Robust point‐to‐point iterative learning control for high speed trains with model uncertainty and wind gust

Li, Zhenxuan; Hou, Zhongsheng; Zhang, Ruikun; Yin, Chun

doi:10.1002/asjc.2745

Cited by 6 publications

(3 citation statements)

References 35 publications

(60 reference statements)

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“…The two groups of wind disturbances are constant disturbance and harmonic disturbance respectively. The former uses the stable wind model of the train in the operating process [42], while the latter is a kind of model of the gust, which can approximately reveal the action process of wind disturbance on the train [43]. The change of disturbance is shown in Fig.…”

Section: Train Operation Scenario With Disturbancementioning

confidence: 99%

Stability-Enhanced Model Predictive Control for Urban Rail Transit Train

Wang,

Xing,

Wang

2024

IEEE Access

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Automatic train operation (ATO) control is a pivotal part of urban rail transit development, where designing the dynamics models and controllers for the ATO control scenarios presents a formidable challenge. To begin with, considering the fundamental resistances encountered by trains during the operation process, including elemental running resistance and time-varying slope resistance, we treat relative distance and relative speed between train carriages as state variables in the control modeling. Considering changes in traction/braking outputs as control variables, we formulate a meticulous dynamics model for urban rail transit trains (URTT). Furthermore, a stability-enhanced model predictive control (SEMPC) approach is proposed for ATO control in URTT, with a terminal term being added to the control objective design for stability requirements. This approach anticipates the future dynamic behaviors of the control system, yielding a stable and convergent predictive controller for the ATO system. Lastly, utilizing operational data from a specific urban rail line as an illustrative example, we conduct comparative analyses of the operational control performance among various controllers in scenarios of the single section, multi-section, and disturbance. Experimental results demonstrate that the proposed SEMPC controller exhibits superior performance to the compared controllers for ATO control in URTT.INDEX TERMS Automatic train operation (ATO), urban rail transit train (URTT), stability-enhanced model predictive control (SEMPC), speed control.

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Section: Train Operation Scenario With Disturbancementioning

confidence: 99%

Stability-Enhanced Model Predictive Control for Urban Rail Transit Train

Wang,

Xing,

Wang

2024

IEEE Access

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“…By replacing the fal function in (21) with nsfal function, the form of improved ESO can be obtained: where 2δ 0 is the length of the approximate linear interval of the nsfal function. If δ 0 is too large, the nsfal function will lose the effect of non-smooth feedback, and if δ 0 is too small, the system will oscillate near the origin.…”

Section: Novel Eso Based On Nsfal Functionmentioning

confidence: 99%

“…In Ref. 21 , an iterative learning method was used to track the desired speed curve of the train. This method improves the multi-point tracking accuracy by using the repeatability of the train motion and does not depend on the accurate model of the system.…”

Section: Introductionmentioning

confidence: 99%

Improved active disturbance rejection speed control for autonomous driving of high-speed train based on feedforward compensation

Yue,

Wang,

Xiao

et al. 2023

Science Progress

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Due to the complex and changeable train operation environment and the unstable and time-varying parameters, accurate modeling is limited. Therefore, a modified active disturbance rejection control algorithm based on feedforward compensation (FC-MADRC) is proposed targeting the speed control problem of trains under the circumstances of external disturbances, which reduces the dependence on the train model. Firstly, the state space equation is established based on the single-particle mathematical model of the train, and all the running resistances are regarded as disturbances. Secondly, the FC-MADRC algorithm is designed. Based on the terminal attractor function and the novel Sigmoid function, an improved tracking differentiator (ITD) is designed. An improved fal (nsfal) function with better smoothness is constructed by using the properties of the Dirac δ function, and an ameliorative nonlinear state error feedback (ANLSEF) and a modified extended state observer (IESO) are designed based on the nsfal function. Furthermore, based on the thought of PID, the integral term of error is introduced into ANLSEF for the nonlinear operation to reduce the steady-state error of train speed tracking. In order to promote the robustness and control accuracy of the system, the feedforward compensation term and disturbance compensation term are combined to perform dynamic compensation for disturbances in real time. Finally, the simulation is carried out with CRH380A train data. The results indicate that compared with conventional ADRC and 2DOF-PID, FC-MADRC has the more vital anti-disturbance ability and higher tracking accuracy. FC-MADRC has the advantages of solid anti-disturbance, fast response, and high tracking accuracy. Under the premise of external disturbance, it can still achieve accurate speed tracking under different road conditions.

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Decentralized Cruise Control of High-Speed Trains with Modelling Uncertainties

Zhao,

2023

2023 35th Chinese Control and Decision Conference (CCDC)

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Robust point‐to‐point iterative learning control for high speed trains with model uncertainty and wind gust

Cited by 6 publications

References 35 publications

Stability-Enhanced Model Predictive Control for Urban Rail Transit Train

Stability-Enhanced Model Predictive Control for Urban Rail Transit Train

Improved active disturbance rejection speed control for autonomous driving of high-speed train based on feedforward compensation

Decentralized Cruise Control of High-Speed Trains with Modelling Uncertainties

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