Passive Lifted FIR Filters for Nonlinear System Identification

Zixing Wang; Fulvio Forni

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arXiv · 2508.05279

Passive Lifted FIR Filters for Nonlinear System Identification

Zixing Wang·Fulvio Forni

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Abstract

Passivity is a fundamental property of physical systems. In data-driven modeling, ensuring that a learned model preserves this structural property is critical to avoiding instability in close loop. Although linear passive system identification is well-established, nonlinear extensions remain challenging. We propose nonlinear operators defined through passivity-preserving lifting of linear passive FIR filters. Passivity is enforced efficiently through frequency-domain constraints, and the nonlinear lifting includes output feedback for expressivity. Numerical and real-world experiments demonstrate the framework capabilities, including the computational advantage of frequency-domain constraints against LMI-based alternatives.

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Zixing Wang, Fulvio Forni. 2025-08-07. Passive Lifted FIR Filters for Nonlinear System Identification. https://arxiv.org/abs/2508.05279

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