This approach demonstrates high-fidelity modeling and superior control in wireless power systems, indicating design independence from physical models.
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.
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Ma et al. (2025) studied this question.
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