Data-Driven Discrepancy Modeling in Higher-Dimensional State Space via Coprime Factorization

Abstract

This work provides a data-driven framework that combines coprime factorization with a lifting linearization technique to model the discrepancy between a nonlinear system and its nominal linear approximation using a linear time-invariant (LTI) state-space model in a higher-dimensional state space. In the proposed framework, the nonlinear system is represented in terms of the left coprime factors of the nominal linear system, along with perturbations modeled as stable, norm-bounded LTI systems in a higher-dimensional state space using a deep learning approach. Our method builds on a recently proposed parametrization for norm-bounded systems, enabling the simultaneous minimization of the H-infinity norm of the learned perturbations. We also provide a coprime factorization-based approach as an alternative to direct methods for learning lifted LTI approximations of nonlinear systems. In this approach, the LTI approximations are obtained by learning their left coprime factors, which remain stable even when the original system is unstable. The effectiveness of the proposed discrepancy modeling approach is demonstrated through multiple examples.