Triggered Communication in Distributed Adaptive High-Gain EKF

Abstract

This paper deals with the design of a distributed adaptive high-gain extended Kalman filtering (AHGEKF) approach based on triggering communication for nonlinear systems being composed of several interconnected subsystems. For each subsystem, a local AHGEKF is designed, which receives local measurements, communicates with other filters, and computes local state estimates. In order to reduce the information transmission frequency among the distributed estimators, a communication trigger is designed for each filter. Each filter transmits its current state estimate when its corresponding triggering criterion is satisfied. Sufficient conditions are provided under which the convergence and ultimate boundedness of the estimation error is guaranteed. A simulated chemical process is used to demonstrate the applicability and performance of the proposed approach.