Event-triggering adaptive neural network output feedback control for networked systems under false data injection attacks

Dongke Zhao, Michael Shi, Huiyan Zhang, Yongchao Liu and Ning Zhao

Chaos, Solitons & Fractals, 2024, vol. 180, issue C

Abstract: This paper investigates the event-triggering adaptive neural network (NN) output feedback control problem for networked control systems subject to false data injection (FDI) attacks. To reduce unnecessary data transmission, an event-triggered mechanism (ETM) related to the measured output is designed on the channel of the sensor-to-observer. Meanwhile, a novel ETM associated with the observer state and parameter estimation signal is devised on the channel of the observer-to-controller to save communication resources on the controller-to-actuator channel. Subsequently, NN technique is employed to approximate FDI attacks, then an adaptive neural output feedback controller is designed to counteract the impact of FDI attacks on the system. Sufficient conditions for the semi-globally uniformly ultimately bounded of the system are obtained by means of the Lyapunov function. Additionally, a co-design scheme for the control gain, observer gain and event-triggered parameters is presented. Finally, a practical example is given to demonstrate the effectiveness of the proposed method.

Keywords: Networked control systems; Neural network; Event-triggered mechanism; False data injection attacks (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:180:y:2024:i:c:s0960077924001358

DOI: 10.1016/j.chaos.2024.114584

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