Koopman Lifted Finite Memory Identification via Truncated Grunwald Letnikov Kernels
This work addresses the challenge of extending Koopman-based identification to non-Markovian settings for systems with memory effects, representing an incremental advancement in system identification methods.
The authors tackled the problem of modeling controlled nonlinear hereditary systems by proposing a data-driven linear framework that combines Koopman lifting with a truncated Grunwald-Letnikov memory term, resulting in improved multi-step open-loop prediction accuracy compared to memoryless Koopman and non-lifted state-space baselines in numerical experiments.
We propose a data-driven linear modeling framework for controlled nonlinear hereditary systems that combines Koopman lifting with a truncated Grunwald-Letnikov memory term. The key idea is to model nonlinear state dependence through a lifted observable representation while imposing history dependence directly in the lifted coordinates through fixed fractional-difference weights. This preserves linearity in the lifted state-transition and input matrices, yielding a memory-compensated regression that can be identified from input-state data by least squares and extending standard Koopman-based identification beyond the Markovian setting. We further derive an equivalent augmented Markovian realization by stacking a finite window of lifted states, thereby rewriting the finite-memory recursion as a standard discrete-time linear state-space model. Numerical experiments on a nonlinear hereditary benchmark with a non-Grunwald-Letnikov Prony-series ground-truth kernel demonstrate improved multi-step open-loop prediction accuracy relative to memoryless Koopman and non-lifted state-space baselines.