文献类型：期刊文献

英文题名：A jointly optimal design of control and scheduling in networked systems under denial-of-service attacks

作者：Lu, Jingyi[1];Quevedo, Daniel E.[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]Queensland Univ Technol, Sch Elect Engn & Robot, Brisbane, Qld, Australia

年份：2023

卷号：148

外文期刊名：AUTOMATICA

收录：;EI(收录号：20225113262968);WOS:【SCI-EXPANDED(收录号:WOS:000928279800024)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), the German Research Foundation (DFG) under Grant 392194080, National Natural Science Fund for Distinguished Young Scholars (61725301), National Natural Science Foundation of China (62173147) and Shanghai AI Lab. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Vijay Gupta under the direction of Editor Christos G. Cassandras.

语种：英文

外文关键词：Networked control; Cyber attacks; Kalman filter; Dynamic programming; Stochastic game

摘要：We consider the joint design of control and scheduling under stochastic Denial-of-Service (DoS) attacks in the context of networked control systems. A sensor takes measurements of the system output and forwards its dynamic state estimates to a remote controller over a packet-dropping link. The controller determines the optimal control law for the process using the estimates it receives. An attacker aims at degrading the control performance by increasing the packet-dropout rate with a DoS attack on the sensor-controller channel. We assume both the controller and the attacker are rational in a game-theoretic sense and establish a partially observable stochastic game to derive the optimal joint design of scheduling and control. Using dynamic programming we prove that the control and scheduling policies can be designed separately without sacrificing optimality, making the problem equivalent to a complete information game. We employ Nash Q-learning to solve the problem and prove that the solution is guaranteed to constitute an epsilon-Nash equilibrium. Numerical examples are provided to illustrate the interactions between the controller and the attacker. (c) 2022 Elsevier Ltd. All rights reserved.