Year: 2025
Type: conference-paper
Abstract: This paper proposes a data-driven framework for optimal pole placement, in which the objective function is defined as a weighted sum of a robustness measure of pole placement and the Frobenius norm of the feedback gain. To derive the optimization algorithm, we propose data-driven methods for solving Sylvester equations, where data-based expressions of Sylvester equations are introduced. By utilizing this result, a gradient-based optimization algorithm is presented. The effectiveness of the proposed algorithm is demonstrated through a numerical example.
DOI: 10.5687/sss.2026.14
Authors: Ikumi Banno, Kaiya Irie, Shun-ichi Azuma
Institution: Kyoto University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7125600619