Distributed System Identification for Linear Stochastic Systems Under an Adaptive Event-Triggered Scheme

Abstract

This article considers a distributed identification problem for linear stochastic systems whose input and output observations are scheduled by an adaptive event-triggered scheme. An event detector with time-varying thresholds is designed to control the transmission of measurements from the sensors to the estimators, which leads to that only a subset of input and output data is available for identification. The estimators exchange information over a network and cooperatively identify the unknown parameters. A distributed recursive identification algorithm under the event-triggered scheme is proposed based on the distributed stochastic approximation algorithm with expanding truncations (DSAAWET). Under mild assumptions, the strong consistency of the algorithm is proved, that is, the estimates generated from each estimator achieve consensus and converge to the true parameters with probability one. Finally, two numerical examples are provided to validate the theoretical results of the algorithm.