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Network-Based H∞ Filtering for Discrete-Time Systems.

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Bibliographic Details
Title: Network-Based H∞ Filtering for Discrete-Time Systems.
Authors: Zhang, Xian-Ming, Han, Qing-Long
Source: IEEE Transactions on Signal Processing; Feb2012, Vol. 60 Issue 2, p956-961, 6p
Subject Terms: DISCRETE-time system stability, TELECOMMUNICATION systems, MARKOV processes, CONTROL theory (Engineering), DATA packeting
Abstract: This correspondence is concerned with network-based H∞ filtering for discrete-time systems. The output signals of the system under consideration are transmitted to the filter through a constraint communication network, which usually leads to network-induced delays and packet dropouts. By introducing a logic data packet processor to choose the newest data signal from the network to actuate the filter, network-induced delays and packet dropouts are modeled as a Markov chain taking values in a finite set. As a result, the filter to be designed is modeled as a Markov jumping linear filter. By introducing some slack matrix variables in terms of probability identity, a less conservative bounded real lemma (BRL) is derived to ensure that the filtering error system is stochastically stable with a prescribed H∞ level. Based on this BRL, suitable H∞ filters are designed by employing a cone complementary approach. A practical example on the Leslie model about some certain pest's structured population dynamics is given to show the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]
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Database: Complementary Index
Description
Abstract:This correspondence is concerned with network-based H<subscript>∞</subscript> filtering for discrete-time systems. The output signals of the system under consideration are transmitted to the filter through a constraint communication network, which usually leads to network-induced delays and packet dropouts. By introducing a logic data packet processor to choose the newest data signal from the network to actuate the filter, network-induced delays and packet dropouts are modeled as a Markov chain taking values in a finite set. As a result, the filter to be designed is modeled as a Markov jumping linear filter. By introducing some slack matrix variables in terms of probability identity, a less conservative bounded real lemma (BRL) is derived to ensure that the filtering error system is stochastically stable with a prescribed H<subscript>∞</subscript> level. Based on this BRL, suitable H<subscript>∞</subscript> filters are designed by employing a cone complementary approach. A practical example on the Leslie model about some certain pest's structured population dynamics is given to show the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]
ISSN:1053587X
DOI:10.1109/TSP.2011.2175224