Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization

被引:4
|
作者
Yin, Mingzhou [1 ]
Iannelli, Andrea [1 ]
Khosravi, Mohammad [1 ]
Parsi, Anilkumar [1 ]
Smith, Roy S. [1 ]
机构
[1] Swiss Fed Inst Technol, Automat Control Lab, Zurich, Switzerland
来源
IFAC PAPERSONLINE | 2020年 / 53卷 / 02期
基金
瑞士国家科学基金会;
关键词
System identification; regularization; periodic systems; SUBSPACE IDENTIFICATION; REGRESSION; NUCLEAR;
D O I
10.1016/j.ifacol.2020.12.1341
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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. Copyright (C) 2020 The Authors.
引用
收藏
页码:1237 / 1242
页数:6
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