Fault diagnosis and minimum entropy fault tolerant control for non-Gaussian singular stochastic distribution systems using square-root approximation

Yao, Lina; Guan, Yacun; Wang, Aiping

doi:10.1504/ijmic.2015.072620

Cited by 14 publications

(9 citation statements)

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“…PI fault tolerant controller is designed and the desired result of the fault tolerant control is obtained. In Yao et al (2015), an iterative learning observer (ILO) is designed for fault estimation. The concept of entropy is introduced to fault tolerant control of the non-Gaussian stochastic distribution system when the target PDF is not known in advance.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis and fault tolerant control for the non-Gaussian nonlinear stochastic distribution control system using Takagi-Sugeno fuzzy model

Yao

Wang

2018

IJMIC

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For the non-Gaussian nonlinear stochastic distribution control system using Takagi-Sugeno fuzzy model, the nonlinear dynamic system is converted to a linear system. A fault diagnosis algorithm using RBF neural network and a sliding mode fault tolerant control algorithm is presented. A new adaptive fault diagnosis algorithm is adopted to diagnose the gradual fault that occurred in the system, and the stability of the observation error system is proved. Differential evolution (DE) algorithm is used to optimise the central vector and width vector of RBF neural network. The sliding mode control algorithm is used to reconfigure the controller, based on the fault estimation information. The post-fault probability density function (PDF) can still track the given distribution. Finally, simulation results show the effectiveness of the proposed fault diagnosis and fault tolerant control algorithm.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis and fault tolerant control for the non-Gaussian nonlinear stochastic distribution control system using Takagi-Sugeno fuzzy model

Yao

Wang

2018

IJMIC

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“…With the study of fault diagnosis of non‐Gaussian SDC systems, some fault diagnosis algorithms have been proposed. For fault diagnosis of SDC systems, in which the information of system output PDF and other measured information to generate residuals in order to analyze and estimate the change of fault, observer or filter‐based methods are mainly used so far . A stable filter‐based residual generator is constructed such that the fault can be detected and diagnosed for general stochastic systems in .…”

Section: Introductionmentioning

confidence: 99%

“…In , an optimal algorithm is proposed to reconfigure the controller to compensate the effect of the fault on the system performance. When the target PDF is unknown, we introduce the concept of entropy to fault‐tolerant control of non‐Gaussian SDC systems; the purpose of fault‐tolerant control is to minimize the uncertainty of the system output after the fault happened .…”

Section: Introductionmentioning

confidence: 99%

Robust fault diagnosis and fault‐tolerant control for non‐Gaussian uncertain stochastic distribution control systems

Sun

Yao

2016

Intl J Robust & Nonlinear

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The purpose of fault diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density function to obtain the fault estimation information. A fault diagnosis and sliding mode fault-tolerant control algorithms are proposed for non-Gaussian uncertain stochastic distribution control systems with probability density function approximation error. The unknown input caused by model uncertainty can be considered as an exogenous disturbance, and the augmented observation error dynamic system is constructed using the thought of unknown input observer. Stability analysis is performed for the observation error dynamic system, and the H 1 performance is guaranteed. Based on the information of fault estimation and the desired output probability density function, the sliding mode fault-tolerant controller is designed to make the post-fault output probability density function still track the desired distribution. This method avoids the difficulties of design of fault diagnosis observer caused by the uncertain input, and fault diagnosis and fault-tolerant control are integrated. Two different illustrated examples are given to demonstrate the effectiveness of the proposed algorithm.Y. SUN AND L.YAO With the study of fault diagnosis of non-Gaussian SDC systems, some fault diagnosis algorithms have been proposed. For fault diagnosis of SDC systems, in which the information of system output PDF and other measured information to generate residuals in order to analyze and estimate the change of fault, observer or filter-based methods are mainly used so far [5][6][7][8][9][10][11][12][13]. A stable filterbased residual generator is constructed such that the fault can be detected and diagnosed for general stochastic systems in [5]. In [6], a nonlinear neural network observer is designed for fault diagnosis in which the adaptive tuning rule for network parameters is determined by the Lyapunov stability theorem. In [7], a fault diagnosis algorithm is proposed based on iterative learning observer for SDC systems. In [8], a fault diagnosis method based on robust filters is proposed for time-delayed SDC systems, and a robust H 1 fault diagnosis scheme is presented in [9]. Otherwise, in [12], a high-gain nonlinear observer-based fault diagnosis approach is proposed for a class of nonlinear uncertain systems with measurable output PDFs. In [13], a novel fault-estimation observer is designed for Takagi-Sugeno (T-S) fuzzy systems with actuator faults, and the problem of fault-tolerant control is addressed. For fault-tolerant control, the active fault-tolerant control is mainly used so far. Combined with the controller design method without fault, such as optimal control, PI control, sliding mode control [14], robust control, iterative learning control, model reference adaptive control, etc, the controller can be reconfigured or reconstructed when fault occurs. In [15], a neural network-based active fault-tolerant control scheme with fault alarm is proposed for a class of nonlinear systems....

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“…In [14], a fuzzy fault tolerant control scheme is developed to guarantee the closed-loop system to be exponentially stable in mean square. When the target PDF is unknown, introduce the concept of entropy to fault tolerant control of non-Gaussian SDC systems, in which the purpose is to minimize the uncertainty of the system output after fault happened [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Although the model descriptions of SDC systems are always the dynamic link relations between the input and the weights of output PDFs, the conventional dynamic links do not meet the practical requirements because of the existence of some algebraic constraint conditions between some state variables. Thus, it is necessary to study the SDC systems in the framework of singular systems [8,13,[16][17][18]. Due to the material delivering by conveyor belt, system modeling, and data operation and transmission, time-delay widely exists in actual industrial systems.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Fault Tolerant Control for Non-Gaussian Singular Time-Delayed Stochastic Distribution Systems with Disturbance Based on the Rational Square-Root Model

Sun

Liang

2016

Journal of Control Science and Engineering

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For the non-Gaussian singular time-delayed stochastic distribution control (SDC) system with unknown external disturbance where the output probability density function (PDF) is approximated by the rational square-root B-spline basis function, a robust fault diagnosis and fault tolerant control algorithm is presented. A full-order observer is constructed to estimate the exogenous disturbance and an adaptive observer is used to estimate the fault size. A fault tolerant tracking controller is designed using the feedback of distribution tracking error, fault, and disturbance estimation to let the postfault output PDF still track desired distribution. Finally, a simulation example is included to illustrate the effectiveness of the proposed algorithms and encouraging results have been obtained.

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Fault diagnosis and minimum entropy fault tolerant control for non-Gaussian singular stochastic distribution systems using square-root approximation

Cited by 14 publications

References 0 publications

Fault diagnosis and fault tolerant control for the non-Gaussian nonlinear stochastic distribution control system using Takagi-Sugeno fuzzy model

Fault diagnosis and fault tolerant control for the non-Gaussian nonlinear stochastic distribution control system using Takagi-Sugeno fuzzy model

Robust fault diagnosis and fault‐tolerant control for non‐Gaussian uncertain stochastic distribution control systems

Fault Diagnosis and Fault Tolerant Control for Non-Gaussian Singular Time-Delayed Stochastic Distribution Systems with Disturbance Based on the Rational Square-Root Model

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