2 citations · 3 across the 10 of their papers we have counts for
10 papers
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…
Artificial Intelligence for Power-Converter-Rich Electrical Systems: A Review
Pengfeng Lin, Yuan Gao, Yuxi Tang +8
Power-converter-rich electrical systems, formed by renewable generation, electrified transportation, and inverter-based resources, exhibit strongly nonlinear dynamics, multi-physic…
Collaborative Optimization Framework of Battery Charging / Swapping Stations for eVTOLs Based on Closed-Loop Supply Chain and Space-Time Network
Pengfeng Lin, Miao Zhu, Jiahui Sun +3
Electric vertical take-off and landing (eVTOL) aircraft, by virtue of their advantages such as high efficiency, environmental friendliness, and low noise, are regarded as an effect…
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…
GenControl: Generative AI-Driven Autonomous Design of Control Algorithms
Chenggang Cui, Jiaming Liu, Peifeng Hui +2
Designing controllers for complex industrial electronic systems is challenging due to nonlinearities and parameter uncertainties, and traditional methods are often slow and costly.…
Research on a Two-Layer Demand Response Framework for Electric Vehicle Users and Aggregators Based on LLMs
Zhaoyi Zhang, Chenggang Cui, Ning Yang +1
The widespread adoption of electric vehicles (EVs) has increased the importance of demand response in smart grids. This paper proposes a two-layer demand response optimization fram…
Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach
Yichen Sun, Chenggang Cui, Chuanlin Zhang +1
This paper presents an enhanced electric vehicle demand response system based on large language models, aimed at optimizing the application of vehicle-to-grid technology. By levera…
Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach
Chuanlin Zhang, Junkang Feng, Chenggang Cui +6
With the growing adoption of electric vehicles (EVs), understanding user charging behavior has become critical for grid stability and transportation planning. This study investigat…
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…
Large Language Models based Multi-Agent Framework for Objective Oriented Control Design in Power Electronics
Chenggang Cui, Jiaming Liu, Junkang Feng +3
Power electronics, a critical component in modern power systems, face several challenges in control design, including model uncertainties, and lengthy and costly design cycles. Thi…