3 papers
cs.LG2025
Theory Foundation of Physics-Enhanced Residual Learning
Shixiao Liang, Wang Chen, Keke Long +3
Intensive studies have been conducted in recent years to integrate neural networks with physics models to balance model accuracy and interpretability. One recently proposed approac…
cs.RO2024
Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning
Peng Zhang, Heye Huang, Hang Zhou +3
This paper introduces a physics enhanced residual learning (PERL) framework for connected and automated vehicle (CAV) platoon control, addressing the dynamics and unpredictability…
cs.AI2024
Physics Enhanced Residual Policy Learning (PERPL) for safety cruising in mixed traffic platooning under actuator and communication delay
Keke Long, Haotian Shi, Yang Zhou +1
Linear control models have gained extensive application in vehicle control due to their simplicity, ease of use, and support for stability analysis. However, these models lack adap…