Decentralized Multiple V-Formation Control in Undirected Time-Varying Network Topologies

Choi, Joonwon; Song, Yeongho; Lim, Seunghan; Oh, Hyondong

doi:10.1109/reduas47371.2019.8999720

Cited by 2 publications

(6 citation statements)

References 22 publications

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“…17 ). Referring to the quasi

-lattice concept in ( Choi et al, 2019 ), this study considers the effect of error

in the formation of the formation. The following inequality is generated.…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…Definition 2. ( Choi et al, 2019 ) For the input ( Eq. 20 ) proposed in this study, the Lyapunov function is defined as follows:…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…The control input function jumps when the network topology graph of the target formation changes. From Choi et al (2019) , we can obtain that the system whose network topology changes with time is the switching system.…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

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Multi-consensus formation control by artificial potential field based on velocity threshold

Chang,

Jiao,

et al. 2024

Front. Neurosci.

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This study proposes a multi-consensus formation control algorithm by artificial potential field (APF) method based on velocity threshold. The algorithm improves the multi-consensus technique. This algorithm can split a group of agents into multiple agent groups. Note that the algorithm can easily complete the queue transformation as long as the entire proxy group is connected initially and no specific edges need to be removed. Furthermore, collision avoidance and maintenance of existing communication connectivity should be considered during the movement of all agents. Therefore, we design a new swarm motion potential function. The stability of multi-consensus formation control has proven to be effective in avoiding collisions, maintaining connectivity, and generating formations. The final numerical simulation results show the role of the controller we designed.

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“…17 ). Referring to the quasi

-lattice concept in ( Choi et al, 2019 ), this study considers the effect of error

in the formation of the formation. The following inequality is generated.…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…Definition 2. ( Choi et al, 2019 ) For the input ( Eq. 20 ) proposed in this study, the Lyapunov function is defined as follows:…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

See 1 more Smart Citation

Multi-consensus formation control by artificial potential field based on velocity threshold

Chang,

Jiao,

et al. 2024

Front. Neurosci.

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show abstract

“…By simply changing ω i , ∀i ∈ V, the allocation of agents into formations can be dynamically reconfigured. Not only the distance between subgroups, but also the subgroup each agent is assigned to is determined by ω i [4]. After the individual agent receives their formation subgroup information that are specified by ω i and ρ i , the user does not have to manually designate any parameter.…”

Section: A Decentralized Multi-subgroup Formation Controlmentioning

confidence: 99%

“…Nevertheless, there have been relatively few studies for creating multiple formations simultaneously [5], [9], [11], [17], [26]. In our preliminary research, the formation algorithm which divides the MAS into several V-shape formations was proposed [4]. However, due to complex interaction between different sub-formation groups during the transient phase, the convergence of the entire MAS may not be guaranteed; they are subject to initial position and velocity conditions.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Multi-Subgroup Formation Control With Connectivity Preservation and Collision Avoidance

et al. 2020

Self Cite

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This paper proposes a formation control algorithm to create separated multiple formations for an undirected networked multi-agent system while preserving the network connectivity and avoiding collision among agents. Through the modified multi-consensus technique, the proposed algorithm can simultaneously divide a group of multiple agents into any arbitrary number of desired formations in a decentralized manner. Furthermore, the agents assigned to each formation group can be easily reallocated to other formation groups without network topological constraints as long as the entire network is initially connected; an operator can freely partition agents even if there is no spanning tree within each subgroup. Besides, the system can avoid collision without loosing the connectivity even during the transient period of formation by applying the existing potential function based on the network connectivity estimation. If the estimation is correct, the potential function not only guarantees the connectivity maintenance but also allows some extra edges to be broken if the network remains connected. Numerical simulations are performed to verify the feasibility and performance of the proposed multi-subgroup formation control.

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Decentralized Multiple V-Formation Control in Undirected Time-Varying Network Topologies

Cited by 2 publications

References 22 publications

Multi-consensus formation control by artificial potential field based on velocity threshold

Multi-consensus formation control by artificial potential field based on velocity threshold

Decentralized Multi-Subgroup Formation Control With Connectivity Preservation and Collision Avoidance

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