Work

Differentiable Predictive Control for Power Electronic Systems

Year: 2025

Type: article

Abstract: This letter presents the first implementation of Differentiable Predictive Control (DPC) in power electronic systems. By embedding the predictive control cost function directly into a differentiable neural network policy, the proposed approach eliminates the need for labeled control data and online optimization solvers. The control policy is trained solely from system trajectories and deployed on a shallow network suitable for an embedded execution. Experimental validation demonstrates that DPC achieves an accurate reference tracking, strong generalization under unseen conditions, and significantly lower computational cost compared to conventional MPC, particularly for extended prediction horizons. These results highlight the potential of DPC as a data-efficient and computationally scalable alternative for advanced power converter control.

Source: IEEE Transactions on Power Electronics

DOI: 10.1109/tpel.2025.3598178

Authors: Yuan Li, Shuai Zhao, Mateja Novak, Yongjie Liu, Huai Wang, Frede Blaabjerg

Institution: Aalborg University

Language: English

Topic: Multilevel Inverters and Converters

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,645