Fuzzy Observer-Based Fault Detection Design Approach for Nonlinear Processes

Li, Linlin; Ding, Steven X.; Qiu, Jianbin; Chen, Xiaolu; Xu, Dongmei

doi:10.1109/tsmc.2016.2576453

Cited by 66 publications

(27 citation statements)

References 49 publications

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“…Further applications of our strategy can be its use in specific control schemes, such as those reported in [31,32,33,34], in order to ensure, besides the stability of the closed loop system, also its positive-realness, and in the design of fault detection systems [35,36,37], in which positive-realness and order reduction may provide higher reliability.…”

Section: Discussionmentioning

confidence: 99%

Positive-real systems under lossless transformations: Invariants and reduced order models

Buscarino

Fortuna

Frasca

et al. 2017

Journal of the Franklin Institute

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Section: Discussionmentioning

confidence: 99%

Positive-real systems under lossless transformations: Invariants and reduced order models

Buscarino

Fortuna

Frasca

et al. 2017

Journal of the Franklin Institute

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“…Proportional integral (PI) observers are linear and have been used in different systems for fault diagnosis, but they are ineffective in the presence of uncertainties and disturbances [12]. To address those issues, nonlinear observers (e.g., adaptive, sliding mode, feedback linearization, and fuzzy) have been introduced [10,[13][14][15]. Feedback linearization observers are stable; however, they are not adequately robust [14].…”

Section: Introductionmentioning

confidence: 99%

Advanced Adaptive Fault Diagnosis and Tolerant Control for Robot Manipulators

Piltan

Kim

2019

Energies

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In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy sliding mode extended autoregressive exogenous input (ARX)–Laguerre proportional integral (PI) observer is proposed. The proposed T–S fuzzy sliding mode extended-state ARX–Laguerre PI observer adaptively improves the reliability, robustness, estimation accuracy, and convergence of fault detection, estimation, and identification. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy sliding mode estimation technique is introduced. The sliding surface slope gain is significant to improve the system’s stability, but the sliding mode technique increases high-frequency oscillation (chattering), which reduces the precision of the fault diagnosis and tolerant control. A fuzzy procedure using a sliding surface and actual output position as inputs can adaptively tune the sliding surface slope gain of the sliding mode fault-tolerant control technique. The proposed robust adaptive T–S fuzzy sliding mode estimation extended-state ARX–Laguerre PI observer was verified with six degrees of freedom (DOF) programmable universal manipulation arm (PUMA) 560 robot manipulator, proving qualified efficiency in detecting, isolating, identifying, and tolerant control for faults inherent in sensors and actuators. Experimental results showed that the proposed technique improves the reliability of the fault detection, estimation, identification, and tolerant control.

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“…For example, in [3], the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme was investigated, in [4], H 2 fault-detection observer for two-dimensional (2-D) discrete-time Markovian jump systems was proposed, and in [5], a simultaneous fault-detection and control strategy was proposed for switched linear systems with mode-dependent average dwell-time. In [6,7], fuzzy fault-detection observers were designed, particularly, a switching mechanism that depends on the lower and upper bounds of the unknown membership functions is provided to reduce conservatism in [6], and an integrated observer-based fault-detection scheme was proposed to meet the real-time fault-detection requirements from industrial processes in [7]. In [8], a fault-detection and -isolation (FDI) scheme for a class of Lipschitz nonlinear systems with nonlinear and unstructured modeling uncertainty was presented, and in [9], actuator stuck faults, including outage cases, were de-tected for linear state-feedback systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fault estimation for T-S fuzzy systems with unmeasurable premise variables

Liu

Wang

et al. 2018

Adv Differ Equ

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This paper is concerned with the fault estimation problem for a class of Takagi-Sugeno (T-S) fuzzy systems with actuator faults and sensor disturbances. Premise variables of the T-S fuzzy systems are assumed to be unmeasurable such that conventional parallel distributed compensation (PDC) methods are not applicable. A modified adaptive observer is designed to estimate states and fault parameters simultaneously. Finally, a simulation example is presented which shows the effectiveness of the proposed method.

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Fuzzy Observer-Based Fault Detection Design Approach for Nonlinear Processes

Cited by 66 publications

References 49 publications

Positive-real systems under lossless transformations: Invariants and reduced order models

Positive-real systems under lossless transformations: Invariants and reduced order models

Advanced Adaptive Fault Diagnosis and Tolerant Control for Robot Manipulators

Adaptive fault estimation for T-S fuzzy systems with unmeasurable premise variables

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