RT - Journal Article T1 - Design of nonlinear parity approach to fault detection and identification based on Takagi-Sugeno fuzzy model and unknown input observer in nonlinear systems JF - joc-isice YR - 2020 JO - joc-isice VO - 14 IS - 3 UR - http://joc.kntu.ac.ir/article-1-641-en.html SP - 1 EP - 11 K1 - fault detection K1 - nonlinear system K1 - nonlinear matrix inequalities K1 - unknown input observer K1 - TS fuzzy model K1 - mapping. AB - 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. LA eng UL http://joc.kntu.ac.ir/article-1-641-en.html M3 10.29252/joc.14.3.1 ER -