Robust Subspace System Identification via Weighted Nuclear Norm Optimization
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.
Cite
@article{arxiv.1312.2132,
title = {Robust Subspace System Identification via Weighted Nuclear Norm Optimization},
author = {Dorsa Sadigh and Henrik Ohlsson and S. Shankar Sastry and Sanjit A. Seshia},
journal= {arXiv preprint arXiv:1312.2132},
year = {2013}
}
Comments
Submitted to the IFAC World Congress 2014