Distributed bounded-error state estimation for partitioned systems based on practical robust positive invariance

TL;DR

This paper introduces a distributed state estimation method for interconnected linear systems with bounded disturbances, avoiding online optimization and ensuring constraint satisfaction through practical robust positive invariance.

Contribution

It presents a novel partition-based estimator that is distributed, computationally efficient, and adaptable to system modifications, based on robust positive invariance.

Findings

Guarantees local estimation error constraints.

Does not require online optimization unlike moving horizon methods.

Easily updates with system topology changes.

Abstract

We propose a partition-based state estimator for linear discrete-time systems composed by coupled subsystems affected by bounded disturbances. The architecture is distributed in the sense that each subsystem is equipped with a local state estimator that exploits suitable pieces of information from parent subsystems. Moreover, differently from methods based on moving horizon estimation, our approach does not require the on-line solution to optimization problems. Our state-estimation scheme, that is based on the notion of practical robust positive invariance developed in Rakovic 2011, also guarantees satisfaction of constraints on local estimation errors and it can be updated with a limited computational effort when subsystems are added or removed.