Restricted accessResearch articleFirst published online 2024-8
Event-triggered communication for H ∞ synchronization and synchronization of multi-weighted coupled neural networks with and without uncertain parameters
In this paper, the problems of H∞ synchronization and synchronization of multi-weighted coupled neural networks (MWCNNs) with event-triggered communication are studied. By using Lyapunov stability theory and some inequality techniques, synchronization and H∞ synchronization criteria of MWCNNs with event-triggered communication are established. Moreover, the parameter uncertainty in the system dynamics is considered. Thus, robust synchronization and robust H∞ synchronization strategies are also established for MWCNNs with uncertain parameters under the event-triggered communication. Finally, two numerical examples are given to verify the effectiveness of the obtained conditions.
Al AelaAMKenneJ-PMintsaHA (2022) Adaptive neural network and nonlinear electrohydraulic active suspension control system. Journal of Vibration and Control28(3-4): 243–259.
2.
AnXZhangLLiY, et al. (2014) Synchronization analysis of complex networks with multi-weights and its application in public traffic network. Physica A: Statistical Mechanics and Its Applications412(10): 149–156.
3.
BevelevichV (1968) Classical Network Synthesis. New York: Van Nostrand.
4.
CaoJChenGLiP (2008) Global synchronization in an array of delayed neural networks with hybrid coupling. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)38(2): 488–498.
5.
ChenWZZhangYZhengYL (2020) Dissipativity of Markovian multipleweighted coupled neural networks with dynamic event-triggered pinning control. IET Control Theory & Applications14(15): 2030–2037.
6.
DongTWangAZhuH, et al. (2018) Event-triggered synchronization for reaction-diffusion complex networks via random sampling. Physica A: Statistical Mechanics and Its Applications495: 454–462.
7.
HouJHuangYRenS (2019a) Anti-synchronization analysis and pinning control of multi-weighted coupled neural networks with and without reaction-diffusion terms. Neurocomputing330: 78–93.
8.
HouJHuangYYangE (2019b) Finite-time anti-synchronization of multi-weighted coupled neural networks with and without coupling delays. Neural Processing Letters50(3): 2871–2898.
9.
HuangYWangS (2018) Synchronisation in an array of spatial diffusion coupled reaction-diffusion neural networks via pinning control. International Journal of Systems Science49(5): 1103–1118.
10.
HuangCWangWCaoJ, et al. (2018) Synchronization-based passivity of partially coupled neural networks with event-triggered communication. Neurocomputing319: 134–143.
11.
HuangYLQiuSHRenSY (2019) Finite-time synchronization and passivity of coupled memristive neural networks. International Journal of Control93(12): 2824–2837.
12.
HubermanABAdamicAL (1999) Internet: growth dynamics of the world wide web. Nature401(16): 23–25.
13.
LiHCaoJKashkynbayevA, et al. (2022) Adaptive dynamic event-triggered cluster synchronization in an array of coupled neural networks subject to cyber-attacks. Neurocomputing511: 380–398.
14.
LinSHuangYLRenSY (2020) Event-triggered passivity and synchronization of delayed multiple-weighted coupled reaction-diffusion neural networks with non-identical nodes. Neural Networks121: 259–275.
15.
LinSLiuXHuangY (2022a) Event-triggered H∞ synchronization of directed and switched coupled stochastic delayed neural networks with multi-weights. IET Control Theory & Applications16(10): 995–1014.
16.
LinSLiuXHuangY (2022b) Event-triggered passivity and synchronization of coupled reaction–diffusion neural networks with and without time-varying delay. Transactions of the Institute of Measurement and Control44(11): 2117–2140.
17.
LuSWangX (2020) Barrier Lyapunov function–based adaptive neural network control for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints via command filtering. Journal of Vibration and Control27(21-22): 2574–2585.
18.
LuoYGaoXCaoJ, et al. (2020) Event-triggered consensus for delayed second-order leader-following heterogeneous multi-agent systems under input saturated condition. Journal of Vibration and Control. doi: 10.1177/10775463221126313, In press.
19.
LvSHeWQianF, et al. (2018) Leaderless synchronization of coupled neural networks with the event-triggered mechanism. Neural Networks105: 316–327.
20.
MihankhahADoustmohammadiA (2022) Event-triggered adaptive fault-tolerant attitude synchronization and tracking control of multiple rigid bodies with finite-time convergence. Journal of Vibration and Control28(9-10): 1095–1108.
21.
StrogatzSHStewartI (1993) Coupled oscillators and biological synchronization. Scientific American269(6): 102–109.
22.
WangJLZhaoLH (2021) PD and PI control for passivity and synchronization of coupled neural networks with multi-weights. IEEE Transactions on Network Science and Engineering8(1): 790–802.
23.
WangADongTLiaoX (2016) Event-triggered synchronization strategy for complex dynamical networks with the markovian switching topologies. Neural Networks74: 52–57.
24.
WangJLWuHNHuangT, et al. (2018a) Output synchronization in coupled neural networks with and without external disturbances. IEEE Transactions on Control of Network Systems5(4): 2049–2061.
25.
WangQWangJLRenSY, et al. (2018b) Analysis and adaptive control for lag H∞ synchronization of coupled reaction-diffusion neural networks. Neurocomputing319: 144–154.
26.
WangJLXuMWuHN, et al. (2018c) Finite-time passivity of coupled neural networks with multiple weights. IEEE Transactions on Network Science and Engineering5(3): 184–197.
27.
WangJLQinZWuHN, et al. (2020a) Finite-time synchronization and H∞ synchronization of multiweighted complex networks with adaptive state couplings. IEEE Transactions on Cybernetics50(2): 600–612.
28.
WangYHuangYYangE (2020b) Event-triggered communication for passivity and synchronisation of multi-weighted coupled neural networks with and without parameter uncertainties. IET Control Theory & Applications14(9): 1228–1239.
29.
WangJLWangQWuHN, et al. (2021) Finite-time output synchronization and H∞ output synchronization of coupled neural networks with multiple output couplings. IEEE Transactions on Cybernetics51(12): 6041–6053.
30.
WuWChenT (2008) Global synchronization criteria of linearly coupled neural network systems with time-varying coupling. IEEE Transactions on Neural Networks19(2): 319–332.
31.
ZhangKZhouBZhengW, et al. (2022) Finite-time stabilization of linear systems by bounded event-triggered and self-triggered control. Information Sciences597: 166–181.
32.
ZhaoLHWenSZhuSGuoZHuangT (2022) Robust H∞ pinning synchronization for multiweighted coupled reaction–diffusion neural networks. IEEE Transactions on Cybernetics. doi: 10.1109/TCYB.2022.3223713, In press.
