CLC number: TP242.6
On-line Access: 2022-07-21
Received: 2021-12-02
Revision Accepted: 2022-05-04
Crosschecked: 2022-07-21
Cited: 0
Clicked: 2502
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-4926-3202
https://orcid.org/0000-0002-0715-987X
Yang YUAN, Yimin DENG, Sida LUO, Haibin DUAN. Distributed game strategy for unmanned aerial vehicle formation with external disturbances and obstacles[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(7): 1020-1031.
@article{title="Distributed game strategy for unmanned aerial vehicle formation with external disturbances and obstacles",
author="Yang YUAN, Yimin DENG, Sida LUO, Haibin DUAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="7",
pages="1020-1031",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100559"
}
%0 Journal Article
%T Distributed game strategy for unmanned aerial vehicle formation with external disturbances and obstacles
%A Yang YUAN
%A Yimin DENG
%A Sida LUO
%A Haibin DUAN
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 7
%P 1020-1031
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100559
TY - JOUR
T1 - Distributed game strategy for unmanned aerial vehicle formation with external disturbances and obstacles
A1 - Yang YUAN
A1 - Yimin DENG
A1 - Sida LUO
A1 - Haibin DUAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 7
SP - 1020
EP - 1031
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100559
Abstract: We investigate a distributed game strategy for unmanned aerial vehicle (UAV) formations with external disturbances and obstacles. The strategy is based on a distributed model predictive control (MPC) framework and levy flight based pigeon inspired optimization (LFPIO). First, we propose a non-singular fast terminal sliding mode observer (NFTSMO) to estimate the influence of a disturbance, and prove that the observer converges in fixed time using a Lyapunov function. Second, we design an obstacle avoidance strategy based on topology reconstruction, by which the UAV can save energy and safely pass obstacles. Third, we establish a distributed MPC framework where each UAV exchanges messages only with its neighbors. Further, the cost function of each UAV is designed, by which the UAV formation problem is transformed into a game problem. Finally, we develop LFPIO and use it to solve the Nash equilibrium. Numerical simulations are conducted, and the efficiency of LFPIO based distributed MPC is verified through comparative simulations.
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