Disturbance rejection for multi-weighted complex dynamical networks with actuator saturation and deception attacks via hybrid-triggered mechanism. (May 2023)

• Record Type: Journal Article
• Authors: Sakthivel, R.; Kwon, O.M.; Park, M.J.; Lee, S.M.; Sakthivel, R.
• Is Part Of: Neural networks. Volume 162(2023)
• Page Start: 225
• Page End: 239
• Publication Date: 2023-05
• Subjects: Complex dynamical networks -- Equivalent-input-disturbance -- Multi-weights -- Actuator saturation -- Deception attacks; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2023.02.031
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this work, we address hybrid-driven-based robust synchronization problem for multi-weighted complex dynamical networks with actuator saturation and deception attacks. The hybrid-triggered mechanism, which combines a switch between the event-triggered scheme and the time-triggered scheme, is often used to reduce the data transmission and the alleviate network burden. Further, the equivalent-input-disturbance technique is applied to eliminate the unknown disturbance effect of the addressed system. Moreover, a memory controller is designed under actuator saturation to ensure that the resultant augmented system is asymptotically synchronized even in the presence of deception attacks. Finally, three numerical examples are given to show the validity of the obtained theoretical results.