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
Article (Journal)
Electronic Resource
English
pattern clustering , predictive function control algorithm , Xie-Beni cluster validity index , time-varying systems , rail traffic control , sublinear models , autonomous train driving , trajectory guidance , multimodel architecture , railways , automatic train operation system , Diophantine equations , PFC algorithm , compensation value , trajectory control , switching performance index , predictive control , fuzzy c-means clustering algorithm , multimodel direct generalised predictive control , fuzzy set theory , online computation time , sublinear model , performance index
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