Bio-Inspired Speed Curve Optimization and Sliding Mode Tracking Control for Subway Trains

Operation optimization for modern subway trains usually requires the speed curve optimization and speed curve tracking simultaneously. For the speed curve optimization, a multi-objective seeking issue should be addressed by considering the requirements of energy saving, punctuality, accurate parking...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2019-07, Vol.68 (7), p.6331-6342
Main Authors: Cao, Yuan, Wang, Zheng-Chao, Liu, Feng, Li, Peng, Xie, Guo
Format: Article
Language:English
Subjects:
Acceleration
Computational modeling
Double sliding surface guidance
Energy conservation
Energy consumption
Force
Fuzzy control
Genetic algorithms
Mode tracking
Multiple objective analysis
Optimization
Parameter robustness
Proportional integral derivative
Public transportation
random reinforcement genetic algorithm (GA)
Resistance
Sliding mode control
speed curve optimization
speed curve tracking control
Subway cars
Tracking control
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Summary:Operation optimization for modern subway trains usually requires the speed curve optimization and speed curve tracking simultaneously. For the speed curve optimization, a multi-objective seeking issue should be addressed by considering the requirements of energy saving, punctuality, accurate parking, and comfortableness at the same time. But most traditional searching methods lack in efficiency or tend to fall into the local optimum. For the speed curve tracking, the widely applied proportional integral differential (PID) and fuzzy controllers rely on complicated parameter tuning, whereas robust adaptive methods can hardly ensure the finite-time convergence strictly, and thus are not suitable for applications in fixed time intervals of trains. To address the above-mentioned two problems, this paper presents a novel approach for speed curve seeking and tracking control. First, we present the random reinforcement genetic algorithm (GA) algorithm to avoid the local optimum efficiently. Then, a sliding mode controller is developed for speed curve tracking with bounded disturbance. The Lyapunov theory is adopted to prove that the system can be stabilized in the finite time. Finally, using the real datasets of Yizhuang Line in Beijing Subway, the proposed approach is validated, demonstrating its effectiveness and superiorities for the operation optimization.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2019.2914936