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Volume 19, Issue 2
Prescribed-Time Adaptive Fuzzy Tracking Control of UAV Swarms Under Deception Attack

Shuchang Liu & Gongfei Song

J. Info. Comput. Sci. , 19 (2024), pp. 155-170.

[An open-access article; the PDF is free to any online user.]

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  • Abstract

In addressing the tracking control problem for unmanned aerial vehicle (UAV) swarms, we consider several challenges: the unmeasurable state of the swarm system, potential deception attacks on actuators, external random disturbances, and the nonlinear dynamics of each UAV. To tackle these issues, we first introduce a time-varying function and utilize a coordinate transformation method to convert the time tracking problem into an error variable constraint problem. Next, we propose an adaptive time tracking control method employing one-to-one mapping and inversion techniques, aimed at achieving system convergence to a specified accuracy within a designated time frame. To mitigate the impact of possible deception attacks on actuators, we design an attack compensator that removes disturbances caused by time-varying attack gains. Additionally, we implement an observer to estimate the unmeasurable state of the system and utilize a fuzzy logic system to manage unknown functions. Finally, we validate the effectiveness of our control method through simulations.

  • AMS Subject Headings

93D15, 93C10

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{JICS-19-155, author = {Liu , Shuchang and Song , Gongfei}, title = {Prescribed-Time Adaptive Fuzzy Tracking Control of UAV Swarms Under Deception Attack}, journal = {Journal of Information and Computing Science}, year = {2025}, volume = {19}, number = {2}, pages = {155--170}, abstract = {

In addressing the tracking control problem for unmanned aerial vehicle (UAV) swarms, we consider several challenges: the unmeasurable state of the swarm system, potential deception attacks on actuators, external random disturbances, and the nonlinear dynamics of each UAV. To tackle these issues, we first introduce a time-varying function and utilize a coordinate transformation method to convert the time tracking problem into an error variable constraint problem. Next, we propose an adaptive time tracking control method employing one-to-one mapping and inversion techniques, aimed at achieving system convergence to a specified accuracy within a designated time frame. To mitigate the impact of possible deception attacks on actuators, we design an attack compensator that removes disturbances caused by time-varying attack gains. Additionally, we implement an observer to estimate the unmeasurable state of the system and utilize a fuzzy logic system to manage unknown functions. Finally, we validate the effectiveness of our control method through simulations.

}, issn = {3080-180X}, doi = {https://doi.org/10.4208/JICS-2024-009}, url = {http://global-sci.org/intro/article_detail/jics/24064.html} }
TY - JOUR T1 - Prescribed-Time Adaptive Fuzzy Tracking Control of UAV Swarms Under Deception Attack AU - Liu , Shuchang AU - Song , Gongfei JO - Journal of Information and Computing Science VL - 2 SP - 155 EP - 170 PY - 2025 DA - 2025/05 SN - 19 DO - http://doi.org/10.4208/JICS-2024-009 UR - https://global-sci.org/intro/article_detail/jics/24064.html KW - UAV swarm control, Deception attack, Prescribed-time control, Fuzzy logic system. AB -

In addressing the tracking control problem for unmanned aerial vehicle (UAV) swarms, we consider several challenges: the unmeasurable state of the swarm system, potential deception attacks on actuators, external random disturbances, and the nonlinear dynamics of each UAV. To tackle these issues, we first introduce a time-varying function and utilize a coordinate transformation method to convert the time tracking problem into an error variable constraint problem. Next, we propose an adaptive time tracking control method employing one-to-one mapping and inversion techniques, aimed at achieving system convergence to a specified accuracy within a designated time frame. To mitigate the impact of possible deception attacks on actuators, we design an attack compensator that removes disturbances caused by time-varying attack gains. Additionally, we implement an observer to estimate the unmeasurable state of the system and utilize a fuzzy logic system to manage unknown functions. Finally, we validate the effectiveness of our control method through simulations.

Liu , Shuchang and Song , Gongfei. (2025). Prescribed-Time Adaptive Fuzzy Tracking Control of UAV Swarms Under Deception Attack. Journal of Information and Computing Science. 19 (2). 155-170. doi:10.4208/JICS-2024-009
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