Interval consensus over random networks

Fu, Weiming; Qin, Jiahu; Wu, Junfeng; Zheng, Wei Xing; Kang, Yufen

doi:10.1016/j.automatica.2019.108603

Cited by 16 publications

(9 citation statements)

References 23 publications

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“…Remark In fact, the proposed intermittent projected subgradient algorithm has drawn on the idea of internal consensus developed in recent article 41 . In Reference 41, the interval consensus problem for discrete‐time multiagent system over random interaction network is investigated. It is shown that the states of all agents converge to a common value inside the intersection if the intersection of the intervals is nonempty.…”

Section: Resultsmentioning

confidence: 99%

Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm

Shi

Zhou

2020

Optim Control Appl Methods

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Summary In this article, a novel intermittent projected subgradient algorithm is presented to solve the randomized optimal consensus problem for heterogeneous multiagent systems with time‐varying communication topologies. The multiagent systems achieve the consensus meanwhile minimizing the global objective function ∑i=1mfi(x) via the proposed algorithm, where fi(x) is the convex objective function of agent i itself. Due to the common Bernoulli distribution adopted in the existing random optimization algorithm without considering the different computing capability of each agent. An individual projection probability is assigned for each agent based on computing capabilities so that either making projection or taking average is chosen according to the above probability which can effectively avoid overload for some agents with lower computing capabilities and improve the reliability of the overall systems. A new sufficient step‐size condition is given to ensure all agents converge to the optimal solution with probability one. Finally, a numerical example is also given to validate the proposed method.

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Section: Resultsmentioning

confidence: 99%

Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm

Shi

Zhou

2020

Optim Control Appl Methods

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“…In the case of M = ∅, the resilient interval consensus reduces to the ordinary interval consensus problem. 5,6 The dynamics of normal agent i ∈ N can be delineated in general as followṡ…”

Section: Resilient Interval Consensus Algorithmmentioning

confidence: 99%

“…It is worth noting that only fixed network is considered previously for interval consensus. 5,6 The rest of the paper is organized as follows. Section 2 presents the graph theoretical results regarding robustness and the resilient interval consensus protocol.…”

Section: Introductionmentioning

confidence: 99%

“…It is shown that unique equilibrium of the multi‐agent system lies in the intersection of these intervals if it is nonempty, and stability analysis is also performed for the empty intersection situation. In the work Reference 6, interval consensus is shown to be achieved in the sense of almost sure convergence when the interaction topology can be characterized by an undirected random network. Although stated constrained consensus problems in general have been investigated extensively by using, for example, projection‐based protocols 7‐9 and discarded methods, 10 the interval consensus framework is unique as the constraints are not hard‐coded meaning that the transient trajectories of agents are allowed to trespass the admissible intervals, and that initial configuration is not required to be confined in the interval intersection.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we establish sufficient conditions for reaching resilient interval consensus over bidirectional robust networks when the interval intersection is non‐empty. It is worth noting that only fixed network is considered previously for interval consensus 5,6 …”

Section: Introductionmentioning

confidence: 99%

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Resilient interval consensus in robust networks

Shang

2020

Intl J Robust & Nonlinear

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This paper considers the resilient interval consensus problems for continuous-time time-varying multi-agent systems when a normal agent is surrounded by no more than r misbehaving agents. Each normal agent individually proposes a constraint interval which specifies their acceptable consensus range and misbehaving agents are anonymous and exert different arbitrary rules posing threats to the global performance of the systems. On the basis of our purely distributed resilient interval consensus strategy, we showed that if the network is (2r + 1)-robust and the interval intersection is nonempty, the normal agents are able to reach an agreement with the final consensus equilibrium in the interval intersection. A numerical example is presented to illustrate the theoretical results.

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Resilient consensus‐based distributed optimization under deception attacks

Qin

et al. 2020

Intl J Robust & Nonlinear

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Summary This article considers distributed optimization problems of complex cyber‐physical networks, whose goal is to minimize a global function consisting of a sum of local functions possessed by each node, when the communication network is suffering L‐local deception attacks. After showing that merely 1‐local deception attacks can arbitrarily affect the outcome of any distributed optimization algorithms without being detected, we propose a resilient consensus‐based distributed optimization algorithm, where the estimation for the optimizer of each node is updated according to its subgradient and its partial neighbors' estimation. Then, we provide the conditions for the proposed algorithm to ensure that all the nodes can make an agreement and converge to the convex hull of the local optimizer of their functions in the presence of L‐local deception attacks. Finally, some simulation examples are presented to demonstrate the effectiveness of the proposed algorithm.

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Interval consensus over random networks

Cited by 16 publications

References 23 publications

Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm

Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm

Resilient interval consensus in robust networks

Resilient consensus‐based distributed optimization under deception attacks

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