3 papers
eess.SY2026
From Privileged Control to Deployable Adaptation:Fusing Mechanism-Guided Task Reduction with Learned Behavior
Xitong Niu, Peifeng Hui, Zheyong Jiang +2
Simultaneous input-gain variation and large additive disturbance create a control problem in which a fixed observer or nominal controller may be unable to reproduce the performance…
eess.SY2026
Model-Free DRL Control for Power Inverters: From Policy Learning to Real-Time Implementation via Knowledge Distillation
Yang Yang, Chenggang Cui, Xitong Niu +2
In response to the trade-off between control performance and computational burden hindering the deployment of Deep Reinforcement Learning (DRL) in power inverters, this paper prese…
eess.SY2024
On Physics-Informed Neural Network Control for Power Electronics
Peifeng Hui, Chenggang Cui, Pengfeng Lin +3
Considering the growing necessity for precise modeling of power electronics amidst operational and environmental uncertainties, this paper introduces an innovative methodology that…