Data-Driven Structured Robust Control of Linear Systems

Fuente: arXiv
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Autori principali: Miller, Jared, Eising, Jaap, Dörfler, Florian, Smith, Roy S.
Natura: Preprint
Pubblicazione: 2024
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author Miller, Jared
Eising, Jaap
Dörfler, Florian
Smith, Roy S.
author_facet Miller, Jared
Eising, Jaap
Dörfler, Florian
Smith, Roy S.
contents Static structured control refers to the task of designing a state-feedback controller such that the control gain satisfies a subspace constraint. Structured control has applications in control of communication-inhibited dynamical systems, such as systems in networked environments. This work performs $H_2$-suboptimal regulation under a common structured state-feedback controller for a class of data-consistent plants. The certification of $H_2$-performance is attained through a combination of standard $H_2$ LMIs, convex sufficient conditions for structured control, and a matrix S-lemma for set-membership. The resulting convex optimization problems are linear matrix inequalities whose size scales independently of the number of data samples collected. Data-driven structured $H_2$-regulation control is demonstrated on example systems.
format Preprint
id arxiv_https___arxiv_org_abs_2411_11542
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Data-Driven Structured Robust Control of Linear Systems
Miller, Jared
Eising, Jaap
Dörfler, Florian
Smith, Roy S.
Optimization and Control
Systems and Control
Static structured control refers to the task of designing a state-feedback controller such that the control gain satisfies a subspace constraint. Structured control has applications in control of communication-inhibited dynamical systems, such as systems in networked environments. This work performs $H_2$-suboptimal regulation under a common structured state-feedback controller for a class of data-consistent plants. The certification of $H_2$-performance is attained through a combination of standard $H_2$ LMIs, convex sufficient conditions for structured control, and a matrix S-lemma for set-membership. The resulting convex optimization problems are linear matrix inequalities whose size scales independently of the number of data samples collected. Data-driven structured $H_2$-regulation control is demonstrated on example systems.
title Data-Driven Structured Robust Control of Linear Systems
topic Optimization and Control
Systems and Control
url https://arxiv.org/abs/2411.11542