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Adaptive Event-Triggered Output-Feedback Stabilization With Exponential Convergence
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DOI：10.1109/TASE.2024.3383415.png)
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Abstract
En 中文
This paper seeks adaptive event-triggered output-feedback control which enables exponential stabilization for nonlinear systems with unknown growth rate. Convergence rate, as an important performance specification, is usually hard to acquire in the context of adaptive control. Besides, the event-triggered architecture could undermine convergence rate, entailing a competent compensation mechanism under reduced execution. As such, we are compelled to pursue a distinctive adaptive event-triggered output-feedback scheme. Specifically, a delicate dynamic gain incorporating exponential-type time-varying information is introduced, which would not only counteract the unknown growth rate, but particularly enable desired convergence. Correspondingly, a compatible event-triggering mechanism, capable of ensuring timely execution for adaptive compensation, is designed by suitably exploiting the gain information. In this way, an adaptive event-triggered output-feedback controller is constructed, which can render exponential convergence for system states, alongside an explicit pre-estimate for inter-execution intervals.
Keywords:
Convergence
Observers
Adaptive control
Nonlinear systems
Vectors
Uncertainty
Closed loop systems
Uncertain nonlinear systems
event-triggered control
adaptive control
output-feedback stabilization
exponential convergence
Journal
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4.9K
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