Koopman-Based Event-Triggered Control from Data

Event-triggered Control (ETC) presents a promising paradigm for efficient resource usage in networked and embedded control systems by reducing communication instances compared to traditional time-triggered strategies. This paper introduces a novel approach to ETC for discrete-time nonlinear systems using a data-driven framework. By leveraging Koopman operator theory, the nonlinear system dynamics are globally linearized (approximately in practical settings) in a higher-dimensional space. We design a state-feedback controller and an event-triggering policy directly from data, ensuring exponential stability in Lyapunov sense. The proposed method is validated through extensive simulation experiments, demonstrating significant resource savings.

Publication Details

Journal
ArXiv.org
Published
2025-04-19
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

Koopman-Based Event-Triggered Control from Data

ArXiv.org
Model Reduction and Neural Networks
article

Koopman-Based Event-Triggered Control from Data

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Abstract

Event-triggered Control (ETC) presents a promising paradigm for efficient resource usage in networked and embedded control systems by reducing communication instances compared to traditional time-triggered strategies. This paper introduces a novel approach to ETC for discrete-time nonlinear systems using a data-driven framework. By leveraging Koopman operator theory, the nonlinear system dynamics are globally linearized (approximately in practical settings) in a higher-dimensional space. We design a state-feedback controller and an event-triggering policy directly from data, ensuring exponential stability in Lyapunov sense. The proposed method is validated through extensive simulation experiments, demonstrating significant resource savings.

ArXiv.org
Decent work and economic growth
Openalex Percentile: Top 52%
Model Reduction and Neural Networks
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Koopman-Based Event-Triggered Control from Data · ArXiv.org (2025) | TGRS Research Map | TGRS