Fault detection for nonlinear networked systems based on quantization and dropout compensation: An interval type-2 fuzzy-model method

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• 作者：Chengwei Wu^aAuthor Vitae ; Hongyi Li^b ; ^{lihongyi2009@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Hak-Keung Lam^cAuthor Vitae ; Hamid Reza Karimi^dAuthor Vitae
• 关键词：Nonlinear networked systems ; Parameter uncertainties ; Fuzzy systems ; Fault detection filter
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：26 May 2016
• 年：2016
• 卷：191
• 期：Complete
• 页码：409-420
• 全文大小：716 K

文摘

This paper investigates the problem of filter-based fault detection for a class of nonlinear networked systems subject to parameter uncertainties in the framework of the interval type-2 (IT2) T–S fuzzy model-based approach. The Bernoulli random distribution process and logarithm quantizer are used to describe the measurement loss and signals quantization, respectively. In the framework of the IT2 T–S fuzzy model, the parameter uncertainty is handled by the membership functions with lower and upper bounds. A novel IT2 fault detection filter is designed to guarantee the residual system to be stochastically stable and satisfy the predefined H_∞$H_{\infty }$ performance. It should be mentioned that the proposed filter does not use the same premise variables, number of fuzzy rules and membership functions as the fuzzy model, which will lead to more flexible design. Finally, two illustrative examples are provided to demonstrate the usefulness of the approach proposed in this paper.