Stability of multiagent systems with time-dependent communication links

Moreau, Luc

doi:10.1109/tac.2004.841888

Cited by 2,391 publications

(1,656 citation statements)

References 29 publications

Supporting

Mentioning

1,636

Contrasting

Unclassified

Order By: Relevance

“…These include problems where the weights are not symmetric, problems where final agreement is achieved, but not necessarily to the average (e.g., [13,20,26]), and problems where the final node values have a specified non-uniform distribution (e.g., [11,27,33]). Convergence conditions have also been established for distributed consensus on dynamically changing graphs (e.g., [13,20,26,34]) and with asynchronous communication and computation ( [2]; see also the early work in [31,32]). Other work gives bounds on the convergence factor W − J for a particular choice of weights, in terms of various geometric quantities such as conductance (e.g., [30]).…”

Section: X(t + 1) − J X(0) W − J X(t) − J X(0) mentioning

confidence: 99%

See 1 more Smart Citation

Distributed average consensus with least-mean-square deviation

Xiao

Boyd

Kim

2007

Journal of Parallel and Distributed Computing

986

732

View full text Add to dashboard Cite

We consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed coordination of mobile autonomous agents, and network synchronization. In this model, each node updates its local variable with a weighted average of its neighbors' values, and each new value is corrupted by an additive noise with zero mean. The quality of consensus can be measured by the total mean-square deviation of the individual variables from their average, which converges to a steady-state value. We consider the problem of finding the (symmetric) edge weights that result in the least mean-square deviation in steady state. We show that this problem can be cast as a convex optimization problem, so the global solution can be found efficiently. We describe some computational methods for solving this problem, and compare the weights and the mean-square deviations obtained by this method and several other weight design methods.

show abstract

Section: X(t + 1) − J X(0) W − J X(t) − J X(0) mentioning

confidence: 99%

“…in distributed coordination of mobile autonomous agents (e.g., [13,20,22,26]), and distributed data fusion in sensor networks (e.g., [28,29,34]). …”

mentioning

confidence: 99%

Distributed average consensus with least-mean-square deviation

Xiao

Boyd

Kim

2007

Journal of Parallel and Distributed Computing

986

732

View full text Add to dashboard Cite

show abstract

“…, C k } be a graph partition, not necessarily an AEP, and let P = P(π ) ∈ R N ×k be its characteristic matrix. As before, the reduced order network is taken to be the Petrov-Galerkin projection of (27) and is represented by…”

Section: The Single Integrator Casementioning

confidence: 99%

“…Again, let S andŜ be the transfer functions of (27) and (28), respectively. We will address the problem of obtaining a priori upper bounds for S −Ŝ H 2 and S − S H ∞ .…”

Section: The Single Integrator Casementioning

confidence: 99%

“…This has brought a significant interest to complex networks, smart-grids, distributed systems, transportation networks, biological networks, and networked multi-agent systems, see, e.g., [2,10,28]. Widely studied topics in networked systems have been the problems of consensus and synchronization, see [19,20,27,30]. Other important subjects in the theory of networked systems are flocking, formation control, sensor placement, and controllability of networks, see, e.g., [8,9,11,12,24,29,34].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model reduction of linear multi-agent systems by clustering with $$\varvec{\mathcal {H}_2}$$ H 2 and $$\varvec{\mathcal {H}_\infty }$$ H ∞ error bounds

Jongsma

Mlinarić

Grundel

et al. 2018

Math. Control Signals Syst.

View full text Add to dashboard Cite

Firstly, we will establish an a priori upper bound for the H 2 model reduction error in case that the agent dynamics is an arbitrary multivariable input-state-output system. Secondly, for the single integrator case, we will derive an explicit formula for the H ∞ model reduction error. Thirdly, we will prove an a priori upper bound for the H ∞ model reduction error in case that the agent dynamics is a symmetric multivariable input-state-output system. Finally, we will consider the problem of obtaining a priori upper bounds if we cluster using arbitrary, possibly non almost equitable, partitions.

show abstract

Distributed Consensus in Networks

Chen

Ren

2016

Wiley Encyclopedia of Electrical and Electronics Engineering

View full text Add to dashboard Cite

Consensus is a fundamental problem and plays an important role in many practical networked systems. This article gives a brief overview on distributed consensus in networks. The main focus is to present the most basic and fundamental results of consensus. Specifically, various definitions on consensus are introduced, such as average consensus, leader‐following consensus, and dynamic average consensus. Three main techniques for convergence analysis of consensus based on decoupling, infinite matrix products, and Lyapunov function are elaborated. Some design issues related to consensus are also discussed. Advanced topics on consensus, such as complicated agent dynamics, communication delays, quantization, event‐triggered mechanism, finite‐time convergence, and state constraints, are presented. The relation between consensus and other cooperative control problems is also established. Finally, future research directions are discussed.

show abstract

Stability of multiagent systems with time-dependent communication links

Cited by 2,391 publications

References 29 publications

Distributed average consensus with least-mean-square deviation

Distributed average consensus with least-mean-square deviation

Model reduction of linear multi-agent systems by clustering with $$\varvec{\mathcal {H}_2}$$ H 2 and $$\varvec{\mathcal {H}_\infty }$$ H ∞ error bounds

Distributed Consensus in Networks

Contact Info

Product

Resources

About