Amidst Data-Driven Model Reduction and Control

Nima Monshizadeh*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)
75 Downloads (Pure)

Abstract

In this note, we explore a middle ground between data-driven model reduction and data-driven control. In particular, we use snapshots collected from the system to build reduced models that can be expressed in terms of data. We illustrate how the derived family of reduced models can be used for data-driven control of the original system under suitable conditions. Finding a control law that stabilizes certain solutions of the original system as well as the one that reaches any desired state in final time are studied in detail.

Original languageEnglish
Pages (from-to)833-838
Number of pages6
JournalIEEE Control Systems Letters
Volume4
Issue number4
Early online date12-May-2020
DOIs
Publication statusPublished - Oct-2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: 14-Dec-202018-Dec-2020

Keywords

  • Data models
  • Reduced order systems
  • Linear systems
  • Complexity theory
  • Tools
  • Analytical models
  • Data-driven model reduction
  • data-driven control
  • linear systems
  • SYSTEMS

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