Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Stability of Recursive Stochastic Tracking Algorithms
SIAM Journal on Control and Optimization
SIAM Journal on Computing
A Layered Analysis of Consensus
SIAM Journal on Computing
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Coordination and Geometric Optimization via Distributed Dynamical Systems
SIAM Journal on Control and Optimization
The Time-Complexity of Local Decision in Distributed Agreement
SIAM Journal on Computing
Distributed algorithms for reaching consensus on general functions
Automatica (Journal of IFAC)
Reaching a Consensus in a Dynamically Changing Environment: A Graphical Approach
SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization
Brief paper: Convergence speed in distributed consensus over dynamically switching random networks
Automatica (Journal of IFAC)
Sensor Networks With Random Links: Topology Design for Distributed Consensus
IEEE Transactions on Signal Processing - Part II
Brief A hierarchical cyclic pursuit scheme for vehicle networks
Automatica (Journal of IFAC)
Randomized consensus algorithms over large scale networks
IEEE Journal on Selected Areas in Communications
Automatica (Journal of IFAC)
Leader-follower consensus over numerosity-constrained random networks
Automatica (Journal of IFAC)
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In this paper, we discuss the consensus problem in networks of multiagents with stochastically switching topologies. The switch of graph topology is modeled as an adapted stochastic process, which in principle can include any stochastic processes such as independent and identically distributed (i.i.d.) processes and Markov chains. We derive the sufficient conditions for consensus in both discrete-time and continuous-time networks in terms of conditional expectations of the underlying graph topology. We prove that if there exist $T0$ and $\delta0$ such that the conditional expectation of the union of the $\delta$-graph topologies across each $T$-length time interval has spanning trees, then the multiagent system reaches consensus. For comparison, we show that some previous results on this topic can be derived from our main theorem as corollaries. This includes important results when the switching topology can be modeled as the special and important stochastic models—the i.i.d. process and the Markov process—which implies that we generalize the previous results to some extent. As applications, we also give some corollaries concerning stochastic processes other than the i.i.d. process and Markov processes, such as independent but not necessarily identically distributed processes, hidden Markov models, and $\phi$-mixing processes.