Distributed observer-based close formation control for UAV swarm under outside disturbances and wake interferences
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Decentralized control paradigms reduce latency using distributed tuning, as seen in UAV swarms with rapid recovery.
“while decentralized control paradigms reduce latency using distributed tuning, as seen in UAV swarms with rapid recovery [27,44].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [27]
Hardware acceleration including GPUs with a 2 GHz clock and FPGA with a 100 MHz clock enables rapid responses in swarms.
“Hardware acceleration, including GPUs with a 2 GHz clock and FPGA with a 100 MHz clock, enables rapid responses in swarms [27,29]”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [27]
Distributed control manages fleets of 5-50 robots with efficiency and achieves rapid coordination in UAV swarms.
“Distributed control manages fleets of 5-50 robots with efficiency [4], achieving rapid coordination in UAV swarms [27]”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [27]
High-Redundancy Actuation retains capacity in UAV swarms when paired with sensor redundancy such as Inertial Measurement Units at 100 Hz and LiDAR for rapid fault detection.
“HRA, detailed in Section Key Technical Methods in Hybrid FTC, retains capacity in UAV swarms when paired with sensor redundancy, such as Inertial Measurement Units (IMUs) at 100 Hz and Light Detection and Ranging (LiDAR) for rapid fault detection [3,27].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [27]
Radial Basis Function networks with a 0.01 learning rate approximate uncertainties with Lyapunov stability, excelling in UAV swarms with rapid recovery.
“Radial Basis Function (RBF) networks with a 0.01 learning rate approximate uncertainties with Lyapunov stability, excelling in UAV swarms with rapid recovery [10,27].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [27]
Neural-adaptive control utilizing a 128-node recurrent neural network delivers rapid responses in surgery and UAV swarms and achieves high accuracy in construction human-robot collaboration, despite significant computational demands.
“Neural-adaptive control, utilizing a 128-node RNN, delivers rapid responses in surgery [11] and UAV swarms [27], achieving high accuracy in construction HRC [40], despite significant computational demands [10].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [27]
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- Authors
- Wang, Z., Wang, T., Li, T., & Mao, Z.
- Affiliations
- Nanjing Forestry University (CN)
- Nanjing University of Aeronautics and Astronautics (CN)
- Corresponding author
- Li, Tao
- Venue
- Journal of the Franklin Institute
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 361
- Issue
- 5
- Pages
- 106651
- ISSN
- 0016-0032 (ISSN-L)
- OpenAlex
- W4391509125
- Cited by
- 25
- References
- 35
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 1
- FWCI
- 4.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Franklin Institute (publishedVersion)
- Funders
- National Natural Science Foundation of China (61922042, 62073164)
- Nanjing University of Aeronautics and Astronautics
- Aeronautical Science Foundation of China (2023Z032052001)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Aerospace Engineering › Guidance and Control Systems
- Keywords
- UAV swarms, distributed observer, prescribed performance control, wake interference, distributed cooperative control, fixed-wing UAV, disturbance estimation, adaptive control, asymptotic stability, Kronecker product
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