期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 6, 页码 4485-4494出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3028988
关键词
Continuous-time; H-2 control; neural modeling; reinforcement learning
类别
资金
- National Council of Science and Technology (CONACYT) [CONACyT-A1-S-8216]
- Center for Research and Advanced Studies (CINVESTAV) [SEP-CINVESTAV-62]
This article discusses the application of continuous-time H-2 control in unknown nonlinear systems. We use differential neural networks to model the system and apply H-2 tracking control based on the neural model. Due to the sensitivity of neural H-2 control to neural modeling errors, we use reinforcement learning to improve control performance. The stability of neural modeling and H-2 tracking control is proven, and the convergence of the approach is also given. The proposed method is validated with two benchmark control problems.
In this article, we discuss continuous-time H-2 control for the unknown nonlinear system. We use differential neural networks to model the system, then apply the H-2 tracking control based on the neural model. Since the neural H-2 control is very sensitive to the neural modeling error, we use reinforcement learning to improve the control performance. The stabilities of the neural modeling and the H-2 tracking control are proven. The convergence of the approach is also given. The proposed method is validated with two benchmark control problems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据