Distributed Flocking Algorithm for Multi-UAV System Based on Behavior Method and Topological Communication

Feng, Yifei; Dong, Jing; Wang, Jianlin; Zhu, Hang

doi:10.1007/s42235-022-00287-w

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…There have been many studies related to this formation protocol with different approaches such as virtual structure [4], [5], behavior based [6], [7], artificial potential field [8], [9], and leader-follower based approach [10]- [12]. Among all these formation approaches, it is noticed that the leaderfollower approach has been the dominant method in realworld applications due to its relatively easy implementation, real-time performance, and good flexibility.…”

Section: Introductionmentioning

confidence: 99%

Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics

Xu,

Yan,

Yang

et al. 2024

IEEE Trans. Intell. Veh.

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This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability. We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue. Furthermore, to address the challenges for robots operating with completely unknown dynamics and disturbances, a learningbased robust dynamic controller is developed. This controller provides real time parameter estimates while maintaining its robustness against disturbances. The overall stability of the proposed method is proved with rigorous mathematical analysis. At last, multiple comprehensive simulation studies have shown the advantages and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics

Xu,

Yan,

Yang

et al. 2024

IEEE Trans. Intell. Veh.

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Distributed fast F‐T control for UAV formation in the presence of unknown input disturbances

Liao,

Chen,

Jia

et al. 2024

Intl J Robust & Nonlinear

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SummaryThis paper addresses the problem of fixed‐time (F‐T) control for unmanned aerial vehicle (UAV) formation under unknown input disturbances to achieve moving target tracking. First, a kinematic model with uncertainties is employed to analyze the transient response of the UAV formation accurately. Next, based on a fast F‐T lemma, an F‐T disturbance observer is constructed to estimate the unknown disturbances. Subsequently, based on the designed observer and fast F‐T lemma, a distributed F‐T guidance law that controls multi‐UAV to track a moving target in a desired formation is introduced. Then, through rigorous mathematical proof, it is established that the control strategy enables the formation system to achieve F‐T stability, and the convergence time is independent of the system's initial states. Finally, the validity of the proposed control method is demonstrated through numerical simulation and presented in a realistic visual simulation scenario created using Unreal Engine 4 (UE4).

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Multi-agent flocking with obstacle avoidance and safety distance preservation: a fuzzy potential-based approach

Ebrahimi,

Farrokhi

2024

Intel Serv Robotics

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Distributed Flocking Algorithm for Multi-UAV System Based on Behavior Method and Topological Communication

Cited by 6 publications

References 22 publications

Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics

Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics

Distributed fast F‐T control for UAV formation in the presence of unknown input disturbances

Multi-agent flocking with obstacle avoidance and safety distance preservation: a fuzzy potential-based approach

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