Abstract
Nowadays, most metro vehicles are equipped with an automatic train operation (ATO) system, and the speed control method, combining cruise speed planning and proportional-integral-derivative (PID) control, is widely used. The automation is achieved, and the energy-efficient can be improved. This paper presents an improved artificial bee colony algorithm for speed profile optimization with coast mode and an adaptive terminal sliding mode method for speed tracking. Specifically, a multi-objective optimization model is established, which considers energy consumption, comfortableness, and punctuality. Then, a novel artificial bee colony algorithm named regional reinforcement artificial bee colony (RR-ABC) is designed, to search the optimal speed profile with coast mode, in which some improvements are made to speed up convergence and to avoid local optimal solutions. For speed-tracking control, the adaptive terminal sliding mode controller (ATSMC) is used to improve the speed error, robustness, and energy saving. In addition, a disturbance observer (DOB) is designed to improve the anti-interference ability of the system and further improve the robustness and anti-disturbance, which are also conducive to speed error and energy saving. Finally, the line and train data of the Qingdao Metro Line 6 are used for simulation, which proves the effectiveness of the study. Specific to the energy saving rate, and compared with normal algorithms, RR-ABC with coast mode is approximately 9.55%, and ATSMC+DOB is 7.58%.
Highlights
In the modern metro system, the quality of operation, including energy saving, punctuality, and comfortableness, is the core factor for operation [1]
Inspired by the above articles, we introduce the additional resistance caused by slope and the basic running resistance of the train into the sliding mode controller, in the form of disturbance, and an adaptive terminal sliding mode controller for train-speed-tracking control (ATSMC) is designed in this paper
In order to further suppress the disturbance and enhance the control accuracy, which are important to energy saving, comfortableness, and punctuality, this paper introduces a disturbance observer (DOB) on the basis of ATSMC to track the speed profile; ATSMC+DOB is designed, and the convergence is proven by Lyapunov theory
Summary
In the modern metro system, the quality of operation, including energy saving, punctuality, and comfortableness, is the core factor for operation [1]. Large speed errors in those controllers result in large energy consumption They are not suitable for metro trains that require fast convergence and high-precision tracking. As a widely used cruise optimization algorithm (cruising mode), it is easy to calculate, in theory It is poor in energy-saving, and difficult to achieve precise control in industry. The initial value of the population has a significant impact on the optimization result, so how to obtain a better initial value is worth studying To solve these problems, the artificial bee colony (ABC) algorithm, which shows good global search ability, may be used in speed profile optimization [34].
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