Volume 14, Issue 3 (Journal of Control, V.14, N.3 Fall 2020)                   JoC 2020, 14(3): 1-11 | Back to browse issues page


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Tolouei H, Aliyari Shoorehdeli M. Design of nonlinear parity approach to fault detection and identification based on Takagi-Sugeno fuzzy model and unknown input observer in nonlinear systems. JoC. 2020; 14 (3) :1-11
URL: http://joc.kntu.ac.ir/article-1-641-en.html
1- K. N. Toosi university of Technology
Abstract:   (5993 Views)
In this study, a novel fault detection scheme is developed for a class of nonlinear system in the presence of sensor noise. A nonlinear Takagi-Sugeno fuzzy model is implemented to create multiple models. While the T-S fuzzy model is used for only the nonlinear distribution matrix of the fault and measurement signals, a larger category of nonlinear systems is considered. Next, a mapping to decouple fault and measurement noise will be used in each fuzzy subsystems. Then, an unknown input observer is implemented to estimate the states of the subsystems subjected to measurement noise. To guarantee asymptotic stability of error dynamic, quadratic Lyapunov function using bilinear matrix inequality is introduced. Finally, the nonlinear parity approach will be used to generate residual to detect and estimate occurred fault(s) in the system. A simulation study on the train system is presented to demonstrate the efficiency of the proposed method.
Full-Text [PDF 573 kb]   (516 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2019/01/17 | Accepted: 2019/10/21 | Published: 2020/11/30

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