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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2100559


Distributed game strategy for UAV formation with external disturbances and obstacles


Author(s):  Yang YUAN, Yimin DENG, Sida LUO, Haibin DUAN

Affiliation(s):  State Key Laboratory of Virtual Reality Technology and Systems, School of Autonomous Science and Electrical Engineering, Beihang University, Beijing 100083, China; more

Corresponding email(s):   yyuan@buaa.edu.cn, ymdeng@buaa.edu.cn, s.luo@buaa.edu.cn, hbduan@buaa.edu.cn

Key Words:  Distributed game strategy, Unmanned aerial vehicle, Distributed-model predictive control, Levy–, flight–, based pigeon-inspired optimization, Non-singular fast terminal sliding-mode observer, Obstacle-avoidance strategy


Yang YUAN, Yimin DENG, Sida LUO, Haibin DUAN. Distributed game strategy for UAV formation with external disturbances and obstacles[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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journal="Frontiers of Information Technology & Electronic Engineering",
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Abstract: 
In this paper, a distributed game strategy for unmanned aerial vehicle (UAV) formations with an external disturbance and with obstacles is investigated. It is based on a distributed model predictive control (MPC) framework and levy–;flight–;based pigeon-inspired optimization (LEPIO). First, we propose a non-singular fast terminal sliding-mode observer (NFTSMO) to estimate the influence of a disturbance, and we 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 only exchanges messages 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 the LEPIO and utilize it to solve the Nash equilibrium. A numerical simulation is conducted, and the efficiency of the LEPIO-based distributed MPC is verified through comparative experiments.

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