Time-varying formation control for unmanned aerial vehicles with switching interaction topologies

2016 ◽  
Vol 46 ◽  
pp. 26-36 ◽  
Author(s):  
Xiwang Dong ◽  
Yan Zhou ◽  
Zhang Ren ◽  
Yisheng Zhong
2019 ◽  
Vol 41 (13) ◽  
pp. 3777-3786 ◽  
Author(s):  
Yu’ang Liu ◽  
Qing Wang ◽  
Chaoyang Dong ◽  
Maopeng Ran

Time-varying formation control for unmanned aerial vehicles (UAVs) swarm systems with external disturbances is investigated via active disturbance rejection control (ADRC). The external disturbances are estimated by a designed extended state observer (ESO). Then, a distributed formation control protocol is designed according to the output of ESO, under which the predefined time-varying formation can be achieved. The closed-loop system under the proposed control strategy is analyzed. In addition, the expression of formation center function of the disturbed formation control is also depicted. Finally, numerical instances are simulated in order to demonstrate the validity and superiority of the proposed control strategy.


2019 ◽  
Vol 13 (3) ◽  
pp. 3580-3589 ◽  
Author(s):  
Jonathan Lwowski ◽  
Abhijit Majumdar ◽  
Patrick Benavidez ◽  
John J. Prevost ◽  
Mo Jamshidi

2019 ◽  
Vol 42 (5) ◽  
pp. 942-950
Author(s):  
Kai Chang ◽  
Dailiang Ma ◽  
Xingbin Han ◽  
Ning Liu ◽  
Pengpeng Zhao

This paper presents a formation control method to solve the moving target tracking problem for a swarm of unmanned aerial vehicles (UAVs). The formation is achieved by the artificial potential field with both attractive and repulsive forces, and each UAV in the swarm will be driven into a leader-centered spherical surface. The leader is controlled by the attractive force by the moving target, while the Lyapunov vectors drive the leader UAV to a fly-around circle of the target. Furthermore, the rotational vector-based potential field is applied to achieve the obstacle avoidance of UAVs with smooth trajectories and avoid the local optima problem. The efficiency of the developed control scheme is verified by numerical simulations in four scenarios.


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