Passive Lifted FIR Filters for Nonlinear System Identification

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.