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

Zongying Feng; Guoqiang Tan; Zongying Feng; Guoqiang Tan

doi:10.3934/era.2025056

Electronic Research Archive

2025, Volume 33, Issue 3: 1267-1284. doi: 10.3934/era.2025056

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Research article

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

Zongying Feng ¹,
Guoqiang Tan ^{2
,
,}

1.
School of Engineering, Qufu Normal University, Rizhao 276826, China
2.
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, U.K

Received: 09 January 2025 Revised: 09 February 2025 Accepted: 14 February 2025 Published: 04 March 2025

This paper is devoted to dealing with the dynamic event-triggered $ H_{\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_{\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.
- event-triggered scheme,
- neural networks,
- sensor saturation,
- quantization,
- cyber-attacks
Citation: Zongying Feng, Guoqiang Tan. Dynamic event-triggered $ H_{\infty} $ control for neural networks with sensor saturations and stochastic deception attacks[J]. Electronic Research Archive, 2025, 33(3): 1267-1284. doi: 10.3934/era.2025056

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Abstract

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

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