Open AccessFault detection for a class of non‐linear networked control systems in the presence of Markov sensors assignment with partially known transition probabilities
Author(s) -
Wang Yanqian,
Lu Junwei,
Li Ze,
Chu Yuming
Publication year - 2015
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.0802
Subject(s) - fault detection and isolation , markov chain , filter (signal processing) , control theory (sociology) , filtering problem , linear matrix inequality , fault (geology) , stochastic matrix , matrix (chemical analysis) , mathematics , markov process , discrete time and continuous time , linear system , filter design , mathematical optimization , computer science , algorithm , control (management) , artificial intelligence , statistics , seismology , actuator , geology , mathematical analysis , materials science , composite material , computer vision
In this study, the problem of fault detection for a class of discrete‐time non‐linear networked control systems is investigated. An event modelled as a Markov chain taking matrix values in a certain set with partially known transition probabilities is utilised to characterise the phenomenon of the sensors assignment. A full‐order mode‐dependent fault detection filter is constructed and the corresponding fault detection problem is converted into an H ∞ filtering problem of a Markov jump system with partially known transition probabilities. Sufficient conditions for the existence of the fault detection filter are formulated as a linear matrix inequality‐based convex optimisation problem. If the convex optimisation problem has a feasible solution, the corresponding fault detection filter parameters are determined. A numerical example with four cases of transition probability matrices is presented to show the usefulness of the developed method.