A Decentralized Formation and Network Connectivity Tracking Controller for Multiple Unmanned Systems

Dutta, Rajdeep; Sun, Liang; Pack, Daniel J.

doi:10.1109/tcst.2017.2740837

Cited by 47 publications

(17 citation statements)

References 19 publications

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“…Ajorlou et al [21] proposed a class of distributed potential-based control laws for avoiding the disconnection of the edge in the information flow graph. Dutta et al [22] presented a decentralized controller for multiple UAVs to make a target-centric formation while maintaining the given algebraic connectivity. Esposito et al [23] proposed a potential-based control law to guide a swarm of robots from the initial position to the final position, while preserving the desired links for the duration of the motion.…”

Section: A Proactive Strategymentioning

confidence: 99%

“…When δ i (t) > R 2 , substituting (22), (27), (28) into (19) yields: (30) Formula (30) can be transformed into a one-variable quadratic inequality of α i (t), as shown in (31). q xs (t) 2 [α i (t)] 2 + 2λ(t) T • q xs (t)α i (t)+ λ(t) 2 −V 2 max ≤ 0 (31) The maximum value of α i (t) which satisfies (31) is shown in (21).…”

Section: Appendixes Appendix I Proof Of Theoremmentioning

confidence: 99%

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SIDR: A Swarm Intelligence-Based Damage-Resilient Mechanism for UAV Swarm Networks

et al. 2020

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Unmanned Aerial Vehicle (UAV) swarm networks have been presented as a promising paradigm for conducting monitoring, and interconnecting tasks in unattended or even hostile environments. However, harsh deployment scenario may make the UAVs susceptible to large-scale damage, and thus degrades the connectivity and performance of the network. None of existing technologies can effectively reorganize the surviving UAVs in a severely damaged UAV swarm into a unified UAV Swarm Network (USNET), this paper presents and analyzes the damage-resilience problem of USNETs for the first time, and put forwards a Swarm Intelligence-based Damage-Resilient (SIDR) mechanism. First, a damage model of USNETs and several metrics are defined before the problem is formally formulated. Second, the SIDR mechanism is detailed based on comprehensively utilizing the storage, communication, positioning, and maneuvering capabilities of UAVs. Third, a potential-field-based solution to the proposed SIDR mechanism is presented, aiming to recover a USNET rapidly and elastically. At last, an evaluation environment is built on the OMNeT++ platform, and the proposed SIDR mechanism is implemented. Extensive simulations are conducted in both dynamic and static scenarios. Simulation results demonstrate that SIDR outperforms the existing algorithms in terms of resilience capability, convergence time and communication overhead. Even if a USNET is divided into multiple disjoint subnets with arbitrary shape, SIDR can aggregate the surviving nodes into a connected network while the network is still flying along the flight path during the recovery process.

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Section: A Proactive Strategymentioning

confidence: 99%

Section: Appendixes Appendix I Proof Of Theoremmentioning

confidence: 99%

SIDR: A Swarm Intelligence-Based Damage-Resilient Mechanism for UAV Swarm Networks

et al. 2020

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“…In recent years, the tracking control for multi-agent systems (MASs) has attracted significant research attentions in the control community [1]- [4], due to its wide application background in engineering and scientific fields, such as spacecraft formation flying [5], cooperative control in robotic systems [6], unmanned systems [7], target tracking in sensor networks [8], and so on. Thus, a great of progress has been made on the tracking control of MASs [9]- [12], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Adaptive Tracking Control of a Class of Nonlinear Discrete-Time Coupled Multi-Agent Systems With Unknown Dynamics

Zhang

2020

IEEE Access

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This paper studies the trajectory tracking control of a class of multi-agent systems, in which, each agent is expressed by a nonlinear discrete-time unknown dynamics and can interact with its neighbors via the history outputs of its neighbors. In order to tackle each unknown dynamics, based on its neighbors' and its own history I/O data and neural network, an approximate model is established by the direct data-driven method. Using the neighbors' history information and the reference trajectory, the decentralized adaptive indirect data-driven control is designed; then, the feedback gain matrix online is designed and adjusted by measured output data and previous estimates. For each agent, this is an adaptive control process of prediction, estimation, and adjustment, which needs to solve some nonlinear optimization problems online, can surmount the negative effects of the modeling errors caused by neural networks, and is the key to making each agent output asymptotically track the given reference trajectory. The convergence analysis shows that the applied method is effective and feasible. INDEX TERMS Multi-agent system, tracking control, indirect data-driven method, adaptive control, nonlinear discrete-time system.

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“…Yu et al [6] considered the event‐triggered tracking consensus under the packet losses and time delays, and concluded that the tracking consensus was faster when using the information of second‐order neighbouring agents. Dutta et al [7] designed the controller, which could maintain the symmetric formation and guarantee the network connectivity. Li et al [8] designed the proportional–integral reference velocity controller for the unmeasurable task‐space velocity, the designed controller could achieve the bearing‐based formation.…”

Section: Introductionmentioning

confidence: 99%

Consensus of multi‐agent system based on novel avoidance function and unequal avoidance strategy

Zhu

Duan

2020

IET Control Theory & Appl

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A Decentralized Formation and Network Connectivity Tracking Controller for Multiple Unmanned Systems

Cited by 47 publications

References 19 publications

SIDR: A Swarm Intelligence-Based Damage-Resilient Mechanism for UAV Swarm Networks

SIDR: A Swarm Intelligence-Based Damage-Resilient Mechanism for UAV Swarm Networks

Decentralized Adaptive Tracking Control of a Class of Nonlinear Discrete-Time Coupled Multi-Agent Systems With Unknown Dynamics

Consensus of multi‐agent system based on novel avoidance function and unequal avoidance strategy

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