دانلود مقاله ISI انگلیسی شماره 67616
ترجمه فارسی عنوان مقاله

فیلتر کردن H∞ ناهمزمان نمونه-داده ی توزیع شده از سیستم های خطی پرش Markovian در شبکه های حسگر

عنوان انگلیسی
Distributed sampled-data asynchronous H∞ filtering of Markovian jump linear systems over sensor networks
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
67616 2016 14 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Signal Processing, Volume 127, October 2016, Pages 86–99

ترجمه کلمات کلیدی
فیلتر کردن H∞H∞ نمونه-داده توزیع شده ؛ سیستم خطی پرش Markovian ؛ سوئیچینگ آسنکرون؛ شبکه حسگر؛ شبکه ناشی از تاخیر؛ توپولوژی سوئیچینگ Markovian
کلمات کلیدی انگلیسی
Distributed sampled-data H∞H∞ filtering; Markovian jump linear system; Asynchronous switching; Sensor network; Network-induced delay; Markovian switching topology
پیش نمایش مقاله
پیش نمایش مقاله  فیلتر کردن H∞ ناهمزمان نمونه-داده ی توزیع شده از سیستم های خطی پرش Markovian در شبکه های حسگر

چکیده انگلیسی

This paper is concerned with distributed sampled-data asynchronous H∞H∞ filtering for a continuous-time Markovian jump linear system over a sensor network, where jumping instants of system modes and filter modes are asynchronous. A group of sensor nodes are deployed to measure the system׳s output and to collaboratively share the measurement with neighboring nodes in accordance with Markovian switching topologies. First, the measurement on each sensor node is sampled at separate discrete instants and transmitted to a remote filter through a communication network. Network-induced signal transmission delays are incorporated in data transmission channels. Second, distributed sampled-data asynchronous H∞H∞ filters, governed by a finite piecewise homogeneous Markov process, are delicately constructed. The resultant filtering error system is transformed into a piecewise homogeneous Markovian jump linear system with delays. Third, sufficient conditions on the existence of desired distributed sampled-data asynchronous H∞H∞ filters are derived such that the filtering error system is stochastically stable with the prescribed weighting average H∞H∞ performance. Finally, three illustrative examples are given to show the effectiveness and advantage of the proposed theoretical results.