Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data

Understand

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.