Paper Info
Title
Fault Diagnosis And Fault Tolerant Control For The Non-Gaussian Nonlinear Stochastic Distribution Control System Using Takagi-Sugeno Fuzzy Model
Abstract
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.
Year
DOI
Venue
2018
10.1504/IJMIC.2018.10010550
INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL
Keywords
Field
DocType
fault diagnosis, RBF neural network, differential evolution, DE, fault tolerant control, sliding mode control
Control theory,Nonlinear system,Linear system,Control theory,Differential evolution,Fault tolerance,Control system,Artificial neural network,Mathematics,Sliding mode control
Journal
Volume
Issue
ISSN
29
1
1746-6172
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Lina Yao198193.63
haoran wang2816.77