Distributed Unknown Input Observer Design: A Geometric Approach

TL;DR

This paper introduces a geometric approach for designing distributed unknown input observers in linear systems, relaxing previous rank conditions and enabling effective state estimation with limited local measurements.

Contribution

It develops a novel geometric method for DUIO design that relaxes rank conditions and applies to both continuous and discrete systems, with proven sufficiency and necessity.

Findings

Effective DUIO design under relaxed conditions

Applicability to continuous and discrete systems

Validated through simulations and a power grid case study

Abstract

We present a geometric approach to designing distributed unknown input observers (DUIOs) for linear time-invariant systems, where measurements are distributed across nodes and each node is influenced by \emph{unknown inputs} through distinct channels. The proposed distributed estimation scheme consists of a network of observers, each tasked with reconstructing the entire system state despite having access only to local input-output signals that are individually insufficient for full state observation. Unlike existing methods that impose stringent rank conditions on the input and output matrices at each node, our approach leverages the $(C,A)$-invariant (conditioned invariant) subspace at each node from a geometric perspective. This enables the design of DUIOs in both continuous- and discrete-time settings under relaxed conditions, for which we establish sufficiency and necessity. The effectiveness of our methodology is demonstrated through extensive simulations, including a practical case study on a power grid system.