Event-triggered attack-tolerant tracking control design for networked nonlinear control systems under DoS jamming attacks  

作　　者：Xiaoli CHEN Youguo WANG 

机构地区：[1]School of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China

出　　处：《Science China(Information Sciences)》2020年第5期83-100,共18页中国科学（信息科学）（英文版）

基　　金：National Natural Science Foundation of China(Grant No.61771256)。

摘　　要：This paper addresses the problem of event-triggered attack-tolerant tracking control for a networked nonlinear system under spasmodic denial-of-service(DoS) attacks. Compared with some existing results, the exact duration of DoS attacks is not required while only assuming attainable bounds of attack frequency and duration. First, a new event-triggered attack-tolerant fuzzy tracking controller is proposed,to reduce the amount of sensor data transmissions over the sensor-to-controller(S-C) channel while counteracting unknown DoS attacks. Second, for the purpose of performance analysis and synthesis, a unified event-triggered Takagi-Sugeno(T-S) fuzzy switched model is established, which accounts for a suitable attackresilient event-triggered communication scheme and unknown DoS jamming signals. Third, using piecewise Lyapunov-Krasovskii functional(PLKF) analysis technique, a new criterion is derived to ensure exponential stability of the resulting switched tracking error system while achieving a weighted H∞performance level.Additionally, the relationship among the parameters of a DoS attack signal, the triggering parameters, the fuzzy controller gains, the sampling period, and the decay rate can be quantitatively characterized. Moreover, the triggering matrix parameter and fuzzy controller gains are obtained by finding a feasible solution to a set of linear matrix inequalities(LMIs). Finally, numerical verification is performed to demonstrate the effectiveness of the proposed control design method.

关 键 词：jamming ATTACKS event-triggered control PIECEWISE LYAPUNOV-KRASOVSKII functional T-S fuzzy system 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP273[自动化与计算机技术—计算机科学与技术]