Crsc Research & Design Institute Group Co., Ltd.

East China Jiaotong University

Beijing National Railway Research & Design Institute of Signal & Communication Co., Ltd.

Junfeng Cui

Qi Wang

Zhiqiang Chen

Bo Zhang

Hui Yang

Jia Wang

Ling Liu

Zhongqi Li

Yating Fu

Yanli Zhou

Changyuan Wang

Kunpeng Zhang

Yinghe Tong

Hairong Dong

Gehui Liu

Pengyu Bao

Hong'en Liu

Kaikuo Zhuo

Disclosed is an adaptive control method and system for train operating condition identification. The method includes acquiring train operating state data under various traction conditions and calculating traction and braking performance data. An adhesion condition characteristic data set is formed, and train adhesion conditions are divided into Ngrades based on this data set, where Nis a natural number. The current adhesion condition grade Nis identified using the number of adhesion condition grades and current operating state data, where N≤N, establishing a mapping relationship between an adhesion coefficient and maximum deceleration for braking under the identified grade. An optimal train protection curve is generated. By clustering and grading adhesion conditions through a clustering algorithm, the method achieves more accurate adhesion condition analysis. Adaptively generating optimal train protection curves based on adhesion condition grades enhances the reliability and safety of train operations.

Adaptive control method and system based on train operating condition identification

The invention discloses a method and a system for state feedback predictive control of a high-speed train based on a forecast error. The method comprises: obtaining a speed prediction model of a high-speed train predicting speeds of the train at times k and k+p according to the speed prediction model; obtaining an actual speed output value of the train; determining a speed prediction error at time k according to the prediction speed of the train and the actual speed output value of the train; correcting a prediction speed of the train at time k+p, according to the speed prediction error, to obtain a corrected prediction speed of the train; calculating a control force uof the train according to u=λ(p)[y−y−Kx+ŷ]; and applying a control force to the train based on the control force u. The disclosure may have features of strong adaptability and easy calculation, and achieve high-precision tracking of train speed.

Method and system for state feedback predictive control of high-speed train based on forecast error

A cooperative control method and system for electro-hydraulic hybrid braking of a middle-low speed maglev train is provided, which relates to the field of vehicle braking control. The method includes: denoising operation data of a middle-low speed maglev train; using a controlled autoregressive integrated moving average model as an electro-hydraulic hybrid braking process model for the middle-low speed maglev train, and processing denoised operation data by using a least square method to determine parameters in the controlled autoregressive integrated moving average model; establishing a generalized predictive control model with time lag compensation according to the controlled autoregressive integrated moving average model and a Smith predictor; and performing cooperative control on an electro-hydraulic hybrid braking process of the middle-low speed maglev train by using the generalized predictive control model with time lag compensation. A time lag in the electro-hydraulic hybrid braking process of the middle-low speed maglev train is reduced; control accuracy of the electro-hydraulic hybrid braking process of the middle-low speed maglev train is improved to a certain extent; and a speed tracking effect is improved.

Cooperative control method for electro-hydraulic hybrid braking of middle-low speed maglev train

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Beijing, China

Junfeng Cui