Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data
Systems and Control
2020-07-06 v2 Systems and Control
Abstract
The study of multiplicative noise models has a long history in control theory but is re-emerging in the context of complex networked systems and systems with learning-based control. We consider linear system identification with multiplicative noise from multiple state-input trajectory data. We propose exploratory input signals along with a least-squares algorithm to simultaneously estimate nominal system parameters and multiplicative noise covariance matrices. Identifiability of the covariance structure and asymptotic consistency of the least-squares estimator are demonstrated by analyzing first and second moment dynamics of the system. The results are illustrated by numerical simulations.
Cite
@article{arxiv.2002.06613,
title = {Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data},
author = {Yu Xing and Ben Gravell and Xingkang He and Karl Henrik Johansson and Tyler Summers},
journal= {arXiv preprint arXiv:2002.06613},
year = {2020}
}