Quantified Event-Triggered Control for Finite-Time Projective Synchronization of Fractional Fuzzy Inertial Delayed Neural Networks With Uncertainties
Wei Wei Zhang, Zhen-Jie Wang, Hai Zhang, Dingyuan Chen, Jinde Cao, Mahmoud Abdel‐Aty, Zhao-Dong Xu · IEEE Transactions on Automation Science and Engineering · 2025
This paper focuses on the finite-time projective synchronization (FTPS) of fractional fuzzy inertial delayed neural networks (FFIDNNs) with parameters perturbation via several quantified event-triggered mechanisms (QETMs). First, applying Laplace transform, a linear fractional finite-time inequality is established for the infinite time interval rather than a finite time interval. Next, three kinds of QETMs are designed, which can respond flexibly to practical challenges. By the established inequalities and the designed controller, some novel criteria are derived to guarantee FTPS for the considered system. Moreover, the settling time is estimated and the event interval has a positive lower bound to avoid the Zeno behavior. Finally, the accuracy of theoretical findings is confirmed via numerical examples.