Building a Self-Evolving Digital Twin System with Bayesian Optimization and Deep Reinforcement Learning for Complex Equipment Optimization and ControlOA
Building a Self-Evolving Digital Twin System with Bayesian Optimization and Deep Reinforcement Learning for Complex Equipment Optimization and Control
Kunyu Wang;Zhen Chen;Lin Zhang;Mohammad S.Obaidat;Jin Cui;Hongbo Cheng;Han Lu
Hangzhou International Innovation Institute of Beihang University,Hangzhou 311115,China||School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,ChinaSchool of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,ChinaHangzhou International Innovation Institute of Beihang University,Hangzhou 311115,China||School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China||State Key Laboratory of Intelligent Manufacturing System Technology,Beijing 100854,ChinaKing Abdullah Ⅱ School of Information Technology,University of Jordan,Amman 999045,JordanSchool of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,ChinaSchool of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,ChinaSchool of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China
equipment digital twinBayesian optimizationdeep reinforcement learning(DRL)dynamic system modelingintelligent control
equipment digital twinBayesian optimizationdeep reinforcement learning(DRL)dynamic system modelingintelligent control
《清华大学学报自然科学版（英文版）》 2026 (1)
199-216,18
This work was supported by the National Natural Science Foundation of China(No.62373026).
评论