Gramians for a New Class of Nonlinear Control Systems Using Koopman and a Novel Generalized SVD

TL;DR

This paper introduces a novel method using Koopman and a generalized SVD to achieve certified model reduction for complex nonlinear control systems while preserving input energy metrics.

Contribution

It proposes a GSVD-based approach that maintains input energy metrics and provides certifiable error bounds for nonlinear system reduction.

Findings

Successfully reduced a 25-dimensional Hodgkin-Huxley network to a single mode.

Maintained certified error bounds in the physical input norm.

Preserved causality and input energy metrics in the reduced model.

Abstract

Certified model reduction for high-dimensional nonlinear control systems remains challenging: unlike balanced truncation for LTI systems, most nonlinear reduction methods either lack computable worst-case error bounds or rely on intractable PDEs. Data-driven Koopman/DMDc surrogates improve tractability, but standard \emph{input lifting} can distort the physical input-energy metric, so $H_\infty$ and Hankel-based bounds computed on the lifted model may be valid only in a lifted-input norm and need not certify the original system. We address this metric mismatch by a Generalized Singular Value Decomposition (GSVD)-based construction that represents general (including non-affine) input nonlinearities in an LTI-like lifted form with a \emph{pointwise norm-preserving} input map $v(x,u)$ satisfying $\|v(x,u)\|_2=\|u\|_2$ and constant matrices $A,B$. This preserves strict causality (constant $B$, no input-history augmentation) and yields computable Hankel-singular-value-based $H_\infty$ error certificates in the physical input norm for reduced-order surrogates. We illustrate the method on a 25-dimensional Hodgkin--Huxley network with saturating optogenetic actuation, reducing to a single dominant mode while retaining certified error bounds.