$H_2$-Optimal Estimation of a Class of Linear PDE Systems using Partial Integral Equations

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 state-space and transfer function representations. To address this problem, we 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 formulate the associated $H_2$-optimal estimation problem. The optimal observer synthesis problem is then recast as an LPI, and the resulting observers are validated using numerical simulation.