Distributed State Estimation for Linear Time-invariant Systems with Aperiodic Sampled Measurement

TL;DR

This paper presents a method for distributed state estimation of linear time-invariant systems using local sampled measurements and asynchronous communication, providing explicit bounds on sampling periods for guaranteed convergence.

Contribution

It introduces a novel approach for distributed observers with aperiodic sampling and asynchronous communication, even when individual observers are not fully observable.

Findings

Explicit upper bounds on sampling periods for convergence.

Distributed observers achieve exponential state estimation.

Numerical example validates theoretical results.

Abstract

This paper deals with the state estimation of linear time-invariant systems using distributed observers with local sampled-data measurement and aperiodic communication. Each observer agent perceives partial information of the system to be observed but does not satisfy the observability condition. Consequently, distributed observers are designed to exponentially estimate the state of the system to be observed by time-varying sampling and asynchronous communication. Additionally, explicit upper bounds on allowable sampling periods for convergent estimation errors are given. Finally, a numerical example is provided to demonstrate the validity of the theoretical results