Similar articles

Abstract: This paper focuses on the design of event-triggered controllers for the synchronization of delayed takagi–;sugeno (T–;S) fuzzy neural networks (NNs) under deception attacks. The traditional event-triggered mechanism (ETM) determines the next trigger based on the current sample, resulting in network congestion. Furthermore, such methods suffer from the issues of deception attacks and unmeasurable system states. To enhance the system stability, we adaptively detect the occurrence of events over a period of time. In addition, deception attacks are recharacterized to describe general scenarios. Specifically, the following enhancements are implemented: First, we use a Bernoulli process to model the occurrence of deception attacks, which can describe a variety of attack scenarios as a type of general Markov process. Second, we introduce a sum-based dynamic discrete event-triggered mechanism (SDDETM), which uses a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events. Finally, we incorporate a dynamic output feedback controller (DOFC) to ensure the system stability. The concurrent design of the DOFC and SDDETM parameters is achieved through the application of the cone complement linearization (CCL) algorithm. We further perform two simulation examples to validate the effectiveness of the algorithm.

基于和值的动态离散事件触发机制在遭受欺骗攻击的时滞神经网络同步中的应用

[1]Bao YG, Zhao D, Sun JY, et al., 2024. Resilient synchronization of neural networks under DoS attacks and communication delays via event-triggered impulsive control. IEEE Trans Syst Man Cybern Syst, 54(1):471-483.

[2]Boyd S, El Ghaoui L, Feron E, et al., 1994. Linear Matrix Inequalities in System and Control Theory.

[3]Chen J, Zhang XM, Park JH, et al., 2022. Improved stability criteria for delayed neural networks using a quadratic function negative-definiteness approach. IEEE Trans Neur Netw Learn Syst, 33(3):1348-1354.

[4]Donkers MCF, Heemels WPMH, 2012. Output-based event-triggered control with guaranteed L∞-gain and improved and decentralized event-triggering. IEEE Trans Autom Contr, 57(6):1362-1376.

[5]El Ghaoui L, Oustry F, AitRami M, 1997. A cone complementarity linearization algorithm for static output-feedback and related problems. IEEE Trans Autom Contr, 42(8):1171-1176.

[6]Guo G, Ding L, Han QL, 2014. A distributed event-triggered transmission strategy for sampled-data consensus of multi-agent systems. Automatica, 50(5):1489-1496.

[7]Heemels WPMH, Donkers MCF, Teel AR, 2013. Periodic event-triggered control for linear systems. IEEE Trans Autom Contr, 58(4):847-861.

[8]Kazemy A, Lam J, Zhang XM, 2022. Event-triggered output feedback synchronization of master–slave neural networks under deception attacks. IEEE Trans Neur Netw Learn Syst, 33(3):952-961.

[9]Lei Y, Hua T, Wang YW, et al., 2024. Robust output regulation of singularly perturbed systems by event-triggered output feedback. IEEE Trans Syst Man Cybern Syst, 54(4):2104-2113.

[10]Liang D, Huang J, 2021. Robust output regulation of linear systems by event-triggered dynamic output feedback control. IEEE Trans Autom Contr, 66(5):2415-2422.

[11]Liu JL, Wei LL, Xie XP, et al., 2018. Quantized stabilization for T–S fuzzy systems with hybrid-triggered mechanism and stochastic cyber-attacks. IEEE Trans Fuzzy Syst, 26(6):3820-3834.

[12]Liu JL, Yin TT, Xie XP, et al., 2019a. Event-triggered state estimation for T–S fuzzy neural networks with stochastic cyber-attacks. Int J Fuzzy Syst, 21(2):532-544.

[13]Liu JL, Gu YY, Xie XP, et al., 2019b. Hybrid-driven-based H_∞control for networked cascade control systems with actuator saturations and stochastic cyber attacks. IEEE Trans Syst Man Cybern Syst, 49(12):2452-2463.

[14]Liu YJ, Fang Z, Park JH, et al., 2023. Quantized event-triggered synchronization of discrete-time chaotic neural networks with stochastic deception attack. IEEE Trans Syst Man Cybern Syst, 53(7):4511-4521.

[15]Liu ZQ, Lou XY, Jia JJ, 2022. Event-triggered dynamic output-feedback control for a class of Lipschitz nonlinear systems. Front Inform Technol Electron Eng, 23(11):1684-1699.

[16]Ma YJ, Wang Y, Li ZJ, et al., 2024. Event-triggered finite-time command-filtered tracking control for nonlinear time-delay cyber physical systems against cyber attacks. Front Inform Technol Electron Eng, 25(2):225-236.

[17]Shen H, Liu YA, Wang J, et al., 2023. Sliding-mode control for IT2 fuzzy nonlinear singularly perturbed systems and its application to electric circuits: a dynamic event-triggered mechanism. IEEE Trans Syst Man Cybern Syst, 53(7):4077-4090.

[18]Song L, Nguang SK, Huang D, 2019. Hierarchical stability conditions for a class of generalized neural networks with multiple discrete and distributed delays. IEEE Trans Neur Netw Learn Syst, 30(2):636-642.

[19]Tan YS, Liu Y, Niu B, et al., 2020. Event-triggered synchronization control for T–S fuzzy neural networked systems with time delay. J Franklin Inst, 357(10):5934-5953.

[20]Wang R, Li YH, Sun H, et al., 2021. Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network. Front Inform Technol Electron Eng, 22(1):51-67.

[21]Wen GH, Chen MZQ, Yu XH, 2016. Event-triggered master–slave synchronization with sampled-data communication. IEEE Trans Circ Syst II Expr Briefs, 63(3):304-308.

[22]Wen GH, Wan Y, Cao JD, et al., 2018. Master–slave synchronization of heterogeneous systems under scheduling communication. IEEE Trans Syst Man Cybern Syst, 48(3):473-484.

[23]Yan S, Shen MQ, Nguang SK, et al., 2019. A distributed delay method for event-triggered control of T–S fuzzy networked systems with transmission delay. IEEE Trans Fuzzy Syst, 27(10):1963-1973.

[24]Yue D, Tian EG, Han QL, 2013. A delay system method for designing event-triggered controllers of networked control systems. IEEE Trans Autom Contr, 58(2):475-481.

[25]Zadeh LA, 1968. Fuzzy algorithms. Inform Contr, 12(2):94-102.

[26]Zhang D, Ouyang D, Shu L, et al., 2023. Sum-based event- triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks. Neur Comput Appl, 35(14):10221-10237.

[27]Zhang LR, Nguang SK, Ouyang DQ, et al., 2020. Synchronization of delayed neural networks via integral-based event-triggered scheme. IEEE Trans Neur Netw Learn Syst, 31(12):5092-5102.

[28]Zhang LR, Nguang SK, Yan S, 2021. Event-triggered H_∞control for networked control systems under denial-of-service attacks. Trans Inst Meas Contr, 43(5):1077-1087.

[29]Zhang XM, Han QL, 2014. Event-triggered dynamic output feedback control for networked control systems. IET Contr Theory Appl, 8(4):226-234.

[30]Zhang XM, Han QL, Wang J, 2018. Admissible delay upper bounds for global asymptotic stability of neural networks with time-varying delays. IEEE Trans Neur Netw Learn Syst, 29(11):5319-5329.