Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization


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Date

2020-11

Publication Type

Conference Paper

ETH Bibliography

yes

Citations

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Data

Abstract

This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.

Publication status

published

Book title

21st IFAC World Congress

Volume

53 (2)

Pages / Article No.

1237 - 1242

Publisher

Elsevier

Event

21st IFAC World Congress (IFAC-V 2020)

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

System identification; regularization; periodic systems

Organisational unit

08814 - Smith, Roy (Tit.-Prof.) (ehemalig) / Smith, Roy (Tit.-Prof.) (former) check_circle

Notes

Due to the Coronavirus (COVID-19) the 21st IFAC World Congress 2020 became the 1st Virtual IFAC World Congress (IFAC-V 2020).

Funding

178890 - Modeling, Identification and Control of Periodic Systems in Energy Applications (SNF)

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