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Safe Q-Learning for Data-Driven Nonlinear Optimal Control With Asymmetric State Constraints

Mingming Zhao Ding Wang Shijie Song Junfei Qiao

自动化学报（英文版）2024，Vol.11Issue(12)：2408-2422,15.

自动化学报（英文版）2024，Vol.11Issue(12)：2408-2422,15.DOI:10.1109/JAS.2024.124509

Safe Q-Learning for Data-Driven Nonlinear Optimal Control With Asymmetric State Constraints

Mingming Zhao ¹Ding Wang ¹Shijie Song ²Junfei Qiao¹

作者信息

1. School of Information Science and Technology,the Beijing Key Laboratory of Computational Intelligence and Intelligent System,the Beijing Laboratory of Smart Environmental Protection,and the Beijing Institute of Artificial Intelligence,Beijing University of Technology,Beijing 100124,China
2. School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
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摘要

关键词

Adaptive critic control/adaptive dynamic program-ming(ADP)/control barrier functions(CBF)/stabilizing value itera-tion Q-learning(SVIQL)/state constraints

Key words

Adaptive critic control/adaptive dynamic program-ming(ADP)/control barrier functions(CBF)/stabilizing value itera-tion Q-learning(SVIQL)/state constraints

引用本文复制引用

Mingming Zhao,Ding Wang,Shijie Song,Junfei Qiao..Safe Q-Learning for Data-Driven Nonlinear Optimal Control With Asymmetric State Constraints[J].自动化学报（英文版）,2024,11(12):2408-2422,15.

基金项目

This work was supported in part by the National Science and Technology Major Project(2021ZD0112302)and the National Natural Science Foundation of China(62222301,61890930-5,62021003). （2021ZD0112302）

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