Paper Info
Title
A neural-fuzzy sliding mode observer for robust fault diagnosis
Abstract
A robust fault diagnosis (FD) scheme using Takagi-Sugeno (T-S) neural-fuzzy model and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A neural-fuzzy observer and neural-fuzzy sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of the two observers. Stability of the observers are analyzed as well. Finally, the proposed FD scheme using these observers is applied to a point mass satellite orbital control system example. Numerical simulation results show that this robust fault diagnosis strategy is effective for the considered class of nonlinear systems.
Year
DOI
Venue
2009
10.1109/ACC.2009.5160193
St. Louis, MO
Keywords
DocType
ISSN
neural-fuzzy observer,observers,point mass satellite orbital control system,proposed fd scheme,considered class,neurocontrollers,neural-fuzzy model,sliding mode observer,robust fault diagnosis,robust control,robust fault diagnosis strategy,t-s fuzzy model,backpropagation,mode technique,modified back-propagation algorithm,nonlinear control systems,fault diagnosis,nonlinear system,mode observer,fuzzy control,stability,variable structure systems,takagi-sugeno neural-fuzzy model,sliding mode control,back propagation,fuzzy systems,stability analysis,robustness,satellites,control system,nonlinear systems,artificial neural networks,control systems,numerical simulation
Conference
0743-1619 E-ISBN : 978-1-4244-4524-0
ISBN
Citations 
PageRank 
978-1-4244-4524-0
0
0.34
References 
Authors
13
2
Name
Order
Citations
PageRank
Qing Wu1203.30
Mehrdad Saif233448.75