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arXiv · 2002.06613

Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data

Yu Xing·Ben Gravell·Xingkang He·Karl Henrik Johansson·Tyler Summers
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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.

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Yu Xing, Ben Gravell, Xingkang He, Karl Henrik Johansson, Tyler Summers. 2020-02-16. Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data. https://arxiv.org/abs/2002.06613

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