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

This paper is devoted to dealing with the dynamic event-triggered $H_{\mathrm{\infty }}$ 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 $H_{\mathrm{\infty }}$ 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.