This work explores the sliding mode tracking control problem of discrete-time T-S fuzzy systems (TSFSs) with DoS attacks under adaptive event-triggered weighted try-once discard (WTOD) protocol. A privacy protection function is introduced to safeguard the security of the reference model. Based on this upon, an augmented system is constructed. An adaptive event triggered WTOD protocol for the augmented systems is proposed, which can not only adaptively adjust threshold parameters, but also dynamically adjust sensor transmission nodes. A sliding mode control (SMC) law depending on transmission nodes and mismatched membership functions are designed to reduce the conservatism of the result. Relaxed sufficient conditions to ensure the stochastic stability of system and the reachability of sliding mode surface (SMS) are derived via matrix inequalities. Finally, two examples are provided to illustrate practicality of the suggested approach.
SuXXiaFLiuJ, et al. Event-triggered fuzzy control of nonlinear systems with its application to inverted pendulum systems. Automatica2018; 94: 236–248.
2.
WenYChangHSuX, et al. Event-triggered fuzzy control of repeated scalar nonlinear systems and its application to Chua’s circuit system. IEEE Trans Circuits Syst I Regul Pap2020; 67(12): 5347–5357.
3.
GuYShenMParkJH, et al. Dynamic guaranteed cost event-triggered-based anti-disturbance control of T–S fuzzy wind-turbine systems subject to external disturbances. IEEE Trans Fuzzy Syst2024; 32: 7063–7072.
4.
ZhangYShiZ.Sliding mode control for uncertain T–S fuzzy singular biological economic system. IEEE Access2019; 7: 14387–14395.
5.
WangWHuangCLamHK, et al. Formation control of T–S fuzzy systems with event-triggered sampling scheme via membership function dependent approach. Inf Sci2023; 622: 842–858.
6.
NguyenKHKimSH.Improved sampled-data control design of T–S fuzzy systems against mismatched fuzzy-basis functions. Appl Math Comput2022; 428: 127150.
7.
ZareIAsemaniMHSetoodehP. Active adaptive observer-based fault-tolerant control strategy for a class of T–S fuzzy systems with unmeasurable premise variables. IEEE Trans Fuzzy Syst2023; 31(10): 3543–3554.
8.
TangXDengL.Multi-step output feedback predictive control for uncertain discrete-time T–S fuzzy system via event-triggered scheme. Automatica2019; 107: 362–370.
9.
CaiXShiKSunY, et al. Intelligent event-triggered control supervised by mini-batch machine learning and data compression mechanism for T–S fuzzy NCS’s under DoS attacks. IEEE Trans Fuzzy Syst2024; 32(3): 804–815.
10.
ZhangZNiuYLamHK.Sliding-mode control of T–S fuzzy systems under weighted try-once-discard protocol. IEEE Trans Cybern2020; 50(12): 4972–4982.
11.
LiuMZhangLShiP, et al. Sliding mode control of continuous-time Markovian jump systems with digital data transmission. Automatica2017; 80: 200–209.
12.
KuppusamySJooYH.Memory-based integral sliding-mode control for T–S fuzzy systems with PMSM via disturbance observer. IEEE Trans Cybern2021; 51(5): 2457–2465.
13.
LiHWangJShiP.Output-feedback based sliding mode control for fuzzy systems with actuator saturation. IEEE Trans Fuzzy Syst2016; 24(6): 1282–1293.
14.
ZhengYZhengJShaoK, et al. Adaptive fuzzy sliding mode control of uncertain nonholonomic wheeled mobile robot with external disturbance and actuator saturation. Inf Sci2024; 663: 120303.
15.
QiuJJiWChadliM.A novel fuzzy output feedback dynamic sliding mode controller design for two-dimensional nonlinear systems. IEEE Trans Fuzzy Syst2021; 29(10): 2869–2877.
16.
LiBZhaoX.Neural network-based adaptive sliding mode control for T–S fuzzy fractional order systems. IEEE Trans Circuits Syst II Express Briefs2023; 70(12): 4549–4553.
17.
YeZZhangDDengC, et al. Finite-time resilient sliding mode control of nonlinear UMV systems subject to DoS attacks. Automatica2023; 156: 111170.
18.
CaiXShiKSheK, et al. Performance degradation estimation mechanisms for networked control systems under DoS attacks and its application to autonomous ground vehicle. IEEE Trans Cybern2024; 54(5): 2992–3002.
19.
DingSXZhangPYinS, et al. An integrated design framework of fault-tolerant wireless networked control systems for industrial automatic control applications. IEEE Trans Ind Inform2013; 9(1): 462–471.
20.
LiTTangXGeJ, et al. Event-based fault-tolerant control for networked control systems applied to aircraft engine system. Inf Sci2020; 512: 1063–1077.
21.
WangTZongGZhaoX, et al. Data-driven-based sliding-mode dynamic event-triggered control of unknown nonlinear systems via reinforcement learning. Neurocomputing2024; 601: 128176.
22.
ZhangZNiuYKarimiHR.Sliding mode control of interval type-2 fuzzy systems under round-robin scheduling protocol. IEEE Trans Syst Man Cybern Syst2021; 51(12): 7602–7612.
23.
YangHYanHZhouJ, et al. Neural-network-based set-membership filtering under WTOD protocols via a novel event-triggered compensation mechanism. IEEE Trans Syst Man Cybern Syst2024; 54(5): 2954–2964.
24.
YangYNiuYLamJ.Interval type-2 fuzzy sliding mode resilient control via probability-dependent adaptive event-triggered scheme. IEEE Trans Syst Man Cybern Syst2023; 53(6): 3474–3484.
25.
YangYNiuYLamHK.Sliding-mode control for interval type-2 fuzzy systems: event-triggering WTOD scheme. IEEE Trans Cybern2023; 53(6): 3771–3781.
26.
YaoLHuangXWangZ, et al. Memory-based event-triggered control of Markov jump systems under hybrid cyber-attacks: a switching-like adaptive law. IEEE Trans Autom Sci Eng2024; 21(4): 6347–6357.
27.
ShenHLiuXWangJ, et al. Non-fragile output feedback control for PDT-switched fuzzy systems under weighted try-once-discard protocol and its application. Inf Sci2023; 626: 390–406.
28.
VashistAKeatsAPudukotai DinakarraoSM, et al. Securing a wireless network-on-chip against jamming-based denial-of-service and eavesdropping attacks. IEEE Trans Very Large Scale Integr (VLSI) Syst2019; 27(12): 2781–2791.
29.
GuoXGWangBQWangJL, et al. Edge-event-triggered encryption-decryption observer-based control of multiagent systems for privacy protection under multiple cyber-attacks. Inf Sci2023; 642: 119128.
30.
DuanZWeiAZhangX, et al. Privacy-preserving secure control for nonlinear multi-agent systems under hybrid attacks. Commun Nonlinear Sci Numer Simul2025; 140: 108356.
31.
LiMChangX.Fuzzy tracking control for discrete-time nonlinear network systems with privacy protection and dynamic quantization. Int J Fuzzy Syst2023; 25: 1227–1238.
32.
LiuJGongEZhaL, et al. Interval type-2 fuzzy-model-based filtering for nonlinear systems with event-triggering weighted try-once-discard protocol and cyberattacks. IEEE Trans Fuzzy Syst2024; 32(3): 721–732.
33.
SunQRenJZhaoF.Sliding mode control of discrete-time interval type-2 fuzzy Markov jump systems with the preview target signal. Appl Math Comput2022; 435: 127479.
34.
NasiriAKiong NguangSSwainA, et al. Robust output feedback controller design of discrete-time Takagi–Sugeno fuzzy systems: a non-monotonic Lyapunov approach. IET Control Theory Appl2016; 10(5): 545–553.
35.
LuJJXiongJ.Energy-to-peak quantized filtering for T–S fuzzy systems with event-triggered-based weighted try-once-discard protocol: the finite-time case. Appl Math Comput2024; 483: 128975.
36.
LiuJWangSHuB, et al. Non-fragile observer-based security control for networked systems via event-triggered WTOD protocol. Int J Robust Nonlinear Control2024; 34(9): 5852–5872.
37.
QiWYangXParkJH, et al. Fuzzy SMC for quantized nonlinear stochastic switching systems with semi-Markovian process and application. IEEE Trans Cybern2022; 52(9): 9316–9325.
38.
ShenHMenYWuZG, et al. Network-based quantized control for fuzzy singularly perturbed semi-Markov jump systems and its application. IEEE Trans Circuits Syst I Regul Pap2019; 66(3): 1130–1140.
