Freight train speed active disturbance rejection tracking and wheel anti-slip based on fuzzy neural network with DBO optimization

In this paper, we present a proposed scheme for speed tracking control specifically designed for freight trains. This innovative speed tracking approach effectively prevents wheel slippage and ensures optimal speed control of the traction motor. The strategy integrates the use of Direct Torque Contr...

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Bibliographic Details
Published in:Electrical engineering 2024-02, Vol.106 (1), p.631-644
Main Authors: Yi, LingZhi, Jiang, WenBo, Yi, Yu, Li, JianLin, Xie, Cheng
Format: Article
Language:English
Subjects:
Adaptive control
Artificial neural networks
Asynchronous motors
Dung
Economics and Management
Electrical Engineering
Electrical Machines and Networks
Energy Policy
Engineering
Freight trains
Fuzzy logic
Kalman filters
Locomotives
Neural networks
Nonlinear control
Original Paper
Parameters
Power Electronics
Predictive control
Railway tracks
Rejection
Slip
Slippage
Speed control
Time delay systems
Tracking control
Traction
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Summary:In this paper, we present a proposed scheme for speed tracking control specifically designed for freight trains. This innovative speed tracking approach effectively prevents wheel slippage and ensures optimal speed control of the traction motor. The strategy integrates the use of Direct Torque Control (DTC), a technique employed by the HXD1 electric traction locomotive to regulate the asynchronous motor. To achieve velocity tracking control, we implement a Predictive Auto Disturbance Rejection Control (PADRC) system. Notably, the PADRC system includes an output prediction module estimator that enables accurate forecasting of time-delay system responses. Additionally, we develop an Unscented Kalman Filter (UKF) observer and seamlessly integrate an adaptive parameter adjustment mechanism, powered by Dung Beetle Optimizer-Fuzzy Neural Network (DBO-FNN), into the observer architecture. By utilizing the anti-slip parameters obtained through this observer, we determine the control scheme for anti-skid control. We validate the efficacy of this scheme through simulations using an actual speed curve of a freight train under both wet and dry pavement conditions. The results show a remarkable improvement in speed tracking accuracy, with respective increases of 66.45% and 56.29% over Adaptive Model Predictive Control (AMPC) and Non-Linear Auto Disturbance Rejection Control (NLADRC), in terms of speed tracking. Moreover, the tracking stationarity witnesses notable enhancements, increasing by 42.07% and 60.87% respectively. Additionally, the anti-slip performance of trains running on dry and wet tracks increased by 12.45% and 26.56%, respectively.
ISSN:0948-7921
1432-0487
DOI:10.1007/s00202-023-02008-w