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A Train Speed ​​Adaptive Control Method Based on System Parameter Identification

A technology of adaptive control and system parameters, applied in the direction of locomotive, railway signal and safety, etc., can solve the problem that the traditional control algorithm is not suitable

Active Publication Date: 2021-04-27
海风智能科技(浙江)有限公司
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Problems solved by technology

Therefore, the train is a typical complex and time-varying system, and the traditional control algorithm is no longer applicable

Method used

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  • A Train Speed ​​Adaptive Control Method Based on System Parameter Identification
  • A Train Speed ​​Adaptive Control Method Based on System Parameter Identification
  • A Train Speed ​​Adaptive Control Method Based on System Parameter Identification

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Embodiment Construction

[0060] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0061] Aiming at the controlled train whose system parameters change with time, the invention designs an identification algorithm to identify the system parameters of the train in real time, and then feeds back the deviation of the identification model to the adaptive PID controller based on the neural network, and at the same time constrains the system identification process and control As a result, train speed adaptive control is realized. The specific process includes: first, according to the mechanical analysis of the train and the torque analysis of the motor, the dynamic model of the train system is established; then, the parameters of the system are identified by the least square method to obtain a complete system model; finally, the design of the system based on BP neural network The adaptive PID speed controller of the system feeds back the residual ...

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Abstract

The invention discloses a train speed adaptive control method based on system parameter identification, which belongs to the field of electric vehicles. First, according to the mechanical analysis of the train and the torque analysis of the motor, the dynamic model of the train including undetermined parameters is established; then several sets of actual input and output data are collected as the fitting data of the least square method; the fitting deviation of the least square method is the most The parameters b and c to be identified corresponding to hours are used as undetermined parameters to obtain a complete dynamic model. Finally, for the train running in real time, according to the established train driving curve, the parameters in the complete dynamic model are adjusted through the neural network, the speed of the train is controlled in real time, and the train speed adaptive control is realized. The invention can not only constrain the learning of the neural network, but also constrain the system identification module, so that both the identification result and the control result are more accurate.

Description

technical field [0001] The invention belongs to the field of electric vehicles and relates to a train speed adaptive control method based on system parameter identification. Background technique [0002] With the development of computer technology and automatic control theory, the function of automatic train control (ATC) system is becoming more and more perfect, which can replace the train driver to realize the safe and punctual operation of the train. The ATC system consists of three subsystems, which are ATP (Automatic Train Protection) system, ATS (Automatic Train Monitoring) system and ATO (Automatic Train Operation) system. [0003] The ATO system accepts the ground information and train control instructions from the ATP / ATS system to control the movement of the train, including acceleration operation, inertia operation, deceleration, parking and return operation control of the terminal station. An important function of the ATO system is to accurately control the spee...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B61C17/00B61L23/14
CPCB61C17/00B61L23/14
Inventor 张辉陶斯友冯敏健李想
Owner 海风智能科技(浙江)有限公司
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