Distributed dynamic event-triggered consensus control for multi-agent systems under fixed and switching topologies

Li, Yifei; Liu, Xiangdong; Liu, Haikuo; Du, Changkun; Lu, Pingli

doi:10.1016/j.jfranklin.2021.03.027

Cited by 38 publications

(13 citation statements)

References 35 publications

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“…Therefore, it is necessary to develop more flexible event‐triggered conditions to further reduce communication costs and save finite resources. A dynamic ETC strategy was proposed in References 31–36. The article 32 investigated a consensus problem for a class of linear multi‐agent with directed networks under distributed dynamic event‐triggered strategy.…”

Section: Introductionmentioning

confidence: 99%

“…The article 32 investigated a consensus problem for a class of linear multi‐agent with directed networks under distributed dynamic event‐triggered strategy. In Reference 35, the dynamic event‐triggered consensus problem of general linear multi‐agent systems under fixed and switching directed topologies was discussed. In Reference 36, the dynamic ETC strategy was applied to solve the cluster consensus problem for linear heterogeneous MASs.…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions of this article can be summarized as follows: In this article, two dynamic ETC protocols are designed for two classes of NMASs, which can realize the cluster consensus instead of complete consensus 32–35 . Compared with the static ETC, 27,29,30 dynamic ETC adopts a dynamic threshold to replace a constant in triggering function.…”

Section: Introductionmentioning

confidence: 99%

“…(1) In this article, two dynamic ETC protocols are designed for two classes of NMASs, which can realize the cluster consensus instead of complete consensus. [32][33][34][35] Compared with the static ETC, 27,29,30 dynamic ETC adopts a dynamic threshold to replace a constant in triggering function. As a result, its next trigger time surpasses that of the static ETC, leading to enhanced trigger time intervals and reduced controller updates.…”

mentioning

confidence: 99%

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Adaptive cluster consensus control of nonlinear multi‐agent system via the dynamic event‐triggered strategy

Xia,

Li,

Liu

et al. 2024

Adaptive Control & Signal

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SummaryIn this article, the cluster consensus problems of two classes of nonlinear multi‐agent systems (NMASs) are studied under the dynamic event‐triggered control (ETC) strategy, where cooperative‐competitive interactions among agents and high‐order dynamics are considered. First, two dynamic ETC protocols are developed to realize the cluster synchronization of NMASs. Different from the static ETC, a dynamic parameter regulated by an adaptive law is used in place of a constant which can improve the trigger time intervals and reduce the energy consumption. Subsequently, an adaptive control mechanism based on the proposed ETC strategy is designed to adjust the controller parameter for agents. Some sufficient conditions for achieving the cluster consensus of NMASs are derived by using the Lyapunov stability theory and algebraic graph theory. Moreover, it is proved in theory that Zeno phenomenon can be excluded with these control schemes. Finally, two simulation examples validate the effectiveness of the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Adaptive cluster consensus control of nonlinear multi‐agent system via the dynamic event‐triggered strategy

Xia,

Li,

Liu

et al. 2024

Adaptive Control & Signal

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“…In many industrial processes, due to the sudden changes from the external environment, component damage, human intervention, and changes in the connection between internal subsystems, the system structure will undergo random mutations. In general, this switch can be arbitrary, [9][10][11][12] or restricted. The Markov jump system is a special switching system.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of semi-Markovian switching complex networks with partial unknown transition rates via event-triggered control

Liu

Yang

Chang

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article investigates the synchronization of semi-Markovian switching complex networks with partial unknown transition rates. Different from Markovian switching system, semi-Markov process is characterized by a fixed transition probability matrix and a probability density function of the sojoturn time, and the sojourn time obeys an expanded exponential distribution, which relaxes the restriction that the sojoturn time obeys memoryless exponential distribution. To realize synchronization, event-triggered control is applied which effectively reduced the control cost. It is demonstrated that the semi-Markovian switching complex networks can realize synchronization under appropriate control gain, event-triggered parameter and average sojourn time. Besides, to cope with the uncertainties that may occur during the switching process, the synchronization of the semi-Markovian switching complex networks with parameter uncertainties and external disturbances is also discussed. In the end, two simulation examples are provided to verify the feasibility of our results.

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Distributed Dynamic Event-Triggered Output Tracking for Heterogeneous Multi-agent Systems with An Input-Bounded Leader

Liu

Bian

et al. 2022

Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021)

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Distributed dynamic event-triggered consensus control for multi-agent systems under fixed and switching topologies

Cited by 38 publications

References 35 publications

Adaptive cluster consensus control of nonlinear multi‐agent system via the dynamic event‐triggered strategy

Adaptive cluster consensus control of nonlinear multi‐agent system via the dynamic event‐triggered strategy

Synchronization of semi-Markovian switching complex networks with partial unknown transition rates via event-triggered control

Distributed Dynamic Event-Triggered Output Tracking for Heterogeneous Multi-agent Systems with An Input-Bounded Leader

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