反推
控制理论(社会学)
边界(拓扑)
指数稳定性
控制(管理)
理论(学习稳定性)
计算机科学
指数函数
数学
事件(粒子物理)
物理
自适应控制
数学分析
非线性系统
量子力学
机器学习
人工智能
作者
Víctor Hernández-Santamaría,Subrata Majumdar,Luz de Teresa
出处
期刊:Automatica
[Elsevier BV]
日期:2025-06-20
卷期号:179: 112447-112447
标识
DOI:10.1016/j.automatica.2025.112447
摘要
In this paper, we address the exponential stabilization of the linearized FitzHugh–Nagumo system using an event-triggered boundary control strategy. Employing the backstepping method, we derive a feedback control law that updates based on specific triggering rules while ensuring the exponential stability of the closed-loop system. We establish the well-posedness of the system and analyze its input-to-state stability in relation to the deviations introduced by the event-triggered control. Numerical simulations demonstrate the effectiveness of this approach, showing that it stabilizes the system with fewer control updates compared to continuous feedback strategies while maintaining similar stabilization performance.
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