This paper presents a data-driven algorithm for simultaneous system identification and parameter estimation in control-affine nonlinear systems. Parameter estimation is achieved by training a data-driven predictive model using state-action measurements and various known values at the parameters of interest. The predictive model is then used in conjunction with state-action data corresponding to unknown values of the parameters to estimate the said unknown value. Numerical experiments on the controlled Duffing oscillator with unknown damping, stiffness, and nonlinearity coefficients demonstrate accurate recovery of both the system trajectories and the unknown parameter values from data collected under open-loop excitation.
@article{arxiv.2604.18783,
title = {A Dynamic Mode Decomposition Approach to Parameter Identification},
author = {Moad Abudia and Opeyemi Owolabi and Joel A. Rosenfeld and Rushikesh Kamalapurkar},
journal= {arXiv preprint arXiv:2604.18783},
year = {2026}
}
Comments
7 pages, 4 figures. Submitted to the 2026 IEEE Conference on Decision and Control