Work

Learning Dynamical Systems by Leveraging Data From Similar Systems

Year: 2025

Type: article

Abstract: We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system. We use a weighted least squares approach, and provide a finite sample error bound of the learned model as a function of the number of samples and various system parameters from the two systems as well as the weight assigned to the auxiliary data. We show that the auxiliary data can help to reduce the intrinsic system identification error due to noise, at the price of adding a portion of error that is due to the differences between the two system models. We further provide general guidelines on how to select the weight assigned to the auxiliary system.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2025.3543574

Authors: Lei Xin, Lintao Ye, George Chiu, Shreyas Sundaram

Institutions: Purdue University West Lafayette, Huazhong University of Science and Technology

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Award: G289220329