Carleman Linearization of Nonlinear Systems and Its Finite-Section Approximations
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Abstract (arXiv)
The Carleman linearization is one of the mainstream approaches to lift a finite-dimensional nonlinear dynamical system into an infinite-dimensional linear system with the promise of providing accurate approximations of the original nonlinear system over larger regions around the equilibrium for longer time horizons with respect to the conventional first-order linearization approach. Finite-section approximations of the lifted system has been widely used to study dynamical and control properties of the original nonlinear system. In this context, some of the outstanding problems are to determine under what conditions, as the finite-section order (i.e., truncation length) increases, the trajectory of the resulting approximate linear system from the finite-section scheme converges to that of the original nonlinear system and whether the time interval over which the convergence happens can be quantified explicitly. In this paper, we provide explicit error bounds for the finite-section approximation and prove that the convergence is indeed exponential with respect to the finite-section order. For a class of nonlinear systems, it is shown that one can achieve exponential convergence over the entire time horizon up to infinity. Our results are practically plausible as our proposed error bound estimates can be used to compute proper truncation lengths for a given application, e.g., determining proper sampling period for model predictive control and reachability analysis for safety verifications. We validate our theoretical findings through several illustrative simulations.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jul 2022.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Learning dynamically inspired bases for Koopman and transfer operator approximationcited by 0
- 2026An explicit operator explains end-to-end computation in the modern neural networks used for sequence and language modelingcited by 0
- 2026Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training errorcited by 0
- 2026Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error29 May 2026
- 2026An explicit operator explains end-to-end computation in the modern neural networks used for sequence and language modeling22 Apr 2026
- 2025Learning dynamically inspired bases for Koopman and transfer operator approximation8 May 2025