Adaptive event-triggered sliding mode secure control for switched systems under PDT switching rules

This paper investigates the problem of adaptive event-triggered sliding mode control (AET-SMC) for uncertain switched systems constrained by persistent dwell time (PDT) switching rules, in which the C/A channel may be subject to random denial-of-service (DoS) attacks. A Markov chain-based model is employed to accurately characterize the time-dependent nature of DoS attack behavior. An adaptive event-triggering mechanism is designed to dynamically adjust the triggering threshold according to the system state variations, thereby effectively reducing the communication burden while preserving system stability. A sliding mode controller is formulated by incorporating an adaptive event-triggered mechanism and a Markov-based DoS attack model, which ensures robustness against system uncertainties and external disturbances. The sliding surface is designed as a function of the system state and the controller gain, enabling effective tracking and disturbance rejection even under random network attacks. Sufficient conditions are derived to guarantee the mean-square exponential stability of the closed-loop switched system and the reachability of the specified sliding surface by constructing appropriate Lyapunov functions and employing Linear Matrix Inequality (LMI) techniques. Numerical simulations and practical DC motor control case studies demonstrate that the proposed method effectively safeguards robust stability and maintains superior dynamic performance under random network attacks and switching disturbances.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering
Published
2026-05-18
DOI
https://doi.org/10.1177/09596518261449561
Primary Topic
Stability and Control of Uncertain Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

Adaptive event-triggered sliding mode secure control for switched systems under PDT switching rules

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering
Stability and Control of Uncertain Systems
article

Adaptive event-triggered sliding mode secure control for switched systems under PDT switching rules

article en

Abstract

This paper investigates the problem of adaptive event-triggered sliding mode control (AET-SMC) for uncertain switched systems constrained by persistent dwell time (PDT) switching rules, in which the C/A channel may be subject to random denial-of-service (DoS) attacks. A Markov chain-based model is employed to accurately characterize the time-dependent nature of DoS attack behavior. An adaptive event-triggering mechanism is designed to dynamically adjust the triggering threshold according to the system state variations, thereby effectively reducing the communication burden while preserving system stability. A sliding mode controller is formulated by incorporating an adaptive event-triggered mechanism and a Markov-based DoS attack model, which ensures robustness against system uncertainties and external disturbances. The sliding surface is designed as a function of the system state and the controller gain, enabling effective tracking and disturbance rejection even under random network attacks. Sufficient conditions are derived to guarantee the mean-square exponential stability of the closed-loop switched system and the reachability of the specified sliding surface by constructing appropriate Lyapunov functions and employing Linear Matrix Inequality (LMI) techniques. Numerical simulations and practical DC motor control case studies demonstrate that the proposed method effectively safeguards robust stability and maintains superior dynamic performance under random network attacks and switching disturbances.

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering
Qingdao University of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province
Openalex Percentile: Top 6%
Stability and Control of Uncertain Systems
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