Robust Subspace System Identification via Weighted Nuclear Norm Optimization

Dorsa Sadigh, Henrik Ohlsson, S. Shankar Sastry, and Sanjit A. Seshia. Robust Subspace System Identification via Weighted Nuclear Norm Optimization. In Proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC), August 2014.

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Abstract

Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter.We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two-dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed.

BibTeX

@InProceedings{sadigh-ifac14,
  author = 	 {Dorsa Sadigh and Henrik Ohlsson and S. Shankar Sastry and Sanjit A. Seshia},
  title = 	 {Robust Subspace System Identification via Weighted Nuclear Norm Optimization},
  booktitle = 	 {Proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC)},
  OPTcrossref =  {},
  OPTkey = 	 {},
  OPTpages = 	 {},
  year = 	 {2014},
  OPTeditor = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  month = 	 {August},
  OPTorganization = {},
  OPTpublisher = {},
  OPTannote = 	 {},
  abstract = {Subspace identification is a classical and very well studied problem in system 
identification. The problem was recently posed as a convex optimization problem via the nuclear 
norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The 
proposed framework takes the form of a convex optimization problem with an objective that 
trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable 
regularization parameter.We show how the space in which a suitable parameter should be sought 
can be limited to a bounded open set of the two-dimensional parameter space. In practice, this 
is very useful since it restricts the parameter space that is needed to be surveyed.}, 
}