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

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. (C) 2020 European Control Association. Published by Elsevier Ltd. All rights reserved.