Exact computation of maximal allowable delay for general second‐order multi‐agents consensus

Ma, Dan; Chen, Jie; Chai, Tianyou

doi:10.1002/rnc.5488

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…Second‐order agents are highly pertinent to the modeling of mechanical systems such as interconnected Lagragian manipulators 21 and interconnected vehicles, 48 where the position and velocity are the states of the system, and to the modeling of electrical systems such as a microgrid, 49,50 where the phase and frequency are the states. It is worth noting that with state‐feedback protocols, the DCM problem was examined in Reference 51 for second‐order systems, where the DCM is also cast as a quasi‐concave optimization problem. It is possible to extend the approach of Reference 51 to output feedback control of nonminimum phase agents.…”

Section: Discussionmentioning

confidence: 99%

“…It is worth noting that with state‐feedback protocols, the DCM problem was examined in Reference 51 for second‐order systems, where the DCM is also cast as a quasi‐concave optimization problem. It is possible to extend the approach of Reference 51 to output feedback control of nonminimum phase agents. Likewise, in such extensions, it is of interest to explore how zero and pole characteristics may confine the consensus performance, and more generally how higher‐order dynamic feedback protocols may mitigate the adversary agent behavior.…”

Section: Discussionmentioning

confidence: 99%

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Output consensus robustness and performance of first‐order agents

Peng,

Ding,

et al. 2024

Intl J Robust & Nonlinear

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In this article, we study consensus robustness and performance problems for continuous‐time multi‐agent systems. We consider first‐order unstable agents coordinated by an output feedback protocol over a network subject to an unknown, uncertain constant delay. Our objectives are twofold. First, we seek to determine the largest range of delay permissible so that the agents may achieve robustly consensus despite variation of the delay length, herein referred to as the delay consensus margin. Second, we attempt to determine consensus error performance quantified under an norm criterion, which measures the disruptive effect of random nodal noises on consensus. We consider both undirected and directed graphs. For undirected graphs, we obtain analytical results for the delay consensus margin and the consensus error performance, while for directed graphs, we develop computational results and analytical bounds. The results provide conceptual insights and exhibit how the agents' unstable pole, nonminimum phase zero, as well as the network topology and network delay may limit fundamentally the consensus robustness and performance of first‐order agents.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Output consensus robustness and performance of first‐order agents

Peng,

Ding,

et al. 2024

Intl J Robust & Nonlinear

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“…Proposition 1. Consider the MAS (1) in the presence of actuator attacks (9) and sensors attacks (10). Under the standard consensus protocol (4), the normal followers that have access to an attacked follower through directed paths are unable to achieve state consensus with the leader.…”

Section: Analysis Of Impacts Of Attacks On Standard Consensus Protocolmentioning

confidence: 99%

“…With technical progress in sensing, communication and control domains, multi‐agent systems (MASs) have attracted an increasing number of attention from researchers 1‐5 . MASs have widespread applications in several areas, such as robot teams, 6 sensor networks, 7 and satellite clusters 8 .…”

Section: Introductionmentioning

confidence: 99%

Secure consensus control for multi‐agent systems against attacks on actuators and sensors

Huo

Zhang

2022

Intl J Robust & Nonlinear

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This article studies the secure leader-following consensus control problem for discrete-time multi-agent systems under false data injection (FDI) attacks on both actuators and sensors. It is shown that under standard consensus protocol, an attacked follower can cause normal followers to fail to follow the leader if the normal followers can access the attacked follower through directed paths of arbitrary length. To mitigate the adverse impacts of FDI attacks, a secure observer-based consensus protocol is presented where local observer is used to estimate the system state and attack signal simultaneously and then the uniformly ultimately boundedness of state consensus error between all followers and leader can be guaranteed. Finally, a simulation example on the LC oscillator networks in the presence of FDI attacks validates the unavailability of standard consensus protocol and the effectiveness of the proposed secure observer-based consensus protocol.

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“…Ma et al 17 investigated consensus robustness with respect to variable network delays, where robustness is measured by the delay consensus margin (DCM), that is, the largest range of delay allowable to ensure consensus attainment in spite of variations in the delay. They considered second‐order multi‐agent systems under distributed PD feedback, alternatively known as position and velocity feedback protocols, and showed that the exact delay consensus margin can be obtained by solving a univariate quasi‐concave optimization problem.…”

Section: Overview Of Articlesmentioning

confidence: 99%