1 citations · 1 across the 10 of their papers we have counts for
11 papers
Recursive Self-Improvement LLM Agents for Inverter Dynamic Model Identification
Jie Feng, Xiaoyang Wang, Xin Chen +1
This is a position paper. We demonstrate that recursive self-improvement (RSI) large language model (LLM) agents are a natural search engine for dynamic model identification of inv…
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
Yuan Zhuang, Yuexin Bian, Sihong He +7
Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and i…
Efficient Policy Adaptation for Voltage Control Under Unknown Topology Changes
Jie Feng, Yuanyuan Shi, Deepjyoti Deka
Reinforcement learning (RL) has shown great potential for designing voltage control policies, but their performance often degrades under changing system conditions such as topology…
RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning
Yuexin Bian, Jie Feng, Tao Wang +3
On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies,…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
We survey classical, machine learning, and data-driven system identification approaches to learn control-relevant and physics-informed models of dynamical systems. Recently, machin…
Stability Constrained Voltage Control in Distribution Grids with Arbitrary Communication Infrastructure
Zhenyi Yuan, Jie Feng, Yuanyuan Shi +1
We consider the problem of designing learning-based reactive power controllers that perform voltage regulation in distribution grids while ensuring closed-loop system stability. In…
Analytical Lyapunov Function Discovery: An RL-based Generative Approach
Haohan Zou, Jie Feng, Hao Zhao +1
Despite advances in learning-based methods, finding valid Lyapunov functions for nonlinear dynamical systems remains challenging. Current neural network approaches face two main is…
DiffOP: Reinforcement Learning of Optimization-Based Control Policies via Implicit Policy Gradients
Yuexin Bian, Jie Feng, Yuanyuan Shi
Real-world control systems require policies that are not only high-performing but also interpretable and robust. A promising direction toward this goal is model-based control, whic…
Online Event-Triggered Switching for Frequency Control in Power Grids with Variable Inertia
Jie Feng, Wenqi Cui, Jorge Cortés +1
The increasing integration of renewable energy resources into power grids has led to time-varying system inertia and consequent degradation in frequency dynamics. A promising solut…
Combining Neural Networks and Symbolic Regression for Analytical Lyapunov Function Discovery
Jie Feng, Haohan Zou, Yuanyuan Shi
We propose CoNSAL (Combining Neural networks and Symbolic regression for Analytical Lyapunov function) to construct analytical Lyapunov functions for nonlinear dynamic systems. Thi…
Stability-Constrained Learning for Frequency Regulation in Power Grids with Variable Inertia
Jie Feng, Manasa Muralidharan, Rodrigo Henriquez-Auba +2
The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequen…