Dynamic event-triggered $H_{\infty}$ control for neural networks with sensor saturations and stochastic deception attacks

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Abstract

This paper is devoted to dealing with the dynamic event-triggered H_∞ quantized control for neural networks with sensor saturations and stochastic deception attacks. To save the limited network resources, a dynamic event-triggered scheme is offered, which includes the general one. And a lower trigger frequency can be obtained by appropriately adjusting the triggering error. Then, a new closed- loop quantized control model is established under a dynamic event-triggered scheme, sensor saturations, and stochastic deception attacks, which is described by two independent Bernoulli-distributed variables. Moreover, by Lyapunov-Krasovskii functional theory, a new H_∞ performance criterion is given, and based on the criterion, the controller design approach is derived. Finally, simulations are listed to verify the validity of derived methods.

Keywords

event-triggered scheme / neural networks / sensor saturation / quantization / cyber-attacks

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