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

• Published in: IEEE transactions on vehicular technology Vol. 68; no. 7; pp. 6331 - 6342
• Main Authors: Cao, Yuan, Wang, Zheng-Chao, Liu, Feng, Li, Peng, Xie, Guo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2019.2914936

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.