$\mathcal{H}_\infty$ performance analysis and distributed controller synthesis for interconnected linear systems from noisy input-state data

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Abstract

The increase in available data and complexity of dynamical systems has sparked the research on data-based system performance analysis and controller design. In this paper, we extend a recent data-based approach for guaranteed performance analysis to distributed analysis of interconnected linear systems. We present a new set of sufficient LMI conditions based on noisy input-state data that guarantees $\mathcal{H}_\infty$ performance and has a structure that is applicable to distributed controller synthesis from data. Sufficient LMI conditions based on noisy data are provided for the existence of a dynamic distributed controller that achieves $\mathcal{H}_\infty$ performance. The presented approach enables scalable analysis and control of large-scale interconnected systems from noisy input-state data.
Original languageEnglish
Title of host publication60th IEEE Conference on Decision and Control (CDC 2021)
PublisherInstitute of Electrical and Electronics Engineers
Publication statusAccepted/In press - 2021
Event60th IEEE Conference on Decision and Control (CDC 2021) - Austin, United States
Duration: 13 Dec 202117 Dec 2021
Conference number: 60
https://2021.ieeecdc.org/

Conference

Conference60th IEEE Conference on Decision and Control (CDC 2021)
Abbreviated titleCDC 2021
Country/TerritoryUnited States
CityAustin
Period13/12/2117/12/21
Internet address

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