Optimal control method for EMU based on multi-working condition anfis model
A technology of optimal control and multiple working conditions, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problem of not considering the operation of multiple working conditions of EMUs
Active Publication Date: 2019-08-13
EAST CHINA JIAOTONG UNIVERSITY
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However, none of the above control methods take into account the characteristics of multiple operating conditions of the EMU, and their ability to deal with the actual high-speed EMU operation needs to be improved.
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[0077] The present invention will be described in detail below in conjunction with specific embodiments.
[0078] The present invention follows the train to collect CRH380AL EMU running speed and control force data of several trips of the same trip on the Beijing-Shanghai high-speed railway from Xuzhou East to Jinan West section, and combines CRH380AL traction and braking characteristic curves to select the global representative traction and braking , Inert the effective data of all working conditions. Using these data, based on the stress conditions of high-speed EMUs under different working conditions, a data-driven ANFIS modeling method is used to establish a multi-working condition ANFIS model. And based on the multi-working-condition ANFIS model, a suitable working-condition selection mechanism is adopted to design a corresponding generalized predictive controller to ensure high-precision high-speed EMU speed control.
[0079] The present invention is based on the high-s...
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The invention discloses a motor train unit optimization operation control method based on a multi-working-condition ANFIS model and belongs to the technical field of precise modeling and optimization operation control during operation of the high-speed motor train unit. According to characteristics of a complex non-linear system of a high-speed motor train unit during operation under multiple working conditions such as pulling, braking and coasting, stress conditions under different working conditions are analyzed. By collecting actual operation data of the high-speed motor train unit and combining a pulling / braking characteristic curve of the motor train unit, the novel multi-working-condition ANFIS model for the motor train unit is established for precisely describing the operation process of the motor train unit. According to the established model, a mechanism is selected by the adoption of appropriate working conditions, and a prediction controller based on the multi-working-condition ANFIS model is designed to complete optimization operation control for the high-speed motor train unit. With the motor train unit optimization operation control method, the modeling precision of the high-speed motor train unit is improved during operation, the operation safety and punctuality and riding comfort are improved, and technical support is provided for automatic driving of the high-speed motor train unit.
Description
technical field [0001] The invention relates to a multiple working condition modeling and optimal operation control method in the operation process of a high-speed EMU, and belongs to the technical field of modeling and optimal operation control in the operation process of a high-speed EMU. Background technique [0002] By 2020, the operating mileage of my country's high-speed rail will reach 30,000 kilometers, covering more than 80% of large cities, and my country will fully enter the era of high-speed rail. There are more and more complex factors affecting the safety of high-speed railway operation than traditional railways. The level of safety is of decisive significance to the sustainable development and stable operation of high-speed railways. Safety assurance is a major scientific issue we need to face. The existing high-speed EMU operation control is mainly based on the manual operation mode of the automatic train protection system (ATP). The safety and other operatio...
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IPC IPC(8): G05B13/04
CPCG05B13/04
Inventor 杨辉付雅婷谭畅
Owner EAST CHINA JIAOTONG UNIVERSITY