Minimax State Estimation for a Dynamic System Described by a Differential-Algebraic Equation

TL;DR

This paper develops a minimax estimation framework for linear state estimation in systems described by differential-algebraic equations, providing optimal algorithms and filters for worst-case error minimization.

Contribution

It introduces a duality-based approach to minimax state estimation for DAEs, deriving explicit solutions and filters for both continuous and discrete systems.

Findings

Derived a duality theorem linking estimation to optimal control.

Provided explicit solutions for minimax estimators as linear operator equations.

Developed online minimax estimators for discrete-time DAEs.

Abstract

In this report we address the linear state estimation problem: to estimate a linear transformation $\ell(\varphi)$ of the state $\varphi$ through an algorithm $\widehat{\ell(\varphi)}$ operating on measurements $y$, where $L\varphi=f,y=H\varphi+\eta$. We study the estimation problem in terms of the minimax estimation framework: to find a linear algorithm $\widehat{\widehat{\ell(\varphi)}}$ that minimizes the worst case error $\sup_{\varphi,\eta}d(\ell(\varphi),\widehat{\ell(\varphi)}) $. A key feature of the presented estimation approach is to fix a class of linear operators $L$, $H$; given any pair $L,H$ from that class we describe a class $\mathcal L$ of all solution operators $\ell$ such that the worst case error is finite. We formulate a duality theorem (like Kalman duality principle) that is the estimation problem is equal to the optimal control problem if $G$ is convex bounded subset of the corresponding Hilbert space, $L$ is a closed linear mapping. We obtain optimal estimations as solutions of the linear operator equations if $G$ is an ellipsoid. Then we apply this to the state estimation for the linear differential-algebraic equations (DAE). The minimax observer for DAE is represented in the form of the minimax filter. For discrete time DAEs we present the online minimax estimator.