$H_2$-Optimal Estimation of Linear Delayed and PDE Systems

TL;DR

This paper develops a novel method for $H_2$-optimal estimation of linear PDE systems using PIE representations, formulating the problem as a convex optimization with LPI inequalities.

Contribution

It introduces a PIE-based framework for $H_2$-optimal estimation of PDEs, enabling convex optimization and systematic observer design.

Findings

Re-characterization of $H_2$-norm for PDEs in terms of initial conditions

Development of convex LPI-based optimization for observer synthesis

Validation through numerical simulations of proposed observers

Abstract

The $H_2$ norm is a commonly used performance metric in the design of estimators. However, $H_2$-optimal estimation of most PDEs is complicated by the lack of transfer function and state-space representations. To address this problem, we first re-characterize the $H_2$-norm in terms of a map from initial condition to output. We then leverage the Partial Integral Equation (PIE) state-space representation of systems of linear PDEs coupled with ODEs to recast this characterization of $H_2$-norm as a convex optimization problem defined in terms of Linear Partial Integral (LPI) inequalities. We then parameterize a class of PIE-based observers and solve the associated $H_2$-optimal estimation problem. The observer synthesis problem is then recast as an LPI, and the resulting observers are validated using numerical simulation.