Paper Info
Title
Generalized Predictive Control Based on Neurofuzzy Model for Electric Multiple Unit
Abstract
In view of the complex, uncertain and nonlinear characteristics of the Electric Multiple Unit (EMU) operation process, the neurofuzzy model based on T-S fuzzy model is presented by data-driven modeling method. On the basis of the train traction characteristic curve and operation data, sub-tractive clustering is employed to ascertain the number of fuzzy rules, and the adaptive neurofuzzy inference system (ANFIS) is used to optimize the T-S fuzzy model parameters. The accuracy of the model is verified with China train control system level 3 (CTCS-3). Together with the neurofuzzy modeling, generalized predictive control (GPC) algorithm is designed to ensure high precision tracking control of train in both position and velocity. Simulation results show the effectiveness and validity of the method.
Year
DOI
Venue
2012
10.1109/ICDMA.2012.106
ICDMA
Keywords
Field
DocType
train traction characteristic curve,electric multiple unit,nonlinear characteristics,fuzzy rule,neurocontrollers,locomotives,subtractive clustering,t-s fuzzy model,ctcs-3,neurofuzzy model,anfis,railways,adaptive neurofuzzy inference system,high precision tracking control,emu,generalized predictive control,china train control system,fuzzy control,data-driven modeling method,nonlinear,t-s fuzzy model parameter,china train control system level 3,gpc,neurofuzzy modeling,fuzzy neural nets,predictive control,data models,prediction algorithms,mathematical model,predictive models
Data modeling,Nonlinear system,Control theory,Fuzzy logic,Model predictive control,Control engineering,Adaptive neuro fuzzy inference system,Engineering,Control system,Fuzzy control system,Cluster analysis
Conference
ISBN
Citations 
PageRank 
978-1-4673-2217-1
0
0.34
References 
Authors
5
3
Name
Order
Citations
PageRank
Hui Yang1188.01
Yating Fu220.70
Kunpeng Zhang36112.54