Speed Tracking and Train Anti-slip Control Based on Active Disturbance Rejection for Freight Trains with Large Inertia.

Rail freight is a common mode of transportation for bulk commodities and is widely used around the world, with heavy quality and large inertia. Conventional control methods are influenced by the dynamic parameters of the train model. To solve this problem, this paper designs a mayfly algorithm-predictive active disturbance rejection controller (MA-PADRC) for speed tracking and wheel anti-slip control of electric freight trains. This control method reduces dependence on accurate modeling. MA-PADRC integrates the searching ability of MA, predicting ability of Smith estimator and high efficiency of ADRC, and it can accurately estimate the driving disturbance of the freight train. Meanwhile, to prevent wheel slip, an Elman neural network-Kalman filter (ENN-KF) is designed. Through it, the precise observation of anti-slip parameters and the constraint of anti-slip control are realized. The proposed control is validated by simulation of both real and virtual driving speeds. The results show that the proposed control has a strong tracking performance, and can effectively prevent wheel slip and guarantee the safe driving of the freight train with large inertia. [ABSTRACT FROM AUTHOR]