Fuzzy modeling-based fault diagnosis and fault tolerant control for the non-Gaussian nonlinear singular stochastic distribution system

Author(s):  
Lifan Li ◽  
Chunhui Lei ◽  
Lina Yao ◽  
Jinglin Zhou ◽  
Hong Wang
2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lina Yao ◽  
Long Feng ◽  
Bin Jiang

New integrated fault diagnosis (FD) and fault tolerant control (FTC) algorithm for non-Gaussian singular time-delayed stochastic distribution control (SDC) system is proposed in the paper. Different from general SDC systems, in singular time-delayed SDC systems, the relationship between the weights and the control input is expressed by a singular time-delayed state space model, which largely increases the difficulty in the FD and FTC design. An iterative learning observer (ILO) is designed to carry out the fault estimation. The influence of the time delay term is eliminated in the process of the proof of the observation error stability. The fault may be constant, slow varying, or fast varying. Combined with the switching control theory, based on the estimated fault information, the fault tolerant controller can be designed to make the postfault probability density function (PDF) still track the given distribution. Simulations are given to show the effectiveness of the proposed integrated FD and FTC algorithms.


2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yuancheng Sun ◽  
Zhanhong Liang

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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