Modeling Nonlinear Energy Transmission Chains Via Volterra Series: Convergence Criteria and Stability Bounds Under Periodic Excitation

This paper derives sufficient conditions for the convergence of Volterra series representing solutions to a class of nonlinear integral equations that model energy objects’ dynamic networks with periodic input signals. By formulating the system’s response through a nonlinear integral equation, we establish rigorous criteria for the absolute convergence of the Volterra series expansion. Specifically, we analyze energy objects’ networks containing ideal bandpass filters excited by trigonometric polynomial inputs, a configuration common in simplified analyses of physically realizable systems. For energy systems’ resistive nonlinear chains, we demonstrate that the Volterra series reduces to a power series and provide explicit estimates of its convergence radius (Theoretical statement 3). Additionally, Theoretical statements 1 and 2 present generalized convergence criteria based on the minimization of a functional over a constrained spatial domain, extending prior results for NARX-type systems. The results can contribute to bridging theoretical analysis with engineering applications, offering practical tools for designing nonlinear energy systems’ chains with predictable dynamics, such as those found in power electronics and signal processing systems.

Institutions

Publication Details

Journal
Mathematical and computer modelling Series Technical sciences
Published
2025-07-18
DOI
https://doi.org/10.32626/2308-5916.2025-27.5-15
Primary Topic
Agriculture, Water, and Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling Nonlinear Energy Transmission Chains Via Volterra Series: Convergence Criteria and Stability Bounds Under Periodic Excitation

Mathematical and computer modelling Series Technical sciences
Agriculture, Water, and Health
article

Modeling Nonlinear Energy Transmission Chains Via Volterra Series: Convergence Criteria and Stability Bounds Under Periodic Excitation

article en

Abstract

This paper derives sufficient conditions for the convergence of Volterra series representing solutions to a class of nonlinear integral equations that model energy objects’ dynamic networks with periodic input signals. By formulating the system’s response through a nonlinear integral equation, we establish rigorous criteria for the absolute convergence of the Volterra series expansion. Specifically, we analyze energy objects’ networks containing ideal bandpass filters excited by trigonometric polynomial inputs, a configuration common in simplified analyses of physically realizable systems. For energy systems’ resistive nonlinear chains, we demonstrate that the Volterra series reduces to a power series and provide explicit estimates of its convergence radius (Theoretical statement 3). Additionally, Theoretical statements 1 and 2 present generalized convergence criteria based on the minimization of a functional over a constrained spatial domain, extending prior results for NARX-type systems. The results can contribute to bridging theoretical analysis with engineering applications, offering practical tools for designing nonlinear energy systems’ chains with predictable dynamics, such as those found in power electronics and signal processing systems.

Mathematical and computer modelling Series Technical sciencesVol. 27
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (UA)
Affordable and clean energy
Openalex Percentile: Top 51%
Agriculture, Water, and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.