Distributed event-triggered collision avoidance coordinated control for QUAVs based on flexible virtual tubes

In this paper, a distributed Event-Triggered (ET) collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles (QUAVs) is proposed based on Virtual Tubes (VTs) with flexible boundaries in the presence of unknown external disturbances. Firstly, VTs are constructed for each QUAV, and the QUAV is restricted into the corresponding VT by the artificial potential field, which is distributed around the boundary of the VT. Thus, the collisions between QUAVs are avoided. Besides, the boundaries of the VTs are flexible by the modification signals, which are generated by the self-regulating auxiliary systems, to make the repulsive force smaller and give more buffer space for QUAVs without collision. Then, a novel ET mechanism is designed by introducing the concept of prediction to the traditional fixed threshold ET mechanism. Furthermore, a disturbance observer is proposed to deal with the adverse effects of the unknown external disturbance. On this basis, a distributed ET collision avoidance coordinated controller is proposed. Then, the proposed controller is quantized by the hysteresis uniform quantizer and then sent to the actuator only at the ET instants. The boundedness of the closed-loop signals is verified by the Lyapunov method. Finally, simulation and experimental results are performed to demonstrate the superiority of the proposed control method.

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Publication Details

Journal
Chinese Journal of Aeronautics
Published
2024-11-15
DOI
https://doi.org/10.1016/j.cja.2024.11.010
Citations
2
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.42

Funders

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article

Distributed event-triggered collision avoidance coordinated control for QUAVs based on flexible virtual tubes

Mou Chen, Mihai Lungu, Baomin Li, Hongzhen Guo
2 citations
Chinese Journal of Aeronautics
Advanced Memory and Neural Computing
0.42
article

Distributed event-triggered collision avoidance coordinated control for QUAVs based on flexible virtual tubes

Mou Chen, Mihai Lungu, Baomin Li, Hongzhen Guo
article en
2 citations

Abstract

In this paper, a distributed Event-Triggered (ET) collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles (QUAVs) is proposed based on Virtual Tubes (VTs) with flexible boundaries in the presence of unknown external disturbances. Firstly, VTs are constructed for each QUAV, and the QUAV is restricted into the corresponding VT by the artificial potential field, which is distributed around the boundary of the VT. Thus, the collisions between QUAVs are avoided. Besides, the boundaries of the VTs are flexible by the modification signals, which are generated by the self-regulating auxiliary systems, to make the repulsive force smaller and give more buffer space for QUAVs without collision. Then, a novel ET mechanism is designed by introducing the concept of prediction to the traditional fixed threshold ET mechanism. Furthermore, a disturbance observer is proposed to deal with the adverse effects of the unknown external disturbance. On this basis, a distributed ET collision avoidance coordinated controller is proposed. Then, the proposed controller is quantized by the hysteresis uniform quantizer and then sent to the actuator only at the ET instants. The boundedness of the closed-loop signals is verified by the Lyapunov method. Finally, simulation and experimental results are performed to demonstrate the superiority of the proposed control method.

Chinese Journal of AeronauticsVol. 38(2)
University of Craiova (RO), Nanjing University of Aeronautics and Astronautics (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Climate action
Openalex Percentile: Top 37%
Advanced Memory and Neural Computing
0.42
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Distributed event-triggered collision avoidance coordinated control for QUAVs based on flexible virtual tubes — Mou Chen, Mihai Lungu, et al. · Chinese Journal of Aeronautics (2024) | TGRS Research Map | TGRS