High-speed electric multiple unit (HSEMU) is a complex non-linear system which runs under three typical operating modes (OMs) including traction, braking, and coasting. With the increasing traffic density of the high-speed railway, the conventionally manual control strategies may be inapplicable for the HSEMU to maintain a good running performance. To enhance the running performances, in this study, a novel multiple OM (MOM) running model is designed to accurately describe the non-linear relationship between running speed and controlling force. By utilising the advantages of adaptive neuro-fuzzy inference system (ANFIS) on non-linear modelling, this study proposes an MOM-ANFIS model of HSEMU. On the basis of the established MOM-ANFIS model, a new speed controller incorporated with OM selection mechanism is designed to achieve speed control of HSEMU followed by a stability analysis of the closed-loop system. Comparative experimental results using practical running data show that the proposed MOM-ANFIS model displays better modelling accuracy and the corresponding control strategy achieves improved speed control performances for the HSEMU.
Multiple operating mode ANFIS modelling for speed control of HSEMU
IET Intelligent Transport Systems ; 12 , 1 ; 31-40
17.10.2017
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
neurocontrollers , multiple operating mode ANFIS modelling , high-speed electric multiple unit , adaptive neuro-fuzzy inference system , running speed , nonlinear relationship , closed-loop system , OM selection mechanism design , nonlinear control systems , nonlinear modelling , complex nonlinear system , coasting mode , adaptive control , multiple OM running model , rail traffic control , stability , high-speed railway , velocity control , closed loop systems , HSEMU , controlling force , control system synthesis , MOM-ANFIS model , traction mode , braking mode , traffic density , fuzzy reasoning , large-scale systems , speed controller , stability analysis , fuzzy neural nets , speed control
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