Moving horizon partition-based state estimation of large-scale systems -- Revised version

TL;DR

This paper introduces three moving horizon estimation methods for large-scale partitioned linear systems, enabling efficient, constrained state estimation with convergence guarantees for each subsystem.

Contribution

It proposes novel MHE algorithms tailored for partitioned systems, analyzing their convergence and computational trade-offs.

Findings

All methods ensure convergence of estimation error to zero.

Different algorithms offer varying computational complexity and accuracy.

The approach exploits physical constraints for improved estimation.

Abstract

This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i.e. systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, conditions for the convergence of the estimation error to zero are analyzed.