Theorems about ergodicity and class-ergodicity of chains with applications in known consensus models

In a multi-agent system, unconditional (multiple) consensus is the property of reaching to (multiple) consensus irrespective of the instant and values at which states are initialized. For linear algorithms, occurrence of unconditional (multiple) consensus turns out to be equivalent to (class-)ergodicity of the transition chain (An). For a wide class of chains, chains with so-called balanced asymmetry property, necessary and sufficient conditions for ergodicity and class-ergodicity are derived. The results are employed to analyze the limiting behavior of agents' states in the JLM model, the Krause model, and the Cucker-Smale model. In particular, unconditional single or multiple consensus occurs in all three models. Moreover, a necessary and sufficient condition for unconditional consensus in the JLM model and a sufficient condition for consensus in the Cucker-Smale model are obtained.

[1]  John N. Tsitsiklis,et al.  Convergence of Type-Symmetric and Cut-Balanced Consensus Seeking Systems , 2011, IEEE Transactions on Automatic Control.

[2]  Felipe Cucker,et al.  Emergent Behavior in Flocks , 2007, IEEE Transactions on Automatic Control.

[3]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[4]  M. Degroot Reaching a Consensus , 1974 .

[5]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[6]  Behrouz Touri,et al.  On Approximations and Ergodicity Classes in Random Chains , 2010, IEEE Transactions on Automatic Control.

[7]  J.N. Tsitsiklis,et al.  Convergence in Multiagent Coordination, Consensus, and Flocking , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[8]  John N. Tsitsiklis,et al.  On the 2R conjecture for multi-agent systems , 2007, 2007 European Control Conference (ECC).

[9]  Sadegh Bolouki,et al.  On consensus with a general discrete time convex combination based algorithm for multi-agent systems , 2011, 2011 19th Mediterranean Conference on Control & Automation (MED).

[10]  Julien M. Hendrickx,et al.  Graphs and networks for the analysis of autonomous agent systems , 2008 .

[11]  V. Blondel,et al.  Convergence of different linear and non-linear Vicsek models , 2006 .

[12]  Jan Lorenz,et al.  A stabilization theorem for dynamics of continuous opinions , 2005, 0708.2981.

[13]  Behrouz Touri,et al.  On backward product of stochastic matrices , 2011, Autom..

[14]  Sadegh Bolouki,et al.  Criteria for Unconditional Convergence to Single or Multiple Consensuses in Discrete Time Linear Consensus Algorithms , 2012 .

[15]  Huaiqing Wang,et al.  J ul 2 00 4 Multi-agent coordination using nearest neighbor rules : a revisit to Vicsek model ∗ , 2008 .

[16]  Huaiqing Wang,et al.  Multi-agent coordination using nearest neighbor rules: revisiting the Vicsek model , 2004, ArXiv.

[17]  John N. Tsitsiklis,et al.  Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms , 1984, 1984 American Control Conference.

[18]  Behrouz Touri,et al.  On Ergodicity, Infinite Flow, and Consensus in Random Models , 2010, IEEE Transactions on Automatic Control.

[19]  E. Seneta,et al.  Towards consensus: some convergence theorems on repeated averaging , 1977, Journal of Applied Probability.

[20]  John N. Tsitsiklis,et al.  On Krause's Multi-Agent Consensus Model With State-Dependent Connectivity , 2008, IEEE Transactions on Automatic Control.

[21]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[22]  Luc Moreau,et al.  Stability of multiagent systems with time-dependent communication links , 2005, IEEE Transactions on Automatic Control.

[23]  John N. Tsitsiklis,et al.  Problems in decentralized decision making and computation , 1984 .