2006
DOI: 10.1007/s11768-006-5086-3
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Robust fault diagnosis for a class of nonlinear systems

Abstract: Robust fault diagnosis based on adaptive observer is studied for a class of nonlinear systems up to output injection. Adaptive fault updating laws are designed to guarantee the stability of the diagnosis system. The upper bounds of the state estimation error and fault estimation error of the adaptive observer are given respectively and the effects of parameter in the adaptive updating laws on fault estimation accuracy are also discussed. Simulation example demonstrates the effectiveness of the proposed methods… Show more

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Cited by 8 publications
(5 citation statements)
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“…where " 1 ; " 2 , and " 3 are positive constants. Substituting (12), (13), (14) into (11), it follows that…”
Section: Theoremmentioning
confidence: 99%
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“…where " 1 ; " 2 , and " 3 are positive constants. Substituting (12), (13), (14) into (11), it follows that…”
Section: Theoremmentioning
confidence: 99%
“…And in recent years, the design of FD methods for nonlinear systems has received much more attention. Especially, various observer-based FD methods have been introduced for nonlinear uncertain systems such as adaptive observer-based FD [10][11][12][13], high gain observer-based FD [14,15], and sliding mode observer-based FD [16][17][18]. The main idea of the observer-based FD for nonlinear uncertain systems is to obtain residuals, robust thresholds [19,20] and analyze the fault detectability [21,22].…”
Section: Introductionmentioning
confidence: 99%
“…Fault detection and accommodation approaches are presented for different systems (Cao and Song, 2020; Gui and Vukovich, 2017; Hu et al, 2018; Liu et al, 2015, 2018; Song et al, 2017; Talebi and Khorasani, 2012; Van et al, 2016; Wang and Zhang, 2006; Xiao et al, 2011, 2017). For instance, assuming that the fault function is known and the derivative of the fault is bounded, a fault diagnosis method is presented for a class of nonlinear systems (Wang and Zhang, 2006). In Liu et al (2015), a fault detection method is proposed for robot manipulators with the assumption of slowly time-varying actuator faults.…”
Section: Introductionmentioning
confidence: 99%
“…Two approaches were developed in , namely, the convex linearization approach and iterative approach, to the quantized H ∞ filtering for a class of continuous‐time Markovian jump linear systems with deficient mode information. By introducing adaptive laws in the observer, designed a robust fault detection technique to compensate for state and fault errors concurrently, with the condition of a known upper error bound. A FTC data‐driven scheme was proposed in for the Tennessee Eastman benchmark, which is realized by an adaptive SMO for identifying faults online with an iterative optimization method for system performance enhancement.…”
Section: Introductionmentioning
confidence: 99%