Work

Data-Driven Modeling and Control of Wireless Power Transfer Systems

Year: 2025

Type: article

Abstract: This paper proposes a data-driven modeling and control method for wireless power transmission systems. To address problems such as parameter deviation and high-order complexity in traditional circuit-theory-based modeling, this paper adopts the data-driven Petrov-Galerkin projection and the generalized Lyapunov balancing method to obtain a reduced-order model directly from experimental data. This approach formulates quadratic matrix inequalities to characterize the data and noise, enabling the direct design of a reduced-order model without intermediate system identification steps. The resulting model order is reduced to merely 2–4. Furthermore, by constructing the extended state space and solving the algebraic Riccati equation, we design a linear quadratic regulator-proportional integral controller with integral action to eliminate steady-state error. Experimentally, the method proves to be independent of detailed physical models while achieving both high-fidelity modeling and superior control.

Source: Electronics

DOI: 10.3390/electronics14183668

Authors: Xiaoguo Ma, Engang Tian, Donghui Xu

Institution: University of Shanghai for Science and Technology

Language: English

Topic: Wireless Power Transfer Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896