Fault-tolerant and attack-tolerant cooperative event-triggered sampled-data security control for synchronization of RDNNs with stochastic actuator failures and random deception attacks

• Published in: Neurocomputing (Amsterdam) Vol. 636; p. 130021
• Main Authors: Zhao, Feng-Liang, Wang, Zi-Peng, Qiao, Junfei, Wu, Huai-Ning, Huang, Tingwen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2025
• Subjects: Event-triggered sampled-data security control; Fault-tolerant and attack-tolerant cooperative control; Space-varying reaction–diffusion neural networks; Spatial linear matrix inequalities; Stochastic actuator failures; Spatial linear matrix inequalities; Stochastic actuator failures; Fault-tolerant and attack-tolerant cooperative control; Space-varying reaction–diffusion neural networks; Event-triggered sampled-data security control
• ISSN: 0925-2312
• DOI: 10.1016/j.neucom.2025.130021

In this article, the fault-tolerant and attack-tolerant cooperative event-triggered sampled-data security (FACETSDS) synchronization problem of space-varying reaction–diffusion neural networks (SVRDNNs) under spatially point measurements (SPMs) with stochastic actuator failures and random deception attacks is investigated. First, to save more communication resources and adapt to the variation of system dynamics subject to stochastic actuator failures and random deception attacks, a FACETSDS control scheme is proposed under SPMs. Second, by constructing a Lyapunov functional and utilizing inequality techniques, some synchronization criteria based on spatial linear matrix inequalities (SLMIs) are derived for SVRDNNs. Then, to solve SLMIs, the FETSDS control for synchronization problem of SVRDNNs under SPMs with stochastic actuator failures and random deception attacks is formulated as an linear matrix inequality feasibility problem. Lastly, the designed FACETSDS synchronization strategy is verified by one numerical example.