Please use this identifier to cite or link to this item:
Title: Fuzzy-model-based fault detection for a class of nonlinear systems with networked measurements
Authors: Zhang, D.
Wang, Q.-G. 
Yu, L.
Song, H.
Keywords: Fault detection (FD)
general medium access constraint
networked measurements
random packet dropouts
sensor saturation
signal quantization
Takagi-Sugeno (T-S) fuzzy systems
time slot assignment
Issue Date: 2013
Citation: Zhang, D., Wang, Q.-G., Yu, L., Song, H. (2013). Fuzzy-model-based fault detection for a class of nonlinear systems with networked measurements. IEEE Transactions on Instrumentation and Measurement 62 (12) : 3148-3159. ScholarBank@NUS Repository.
Abstract: This paper is concerned with the fuzzy-model-based fault detection for a class of nonlinear systems with networked measurements where there are significant uncertainties on information. A unified model is proposed to capture four sources of these uncertainties, namely, the sensor saturation, the signal quantization, the general medium access constraint, and the multiple packet dropouts. A simultaneous consideration of these issues reflects the practical networked systems much more closely than the existing works. The goal of this paper is to design a fault detector such that, for all unknown input, control input, and uncertain information, the estimation error between the residual and the fault is minimized. Using the switched system approach and some stochastic analyses, a sufficient condition for the existence of desired fault detector is established and the fault detector gains are computed by solving an optimization problem. Two numerical examples are given to show the effectiveness of the proposed design. © 1963-2012 IEEE.
Source Title: IEEE Transactions on Instrumentation and Measurement
ISSN: 00189456
DOI: 10.1109/TIM.2013.2272865
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jul 7, 2020


checked on Jun 29, 2020

Page view(s)

checked on Jun 28, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.