$H_\infty$-Optimal Estimator Synthesis for Coupled Linear 2D PDEs using Convex Optimization
<|reference_start|>$H_\infty$-Optimal Estimator Synthesis for Coupled Linear 2D PDEs using Convex Optimization: Any suitably well-posed PDE in two spatial dimensions can be represented as a Partial Integral Equation (PIE) -- with system dynamics parameterized using Partial Integral (PI) operators. Furthermore, $L_2$-gain analysis of PDEs with a PIE representation can be posed as a linear operator inequality, which can be solved using convex optimization. In this paper, these results are used to derive a convex-optimization-based test for constructing an $H_{\infty}$-optimal estimator for 2D PDEs. In particular, we first use PIEs to represent an arbitrary well-posed 2D PDE where sensor measurements occur along some boundary of the domain. An associated Luenberger-type estimator is then parameterized using a PI operator $\mathcal{L}$ as the observer gain. Examining the error dynamics of this estimator, it is proven that an upper bound on the $H_{\infty}$-norm of these error dynamics can be minimized by solving a linear operator inequality on PI operator variables. Finally, an analytical formula is proposed for inversion of a class of 2D PI operators, which is then used to reconstruct the Luenberger gain $\mathcal{L}$. Results are implemented in the PIETOOLS software suite -- applying the methodology and simulating the resulting observer for an unstable 2D heat equation with boundary observations.<|reference_end|>
@article{jagt2024$h_{\infty}$-optimal,
    title={$H_{\infty}$-Optimal Estimator Synthesis for Coupled Linear 2D PDEs
  using Convex Optimization},
    author={Declan S. Jagt, Matthew M. Peet},
    journal={arXiv preprint arXiv:2402.05061},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05061},
    primaryClass={math.OC cs.SY eess.SY}
}