiv:2501.00739

Help | Advanced Search

Electrical Engineering and Systems Science > Systems and Control

arXiv:2501.00739 (eess)
[Submitted on 1 Jan 2025 ]

Title: Smooth Reference Command Generation and Control for Transition Flight of VTOL Aircraft Using Time-Varying Optimization

Title: 基于时变优化的倾转旋翼机过渡飞行平滑参考指令生成与控制

Authors:Jinrae Kim, John L. Bullock, Sheng Cheng, Naira Hovakimyan
Abstract: Vertical take-off and landing (VTOL) aircraft pose a challenge in generating reference commands during transition flight. While sparsity between hover and cruise flight modes can be promoted for effective transitions by formulating $\ell_{1}$-norm minimization problems, solving these problems offline pointwise in time can lead to non-smooth reference commands, resulting in abrupt transitions. This study addresses this limitation by proposing a time-varying optimization method that explicitly considers time dependence. By leveraging a prediction-correction interior-point time-varying optimization framework, the proposed method solves an ordinary differential equation to update reference commands continuously over time, enabling smooth reference command generation in real time. Numerical simulations with a two-dimensional Lift+Cruise vehicle validate the effectiveness of the proposed method, demonstrating its ability to generate smooth reference commands online.
Abstract: Vertical take-off and landing (VTOL) aircraft pose a challenge in generating reference commands during transition flight. While sparsity between hover and cruise flight modes can be promoted for effective transitions by formulating $\ell_{1}$-norm minimization problems, solving these problems offline pointwise in time can lead to non-smooth reference commands, resulting in abrupt transitions. This study addresses this limitation by proposing a time-varying optimization method that explicitly considers time dependence. By leveraging a prediction-correction interior-point time-varying optimization framework, the proposed method solves an ordinary differential equation to update reference commands continuously over time, enabling smooth reference command generation in real time. Numerical simulations with a two-dimensional Lift+Cruise vehicle validate the effectiveness of the proposed method, demonstrating its ability to generate smooth reference commands online.
Comments: 10 pages, 7 figures, AIAA SciTech 2025 Forum
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2501.00739 [eess.SY]
  (or arXiv:2501.00739v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2501.00739
arXiv-issued DOI via DataCite
Related DOI: https://doi.org/10.2514/6.2025-1123
DOI(s) linking to related resources

Submission history

From: Jinrae Kim [view email]
[v1] Wed, 1 Jan 2025 06:05:10 UTC (1,023 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled
  • View Chinese PDF
  • View PDF
  • HTML (experimental)
  • TeX Source
view license
Current browse context:
eess.SY
< prev   |   next >
new | recent | 2025-01
Change to browse by:
cs
cs.SY
eess

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
a export BibTeX citation Loading...

BibTeX formatted citation

×
Data provided by:

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

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

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)
  • Author
  • Venue
  • Institution
  • Topic

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?)
  • About
  • Help
  • contact arXivClick here to contact arXiv Contact
  • subscribe to arXiv mailingsClick here to subscribe Subscribe
  • Copyright
  • Privacy Policy
  • Web Accessibility Assistance
  • arXiv Operational Status
    Get status notifications via email or slack

京ICP备2025123034号