The authors propose a novel multi‐model direct generalised predictive control based on predictive function control (PFC) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi‐model architecture based on fuzzy c‐means clustering algorithm. In order to obtain the optimal number of sub‐linear models, they apply Xie–Beni cluster validity index. In this regards, the multi‐model set is established off‐line. Secondly, the proper sub‐linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFC. The control algorithm is simple, and can reduce the on‐line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFC. Finally, simulation results are provided to show the effectiveness of the proposed scheme.
Multi‐model direct generalised predictive control for automatic train operation system
IET Intelligent Transport Systems ; 9 , 1 ; 86-94
2015-02-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
fuzzy set theory , Diophantine equations , time‐varying systems , predictive function control algorithm , sublinear models , sublinear model , Xie‐Beni cluster validity index , trajectory guidance , autonomous train driving , automatic train operation system , predictive control , multimodel architecture , performance index , railways , multimodel direct generalised predictive control , PFC algorithm , online computation time , fuzzy c‐means clustering algorithm , compensation value , switching performance index , rail traffic control , trajectory control , pattern clustering
Multi-model direct generalised predictive control for automatic train operation system
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