In-vehicle pressure fluctuation iterative learning control method and system under carbon dioxide constraint

Abstract

The invention discloses an in-vehicle pressure fluctuation iterative learning control method and system under carbon dioxide constraint. The method comprises the following steps: initializing parameters such as a carbon dioxide volume concentration limit value; detecting the carbon dioxide concentration in the vehicle in real time, comparing the carbon dioxide concentration with a limit value, and determining whether to open a ventilation air duct, execute pressure control in the vehicle or obtain a pressure signal in the vehicle; the 1s change rate of the pressure in the vehicle is calculated for continuous comparison; further judging whether the air duct leaves the tunnel or not, if so, performing variable amplitude processing, control error calculation and variable scale processing, and correcting the air duct valve opening control quantity according to an iterative learning control algorithm; the system comprises a detection module, a signal acquisition and processing module, an evaluation module, an in-vehicle pressure fluctuation control module, an output module and a vehicle body ventilation system module. The system can meet the requirements for the in-vehicle pressure comfort and the in-vehicle fresh air amount at the same time, and has the engineering application value.

Description

technical field [0001] The invention belongs to the technical field of in-vehicle pressure fluctuation control of high-speed trains, and in particular relates to an iterative learning control method and system for in-vehicle pressure fluctuation under the constraint of carbon dioxide. Background technique [0002] When the train runs at high speed in the tunnel, a severe tunnel pressure wave will be generated outside the car. The pressure change enters the car through the ventilation ducts, car body gaps and car body deformation, etc., resulting in pressure fluctuations in the car, which in turn affects the drivers and passengers. of comfort. [0003] According to existing literature, train air tightness is an important factor affecting passenger comfort. However, improving the air tightness of the train will increase the cost of train manufacturing. Therefore, after the air-tightness of the train is improved to a certain level, it is necessary to design a reasonable inter...

AI Technical Summary

Problems solved by technology

Due to the influence of the speed of the vehicle and the random disturbance of the surrounding environment, the tunnel pressure wave generated when the same high-speed train passes through the same tunnel has a morphological change in time scale and amplitude (the present invention refers to it as a fixed-shaped tunnel pressure wave), which is not It has strict periodicity, which makes the traditional iterative learning control limited when dealing with the pressure fluctuation control problem in the vehicle under the excitation of the fixed-shaped tunnel pressure wave

In addition, according to the existing literature, the current research on iterative learning control of pressure fluctuations in the vehicle does not consider the constraints of carbon dioxide

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