CN110758487A - Automatic driving speed control method for train in rainy and snowy weather - Google Patents
Automatic driving speed control method for train in rainy and snowy weather Download PDFInfo
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- ZKHQWZAMYRWXGA-UHFFFAOYSA-N Adenosine triphosphate Natural products C1=NC=2C(N)=NC=NC=2N1C1OC(COP(O)(=O)OP(O)(=O)OP(O)(O)=O)C(O)C1O ZKHQWZAMYRWXGA-UHFFFAOYSA-N 0.000 description 6
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
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Abstract
The invention discloses a method for controlling the speed of an automatic driving train in rainy and snowy weather, which comprises the steps of presetting driving modes under a plurality of scenes according to the speed range and the change of the rain and snow grade, wherein the driving modes comprise speed and acceleration curves under each scene. And then, acquiring weather information through a network, correcting the rain and snow weather information by using the vehicle-mounted sensor, and grading the rain and snow weather. And finally, selecting a preset driving mode according to the rain and snow classification and the train operation parameters, and performing tracking control on the train speed by adopting fuzzy PID control. The invention solves the problem of difficult speed control of train automatic driving in rainy and snowy weather, and improves the safety of train automatic driving in rainy and snowy weather.
Description
Technical Field
The invention relates to the field of automatic train driving, in particular to a speed control method of an automatic driving train in rainy and snowy weather.
Background
With the continuous expansion of domestic high-speed railway networks and the continuous improvement of transportation pressure, an Automatic Train Operation (ATO) system for high-speed railways, which can improve the Operation efficiency of the high-speed railways, is developed rapidly, can not only improve the Automatic Operation degree of trains, but also ensure the safety, and is a key technology for the recent research of the high-speed railways in China.
The Chinese Train operation Control System (CTCS) is an important Control System for high-speed railways in China, and ensures safe and reliable operation of high-speed trains. Although the automatic driving function realized by the ATO system superimposed on the CTCS can be effectively used in most scenes, the rail adhesion is reduced under severe weather conditions such as heavy rain and snow, rapid weather change, and the like, and the automatic driving control of the train is difficult.
Disclosure of Invention
The invention provides a method for controlling the speed of an automatic driving train in rainy and snowy weather, aiming at solving the technical problem that the speed of the automatic driving train is difficult to control in rainy and snowy weather.
The invention discloses an automatic driving speed control method under a train in rainy and snowy weather, which comprises the following specific steps:
the method comprises the following steps: the driving mode under the preset scene comprises speed and acceleration curves under all scenes.
Step two: and acquiring precipitation data by a precipitation sensor on the rail, grading the rain and snow weather according to the precipitation data, and judging the current rain and snow weather grade.
Step three: and determining a driving mode according to the current environment data, namely the speed and the acceleration data of the train.
Step four: and according to the selected driving mode, carrying out tracking control on the train according to the speed and acceleration curve by adopting fuzzy PID control.
The invention has the advantages that:
(1) according to the method for controlling the automatic driving speed of the train in the rainy and snowy weather, the driving experience and the method of an expert in the rainy and snowy weather are integrated into the automatic driving speed control of the train, the problem that the speed of the automatic driving of the train in the rainy and snowy weather is difficult to control is solved, and the safety of the automatic driving of the train in the rainy and snowy weather is improved.
(2) The method for controlling the automatic driving speed of the train in the rainy and snowy weather learns the speed curve of the train driven by the driver, and performs tracking control through the fuzzy PID control algorithm, so that the automatic train driving system simulates the driving style of a human, and the riding comfort of the train is improved.
Drawings
Fig. 1 is a flow chart of the method for controlling the speed of an automatic driving train in rainy and snowy weather according to the invention.
Fig. 2 is a flow chart of the rain and snow weather grading in the method for controlling the speed of the automatic driving train in the rain and snow weather.
FIG. 3 is a fuzzy PID control structure in the method for controlling the speed of an automatic driving train in rainy and snowy weather.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention discloses an automatic driving speed control method under a train in rainy and snowy weather, which comprises the following specific steps:
the method comprises the following steps: presetting speed and acceleration curves under various scenes;
the driving records of the driver are classified according to scenes, and the scene classification is performed according to speed ranges (300-250, 250-200, 200-150, 150-100 and less than 100) and rain and snow weather grade changes (0-1, 1-2, 2-3, 3-2, 2-1 and 1-0), and 30 scenes are used in total.
And (3) acquiring driving operation behavior characteristics (speed and acceleration) of a speed change time period, namely performing the whole driving process of one line in each scene, and collecting multiple times of running data for comprehensive analysis in each scene. Fitting the multiple times of speed data under each scene by using a fourth-order polynomial to construct a speed curve as shown in formula (1), wherein formula (2) is an acceleration function
v(t)＝n4t4+n3t3+n2t2+n1t+n0(1)
a(t)＝4n4t3+3n3t2+2n2t+n1(2)
Wherein v (t) is a speed change function of the driver in a speed change time period, a (t) is an acceleration change function of the driver in the speed change time period, t is time, n0,n1,n2,n3,n4Is an unknown undetermined coefficient. Total 5 unknowns in the formulaFitting the formula (1) with driving speed data in the same scene, taking 5 moments, listing 5 equations to solve speed and acceleration curves, and finally obtaining the speed and acceleration curves in 30 scenes, namely presetting 30 driving modes aiming at rainy and snowy weather.
Step 2: and classifying the rain and snow weather according to the rainfall, judging the current rain and snow weather grade, and verifying the current rain and snow weather grade.
Weather information is acquired through a network, and weather data is transmitted to a Train operation control system (ATP) in real time in a TCP/IP mode through a wireless communication unit by using a GPRS (general packet radio service); because the train speed is high during actual operation and the weather changes rapidly, the rain and snow information needs to be verified and corrected by using a ground sensor and a vehicle-mounted vision system on site; that is, the data of the rainfall sensor on the rail on the ground is collected by a ground temporary speed limit server (TSRS) unit, and the data of the rainfall and snowfall is transmitted to the ATP of the vehicle through an ATO-RCS communication unit. And finally, weighting the hourly precipitation data of the ground automatic station issued by the Chinese meteorological data network and the precipitation data of the track ground precipitation sensor by ATP (adenosine triphosphate), wherein the formula is shown in a formula (3).
R＝0.3·R1+0.7·R2(3)
Wherein R is the total precipitation, R1Is the precipitation data, R, of the China meteorological data network2And the data of the precipitation amount of the track ground precipitation sensor.
The rain and snow weather is graded by using the total precipitation R, the total precipitation is the first grade at 1.3-3.7 mm, the total precipitation is the second grade at 3.8-7.4 mm, and the total precipitation is the third grade at 7.5-10 mm.
Finally, verifying rain and snow weather information by using an external sensor such as a camera through algorithms such as machine vision and the like, calibrating a camera picture according to the rain and snow weather grades to be divided into three observation grades, and if the precipitation obtained through ATP processing accords with the observation grade of the camera, passing verification; if the precipitation obtained by ATP processing does not accord with the camera observation grade, the rain and snow grade is calculated by the higher grade.
And step 3: and selecting a preset speed curve and an acceleration curve of each scene.
Combining the change of rain and snow grade and environmental data obtained by receiving and demodulating ground transponder signals by the ATO-RCS combined transponder receiving unit, acquiring speed and acceleration information from a speed sensor signal and obtained by calculation by the speed and distance measuring unit, calculating and judging the current train scene in real time by the ATO-RCS, and selecting a driving mode corresponding to the current scene.
And 4, step 4: the ATO-RCS receives speed and acceleration curves corresponding to the scenes selected by the ATP, receives information such as an operation plan, line data and temporary speed limit in combination with wireless communication, and adopts fuzzy PID control to enable the train to carry out tracking control according to the speed and acceleration curves.
Firstly, dynamic modeling needs to be carried out on a train, the train is simplified into a single mass point for modeling, and the stress of the train is mainly considered as gravity, supporting force, traction force, braking force, basic resistance and additional resistance. The basic resistance is the resistance borne by the train when the train runs on a straight road, and can be written into the following form through a large number of experiments and simulation verification:
W0＝C0+C1V+C2V2(4)
in the formula, W0The unit is the basic resistance of the train in unit time, and the unit is N/t; v is train speed, and the unit is km/h; c0The coefficient of rolling friction being the speed independent part; c1The coefficients related to the mechanical resistance and the rolling friction force in the running process of the train are obtained; c2Is the aerodynamic section coefficient related to the velocity squared.
According to Newton's law of kinematics, the motion characteristics of a train are:
U＝Ma+(C0+C1+C2V2)+Wf(5)
wherein a is acceleration in m/s2；WfThe additional resistance of the train is shown in the unit of N; u is traction (braking) force, and the unit is N; m is train mass, and the unit is t.
Establishing a train transfer function model according to the motion characteristics of the train:
in the formula, K1The proportional coefficient of the speed-regulating gear and the acceleration of the train is obtained; k2Is an acceleration correction coefficient; m' is the current train quality value; and M is the train quality when the train is empty. E in pure hysteresis loop of the transfer function model-τsPure delay time of model command to actual output, T in inertial link1The train transfer function model can be simplified by eliminating the pure hysteresis characteristic, namely approximating the transfer function proportionality coefficient to a constant 1 when the control quantity is the acceleration:
the inertia time of the traction working condition is 500ms, and the transfer function of a train motion model is as follows:
a speed controller is designed by adopting a fuzzy PID algorithm, as shown in figure 3, the fuzzy PID controller takes the error e and the error change rate de/dt of the speed output by the following formula (8) in the same state and the speed output by a preset mode as input signals, simultaneously the error e and the error change rate de/dt enter fuzzy reasoning, and a proportional gain k is output on line by adopting a fuzzy control rulepIntegral gain kiAnd a differential gain kdPID control is carried out to realize real-time adaptive modification of PID parameters, and by combining a train operation control model, the actual speed of a train can be obtained through simulation, and then the speed error e and the error change rate de/dt are formed by feedback with the target speed. Wherein the fuzzy reasoning summarizes the technical knowledge of engineering designers and the actual operation experience of the train driver on site, establishes a scientific and reasonable 'if … then' format fuzzy rule, and obtains a correction parameter delta kp,Δki,ΔkdEach of which isThe setting fuzzy control tables are listed in tables 1 to 3.
TABLE 1 Δ kpFuzzy control table of
TABLE 2 Δ kiFuzzy control table of
TABLE 3 Δ kdFuzzy control table of
According to tables 1-3, the fuzzy subsets defining e and de/dt fuzzy quantities are { NB, NM, NS, ZO, PS, PM, PB }, and the domains of discourse are [ -13, +13 }]；Δkp,Δki,ΔkdIs { NB, NM, NS, ZO, PS, PM, PB }, Δ k }p,ΔkiAll of them are [ -5, +5 [)]，ΔkdHas a discourse field of [ -0.5, +0.5]. Fully considering the delta kp,Δki,ΔkdAnd for the coverage degree and sensitivity requirement of the domain of interest and the stability and robustness principle of the controller, the fuzzy subsets of all variables and parameters in the PID controller are represented in a triangular membership function form. According to the specific conditions of e and de/dt, the fuzzy PID controller determines the parameter adjustment amount of the fuzzy set at each moment according to the actual values of e and de/dt, and adaptively adjusts the PID controller parameters on line in real time, so as to obtain the corrected parameters at the next moment:
in the formula, kp′,ki′,kd' means a determination section; Δ kp,Δki,ΔkdIndicating the adjusted portion after the fuzzy control. And finally, the train automatically enters a preset speed control mode.
When the number of times of the bogie turning is larger than a preset value or the speed of the train is abnormal and the speed tracking error is large, the ATP returns control failure information to give an alarm and immediately perform emergency braking.
Claims (7)
1. A method for controlling the automatic driving speed of a train in rainy and snowy weather is characterized by comprising the following steps: the method comprises the following specific steps:
the method comprises the following steps: the driving mode under the preset scene comprises speed and acceleration curves under all scenes;
step two: acquiring rainfall data by a rainfall sensor on a rail, grading the rain and snow weather according to the rainfall data, and judging the current rain and snow weather grade;
step three: determining a driving mode according to the current environment data, namely the speed and acceleration data of the train;
step four: and according to the selected driving mode, carrying out tracking control on the train according to the speed and acceleration curve by adopting fuzzy PID control.
2. The automatic driving speed control method under the train in the rainy and snowy weather as claimed in claim 1, characterized in that: in the first step, 30 scenes are classified according to the speed ranges of 300-250, 250-200, 200-150, 150-100 and below 100 and the rain and snow grade changes of 0-1, 1-2, 2-3, 3-2, 2-1 and 1-0;
collecting multiple running speed data in each scene, fitting by using a fourth-order polynomial, constructing a speed and acceleration curve function, and solving to obtain a speed and acceleration curve corresponding to the current scene.
3. The automatic driving speed control method under the train in the rainy and snowy weather as claimed in claim 1, characterized in that: and in the second step, the precipitation data acquired by the precipitation sensor is compared with precipitation observation data measured by the image acquisition sensor outside the vehicle, and if the precipitation data acquired by the precipitation sensor is inconsistent with the precipitation observation data measured by the image acquisition sensor outside the vehicle, the precipitation with a large precipitation is selected as the current precipitation.
4. The automatic driving speed control method under the train in the rainy and snowy weather as claimed in claim 1, characterized in that: and 4, a specific design method of the speed controller comprises the following steps:
the method comprises the steps of designing a speed controller by adopting a fuzzy PID algorithm, taking an error e and an error change rate de/dt of a speed output by a transfer function of a train model and a speed output by a preset mode under the same state as input signals by the fuzzy PID controller, simultaneously carrying out fuzzy reasoning on the error e and the error change rate de/dt, and outputting correction parameters including a proportional gain k on line by adopting a fuzzy control rulepIntegral gain kiAnd a differential gain kdAnd entering a PID controller to realize real-time adaptive modification of PID parameters, combining a train operation control model, obtaining the actual speed of the train through simulation, and feeding back the error e and the error change rate de/dt of the speed formed by the actual speed and the target speed.
5. The method for controlling the automatic driving speed under the train in the rainy and snowy weather as claimed in claim 4, wherein: the respective setting fuzzy control tables of the correction parameters are respectively as follows:
Δkpthe fuzzy control table of (1):
Δkithe fuzzy control table of (1):
Δkdthe fuzzy control table of (1):
6. the method for controlling the automatic driving speed of the train in the rainy and snowy weather as claimed in claim 4, wherein fuzzy subsets of variables and parameters in the PID controller are all represented by a triangular membership function form; and the PID controller determines the parameter adjustment amount of the fuzzy set at each moment by depending on the actual values of e and de/dt, and adaptively adjusts the parameters of the PID controller on line in real time, so as to obtain the corrected correction parameters at the next moment.
7. The method for controlling the automatic driving speed of the train in the rainy and snowy weather as claimed in claims 1 to 6, wherein when the number of times of turning of the bogie is larger than a predetermined value, or the speed of the train is abnormal and the speed tracking error is large, the ATP returns control failure information to give an alarm and immediately perform emergency braking.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112632750A (en) * | 2020-12-01 | 2021-04-09 | 北方信息控制研究院集团有限公司 | Simulation modeling method and system for mutual coupling of different elements in virtual battlefield environment |
| CN112729366A (en) * | 2020-12-22 | 2021-04-30 | 同济大学 | Test evaluation method and device for weather simulation equipment for automatic driving field test |
| CN112757914A (en) * | 2021-02-03 | 2021-05-07 | 青岛慧拓智能机器有限公司 | Speed following control method of unmanned electrically-driven mine car |
| CN113147843A (en) * | 2021-05-21 | 2021-07-23 | 上海电气泰雷兹交通自动化***有限公司 | Automatic train control method based on environment perception and signal system |
| CN113859313A (en) * | 2021-10-29 | 2021-12-31 | 中车株洲电力机车有限公司 | Freight train and speed tracking method and system thereof |
| CN114194249A (en) * | 2020-09-17 | 2022-03-18 | 比亚迪股份有限公司 | Train driving control method and control system |
| CN115352496A (en) * | 2022-07-29 | 2022-11-18 | 中车唐山机车车辆有限公司 | Speed limit control method, device and system and train |
| CN117189727A (en) * | 2023-11-07 | 2023-12-08 | 合肥合锻智能制造股份有限公司 | Moment leveling system control system based on fuzzy PID |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108674447A (en) * | 2018-05-02 | 2018-10-19 | 北京交通大学 | Operation control method for train under special external environment |
-
2019
- 2019-11-05 CN CN201911072952.3A patent/CN110758487A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108674447A (en) * | 2018-05-02 | 2018-10-19 | 北京交通大学 | Operation control method for train under special external environment |
Non-Patent Citations (2)
| Title |
|---|
| 闻一龙: "针对城市轨道交通的复杂天气环境应对方法研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
| 陈小强等: "预测模糊PID在高速列车速度控制中的研究", 《***仿真学报》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114194249A (en) * | 2020-09-17 | 2022-03-18 | 比亚迪股份有限公司 | Train driving control method and control system |
| CN114194249B (en) * | 2020-09-17 | 2023-02-07 | 比亚迪股份有限公司 | Train driving control method and control system |
| CN112632750A (en) * | 2020-12-01 | 2021-04-09 | 北方信息控制研究院集团有限公司 | Simulation modeling method and system for mutual coupling of different elements in virtual battlefield environment |
| CN112632750B (en) * | 2020-12-01 | 2023-09-26 | 北方信息控制研究院集团有限公司 | Simulation modeling method and system for mutual coupling of different elements of virtual battlefield environment |
| CN112729366A (en) * | 2020-12-22 | 2021-04-30 | 同济大学 | Test evaluation method and device for weather simulation equipment for automatic driving field test |
| CN112729366B (en) * | 2020-12-22 | 2022-04-05 | 同济大学 | Test evaluation method and device for weather simulation equipment for automatic driving field test |
| CN112757914A (en) * | 2021-02-03 | 2021-05-07 | 青岛慧拓智能机器有限公司 | Speed following control method of unmanned electrically-driven mine car |
| CN113147843A (en) * | 2021-05-21 | 2021-07-23 | 上海电气泰雷兹交通自动化***有限公司 | Automatic train control method based on environment perception and signal system |
| CN113859313A (en) * | 2021-10-29 | 2021-12-31 | 中车株洲电力机车有限公司 | Freight train and speed tracking method and system thereof |
| CN115352496A (en) * | 2022-07-29 | 2022-11-18 | 中车唐山机车车辆有限公司 | Speed limit control method, device and system and train |
| CN117189727A (en) * | 2023-11-07 | 2023-12-08 | 合肥合锻智能制造股份有限公司 | Moment leveling system control system based on fuzzy PID |
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