Flocking of multi-agents with time delay

Yang, Zhengquan; Zhang, Qing; Jiang, Zuolian; Chen, Zengqiang

doi:10.1080/00207721.2011.564675

Cited by 34 publications

(14 citation statements)

References 16 publications

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“…Moreover from (14), it follows that a ep T ip i ≤p T (a e l nN + b e ((L + c 2 H ) ⊗ l n ))p ≤ 2Q max (21) Thus p i ≤ √ 2Q max /a e . Therefore the set such that Q(t) ≤ Q max is closed and bounded then it is compact set.…”

Section: Collisions Between Agents Are Avoidedmentioning

confidence: 92%

Position convergence of informed agents in flocking problem with general linear dynamic agents

Yazdani

Haeri

2015

IET Control Theory & Applications

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In most existing works on the flocking problem, results were obtained for the case where a double integrator dynamic is used to describe motion of agents and virtual leader. These results, however, may not be generalised to agents with general linear dynamic. In this study, it is shown that in the flocking of agents with a linear dynamic, the velocity convergence of agents does not generally lead to the position convergence of informed agents and the position convergence is possible only for a specified group of linear dynamics. In this study, a control protocol is proposed to flock agents with specified linear dynamics and guarantee the velocity convergence of all agents as well as average position convergence of informed agents to those of the virtual leader.

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Section: Collisions Between Agents Are Avoidedmentioning

confidence: 92%

Position convergence of informed agents in flocking problem with general linear dynamic agents

Yazdani

Haeri

2015

IET Control Theory & Applications

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“…In this model, all agents determine their next velocity by calculating the weighted average of the speed difference of their neighbors, while the weight is determined by the distance between the agent and its neighbors. The study of the C-S model has received much attention, and the model has been generalized by considering more factors such as time lag [9,10] and noise [11,12]. Besides these factors, the connecting structure among the agents is yet another important factor affecting the dynamics of the C-S system.…”

Section: Introductionmentioning

confidence: 99%

Flocking of discrete Cucker–Smale model with packet loss

Fang

2019

Adv Differ Equ

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“…Yu studied the leader–follower flocking control with time‐varying velocities. Yang considered flocking with time delay. However, these works concentrate on agents with holonomic kinematics, which are not adequate to model many practical systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed control for flocking and group maneuvering of nonholonomic agents

Cai

Chang

Wang

et al. 2017

Computer Animation & Virtual

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In this paper, we propose a distributed control approach for flocking and group maneuvering of nonholonomic agents, with constrained kinematic properties commonly found in practical systems, such as fixed-wing unmanned aerial vehicles.Flocking of agents with differential drive kinematics is addressed by introducing a virtual leader-follower mechanism into the Olfati-Saber's algorithm, which is originally proposed for holonomic agents with double integrator kinematics. Then, group maneuverability of the flock is achieved by superimposing a group motion onto each agent's flocking motion. Moreover, it is proven that speed limits are intrinsically guaranteed by the approach, which renders it more applicable in practical systems.Experimental results in MATLAB and Gazebo, a popular robotic simulator, are presented to evaluate the performance and demonstrate the effectiveness of the proposed approach.

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Flocking of multi-agents with time delay

Cited by 34 publications

References 16 publications

Position convergence of informed agents in flocking problem with general linear dynamic agents

Position convergence of informed agents in flocking problem with general linear dynamic agents

Flocking of discrete Cucker–Smale model with packet loss

Distributed control for flocking and group maneuvering of nonholonomic agents

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