Stable Linear Subspace Identification: A Machine Learning Approach

arXiv:2311.03197v4 Announce Type: replace-cross
Abstract: Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the automatic differentiation framework - to introduce SIMBa, a family of discrete linear multi-step-ahead state-space SI methods using backpropagation. SIMBa relies on a novel Linear-Matrix-Inequality-based free parametrization of Schur matrices to ensure the stability of the identified model.
We show how SIMBa generally outperforms traditional linear state-space SI methods, and sometimes significantly, …

abstract arxiv backpropagation cs.lg cs.sy differentiation eess.sy family framework identification linear machine machine learning ml tools novel paper space state tools type