Abstract
Considering that the speed control system of the suspended permanent magnetic maglev train is more complicated and the parameters are more unstable than those of other trains, the traditional speed-tracking algorithm has large tracking errors, frequent controller output changes, high energy consumption, and decreasing the passengers’ riding comfort. To improve the shortcomings of the traditional automatic train operation (ATO) control algorithm, this paper proposes a predictive fuzzy proportional-integral-derivative control algorithm with weights (WM-F-PID). The main contribution of this work is to propose a cascaded predictive fuzzy PID (F-PID) control algorithm architecture with weights and use an improved steepest descent method to calculate online the weight of the F-PID controller input occupied by the predictive controller output. Compared with the proportional-integral-derivative (PID), F-PID, model predictive control (MPC), and simple cascade predictive fuzzy PID (M-F-PID) control algorithms, this control algorithm effectively improves train tracking accuracy and comfort and reduces train energy consumption and stopping errors.
Highlights
In recent years, the research on the automatic train operation (ATO) technology has yielded outstanding achievement together with the advances in computer and sensor technologies
ATO for suspended permanent magnetic maglev trains involves a series of technologies such as train speed curve optimization and speed curve tracking, among which speed curve tracking is a key step of ATO technology [1]–[4]
Compared with the PID, fuzzy PID (F-PID), model predictive control (MPC), and simple cascade M-F-PID control algorithms, the WM-F-PID control algorithm reduces energy consumption per unit mass by 18.6%, 13.8%, 3.9% and 7.8%, respectively, which is more conducive to saving energy
Summary
The research on the automatic train operation (ATO) technology has yielded outstanding achievement together with the advances in computer and sensor technologies. Y. Liu et al.: Intelligent Traction Control Method Based on Model Predictive F-PID Control and Online Optimization train speed-tracking control algorithm that combines PID and a fuzzy control algorithm, which can adjust the parameters of PID controller in real-time according to external disturbances. With the development of intelligent control algorithms, a series of intelligent control algorithms has been proposed [15]–[23] These methods rely on the instantaneous state response of the system and cannot predict the future behavior of the system, and perform poorly on systems with large time delays. According to the above analysis, the main work of this article is summarized as follows: 1) To solve the problems of the inability of the current train speed control algorithm to predict future behavior and of the poor performance of the system time delay, a WM-F-PID control algorithm is proposed.
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