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Protocol‐based H ∞ filtering for piecewise linear systems: A measurement‐dependent equivalent reduction approach
Author(s) -
Li Jiajia,
Wei Guoliang,
Ding Derui,
Tian Engang
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5445
Subject(s) - filter (signal processing) , asynchronous communication , control theory (sociology) , reduction (mathematics) , piecewise linear function , computer science , filtering problem , markov chain , linear system , markov process , schedule , piecewise , mathematical optimization , transmission (telecommunications) , state space , mathematics , filter design , computer network , mathematical analysis , telecommunications , statistics , geometry , control (management) , artificial intelligence , machine learning , computer vision , operating system
In this article, a protocol‐based H ∞ filtering problem is investigated for piecewise linear (PWL) systems with mixed delays. Stochastic access protocol (SAP) is utilized to schedule the signal transmission via a constrained communication channel, under which only one measurement value can be transmitted to the filter at each time instant following a Markov process. When SAP is applied to PWL systems, only a part of system modes can be observed due to the incomplete measurement information. Therefore, a measurement‐dependent parameter storage rule with a certain probability distribution is introduced to overcome the difficulties coming from SAP‐induced incomplete information. By resorting to the proposed mode storage rule, a novel measurement‐dependent asynchronous H ∞ filter model is constructed and the problem is converted into a new switching process of a larger state space following a reconstructed Markov process. Subsequently, a sufficient condition is established to guarantee the H ∞ performance, and the probability‐dependent filter parameters are obtained by applying the stochastic analysis technique. Finally, a numerical example is given to show the effectiveness of the design approach.