Fully Automatic Operation Algorithm of Urban Rail Train Based on RBFNN Position Output Constrained Robust Adaptive Control

Junxia Yang,Youpeng Zhang,Yuxiang Jin

doi:10.3390/a14090264

Junxia Yang, Youpeng Zhang + Show 1 more

Open Access

https://doi.org/10.3390/a14090264

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Journal: Algorithms	Publication Date: Sep 9, 2021
Citations: 3	License type: CC BY 4.0

Affiliation: Lanzhou Jiaotong University

Abstract

High parking accuracy, comfort and stability, and fast response speed are important indicators to measure the control performance of a fully automatic operation system. In this paper, aiming at the problem of low accuracy of the fully automatic operation control of urban rail trains, a radial basis function neural network position output-constrained robust adaptive control algorithm based on train operation curve tracking is proposed. Firstly, on the basis of the mechanism of motion mechanics, the nonlinear dynamic model of train motion is established. Then, RBFNN is used to adaptively approximate and compensate for the additional resistance and unknown interference of the train model, and the basic resistance parameter adaptive mechanism is introduced to enhance the anti-interference ability and adaptability of the control system. Lastly, on the basis of the RBFNN position output-constrained robust adaptive control technology, the train can track the desired operation curve, thereby achieving the smooth operation between stations and accurate stopping. The simulation results show that the position output-constrained robust adaptive control algorithm based on RBFNN has good robustness and adaptability. In the case of system parameter uncertainty and external disturbance, the control system can ensure high-precision control and improve the ride comfort.

Highlights

The authors of [14] proposed an accurate parking control algorithm of an urban rail transit train based on adaptive terminal sliding mode control, which can adaptively adjust the control input according to the model parameter changes caused by the uncertainty of train dynamic model parameters and unknown disturbance to ensure that the train can accurately track the desired parking curve
Due to the influence of factors such as parameter uncertainty and external disturbance in the fully automatic operation system (FAO), this paper introduces the additional resistance and unknown disturbances that cannot be accurately modeled in the train dynamics model into the position output-constrained adaptive controller in the form of disturbance by radial basis function neural network (RBFNN), so as to enhance the ability of the system to deal with disturbances such as ramps and curves, as well as realize accurate tracking control of the urban rail train operation curve
The first used the actual parameters of urban rail trains to carry out the RBFNN position outputconstrained robust adaptive control (RBFNN-position output-constrained robust adaptive control (POCRAC)) design and desired curve tracking

Summary

Introduction

The authors of [13] designed a sliding model using PID control for an automatic train operation system, which constrained the train operation to the sliding hyperplane so as to realize tracking and accurate parking of the desired operation curve, but the influence of unknown disturbances on the train operation control was not considered, and the control accuracy could not be guaranteed under complex road conditions. The authors of [14] proposed an accurate parking control algorithm of an urban rail transit train based on adaptive terminal sliding mode control, which can adaptively adjust the control input according to the model parameter changes caused by the uncertainty of train dynamic model parameters and unknown disturbance to ensure that the train can accurately track the desired parking curve.

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Conclusion