Containment control for distributed networks subject to multiplicative and additive noises with stochastic approximation–type protocols

Du, Yunfei; Wang, Yijing; Zuo, Zhiqiang; Zhang, Wentao

doi:10.1002/rnc.4791

Cited by 19 publications

(6 citation statements)

References 38 publications

(117 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing with existing works concerning with noises, general linear MASs with communication noises are considered in this paper. For MASs with additive noises, stochastic approximation technique was widely adopted, which resulted in time-varying coupling strengths [12,21]. In this paper, by employing Riccati equation and It 􏽢 o's formula, the coupling strength in the consensus protocol is time-invariant, but restricted in a given interval.…”

Section: Consensus On Fixed Topologymentioning

confidence: 99%

“…In practical applications, the two kinds of noise may coexist in MASs [20]. Motivated by this phenomenon, MASs with both additive and multiplicative noises were considered in [10] for the consensus problem, and in [21], for the containment control problem. However, in the above studies, the concerned MASs were in first-or second-order dynamics under fixed topology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mean Square Consensus of General Linear Multiagent Systems with Communication Noises under Switching Topologies

et al. 2022

View full text Add to dashboard Cite

This paper investigates the distributed consensus problem of general linear multiagent systems (MASs) with communication noises under fixed and Markovian switching topologies, respectively. Each agent can obtain full state of itself and receive its neighbors’ state with noises, where intensities of noises are vector functions of relative states of agents. Bearing in mind the above constrains, a consensus protocol is proposed, where the gain matrix is obtained by the algebraic Riccati equation and the coupling strength is restricted in a given interval. By using the stochastic stability theorem, we show that mean square consensus is achieved in fixed topology case and switching topologies case, respectively. Furthermore, an estimation of the exponential convergence rate of consensus is given explicitly. Finally, simulation examples are given to show the correctness of the proposed results.

show abstract

Section: Consensus On Fixed Topologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mean Square Consensus of General Linear Multiagent Systems with Communication Noises under Switching Topologies

et al. 2022

View full text Add to dashboard Cite

show abstract

“…(3) For the stochastic perturbed consensus problem in real system, the additive or multiplicative noise is the more realistic stochastic one [52,53], which deserves further deep explanation.…”

Section:  mentioning

confidence: 99%

Leader-Following Consensus of Stochastic Perturbed Multi-Agent Systems via Variable Impulsive Control and Comparison System Method

Xiao¹,

Guo²,

Feng³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this paper, we explore the consensus control scheme of stochastic perturbed nonlinear multi-agent systems with impulsive protocol and comparison system method. The effective variable impulsive consensus method is used to remove the restriction of fixed impulsive instants, which is more reliable and flexible in practical applications. From the theory of impulsive differential system, comparison system and stochastic differential system, some sufficient consensus conditions are derived and the relation between the system parameters and impulsive time window is analyzed extensively. The effectiveness of the proposed control method is confirmed by the numerical simulations finally.

show abstract

“…For signed graph with directed spanning tree, Reference 24 shown that agents in general do not enjoy bipartite consensus, but the interval bipartite consensus. Further extension and generalization can be subsequently found in finite‐time convergence, 25 time‐delay bipartite consensus, 26 impulsive consensus, 27 fractional‐order multi‐agent systems, 28 and stochastic approximation, 29 etc.…”

Section: Introductionmentioning

confidence: 99%

Consensus for cooperative–competitive network systems: An impulsive perspective

Zhang

Liu

Lou

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Usually, hypotheses involving cooperative interactions and continuous interplay among interacting agents may result in somewhat conservatism, drastically hurdling potential about interpretations of prevalent aggregation phenomena arising in both nature and practical application. Of interest uncovered in this article is the cooperative-competitive consensus problems for multi-agent systems by leverage of impulsive-based control perspective. We first discuss the possibility of quantifying interaction relation of the agents and the description of cooperative-competitive phenomena that capture massive interest recently.Specifically, the former is done by algebraic graph theory, following the same research avenue as the convention; while the cooperative-competitive information is described by some nonzero scaling parameter. We shall pursue the tie that enables to anchor the consensus of participating agents, without depending on the signed graph theory. Subsequently, a manner relying on the local information is carried out to induce the consensus error to circumvent the global information. Then, consensus condition, in connection with the features of both continuous and impulsivebased dynamics, is obtained, as well as several typical circumstances are also elaborated. Both the proposed consensus algorithms and the derived results are underpinned via a numerical study eventually.

show abstract

Containment control for distributed networks subject to multiplicative and additive noises with stochastic approximation–type protocols

Cited by 19 publications

References 38 publications

Mean Square Consensus of General Linear Multiagent Systems with Communication Noises under Switching Topologies

Mean Square Consensus of General Linear Multiagent Systems with Communication Noises under Switching Topologies

Leader-Following Consensus of Stochastic Perturbed Multi-Agent Systems via Variable Impulsive Control and Comparison System Method

Consensus for cooperative–competitive network systems: An impulsive perspective

Contact Info

Product

Resources

About