Event‐Triggered Model‐Free Adaptive Sliding Mode Control of Networked Control Systems Under IDoS Attacks

ABSTRACT An event‐triggered model‐free adaptive sliding mode control (MFASMC) method is investigated in this article for networked control systems under intermittent denial‐of‐service (IDoS) attacks. To address unmodeled dynamics and bandwidth limitations, MFASMC is integrated with an event‐triggered mechanism and formulated within a data‐driven framework. By combining optimization theory with sliding mode reaching laws, an event‐triggered MFASMC scheme is developed, enabling adaptive responses to system states and external disturbances. Pseudo partial derivatives are employed to transform the nonlinear system into an equivalent data‐driven model. IDoS attacks are modeled as processes constrained by both frequency and duration, without specific assumptions regarding their structure or strategy. Through theoretical analysis, stability conditions for closed‐loop operation are derived. The effectiveness of the proposed method is validated by simulation results, which show significant improvements in system performance under complex network conditions.

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Publication Details

Journal
Optimal Control Applications and Methods
Published
2026-11-11
DOI
https://doi.org/10.1002/oca.70152
Primary Topic
Stability and Control of Uncertain Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Event‐Triggered Model‐Free Adaptive Sliding Mode Control of Networked Control Systems Under IDoS Attacks

Bei Chen, Tingting Liu
Optimal Control Applications and Methods
Stability and Control of Uncertain Systems
article

Event‐Triggered Model‐Free Adaptive Sliding Mode Control of Networked Control Systems Under IDoS Attacks

Bei Chen, Tingting Liu
article en

Abstract

ABSTRACT An event‐triggered model‐free adaptive sliding mode control (MFASMC) method is investigated in this article for networked control systems under intermittent denial‐of‐service (IDoS) attacks. To address unmodeled dynamics and bandwidth limitations, MFASMC is integrated with an event‐triggered mechanism and formulated within a data‐driven framework. By combining optimization theory with sliding mode reaching laws, an event‐triggered MFASMC scheme is developed, enabling adaptive responses to system states and external disturbances. Pseudo partial derivatives are employed to transform the nonlinear system into an equivalent data‐driven model. IDoS attacks are modeled as processes constrained by both frequency and duration, without specific assumptions regarding their structure or strategy. Through theoretical analysis, stability conditions for closed‐loop operation are derived. The effectiveness of the proposed method is validated by simulation results, which show significant improvements in system performance under complex network conditions.

Optimal Control Applications and Methods
Shanghai University of Engineering Science (CN)
Stability and Control of Uncertain Systems
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