NDLI: Event-Triggered Filtering for Delayed Markov Jump Nonlinear Systems with Unknown Probabilities

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Event-Triggered Filtering for Delayed Markov Jump Nonlinear Systems with Unknown Probabilities

Content Provider	MDPI
Author	Chen, Huiying Liu, Renwei Xia, Weifeng Li, Zuxin
Copyright Year	2022
Description	This paper focuses on the problem of event-triggered $H\infty$ asynchronous filtering for Markov jump nonlinear systems with varying delay and unknown probabilities. An event-triggered scheduling scheme is adopted to decrease the transmission rate of measured outputs. The devised filter is mode dependent and asynchronous with the original system, which is represented by a hidden Markov model (HMM). Both the probability information involved in the original system and the filter are assumed to be only partly available. Under this framework, via employing the Lyapunov–Krasovskii functional and matrix inequality transformation techniques, a sufficient condition is given and the filter is further devised to ensure that the resulting filtering error dynamic system is stochastically stable with a desired $H\infty$ disturbance attenuation performance. Lastly, the validity of the presented filter design scheme is verified through a numerical example.
Starting Page	769
e-ISSN	22279717
DOI	10.3390/pr10040769
Journal	Processes
Issue Number	4
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2022-04-14
Access Restriction	Open
Subject Keyword	Processes Industrial Engineering Event-triggered Scheduling Markov Jump Nonlinear Systems(mjnss) Error Threshold Partly Unknown Probabilities Asynchronous Filtering
Content Type	Text
Resource Type	Article

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