Title
A Super-Twisting-Like Algorithm and Its Application to Train Operation Control With Optimal Utilization of Adhesion Force.
Abstract
The friction between wheel and track is usually called adhesion force, and it is the critical factor for the movement of trains. On one hand, excessive driving force of a train may lead to insufficient utilization of the adhesion effect and cause wasted energy; on the other hand, insufficient driving force of a train brings inefficient train operation. To balance the issues of energy consumption, operational efficiency, and security, it is necessary to control a train to obtain its maximal adhesion force, particularly in the cases of fast acceleration and emergency braking. However, since engineering experiments indicate a complex nonlinear relationship between the adhesion force and the slip ratio of a train, such a control problem is difficult and challenging, particularly when the optimal slip ratio is unknown. Facing this problem, this paper proposes a novel control method based on the modification of the famous super-twisting sliding mode algorithm, and rigorous mathematical analysis is given to guarantee the ultimate boundedness of the proposed algorithm. Furthermore, by considering four different control scenarios, detailed control and estimation algorithms are both proposed. Simulation result verifies that the proposed control strategy can control the train to obtain its maximum adhesion force.
Year
DOI
Venue
2016
10.1109/TITS.2016.2539361
IEEE Trans. Intelligent Transportation Systems
Keywords
Field
DocType
Force,Adhesives,Algorithm design and analysis,Heuristic algorithms,Wheels,Friction,Torque
Torque,Control theory,Control engineering,Positive train control,Slip ratio,Algorithm design,Simulation,Algorithm,Acceleration,Engineering,Rolling resistance,Train,Energy consumption
Journal
Volume
Issue
ISSN
17
11
1524-9050
Citations 
PageRank 
References 
1
0.39
13
Authors
5
Name
Order
Citations
PageRank
Yao Chen119310.85
Hairong Dong229749.85
Lv Jinhu32906244.29
Xubin Sun4336.12
Liang Guo510.39