Three-dimensional formation merging control under directed and switching topologies

Han, Tingrui; Lin, Zhiyun; Fu, Minyue

doi:10.1016/j.automatica.2015.04.027

Cited by 22 publications

(17 citation statements)

References 15 publications

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“…End terms of rigidity maintenance [22]- [25]. It must be noted that having a clear picture of the lack of connectivity and of the specific links that are needed for it to be restored requires in general global information which cannot be directly obtained in a decentralized way.…”

Section: B Decentralized Topology Updatementioning

confidence: 99%

Fault-Tolerant Formation Control of Passive Multi-Agent Systems Using Energy Tanks

Cristofaro

Giunta

Giordano

2022

IEEE Control Syst. Lett.

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This letter considers the design of a faulttolerant formation control law for multi-agent systems using energy balancing methods. Decentralized algorithms for fault diagnosis and graph topology update are proposed, and a formation reconfiguration procedure is developed based on energy tanks, which are able to guarantee that new connections among the agents can be established in a passive way. A simulation study supports and corroborates the theoretical findings.

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Section: B Decentralized Topology Updatementioning

confidence: 99%

Fault-Tolerant Formation Control of Passive Multi-Agent Systems Using Energy Tanks

Cristofaro

Giunta

Giordano

2022

IEEE Control Syst. Lett.

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“…where we used the equations qij = q ij + qij and q ij − σ ij = pi − pj = qi − qj from ( 4) and (6). Then the dynamics of the formation error can be found as…”

Section: B Convergence Of Tvf Errormentioning

confidence: 99%

Persistently Excited Adaptive Relative Localization and Time-Varying Formation of Robot Swarms

et al. 2020

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In this paper we investigate the problem of controlling a multi-robot team to follow a leader in formation, supported by relative position estimate derived from distance and selfdisplacement measurements, thus waiving the need of external localization infrastructure. The main challenge of the problem, which is to simultaneously fulfill both relative localization and control tasks, is efficiently and novelly resolved by embedding a distance-displacement-based persistently excited adaptive relative localization technique into a time-varying formation with bounded control input (PEARL-TVF). By assuming that the leader is globally reachable and selecting proper parameters, it is shown that PEARL-TVF ensures exponentially convergent localization, which leads to exponentially convergent formation when the leader's behavior is deterministic, and bounded formation error for a non-deterministic leader. Numerical simulations and experiments on quadcopters are provided to verify the theoretical findings.

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“…Because the shape of the formation is flexible and variable, there is a chance to settle the problem of passing a narrow space by changing the shape of the desired region directly. In addition, it is complicated to solve the formation reconstruction and formation merging problems by the traditional distance-based formation control schemes [22][23][24]. The region-based formation control scheme provides a new way to deal with these problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive‐Neural‐Network‐Based Shape Control for a Swarm of Robots

Lan

et al. 2018

Complexity

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This paper considers the region-based formation control for a swarm of robots with unknown nonlinear dynamics and disturbances. An adaptive neural network is designed to approximate the unknown nonlinear dynamics, and the desired formation shape is achieved by designing appropriate potential functions. Moreover, the collision avoidance, velocity consensus, and region tracking are all considered in the controller. The stability of the multirobot system has been demonstrated based on the Lyapunov theorem. Finally, three numerical simulations show the effectiveness of the proposed formation control scheme to deal with the narrow space, loss of robots, and formation merging problems.

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Three-dimensional formation merging control under directed and switching topologies

Cited by 22 publications

References 15 publications

Fault-Tolerant Formation Control of Passive Multi-Agent Systems Using Energy Tanks

Fault-Tolerant Formation Control of Passive Multi-Agent Systems Using Energy Tanks

Persistently Excited Adaptive Relative Localization and Time-Varying Formation of Robot Swarms

Adaptive‐Neural‐Network‐Based Shape Control for a Swarm of Robots

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