A Dual System-Level Parameterization for Identification from Closed-Loop Data
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Date
2023
Publication Type
Conference Paper
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yes
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Abstract
This work presents a dual system-level parameterization (D-SLP) method for closed-loop system identification. The recent system-level synthesis framework parameterizes all stabilizing controllers via linear constraints on some closed- loop response functions, known as system-level parameters. It was demonstrated that several structural, locality, and communication constraints on the controller can be posed as convex constraints on these system-level parameters. In the current work, the identification problem is treated as a dual of the system-level synthesis problem. The plant model is identified from the dual system-level parameters associated to the plant. In comparison to existing closed-loop identification approaches (such as the dual-Youla parameterization), the D-SLP framework neither requires the knowledge of a nominal plant that is stabilized by the known controller, nor depends upon the choice of factorization of the nominal plant and the stabilizing controller. Numerical simulations demonstrate the efficacy of the proposed D-SLP method in terms of identification errors, compared to existing closed-loop identification techniques.
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Publication status
published
Editor
Book title
2023 62nd IEEE Conference on Decision and Control (CDC)
Journal / series
Volume
Pages / Article No.
4506 - 4511
Publisher
IEEE
Event
62nd IEEE Conference on Decision and Control (CDC 2023)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Benchmark testing; Numerical simulation
Organisational unit
08814 - Smith, Roy (Tit.-Prof.) (ehemalig) / Smith, Roy (Tit.-Prof.) (former)
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)
Related publications and datasets
Is supplemented by: https://doi.org/10.3929/ethz-b-000628973