Consensus-based decentralized real-time identification of large-scale systems

In this paper an approach is proposed to decentralized multi-agent identification of large-scale systems represented by linear discrete-time stochastic MIMO models. It is assumed that each agent: (a) has access only to a subset of noisy input–output variables; (b) communicates local data processing results to its neighborhood. The proposed algorithm consists of two stages. The first stage is a consensus-based stochastic approximation algorithm for estimating input–output correlation functions, while at the second stage each agent utilizes a stochastic approximation algorithm with expanding truncations derived from the modified Yule–Walker equations in order to generate all the system parameter estimates. It is proved that under nonrestrictive assumptions concerning the system properties and the multi-agent network topology the estimates of the correlation functions converge almost surely to their true values and those of the system parameters to a solution of the modified Yule–Walker equations, assuming intermittent observations and communication outages. Conditions are also given for the strong consistency of the parameter estimates. Simulation results provide an illustration of the algorithm properties.