Fault Diagnosis and Sliding Mode Fault Tolerant Control for Non-Gaussian Stochastic Distribution Control Systems Using T-s Fuzzy Model

2016 ◽  
Vol 19 (2) ◽  
pp. 636-646 ◽  
Author(s):  
Yao Lina ◽  
Lei Chunhui
Keyword(s):  
Fault Diagnosis ◽  
Control Systems ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fuzzy Model ◽  
Fault Tolerant Control ◽  
Non Gaussian ◽  
Stochastic Distribution

scholarly journals Sensor Fault Diagnosis and Fault-Tolerant Control for Non-Gaussian Stochastic Distribution Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Hao Wang ◽  
Lina Yao
Keyword(s):  
Fault Diagnosis ◽  
Distribution Systems ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fault Tolerant Control ◽  
Fault Estimation ◽  
Sensor Fault ◽  
System Output ◽  
Non Gaussian ◽  
Stochastic Distribution

A sensor fault diagnosis method based on learning observer is proposed for non-Gaussian stochastic distribution control (SDC) systems. First, the system is modeled, and the linear B-spline is used to approximate the probability density function (PDF) of the system output. Then a new state variable is introduced, and the original system is transformed to an augmentation system. The observer is designed for the augmented system to estimate the fault. The observer gain and unknown parameters can be obtained by solving the linear matrix inequality (LMI). The fault influence can be compensated by the fault estimation information to achieve fault-tolerant control. Sliding mode control is used to make the PDF of the system output to track the desired distribution. MATLAB is used to verify the fault diagnosis and fault-tolerant control results.

scholarly journals Discrete-Time 2-Order Sliding Mode Fault-Tolerant Tracking Control for Non-Gaussian Nonlinear Stochastic Distribution Control Systems with Missing Measurements

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Lifan Li ◽  
Lina Yao
Keyword(s):  
Fault Diagnosis ◽  
Discrete Time ◽  
Tracking Control ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
General System ◽  
Missing Measurements ◽  
Non Gaussian ◽  
Probability Density Function Pdf ◽  
Stochastic Distribution

In this paper, a method of fault diagnosis (FD) and fault-tolerant tracking control (FTTC) is investigated for non-Gaussian nonlinear stochastic distribution control (SDC) systems with missing measurements. The phenomenon of the randomly occurring missing measurements is described as a Bernoulli process. The missing measurements during transmission are compensated with the data successfully transmitted at the previous moment. The residual signal of the fault diagnosis observer is different from that of the general system. Using the integral of the error of the output probability density function (PDF) as the driving information, the system state and fault can be estimated by an adaptive fault diagnosis observer. Then, a novel fault-tolerant tracking controller is designed based on a discrete-time 2-order sliding mode to make the post-fault PDF still track the target. Two simulated examples are included to illustrate the effectiveness of the theoretical results.

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