Event-Triggered Control of Saturated Switched System Based on Composite Nonlinear Feedback

In this paper, the event-triggered control problem of switched saturated systems with composite nonlinear feedback (CNF) control is investigated. We combine event-triggered control with CNF control and obtain the sufficient conditions for the asymptotic tracking problem of saturated switched systems under the dwell time scheme. The introduction of CNF control can improve the system response speed. For the proposed event-triggered scheme, the control task can only be executed when the trigger conditions are met, which further reduces the cost of system resources to some extent. Zeno behavior is eliminated by proving that the minimum time interval is greater than zero. Finally, the correctness of the results is verified by simulation.

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Publication Details

Published
2025-03-21
DOI
https://doi.org/10.1109/icaace65325.2025.11019126
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Event-Triggered Control of Saturated Switched System Based on Composite Nonlinear Feedback

Fuyou Mao, Bing Wu, Hong Zhu
Neural Networks Stability and Synchronization
article

Event-Triggered Control of Saturated Switched System Based on Composite Nonlinear Feedback

Fuyou Mao, Bing Wu, Hong Zhu
article en

Abstract

In this paper, the event-triggered control problem of switched saturated systems with composite nonlinear feedback (CNF) control is investigated. We combine event-triggered control with CNF control and obtain the sufficient conditions for the asymptotic tracking problem of saturated switched systems under the dwell time scheme. The introduction of CNF control can improve the system response speed. For the proposed event-triggered scheme, the control task can only be executed when the trigger conditions are met, which further reduces the cost of system resources to some extent. Zeno behavior is eliminated by proving that the minimum time interval is greater than zero. Finally, the correctness of the results is verified by simulation.

Central South University (CN), Hangzhou Dianzi University (CN)
Affordable and clean energy
Openalex Percentile: Top 86%
Neural Networks Stability and Synchronization
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