CN115257882A - Method, equipment and storage medium for accurately stopping ATO of train - Google Patents

Method, equipment and storage medium for accurately stopping ATO of train Download PDF

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Publication number
CN115257882A
CN115257882A CN202210890599.5A CN202210890599A CN115257882A CN 115257882 A CN115257882 A CN 115257882A CN 202210890599 A CN202210890599 A CN 202210890599A CN 115257882 A CN115257882 A CN 115257882A
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train
control quantity
period
speed
current
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CN115257882B (en
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陈明亮
王伟
郜春海
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Traffic Control Technology TCT Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/04—Automatic systems, e.g. controlled by train; Change-over to manual control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or vehicle trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or vehicle trains
    • B61L25/021—Measuring and recording of train speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or vehicle trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or vehicle trains
    • B61L25/025—Absolute localisation, e.g. providing geodetic coordinates
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The application provides a method, equipment and a storage medium for ATO accurate parking of a train, wherein the method comprises the following steps: after the train enters an accurate parking stage, acquiring a dynamic model of the train; periodically acquiring speed and position state information of the train; after the speed and position state information of the train is obtained each time, determining the control quantity of the current period through a dynamic model according to the speed and position state information obtained in the current period; and controlling the train operation according to the control quantity of the current period. According to the method and the device, after the train enters the accurate parking stage, the dynamic model of the train is obtained, the control quantity of each period is determined through the dynamic model according to the speed and position state information obtained in each period, the train operation is controlled according to the control quantity of each period, and the train ATO accurate parking control through incremental prediction of the control quantity of each period is realized.

Description

Method, equipment and storage medium for accurately stopping ATO of train
Technical Field
The application relates to the technical field of rail transit, in particular to a method, equipment and a storage medium for ATO accurate parking of a train.
Background
Urban rail transit is one of the ways for solving traffic jam in large cities due to the fact that the urban rail transit is convenient, rapid, on-time, green, low-carbon and environment-friendly and has little influence on road traffic. Along with the continuous development of the automatic train driving technology, the running of the urban rail transit train is more automatic and intelligent. The accurate parking of the Train is used as a core function of an Automatic Train Operation (ATO), and the safe, stable and accurate parking of the Train at a preset parking position can be guaranteed. However, the difficulty of accurate train stop control is high due to the fact that the train is influenced by factors of full load rate, weather reasons and uncertainty of line characteristics in the running process.
The conventional ATO system mostly adopts a conventional Proportional-Integral-Derivative (PID) controller to perform parking control on a train, so as to realize accurate parking control of a platform. However, in practical engineering application, parameters of the conventional PID controller need to be obtained through a large number of tests and repeated debugging according to different line parameters, train traction braking characteristics and the like, so that the working strength is high, the time consumption is long, and accurate parking is difficult to guarantee.
Disclosure of Invention
In order to solve one of the technical defects, the application provides a method, equipment and a storage medium for ATO accurate parking of a train.
In a first aspect of the present application, a method for accurately stopping an ATO of a train is provided, the method comprising:
after the train enters an accurate parking stage, acquiring a dynamic model of the train;
periodically acquiring speed and position state information of the train;
after the speed and position state information of the train is obtained each time, determining the control quantity of the current period through the dynamic model according to the speed and position state information obtained in the current period; and controlling the train to run according to the control quantity of the current period.
Optionally, the kinetic model is:
Figure BDA0003767416080000021
wherein a is the actual acceleration of the train, adesFor the desired acceleration of the train, K is the system gain, and τ is the timeA constant.
Optionally, the determining, according to the speed and position state information obtained in the current period, a control quantity of the current period through the dynamic model includes:
constructing an optimization problem according to the dynamic model;
and taking the speed and position state information acquired in the current period as an initial state, and solving the optimization problem to obtain the control quantity of the current period.
Optionally, the constructing an optimization problem according to the dynamical model includes:
determining a train dynamics discrete system equation according to the dynamics model;
determining a target function and a constraint condition according to the train dynamics discrete system equation;
and constructing an optimization problem according to the objective function and the constraint condition.
Optionally, the discrete system equation of train dynamics is:
x(k+1)＝Ax(k)+Bu(k)；
wherein the content of the first and second substances,
Figure BDA0003767416080000022
Tsis the sampling interval; k is the current sampling time, x (k) is the system state value of the current sampling time, x (k + 1) is the system state value of the next sampling time, u (k) is the control quantity of the current sampling time, and the system state value is [ sv a ]]T，u(k)＝adesS is train position and v is train speed.
Optionally, the objective function is:
Figure BDA0003767416080000023
Figure BDA0003767416080000031
wherein N ispTo predict the step size, i is the step size identification, and x (k + i | k) represents the current sample time basedA predicted system state value after a k + i sampling period; x is the number ofref(k + i | k) represents the expected value of the system state after k + i sampling period predicted based on the current sampling instant, Q is the system state weight coefficient matrix, NcFor the control step length, R is the weight of the control quantity increment, and Δ u (k + i) is the control quantity increment after the sampling period of k + i.
Optionally, the constraint condition is:
umin≤u(k+i)≤umax；
Δumin≤Δu(k+i)≤Δumax；
wherein u isminTo control the maximum value of quantity, umaxFor maximum control quantity, Δ uminFor control quantity increment minimum, Δ umaxFor the control quantity increment maximum, u (k + i) is the control quantity after the k + i sampling period.
Optionally, the obtaining the control quantity of the current cycle by solving the optimization problem with the speed and position state information obtained in the current cycle as the initial state includes:
taking the speed and position state information acquired in the current period as initial states, and solving the optimal solution delta u of the optimization problem*(k)；
Determining the train control quantity u (k) = u (k-1) + delta u of the current period*(k)。
In a second aspect of the present application, there is provided an electronic device comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method according to the first aspect.
In a third aspect of the present application, there is provided a computer readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement the method according to the first aspect as described above.
The application provides a method, equipment and a storage medium for ATO accurate parking of a train, wherein the method comprises the following steps: after the train enters an accurate parking stage, acquiring a dynamic model of the train; periodically acquiring speed and position state information of the train; after the speed and position state information of the train is acquired each time, determining the control quantity of the current period through a dynamic model according to the speed and position state information acquired in the current period; and controlling the train operation according to the control quantity of the current period.
According to the method and the device, after the train enters the accurate parking stage, the dynamic model of the train is obtained, the control quantity of each period is determined through the dynamic model according to the speed and position state information obtained in each period, the train operation is controlled according to the control quantity of each period, and the train ATO accurate parking control through incremental prediction of the control quantity of each period is realized.
In addition, in one implementation, the accuracy of the control quantity of each period is ensured by limiting the dynamic model, and further the precise ATO stopping control of the train is ensured.
In addition, in one implementation, an optimization problem is constructed according to the dynamic model, and the control quantity of the current period is obtained through the optimization problem, so that the determination of the control quantity of each period is more accurate, and the precise ATO parking control of the train is further ensured.
In addition, in one implementation, a train dynamics discrete system equation is determined according to the dynamics model, a target function and a constraint condition are obtained, and then an optimization problem is constructed, so that the construction of the optimization problem is accurate, and the precise ATO parking control of the train is ensured.
In addition, in one implementation, the discrete system equation of train dynamics is limited, so that the construction of the optimization problem is accurate, and the precise ATO stopping control of the train is further ensured.
In addition, in one implementation, the objective function is limited, so that the construction of the optimization problem is accurate, and the precise ATO parking control of the train is further ensured.
In addition, in one implementation, the constraint conditions are limited, so that the construction of the optimization problem is accurate, and the precise ATO parking control of the train is further ensured.
In addition, in one implementation, the speed and position state information acquired in the current period is used as an initial state, the optimal solution of the optimization problem is solved, and the train control quantity in the current period is determined based on the optimal solution, so that the determination of the control quantity in each period is more accurate, and the precise ATO parking control of the train is further ensured.
According to the electronic equipment, the computer program is executed by the processor so as to obtain the dynamic model of the train after the train enters the accurate parking stage, the control quantity of each period is determined through the dynamic model according to the speed and position state information obtained in each period, the train operation is controlled according to the control quantity of each period, and the train ATO accurate parking control through the incremental prediction of the control quantity of each period is realized.
The computer readable storage medium provided by the application has the computer program executed by the processor to obtain the dynamic model of the train after the train enters the precise parking stage, determine the control quantity of each period through the dynamic model according to the speed and position state information obtained in each period, and further control the train operation according to the control quantity of each period, thereby realizing the ATO precise parking control of the train through the incremental prediction of the control quantity of each period.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a method for accurately stopping an ATO of a train according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a predictive control flow according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the following description of the exemplary embodiments of the present application with reference to the accompanying drawings is made in further detail, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all the embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
In the process of implementing the present application, the inventor finds that, in an existing ATO (Automatic Train Operation) system, a conventional Proportional-Integral-Derivative (PID) controller is mostly used to perform parking control on a Train, so as to implement accurate parking control on a platform. However, in practical engineering application, parameters of the conventional PID controller need to be obtained through a large number of tests and repeated debugging according to different line parameters, train traction braking characteristics and the like, so that the working strength is high, the time consumption is long, and accurate parking is difficult to guarantee.
In view of the above problems, embodiments of the present application provide a method, an apparatus, and a storage medium for ATO precise stop of a train, where the method includes: after the train enters a precise parking stage, acquiring a dynamic model of the train; periodically acquiring speed and position state information of the train; after the speed and position state information of the train is acquired each time, determining the control quantity of the current period through a dynamic model according to the speed and position state information acquired in the current period; and controlling the train operation according to the control quantity of the current period. According to the method and the device, after the train enters the accurate parking stage, the dynamic model of the train is obtained, the control quantity of each period is determined through the dynamic model according to the speed and position state information obtained in each period, the train operation is further controlled according to the control quantity of each period, and the train ATO accurate parking control through incremental prediction of the control quantity of each period is achieved.
Referring to fig. 1, the implementation process of the method for accurately stopping the ATO of the train provided by the embodiment is as follows:
101, acquiring a dynamic model of the train after the train enters an accurate parking stage.
In order to make the train stop at the predetermined stop position more smoothly and accurately, a distance before the train enters the station is generally used as the position where the train enters the accurate stop position. And when the train enters the accurate stopping stage, executing the step to obtain a dynamic model of the train.
The dynamic characteristic of the controlled object train can be modeled in the step, and then a dynamic model is obtained.
The modeling method may be various, such as parameter identification. The embodiment provides an exemplary modeling scheme, namely a scheme for parameter identification according to historical data, and an inertia link is adopted to build a model for train braking delay, so that the following dynamic model is obtained:
Figure BDA0003767416080000061
a is the actual acceleration of the train, adesFor the desired acceleration of the train, K is the system gain and τ is the time constant.
During specific implementation, control instructions in vehicle-mounted ATO communication control data and data information such as acceleration of an actual train are extracted, invalid data are screened and removed, and system gain and time constants in the model are identified by a data identification method.
The dynamic model is limited in the step, so that the accuracy of the control quantity of each period is guaranteed, and the precise ATO stopping control of the train is further guaranteed.
And 102, periodically acquiring the speed and position state information of the train.
For example, the speed information of the train is obtained by measuring through a vehicle-mounted wheel diameter sensor, a radar speed measurement sensor or an accelerometer of the train, and the position state information of the train is obtained by combining vehicle-mounted speed integral calculation and correction position information of the trackside transponder.
103, after the speed and position state information of the train is obtained each time, determining the control quantity of the current period through a dynamic model according to the speed and position state information obtained in the current period; and controlling the train operation according to the control quantity of the current period.
After the speed and position state information of the train is obtained every time, determining the control quantity of the current period through a dynamic model according to the speed and position state information obtained in the current period 1. 2. And controlling the train operation according to the control quantity of the current period.
The specific implementation scheme of the process is as follows:
1. the process of determining the control quantity of the current period through a dynamic model according to the speed and position state information acquired in the current period comprises the following specific implementation schemes:
1) And constructing an optimization problem according to the dynamic model.
The construction process of the optimization problem comprises the following steps:
(1) And determining a train dynamics discrete system equation according to the dynamics model.
And determining a train dynamics discrete system equation by taking the minimized speed tracking error and the minimized distance error as targets. Acquiring the position, speed and acceleration of the train as system state variables, namely x = [ s va =]T，u＝adesIs a control input for the train. The discrete system equation of train dynamics is:
x(k+1)＝Ax(k)+Bu(k)。
Figure BDA0003767416080000071
Tsis the sampling interval. k is the current sampling time, x (k) is the system state value of the current sampling time, x (k + 1) is the system state value of the next sampling time, u (k) is the control quantity of the current sampling time, and the system state value is [ sv a ]]T，u(k)＝adesS is train position and v is train speed.
By the train dynamics discrete system, the construction of the optimization problem is accurate, and the precise ATO stopping control of the train is ensured
(2) And determining an objective function and a constraint condition according to a train dynamics discrete system equation.
An objective function
The control objectives are velocity tracking and position tracking while avoiding excessive acceleration and jerk, so the objective function is:
Figure BDA0003767416080000081
wherein, NpTo predict the step size, i is the step size identification, and x (k + i | k) represents the system state value after the k + i sampling period predicted based on the current sampling time. x is a radical of a fluorine atomref(k + i | k) represents the expected value of the system state after k + i sampling period predicted based on the current sampling time, i.e., the expected value of the system state, Q is a matrix of weight coefficients of the system state, NcFor the control step length, R is the weight of the controlled variable increment, and Δ u (k + i) is the controlled variable increment after the sampling period of k + i.
The Q dimension is kept consistent with the system matrix a, e.g., the Q first term represents the velocity tracking weight, the second term represents the distance tracking weight, and the third term represents the acceleration tracking weight (typically set to 0).
The constraint is:
umin≤u(k+i)≤umax。
Δumin≤Δu(k+i)≤Δumax。
wherein u isminTo control the maximum value of quantity, umaxFor maximum control quantity, Δ uminFor control quantity increment minimum, Δ umaxFor the control quantity increment maximum, u (k + i) is the control quantity after the k + i sampling period. u. ofmin、umaxRespectively corresponding to the maximum value and the minimum value of the expected braking rate when the train is stopped; Δ umin、ΔumaxThe maximum value and the minimum value of the expected braking rate change (corresponding to the level) are respectively corresponding to the train stopping control.
By the aid of the objective function and the constraint conditions, the optimization problem is accurately constructed, and ATO accurate parking control of the train is guaranteed.
(3) And constructing an optimization problem according to the objective function and the constraint condition.
Namely that
Figure BDA0003767416080000082
Figure BDA0003767416080000091
According to the dynamic model, a train dynamic discrete system equation is determined, a target function and a constraint condition are obtained, and then an optimization problem is constructed, so that the construction of the optimization problem is accurate, and the precise ATO parking control of the train is ensured.
2) And taking the speed and position state information acquired in the current period as an initial state, and solving an optimization problem to obtain the control quantity of the current period.
In specific implementation, the speed and position state information acquired in the current period can be used as an initial state to solve the optimal solution delta u of the optimization problem*(k) In that respect And determining the train control quantity u (k) = u (k-1) + delta u x k of the current period.
When solving the optimal solution of the optimization problem, the optimization problem can be converted into a standard quadratic programming problem, and the optimal solution delta u of the optimization problem can be obtained by solving the quadratic programming problem*(k) And further obtaining an optimal solution set delta U = [ delta U = [ [ delta U ]*(k)，Δu*(k+1)，...，Δu*(k+Nc-1)]
By adopting the process, the speed and position state information acquired in the current period is used as the initial state, the optimal solution of the optimization problem is solved, and the train control quantity in the current period is determined based on the optimal solution, so that the determination of the control quantity in each period is more accurate, and the precise ATO (automatic train operation) stop control of the train is further ensured.
The method comprises the steps of taking speed and position state information acquired at each sampling moment of a current period as an initial state of a model predictive control optimization problem, obtaining a series of control input increments of a control time domain by solving the optimization problem, and acting a first element of an optimal control sequence as a control quantity of an actual control system on the actual system, namely, the optimal control rate obtained at the current sampling moment is u (k) = u (k-1) + delta u*(k) .1. The At the next sampling moment, resampling to obtain the current state information of the train, such as speed, position and the like, repeating the optimization process, and finishing the rolling time domain optimization controlAnd (5) preparing. The predictive control flow is shown in fig. 2.
By the process of constructing the optimization problem according to the dynamic model and obtaining the control quantity of the current period through the optimization problem, the control quantity of each period can be more accurately determined, and the precise ATO (automatic train operation) stop control of the train is further ensured
2. The process of controlling the train operation according to the control quantity of the current period comprises the following specific implementation schemes:
and (3) issuing the control quantity of the current period, such as the first numerical value in the train expected acceleration/braking speed control sequence, to the controlled train, and controlling the train to run by obtaining the traction/braking force to be applied by the controlled train through a vehicle control system according to the calculated expected acceleration.
According to the method provided by the embodiment, the accuracy of the prediction model in model prediction control is improved by continuously comparing, feeding back and correcting the train state information sampled by the sensor with the prediction value of the model, so that the ATO accurate parking control of the train based on incremental prediction control is completed.
The method provided by the embodiment can accurately control the train braking and parking stage, realize accurate parking control and perform optimal control according to real-time data aiming at the problems of variable environment, high parameter debugging working strength of the traditional PID controller, poor anti-interference capability and the like in the actual engineering.
In addition, the method provided by the embodiment can perform comparison and correction according to the actually output measured value and the predicted value to form closed-loop optimization, and improve the control accuracy and robustness of model prediction control.
The embodiment provides a method for accurately stopping an ATO (automatic train operation), which comprises the following steps: after the train enters an accurate parking stage, acquiring a dynamic model of the train; periodically acquiring speed and position state information of the train; after the speed and position state information of the train is acquired each time, determining the control quantity of the current period through a dynamic model according to the speed and position state information acquired in the current period; and controlling the train operation according to the control quantity of the current period. In the embodiment, after the train enters the accurate stopping stage, the dynamic model of the train is obtained, the control quantity of each period is determined through the dynamic model according to the speed and position state information obtained in each period, the train operation is further controlled according to the control quantity of each period, and the train ATO accurate stopping control through incremental prediction of the control quantity of each period is realized.
Based on the same inventive concept of the train ATO accurate parking method, the embodiment provides an electronic device, which includes: memory, processor, and computer programs.
Wherein the computer program is stored in the memory and configured to be executed by the processor to implement the above-described ATO precision stop method for a train.
In particular, the method comprises the following steps of,
and acquiring a dynamic model of the train after the train enters the accurate parking stage.
And periodically acquiring the speed and position state information of the train.
After the speed and position state information of the train is obtained each time, the control quantity of the current period is determined through a dynamic model according to the speed and position state information obtained in the current period. And controlling the train operation according to the control quantity of the current period.
Optionally, the kinetic model is:
Figure BDA0003767416080000111
wherein a is the actual acceleration of the train, adesFor the desired acceleration of the train, K is the system gain and τ is the time constant.
Optionally, determining the control quantity of the current period through a dynamic model according to the speed and position state information acquired in the current period, including:
and constructing an optimization problem according to the dynamic model.
And taking the speed and position state information acquired in the current period as an initial state, and solving an optimization problem to obtain the control quantity of the current period.
Optionally, building an optimization problem from the kinetic model, comprising:
and determining a train dynamics discrete system equation according to the dynamics model.
And determining an objective function and a constraint condition according to a train dynamics discrete system equation.
And constructing an optimization problem according to the objective function and the constraint condition.
Optionally, the discrete system equation of train dynamics is:
x(k+1)＝Ax(k)+Bu(k)。
wherein the content of the first and second substances,
Figure BDA0003767416080000112
Tsis the sampling interval. k is the current sampling time, x (k) is the system state value of the current sampling time, x (k + 1) is the system state value of the next sampling time, u (k) is the control quantity of the current sampling time, and the system state value is [ sv a ]]T，u(k)＝adesS is train position and v is train speed.
Optionally, the objective function is:
Figure BDA0003767416080000113
Figure BDA0003767416080000121
wherein N ispTo predict the step size, i is the step size identification, and x (k + i | k) represents the system state value after the k + i sampling period predicted based on the current sampling time. x is a radical of a fluorine atomref(k + i | k) represents the expected value of the system state after k + i sampling period predicted based on the current sampling instant, Q is the system state weight coefficient matrix, NcFor the control step length, R is the weight of the control quantity increment, and Δ u (k + i) is the control quantity increment after the sampling period of k + i.
Optionally, the constraint is:
umin≤u(k+i)≤umax。
Δumin≤Δu(k+i)≤Δumax。
wherein u isminTo control the maximum value of quantity, umaxFor maximum control quantity, Δ uminFor control quantity increment minimum, Δ umaxFor the control quantity increment maximum, u (k + i) is the control quantity after the k + i sampling period.
Optionally, the step of solving an optimization problem to obtain the control quantity of the current period by using the speed and position state information obtained in the current period as an initial state includes:
taking the speed and position state information acquired in the current period as initial states, and solving the optimal solution delta u of the optimization problem*(k)。
Determining the train control quantity u (k) = u (k-1) + delta u of the current period*(k)。
In the electronic device provided by this embodiment, the computer program is executed by the processor to obtain the dynamic model of the train after the train enters the precise stop stage, determine the control amount of each period through the dynamic model according to the speed and position state information obtained in each period, and further control the train operation according to the control amount of each period, thereby implementing the ATO precise stop control of the train through incremental prediction of the control amount of each period.
Based on the same inventive concept of the train ATO precision parking method, the present embodiment provides a computer on which a computer program is stored. The computer program is executed by a processor to implement the above-described ATO accurate train stop method.
In particular, the method comprises the following steps of,
and acquiring a dynamic model of the train after the train enters the accurate parking stage.
And periodically acquiring the speed and position state information of the train.
After the speed and position state information of the train is obtained each time, the control quantity of the current period is determined through a dynamic model according to the speed and position state information obtained in the current period. And controlling the train operation according to the control quantity of the current period.
Optionally, the kinetic model is:
Figure BDA0003767416080000131
wherein a is the actual acceleration of the train, adesFor the desired acceleration of the train, K is the system gain and τ is the time constant.
Optionally, determining the control quantity of the current period through a dynamic model according to the speed and position state information acquired in the current period, including:
and constructing an optimization problem according to the dynamic model.
And taking the speed and position state information acquired in the current period as an initial state, and solving an optimization problem to obtain the control quantity of the current period.
Optionally, constructing an optimization problem according to the kinetic model, comprising:
and determining a train dynamics discrete system equation according to the dynamics model.
And determining an objective function and a constraint condition according to a train dynamics discrete system equation.
And constructing an optimization problem according to the objective function and the constraint condition.
Optionally, the discrete system equation of train dynamics is:
x(k+1)＝Ax(k)+Bu(k)。
wherein the content of the first and second substances,
Figure BDA0003767416080000132
Tsis the sampling interval. k is the current sampling time, x (k) is the system state value of the current sampling time, x (k + 1) is the system state value of the next sampling time, u (k) is the control quantity of the current sampling time, and the system state value is [ sv a ]]T，u(k)＝adesS is train position and v is train speed.
Optionally, the objective function is:
Figure BDA0003767416080000133
Figure BDA0003767416080000141
wherein N ispTo predict the step size, i is the step size identification, and x (k + i | k) represents the system state value after the k + i sampling period predicted based on the current sampling time. x is the number ofref(k + i | k) represents the expected value of the system state after the k + i sampling period predicted based on the current sampling time, Q is the system state weight coefficient matrix, NcFor the control step length, R is the weight of the controlled variable increment, and Δ u (k + i) is the controlled variable increment after the sampling period of k + i.
Optionally, the constraint is:
umin≤u(k+i)≤umax。
Δumin≤Δu(k+i)≤Δumax。
wherein u isminTo control the maximum value of quantity, umaxFor maximum control quantity, Δ uminFor control quantity increment minimum, Δ umaxFor the control quantity increment maximum, u (k + i) is the control quantity after the k + i sampling period.
Optionally, the method for obtaining the control quantity of the current period by solving the optimization problem by using the speed and position state information obtained in the current period as an initial state includes:
taking the speed and position state information acquired in the current period as initial states, and solving the optimal solution delta u of the optimization problem*(k)。
Determining the train control quantity u (k) = u (k-1) + delta u of the current period*(k)。
In the computer-readable storage medium provided by this embodiment, the computer program on the computer-readable storage medium is executed by the processor to obtain a dynamic model of the train after the train enters the precise stopping stage, determine the control amount of each period through the dynamic model according to the speed and position state information obtained in each period, and further control the train operation according to the control amount of each period, thereby implementing the ATO precise stopping control of the train through incremental prediction of the control amount of each period.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the present application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims (10)

1. A method for ATO accurate stop of a train, the method comprising:
after the train enters an accurate parking stage, acquiring a dynamic model of the train;
periodically acquiring speed and position state information of the train;
after the speed and position state information of the train is obtained each time, determining the control quantity of the current period through the dynamic model according to the speed and position state information obtained in the current period; and controlling the train to run according to the control quantity of the current period.
2. The method of claim 1, wherein the kinetic model is:
Figure FDA0003767416070000011
wherein a is the actual acceleration of the train, abesFor the desired acceleration of the train, K is the system gain and τ is the time constant.
3. The method of claim 2, wherein determining the control quantity of the current cycle through the dynamic model according to the speed and position state information acquired by the current cycle comprises:
constructing an optimization problem according to the dynamic model;
and taking the speed and position state information acquired in the current period as an initial state, and solving the optimization problem to obtain the control quantity of the current period.
4. The method of claim 3, wherein said constructing an optimization problem from said kinetic model comprises:
determining a discrete system equation of train dynamics according to the dynamic model;
determining a target function and a constraint condition according to the train dynamics discrete system equation;
and constructing an optimization problem according to the objective function and the constraint condition.
5. The method of claim 4, wherein the discrete system of train dynamics equation is:
x(k+1)＝Ax(k)+Bu(k)；
wherein the content of the first and second substances,
Figure FDA0003767416070000021
Tsis the sampling interval; k is the current sampling time, x (k) is the system state value of the current sampling time, x (k + 1) is the system state value of the next sampling time, u (k) is the control quantity of the current sampling time, and the system state value is [ sv a ]]T，u(k)＝adesS is train position and v is train speed.
6. The method of claim 5, wherein the objective function is:
Figure FDA0003767416070000022
wherein N ispTo predict the step size, i is the step size indicator, and x (k + i | k) represents the predicted step size after the k + i sampling period based on the current sampling instantA system state value; x is the number ofref(k + i | k) represents the expected value of the system state after k + i sampling period predicted based on the current sampling instant, Q is the system state weight coefficient matrix, NcFor the control step length, R is the weight of the control quantity increment, and Δ u (k + i) is the control quantity increment after the sampling period of k + i.
7. The method of claim 6, wherein the constraint is:
umin≤u(k+i)≤umax；
Δumin≤Δu(k+i)≤Δumax；
wherein u isminTo control the maximum value of quantity, umaxFor maximum control quantity, Δ uminFor control quantity increment minimum, Δ umaxFor the control quantity increment maximum value, u (k + i) is the control quantity after the k + i sampling period.
8. The method according to claim 7, wherein the solving the optimization problem to obtain the control quantity of the current cycle by taking the speed and position state information obtained in the current cycle as an initial state comprises:
taking the speed and position state information acquired in the current period as initial states, and solving the optimal solution delta u of the optimization problem*(k)；
Determining the train control quantity u (k) = u (k-1) + delta u of the current period*(k)。
9. An electronic device, comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-8.
10. A computer-readable storage medium, having stored thereon a computer program; the computer program is executed by a processor to implement the method of any one of claims 1-8.
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US20160244077A1 (en) * 2015-02-23 2016-08-25 Mitsubishi Electric Research Laboratories, Inc. System and Method for Stopping Trains Using Simultaneous Parameter Estimation
CN107399333A (en) * 2017-07-24 2017-11-28 清华大学 A kind of accurate parking method of train towards train automatic Pilot
CN107515533A (en) * 2017-07-21 2017-12-26 北京交通大学 A kind of robust non-singular terminal sliding-mode control for train ATO systems
CN109625033A (en) * 2018-12-13 2019-04-16 交控科技股份有限公司 ATO controls accurate shutdown phase stopping accuracy prediction technique and device when vehicle
CN112208581A (en) * 2020-09-25 2021-01-12 通号城市轨道交通技术有限公司 ATO vehicle control method, ATO vehicle control device, electronic equipment and storage medium

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160244077A1 (en) * 2015-02-23 2016-08-25 Mitsubishi Electric Research Laboratories, Inc. System and Method for Stopping Trains Using Simultaneous Parameter Estimation
CN107515533A (en) * 2017-07-21 2017-12-26 北京交通大学 A kind of robust non-singular terminal sliding-mode control for train ATO systems
CN107399333A (en) * 2017-07-24 2017-11-28 清华大学 A kind of accurate parking method of train towards train automatic Pilot
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