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An Optimal Hybrid Learning Approach for Attack Detection in Linear Networked Control Systems

Haifeng Niu Avimanyu Sahoo Chandreyee Bhowmick S.Jagannathan

自动化学报（英文版）2019，Vol.6Issue(6)：1404-1416,13.

自动化学报（英文版）2019，Vol.6Issue(6)：1404-1416,13.DOI:10.1109/JAS.2019.1911762

An Optimal Hybrid Learning Approach for Attack Detection in Linear Networked Control Systems

Haifeng Niu ¹Avimanyu Sahoo ²Chandreyee Bhowmick ³S.Jagannathan³

作者信息

1. Google Inc, Seattle, WA 98103 USA
2. School of Electrical and Computer Engineering, Oklahoma State University, Still Water, OK 74078 USA
3. Department of Electrical and Computer Engineering, Missouri University of Science and Technology,Rolla, MO 65409 USA
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摘要

关键词

Attack detection/attack estimation/event-triggered control/Lyapunov stability/networked control system (NCS)/optimal control/Q-learning

Key words

Attack detection/attack estimation/event-triggered control/Lyapunov stability/networked control system (NCS)/optimal control/Q-learning

引用本文复制引用

Haifeng Niu,Avimanyu Sahoo,Chandreyee Bhowmick,S.Jagannathan..An Optimal Hybrid Learning Approach for Attack Detection in Linear Networked Control Systems[J].自动化学报（英文版）,2019,6(6):1404-1416,13.

基金项目

This work was supported in part by the National Science Foundation (ⅡP 1134721,ECCS 1406533,CMMI 1547042). （ⅡP 1134721,ECCS 1406533,CMMI 1547042）

自动化学报（英文版）

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