Finite-time decentralized event-triggering non-fragile control for fuzzy neural networks with cyber-attack and energy constraints

Abstract

In this study, we concerned with non-fragile control problem for T-S fuzzy neural networks (TSFNNs) within finite-time domain under decentralized event-triggered scheme, limited network-bandwidth and cyber-attack. Precisely, the event-triggered mechanism and energy constraints are introduced to mitigate the network traffic and to protect the network resources. To be specific, an event-triggered mechanism relieves the network transmission burden and the sensors which decide the measurement transmissions in accordance with event-triggered scheme. The main intention of this work is to design a decentralized event-triggered scheme and non-fragile controller for ensuring the stochastic finite-time boundedness for the desired TSFNNs with optimal mixed H∞ and passivity performance index within the prescribed time interval. In accordance with Lyapunov–Krasovskii stability theory, an adequate condition in the frame of linear matrix inequalities is established to signify the stochastic stability of the resulting closed-loop TSFNNs. Moreover, the projected gain matrix is characterized by the obtained linear matrix inequalities. At long last, a numerical example is framed to substantiate the effectiveness and superiority of the proposed control strategy.