Resilient Formation Control Based on Watermarks for Networked Quadrotors Under Deception Attacks
This article addresses the problem of formation control of networked AAVs under deception attacks. A lightweight resilient formation control framework based on watermarks is proposed to achieve the desired formation configuration without extra monitoring attack status. Since deception attacks are covert, the shared information is watermarked before transmission to establish the protocol among networked AAVs. To facilitate the resilient formation controller design, a decoder is first developed to decipher the inverse transformation of deception attacks, where the watermark is the criterion for approximating the inverse transformation. The Gauss error function is introduced to eliminate some unrealistic input commands under deception attacks, obtaining the predefined input performance. Then, a resilient formation controller is developed to guarantee the global boundedness of all signals within the inner-loop and outer-loop system, while the formation error converges to a small neighborhood of zero by solving the linear matrix inequality. Two challenging deception attack examples in simulations and experiments are conducted to verify the effectiveness and performance of the proposed resilient formation control framework.
Authors
- Zhiqiang Miao
- Yaonan Wang (ORCID: https://orcid.org/0000-0002-0519-6458)
- Yanjie Chen (ORCID: https://orcid.org/0000-0001-9750-9177)
- Weiwei Zhan
- Wei He
- Jianxin Zeng (ORCID: https://orcid.org/0009-0007-7271-1855)
- Zheng‐Guang Wu (ORCID: https://orcid.org/0000-0002-7054-0008)
Institutions
- Hunan University (CN)
- Zhejiang University (CN)
- Beijing Information Science & Technology University (CN)
- Fuzhou University (CN)
- University of Science and Technology Beijing (CN)
Publication Details
- Journal
- IEEE Transactions on Vehicular Technology
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1109/tvt.2025.3541455
- Citations
- 1
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 1.49