Virtual rail train trajectory tracking method and device and train

Abstract

The invention provides a virtual rail train trajectory tracking method which can be applied to the technical field of intelligent transportation and automatic control. The method comprises the steps that a dynamic model is built based on an energy conversion strategy, initial state information of a train and initial control information, and the initial state information of the train is converted into target state information of the train in a vehicle coordinate system through the dynamic model; based on the speed component, reference position information corresponding to the virtual track is determined according to the actual position information of the train, and the reference position information represents position information obtained by projecting the actual position information of the train into the virtual track and comprises reference angle information, angle deviation information and reference point position information; the angle deviation information represents yaw angle deviation among a plurality of carriages under a vehicle coordinate system; determining the optimal control quantity of the train based on the target function and the reference position information; and performing trajectory tracking on the train by using the optimal control quantity. The invention further provides a virtual rail train trajectory tracking device and a train.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent transportation and automatic control technology, and in particular to a virtual track train trajectory tracking method, device and train. Background Art

[0002] The longer body of the virtual rail train increases the passenger capacity, but the double-articulated structure also puts higher requirements on steering control. In order to overcome the backward amplification effect brought by the articulated structure, the vehicle generally adopts a full-axis active steering strategy to eliminate the tail swing caused by the response of the first-axis steering being delayed step by step through the articulated plate. However, after the introduction of the full-axis steering system, the number of actuators is greater than the train's planar motion degrees of freedom, which is prone to over-driving problems, resulting in possible motion interference between the carriages, causing the torsion of the body and excessive slip of the wheels. In related technologies, the control is mainly carried out by the driver, and the steering angle of each axis is determined based on geometric methods and related principles, or a decoupled model is used to predict the control quantity.

[0003] However, the relevant technology is not suitable for fully autonomous driving. The geometry-based method lacks consideration of vehicle dynamics characteristics, and the decoupled model lacks the interaction between vehicles, making it difficult to cope with complex working conditions, and the control accuracy and computational efficiency are low. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a virtual track train trajectory tracking method, apparatus, train, device, storage medium and program product.

[0005] According to a first aspect of the present disclosure, a virtual track train trajectory tracking method is provided, including: constructing a dynamic model based on an energy conversion strategy, initial state information of a train and initial control information, using the dynamic model to convert the initial state information of the train into target state information of the train in a vehicle coordinate system, wherein the initial state information represents the state information of the train in a global coordinate system, the target state information includes velocity components of the actual point position on the train in multiple dimensions, the energy conversion strategy represents the relationship between the initial kinetic energy information and initial potential energy information of the train and the initial state information, and the train includes multiple carriages; determining reference position information corresponding to the virtual track based on the actual position information of the train based on the velocity component, wherein the reference position information represents the position information obtained by projecting the actual position information of the train onto the virtual track, including reference angle information, angle deviation information and reference point information, and the angle deviation information represents the yaw angle deviation between the multiple carriages in the vehicle coordinate system; determining the optimal control amount of the train based on the objective function and the reference position information, wherein the objective function is obtained based on the angle deviation information and the lateral deviation information, and the lateral deviation information represents the distance between the actual point position and the reference point position; and tracking the trajectory of the train using the optimal control amount.

[0006] According to an embodiment of the present disclosure, the initial state information includes a state matrix and an initial state quantity, the carriages include a head carriage, a middle carriage and a tail carriage, and the actual point positions include the first point position of the head carriage; the method also includes: determining the state matrix based on the coordinate information of the first point position and the first yaw angles of multiple carriages, the state matrix representing the state information of the train in the global coordinate system; determining the initial state quantity based on the state matrix, the instantaneous rate of change information of the state matrix and the longitudinal driving torque of the train.

[0007] According to an embodiment of the present disclosure, the head car, the middle car and the rear car each include a plurality of lateral axes; the method further includes: determining initial control information based on the lateral axis yaw angles of the plurality of lateral axes.

[0008] According to an embodiment of the present disclosure, a dynamic model is constructed based on an energy conversion strategy, initial state information of the train and initial control information, including: using a state matrix and an energy conversion strategy to determine mass-related information and force-related information of the train; adding mass-related information and force-related information to obtain energy information; and constructing a dynamic model based on mass-related information, force-related information and energy information.

[0009] According to an embodiment of the present disclosure, the method further includes: obtaining a second yaw angle of the head car in the vehicle coordinate system; and using the difference between the first yaw angle and the second yaw angle as the yaw angle of the middle car or the rear car in the vehicle coordinate system.

[0010] According to an embodiment of the present disclosure, the method further includes: determining a velocity component based on the second yaw angle and coordinate information of the first point.

[0011] According to an embodiment of the present disclosure, the method further includes: converting initial orbit information of the virtual orbit into orbit parameter information based on a preset conversion strategy, wherein the initial orbit information is characterized as a longitude and latitude sequence of the virtual orbit.

[0012] According to an embodiment of the present disclosure, the actual point positions include a first point position, a second point position between the head car and the middle car, a third point position between the middle car and the rear car, and a fourth point position of the rear car; the method also includes: taking the yaw angle between the first point position and the second point position, the yaw angle between the second point position and the third point position, and the yaw angle between the third point position and the fourth point position as reference angle information.

[0013] According to an embodiment of the present disclosure, the method further includes: determining the reference position information of the first point as the first reference point information; and determining the lateral deviation information based on the difference between the actual position information of the first point and the first reference point information.

[0014] According to an embodiment of the present disclosure, the actual position information of the train is used based on the speed component to determine the reference position information corresponding to the virtual track, including: determining the moving speed of the train based on the reference curvature, lateral deviation information, angular deviation information and speed component of the train; determining the reference position information based on the moving speed and the time corresponding to the moving speed.

[0015] A second aspect of the present disclosure provides a virtual track train trajectory tracking device, including: a model construction module, which is used to construct a dynamic model based on an energy conversion strategy, initial state information of the train and initial control information, and use the dynamic model to convert the initial state information to obtain the target state information of the train in the vehicle coordinate system, wherein the initial state information represents the state information of the train in the global coordinate system, and the target state information includes the velocity components of the actual point position on the train in multiple dimensions, and the energy conversion strategy is used to convert the initial kinetic energy information and initial potential energy information of the train into the initial state information, and the train includes multiple carriages; an information conversion module, which is used to determine the reference position information corresponding to the virtual track based on the actual position information of the train based on the velocity component, wherein the reference position information represents the position information obtained by projecting the actual position information of the train onto the virtual track, including reference angle information, angle deviation information and reference point information, and the angle deviation information represents the yaw angle deviation between the multiple carriages in the vehicle coordinate system; a control amount determination module, which is used to determine the optimal control amount of the train based on the objective function and the reference position information, wherein the objective function is obtained based on the angle deviation information and the lateral deviation information, and the lateral deviation information represents the distance between the actual point position and the reference point position. The trajectory tracking module is used to track the train trajectory using the optimal control quantity.

[0016] A third aspect of the present disclosure provides a train, comprising: a carriage; a steering actuator, used to track the trajectory of the train according to an optimal control amount; and a virtual track train trajectory tracking device as described above.

[0017] A fourth aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above method.

[0018] The fifth aspect of the present disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above method.

[0019] The sixth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0020] According to the virtual track train trajectory tracking method, device, train, equipment, storage medium and program product provided by the present disclosure, the dynamic model fully considers the dynamic characteristics of the vehicle and the real-time changes in the virtual track curvature, and the tracking response can be made in advance, thereby improving the tracking accuracy of the train. Since the energy conversion strategy can simplify the initial kinetic energy information and initial potential energy information of the train to obtain the dynamic model, the complexity of the model prediction is reduced, and the consumption of system computing resources is reduced. The reference position information is obtained by real-time conversion based on the actual point information, which realizes the real-time linear optimization of the nonlinear optimization problem, further improving the computing efficiency and the control accuracy of the train trajectory tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0022] Figure 1 A diagram schematically shows an application scenario of a virtual track train trajectory tracking method, device, and train according to an embodiment of the present disclosure;

[0023] Figure 2 A flowchart of a virtual track train trajectory tracking method according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 3A A schematic diagram of the structure of a virtual track train according to an embodiment of the present disclosure is shown;

[0025] Figure 3B A schematic diagram schematically shows an example of a process of projecting the actual position of a train to a reference position in a virtual track according to an embodiment of the present disclosure;

[0026] Figure 4 A structural block diagram of a virtual track train trajectory tracking device according to an embodiment of the present disclosure is schematically shown;

[0027] Figure 5 A block diagram of an electronic device suitable for implementing a virtual track train trajectory tracking method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0029] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0030] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0031] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0032] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0033] Regarding the steering control of virtual track trains, the relevant technologies are used in situations where a driver is present, but are not suitable for fully autonomous driving. The geometry-based method lacks consideration of vehicle dynamics characteristics, and the decoupled model lacks consideration of the forces between carriages. It is difficult to cope with complex working conditions, and the control accuracy and computational efficiency are low.

[0034] The present disclosure fully considers the dynamic characteristics of the vehicle and the real-time changes in the curvature of the virtual track through the power model, makes tracking responses in advance, and improves the tracking accuracy of the train. Since the energy conversion strategy can simplify the initial kinetic energy information and initial potential energy information of the train to obtain the power model, the complexity of the model prediction is reduced, and the consumption of system computing resources is reduced. The reference position information is obtained by real-time conversion based on the actual point information, and the nonlinear optimization problem is linearly optimized in real time, which further improves the computing efficiency and the control accuracy of the train trajectory tracking.

[0035] An embodiment of the present disclosure provides a virtual track train trajectory tracking method, including: constructing a dynamic model based on an energy conversion strategy, initial state information of a train, and initial control information, using the dynamic model to convert the initial state information of the train into target state information of the train in a vehicle coordinate system, wherein the initial state information represents the state information of the train in a global coordinate system, the target state information includes velocity components of the actual point position on the train in multiple dimensions, the energy conversion strategy represents the relationship between the initial kinetic energy information and initial potential energy information of the train and the initial state information, and the train includes multiple carriages; based on the velocity component, determining the reference position information corresponding to the virtual track from the actual position information of the train, wherein the reference position information represents the position information obtained by projecting the actual position information of the train onto the virtual track, including reference angle information, angle deviation information, and reference point information, and the angle deviation information represents the yaw angle deviation between the multiple carriages in the vehicle coordinate system; determining the optimal control amount of the train based on the objective function and the reference position information, wherein the objective function is obtained based on the angle deviation information and the lateral deviation information, and the lateral deviation information represents the distance between the actual point position and the reference point position; and using the optimal control amount to track the train trajectory.

[0036] Figure 1 The application scenario diagram of the virtual track train trajectory tracking method, device and train according to the embodiments of the present disclosure is schematically shown.

[0037] like Figure 1 As shown, the application scenario according to this embodiment may include a train 101, a network 102, a sensor 103, and a server 104. The network 102 is used to provide a medium for a communication link between the sensor 103 and the server 104. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc. The train 101 may include a steering actuator.

[0038] The train 101 may be a trackless rubber-wheeled train using electronic guidance, with ground traffic lines as virtual tracks, and may identify and autonomously travel along the virtual tracks. It is understood that the specific type of the train 101 may be determined according to actual conditions, such as a 3-set 8-axle train. The trackless rubber-wheeled train generally uses an articulated vehicle, which may be a fully axle-steering train.

[0039] Sensor 103 can be used to collect relevant information of the train, including but not limited to visual sensors, radar sensors, navigation sensors and magnetic nail sensors. It can be understood that the sensor can be set on the train body or other locations outside the body, such as on the virtual track, and can be determined according to actual needs.

[0040] The server 104 may be a server that provides various services, for example, building a dynamic model based on energy conversion strategy, initial state information of the train and initial control information, for example, determining reference position information corresponding to the virtual track based on the actual position information of the train based on the velocity component.

[0041] For example, the sensor 103 can be used to collect the initial state information and initial control information of the train, and transmit the data to the server 104 through the network 102. The server 104 analyzes the collected information and generates a control instruction. The server 104 sends the control instruction to the steering actuator of the train through the network 102, and the steering actuator adjusts the steering angle of the train according to the instruction. After the steering actuator executes the control instruction, the sensor 103 continues to monitor the running state of the train and feeds back the new data to the server 104, and the server 104 performs steering control and trajectory tracking according to the feedback data.

[0042] It should be noted that the virtual track train trajectory tracking method provided in the embodiment of the present disclosure can generally be executed by the server 104. Accordingly, the virtual track train trajectory tracking device provided in the embodiment of the present disclosure can generally be set in the server 104. The virtual track train trajectory tracking method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 104 and can communicate with the server 104. Correspondingly, the virtual track train trajectory tracking device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 104 and can communicate with the server 104.

[0043] It should be understood that Figure 1 The number of trains, networks, sensors and servers in the embodiment is only for illustration. Any number of trains, networks, sensors and servers may be provided as required.

[0044] Figure 2 The flowchart of the virtual track train trajectory tracking method according to an embodiment of the present disclosure is schematically shown.

[0045] like Figure 2 As shown, the virtual track train trajectory tracking method of this embodiment may include operations S210 to S240.

[0046] In operation S210, a dynamic model is constructed based on an energy conversion strategy, initial state information of the train and initial control information, and the dynamic model is used to convert the initial state information of the train into target state information of the train in a vehicle coordinate system, wherein the initial state information represents state information of the train in a global coordinate system, the target state information includes velocity components of actual points on the train in multiple dimensions, the energy conversion strategy represents the relationship between the initial kinetic energy information and initial potential energy information of the train and the initial state information, and the train includes multiple carriages.

[0047] In an embodiment of the present disclosure, a train may include multiple carriages and multiple actual points, a single carriage may be regarded as a marshaling, and each marshaling may include multiple transverse axes and actual points corresponding to the marshaling. An energy conversion strategy may be used to perform dynamic modeling on a multi-marshaling articulated train, and may include a Lagrangian method and a Newton-Euler method. The initial state information may be represented by a state matrix of the train in a global coordinate system. The initial control information may be determined by the initial yaw angles of multiple transverse axes. The velocity component may characterize the longitudinal and lateral velocities of the first point of the train head car in the vehicle coordinate system.

[0048] For example, using the Lagrangian method, the initial kinetic energy information and initial potential energy information of the train system can be expressed in the form of system variables, and then substituted into the Lagrangian equation, and the partial derivatives can be obtained to obtain the train's yaw dynamics model. For example, the Newton-Euler method can be used to simulate and analyze the yaw and braking stability of the train, and the dynamic response of the train during the turning process along the virtual track can be analyzed by constructing a dynamic model of the train vehicle.

[0049] In operation S220, the actual position information of the train is used to determine reference position information corresponding to the virtual track based on the velocity component, wherein the reference position information represents position information obtained by projecting the actual position information of the train onto the virtual track, including reference angle information, angle deviation information and reference point information, and the angle deviation information represents the yaw angle deviation between multiple cars in the vehicle coordinate system.

[0050] In the embodiments of the present disclosure, the actual position information may represent the position information of the train during travel. Multiple actual points of the train may be projected onto the virtual track to obtain multiple reference points, and the reference angle information may represent the angle information between multiple references of the train.

[0051] For example, the actual position of the train can be projected onto the virtual track to obtain the reference position, and then the reference angle information can be determined using the angle between adjacent reference points, and then converted to the vehicle coordinate system to obtain the angle deviation information of each car.

[0052] In operation S230, the optimal control amount of the train is determined based on the objective function and the reference position information, wherein the objective function is obtained based on the angle deviation information and the lateral deviation information, and the lateral deviation information represents the distance between the actual point and the reference point.

[0053] In operation S240, the train is tracked using the optimal control amount.

[0054] In the embodiment of the present disclosure, the objective function can be used to predict the vehicle state information of the train and the optimal control information of the vehicle steering within the target period. After the optimal control amount is determined by using the objective function, the optimal control amount can be used to perform trajectory tracking control of the train based on rolling optimization and hot start strategy.

[0055] For example, the train state prediction and optimal control quantity within the target period are combined, the target period is divided into multiple small optimization intervals, and the optimization solution is performed in each interval, so that the existing optimization results can be used as the initial solution to the next new optimization problem to accelerate the convergence of the optimization process.

[0056] According to the embodiments of the present disclosure, the dynamic model fully considers the dynamic characteristics of the vehicle and the real-time changes in the curvature of the virtual track, makes tracking responses in advance, and improves the tracking accuracy of the train. Since the energy conversion strategy can simplify the initial kinetic energy information and initial potential energy information of the train to obtain the dynamic model, the complexity of the model prediction is reduced, and the consumption of system computing resources is reduced. The reference position information is obtained by real-time conversion based on the actual point information, which realizes the real-time linear optimization of the nonlinear optimization problem, further improving the computing efficiency and the control accuracy of the train trajectory tracking.

[0057] It can be understood that the above has explained how to track the trajectory of the train, and the following will explain how to determine the initial state quantity.

[0058] According to an embodiment of the present disclosure, the initial state information includes a state matrix and an initial state quantity, the carriages include a head carriage, a middle carriage and a tail carriage, and the actual point positions include the first point position of the head carriage; the method also includes: determining the state matrix based on the coordinate information of the first point position and the first yaw angles of multiple carriages, the state matrix representing the state information of the train in the global coordinate system; determining the initial state quantity based on the state matrix, the instantaneous rate of change information of the state matrix and the longitudinal driving torque of the train.

[0059] In an embodiment of the present disclosure, the first yaw angle may characterize the yaw angles of multiple carriages in a global coordinate system, including a first head yaw angle corresponding to a head carriage, a first middle yaw angle corresponding to a middle carriage, and a first tail yaw angle corresponding to a tail carriage. The instantaneous rate of change information may be obtained by converting the state matrix to obtain the instantaneous speed and instantaneous acceleration of the vehicle. The longitudinal driving torque may characterize the longitudinal driving torque of the vehicle of the train in a global coordinate system. The initial state quantity may be determined based on the state matrix, the instantaneous rate of change information of the state matrix, and the longitudinal driving torque.

[0060] For example, a dynamic model can be constructed in a global coordinate system, where the origin is the starting point of the virtual track, the east direction of the vehicle's forward direction is the X-axis, and the north direction of the vehicle's forward direction is the Y-axis. The state matrix and initial state information are determined. The state matrix and initial state information can be recorded as q and x, respectively. vs , as shown in the following formulas (1)-(2):

[0061] (1);

[0062] (2);

[0063] Among them, x vs,Mc1 and vs,Mc1 The global coordinate information of the first point at the front end of the head compartment Mc1 can be represented respectively. The first head yaw angle, the first middle yaw angle and the first tail yaw angle in the global coordinate system can be represented respectively. It can represent the instantaneous speed in the instantaneous rate of change information. lon It can characterize the longitudinal driving torque of the train.

[0064] According to an embodiment of the present disclosure, the head car, the middle car and the rear car each include a plurality of lateral axes; the method further includes: determining initial control information based on the lateral axis yaw angles of the plurality of lateral axes.

[0065] In the embodiments of the present disclosure, the transverse axis may represent the steering actuator (eg, steering shaft) of each carriage, and the number of transverse axes of each carriage may be determined based on actual conditions.

[0066] Figure 3A The structural diagram of a virtual rail train according to an embodiment of the present disclosure is schematically shown.

[0067] like Figure 3AAs shown, taking a train with three marshaling units and eight axles as an example, in the vehicle coordinate system x, y, the head car 31 has three horizontal axes, namely the first horizontal axis 311, the second horizontal axis 312 and the third horizontal axis 313; in turn, the middle car 32 has two horizontal axes, namely the fourth horizontal axis 321 and the fifth horizontal axis 322; the rear car 33 has three horizontal axes, namely the sixth horizontal axis 331, the seventh horizontal axis 332 and the eighth horizontal axis 333, which are respectively denoted as δ 1 , δ 2 , δ 3 , δ 4 , δ 5 , δ 6 , δ 7 and δ 8 The first transverse axis 311, the second transverse axis 312, the seventh transverse axis 332 and the eighth transverse axis 333 are steered in coordination, and the remaining transverse axes can be steered independently. The actual point 34 may include a first point 341 at the front end of the head carriage Mc1, a second point 342 between the head carriage and the middle carriage, a third point 343 between the middle carriage and the rear carriage, and a fourth point 344 at the rear carriage.

[0068] After obtaining the yaw angles of multiple carriages, the initial control information of the train can be determined using the yaw angles of multiple lateral axes, which is denoted as u. The initial control information u is shown in the following formula (3):

[0069] (3);

[0070] Among them, δ i The yaw angle of the i-th transverse axis can be represented.

[0071] According to an embodiment of the present disclosure, a dynamic model is constructed based on an energy conversion strategy, initial state information of the train and initial control information, including: using a state matrix and an energy conversion strategy to determine mass-related information and force-related information of the train; adding mass-related information and force-related information to obtain energy information; and constructing a dynamic model based on mass-related information, force-related information and energy information.

[0072] In an embodiment of the present disclosure, mass-related information can be represented by a mass-related matrix of the train, which can be determined based on the state matrix, instantaneous velocity, and instantaneous acceleration. Force-related information can represent a matrix of Coriolis forces on the train. Energy information can be represented by an energy matrix of the train.

[0073] For example, taking the Lagrangian method as an example, the Lagrangian function is used to solve the vehicle kinetic energy according to the vehicle geometric parameters and inertia parameters, and the generalized force is solved by the principle of virtual work, and the vehicle dynamics equation of the following form can be obtained:

[0074] (4);

[0075] Among them, M, C, τ does not contain x vs,Mc1 and vs,Mc1 Related items. It can characterize the instantaneous speed in the instantaneous rate of change information. It can represent the instantaneous acceleration in the instantaneous rate of change information, F lon It can characterize the longitudinal driving torque of the train. It should be noted that for the dynamic modeling of multi-unit articulated vehicles, the Newton-Euler method can also be used to eliminate the workshop constraint force to obtain the dynamic model of the vehicle.

[0076] In the embodiments of the present disclosure, based on the energy conversion strategy, by introducing the geometric parameters and inertia parameters of the vehicle, the dynamic behavior of the vehicle can be more accurately described, and the dynamic model is used for the design of the vehicle control system to improve the handling performance and stability of the vehicle.

[0077] According to an embodiment of the present disclosure, the method further includes: obtaining a second yaw angle of the head car in the vehicle coordinate system; and using the difference between the first yaw angle and the second yaw angle as the yaw angle of the middle car or the rear car in the vehicle coordinate system.

[0078] In the embodiment of the present disclosure, the second yaw angle can represent the yaw angle of the head car at the target time (sampling time). After obtaining the second yaw angle, the yaw angle of the middle car and the rear car in the vehicle coordinate system can be obtained by subtracting the first yaw angle from the second yaw angle, which is recorded as , as shown in the following formula (5):

[0079] (5);

[0080] in, The initial yaw angle of the ith car in the vehicle coordinate system can be represented as the angle between the target moment and the initial yaw angle of the head car.

[0081] According to an embodiment of the present disclosure, the method further includes: determining a velocity component based on the second yaw angle and coordinate information of the first point.

[0082] In the embodiment of the present disclosure, after determining the initial yaw angle of the carriage in the vehicle coordinate system, the first actual point position J of the head carriage can be determined according to the global coordinate information of the first point position of the front end of the head carriage Mc1. 1 The longitudinal and lateral velocity components. Assume that the longitudinal driving torque F of the train is lon At the target time, it is a constant value, and the first actual point J 1 The longitudinal and lateral velocity components can be recorded as v x 、vy It can be shown as the following formula (6)-(7):

[0083] (6);

[0084] (7);

[0085] in, It can represent the initial yaw angle of the i-th car in the vehicle coordinate system, x vs,Mc1 and vs,Mc1 The global coordinate information of the first point at the front end of the head compartment can be represented respectively. After obtaining the velocity component, the second yaw angle and the dynamic model, the target state information of the vehicle in the vehicle coordinate system can be obtained, as shown in the following formula (8):

[0086] (8);

[0087] in, The instantaneous rate of change information of the yaw angle of the middle car or the rear car in the vehicle coordinate system can be represented respectively.

[0088] It can be understood that the above has explained how to use the energy conversion strategy to construct a power model of a train. The following will explain how to determine the parameter information of the virtual track.

[0089] According to an embodiment of the present disclosure, the method further includes: converting initial orbit information of the virtual orbit into orbit parameter information based on a preset conversion strategy, wherein the initial orbit information is characterized as a longitude and latitude sequence of the virtual orbit.

[0090] In the embodiments of the present disclosure, the initial track information may be a series of discrete points or paths, indicating the route that the train should follow. Depending on the actual application scenario, these points may be coordinates in a two-dimensional space or in a three-dimensional space. The virtual track may be obtained through map data, global positioning signals, sensor data, and other methods.

[0091] In the embodiment of the present disclosure, the initial orbit information of the virtual orbit may be a set of longitude and latitude sequences of length n. To simplify processing and improve computational efficiency, the initial orbit information may be converted into an n×8 information matrix. Each row of the information matrix is ​​formed as shown in the following formula (9):

[0092] (9);

[0093] Among them, s can represent the absolute distance from the starting point of the virtual track to the current point at the target time. can represent the global coordinates of the current point, t and n can represent the tangent vector and normal vector of the current point respectively, and c can be the curvature of the point.

[0094] For example, converting the initial orbit information into orbit parameter information may include the following operations S401 to S405.

[0095] In operation S401, the longitude and latitude may be converted into rectangular coordinates to obtain a longitude and latitude sequence.

[0096] In operation S402, the entire longitude and latitude sequence is traversed, and every three points can be fitted into a circle.

[0097] In operation S403, the curvature of the fitted circle is used as the curvature of the middle point, which is positive in the counterclockwise direction. The tangent vector of the middle point on the circle is used as the tangent vector of the point, which is rotated 90° counterclockwise as the normal vector.

[0098] In operation S404, the start point and the end point use the center of the circle of the adjacent points as their center, and the calculation of each quantity is the same as above.

[0099] In operation S405, the arc lengths of the arcs are accumulated from the starting point to determine the absolute distance from the starting point of the virtual track to the point.

[0100] According to an embodiment of the present disclosure, by converting a virtual track into a sequence of discrete points and calculating the curvature, tangent vector, and normal vector of each point, the shape and dynamic characteristics of the track can be described more accurately. This parameterization method can effectively improve the accuracy of trajectory generation, especially in trajectory planning in complex environments. In a virtual track train or an autonomous driving vehicle, more precise tracking control can be achieved by calculating the geometric features of the trajectory points. For example, a longitudinal speed decision model and speed regulation method based on curvature can ensure the stability of the vehicle in different operating modes. In addition, the accuracy of tracking control can be further improved by estimating the coordinates of discrete points of the target trajectory through multi-sensor fusion.

[0101] It can be understood that an example of how to determine orbit parameter information has been described above, and how to determine reference angle information will be further described below.

[0102] According to an embodiment of the present disclosure, the actual point positions include a first point position, a second point position between the head car and the middle car, a third point position between the middle car and the rear car, and a fourth point position of the rear car; the method also includes: taking the yaw angle between the first point position and the second point position, the yaw angle between the second point position and the third point position, and the yaw angle between the third point position and the fourth point position as reference angle information.

[0103] In the embodiment of the present disclosure, in order to evaluate the degree of deviation of the train from the virtual track for feedback control, the actual position information of the train can be projected onto the virtual track as reference position information. Unlike ordinary four-wheeled vehicles, virtual track trains cannot be projected as mass points because they have multiple groups (carriages). Since the vehicle has rigid body characteristics and includes a total of 4 degrees of freedom of lateral movement, there may be 4 control points on the virtual track at the same time. The second point between the head car and the middle car is recorded as the first hinge point J 2 The third point between the middle carriage and the rear carriage is recorded as the second hinge point J 3 , the fourth point of the rear carriage is marked as J 4 .

[0104] Figure 3B A schematic diagram schematically shows an example of a process of projecting the actual position of a train to a reference position in a virtual track according to an embodiment of the present disclosure.

[0105] like Figure 3B As shown, the first point J at the front end of the head carriage 31 in the actual position of the train 1 , first hinge point J 2 , the second hinge point J 3 , the fourth point J at the rear end of the tail carriage 33 4 As control points, the four actual points are subject to rigid constraints of the vehicle body length. Once the position of one point on the virtual track is determined, the positions of other points can also be determined accordingly.

[0106] For example, the projection method may include: 1 Vertical projection to the virtual track R 1 point, so that R 1 As the center of the circle, take J 1 With J 2 The distance between them is the radius of the circle, and the virtual track intersects at R 2 Points, get J 2 The projection point of R 3 and R 4 ; and then R 1 R 2 , R 2 R 3 , R 3 R 4 The angle is taken as the reference yaw angle, denoted as φ ref , and converted to the vehicle coordinate system to obtain the yaw angle deviation and the reference position of each carriage can be expressed as follows:

[0107] (10);

[0108] in, It can represent the yaw angle of the i-th car in the vehicle coordinate system.

[0109] According to an embodiment of the present disclosure, the method further includes: determining the reference position information of the first point as the first reference point information; and determining the lateral deviation information based on the difference between the actual position information of the first point and the first reference point information.

[0110] In the embodiment of the present disclosure, after determining the first point position J 1 After obtaining the reference position information, the actual position information of the first point can be subtracted from the first reference point information to obtain the sampling time J 1 The distance between it and its reference position, i.e. J 1 After determining the lateral deviation information, the real-time moving speed of the train can be calculated.

[0111] According to the embodiments of the present disclosure, the control points are projected onto the virtual track according to the rigid body characteristics of the train vehicle to calculate the reference yaw angle and yaw angle deviation, which can more accurately determine the position and posture of the vehicle on the virtual track, effectively reduce the trajectory deviation, and improve the trajectory tracking accuracy of the vehicle. At the same time, the method can dynamically adjust the control strategy according to the rigid body characteristics of the vehicle. For example, under different loads, road adhesion coefficients and other working conditions, by calculating the yaw angle deviation, the control torque of the vehicle can be adjusted in real time, thereby optimizing the adaptability of the trajectory tracking algorithm.

[0112] According to an embodiment of the present disclosure, the actual position information of the train is used based on the speed component to determine the reference position information corresponding to the virtual track, including: determining the moving speed of the train based on the reference curvature, lateral deviation information, angular deviation information and speed component of the train; determining the reference position information based on the moving speed and the time corresponding to the moving speed.

[0113] In an embodiment of the present disclosure, after determining the reference curvature, lateral deviation information, angular deviation information and speed component of the train, prediction may be performed using a vehicle dynamics model by adopting Model Predictive Control (MPC).

[0114] Since the train moves along the track, the projected position of the train can also move along the track. The moving speed of the projected position along the track is It can be expressed as the following formula (11):

[0115] (11);

[0116] Among them, K ref The reference curvature can be characterized, It can characterize the yaw angle deviation of the car body, e d The sampling time J can be characterized 1 The distance between the reference position (J 1 lateral deviation information). Assuming that the moving speed during the forecast period is a fixed value, which can be adjusted according to the movement speed. and time step to estimate the reference position information corresponding to each time step of the train during the prediction period.

[0117] In an embodiment of the present disclosure, determining the optimal control amount of the train based on the objective function and the reference position information may include: when J 1 When the point lateral deviation information and the yaw angle deviation of each car are preset values ​​(for example, 0), the four point locations are located on the virtual track. 1 The point lateral deviation and the yaw angle deviation of each car are taken as optimization targets.

[0118] For example, the cost function is constructed as shown in the following formula (12):

[0119] (12);

[0120] Among them, N p It can characterize the forecast period. , Q can be a representation of a weighted matrix; u k The control quantity that can represent the train steering at time k, ξ, and the initial state information x in the global coordinate system vs The relationship between is shown in the following formula (13):

[0121] (13);

[0122] in, For Figure 3A The vehicle coordinate system shown points to R 1 The angle of the point, initial state information x vs The dynamic equation shown in formula (4) is satisfied. For the convenience of solving, the values ​​of M, C, and τ at the initial time can be used as a constant matrix during the prediction period. vs Perform a first-order Taylor expansion to obtain the linear dynamic equation near the point. The discrete form can be obtained by the forward Euler method, as shown in the following formula (14):

[0123] (14);

[0124] Among them, u(k) can represent the control quantity at time k, x vs (k) can represent the initial state information corresponding to time k, x vs(k + 1) can represent the initial state information corresponding to the (k + 1)-th moment. Due to the physical limitations of the steering mechanism, the control variable u can satisfy u min ≤ u ≤ u max .

[0125] Thus, the standard form of the time-varying linear model predictive control (MPC) problem can be constructed. This problem can be transformed into a quadratic programming problem and numerically solved by the interior point method to obtain the optimal control variable corresponding to each time step in the prediction period.

[0126] In the embodiments of the present disclosure, after transforming the linear model predictive control problem into a quadratic programming problem, the optimal control variable can be determined by various methods.

[0127] For example, the ways to determine the optimal control variable using the interior point method include: transforming the above model predictive control problem into a standard quadratic programming form, extracting the objective function and constraint conditions; selecting an initial feasible solution A 0 , and initializing the dual variable B and the slack variable s; constructing the central path equation by introducing the logarithmic barrier function; using the Newton method to solve the central path equation to obtain the search directions of the primal variable A and the dual variable B. Update A and B according to the search directions and perform a line search to ensure that the iteration points remain within the feasible region; calculate the duality gap, and when the duality gap is less than the set threshold, the iteration can be stopped. It should be noted that the active set method or gradient projection can also be used to determine the optimal control variable.

[0128] In the embodiments of the present disclosure, after determining the optimal control variable, the optimal control variable can be applied to the steering actuator to perform trajectory tracking and steering control on the train. It can be understood that the obtained optimal control variable is represented by the optimal control variable sequence in the prediction period. The first control variable in the sequence can be applied to the steering actuator (for example, the steering axle - tire), and after updating the state variables from the sensor at the next sampling moment, the optimal control is solved again to achieve the rolling optimization process; the optimal control variable sequence obtained at the previous sampling time can be used as the initial value for the next solution to achieve the warm start of the solution.

[0129] According to the embodiments of the present disclosure, the virtual track train trajectory tracking method based on Model Predictive Control (MPC) can reduce the calculation time of the system by adopting the warm start technology and using the optimal control sequence at the last sampling moment as the initial guess for the current optimization. In particular, for vehicle control systems with high real-time requirements, the warm start can shorten the single solution time by 30%-70%, which can meet the millisecond-level control cycle requirements. Furthermore, the hot start uses historical information to accelerate convergence, allowing the controller to adapt to environmental disturbances (such as trajectory deviation and sudden change in wind resistance) more quickly, and uses the smoothness of the historical solution to transfer to the current optimization, suppressing high-frequency jitter of the control quantity (such as sudden jump in the steering angle), and improving the robustness of the vehicle control system to cope with dynamic environments.

[0130] Based on the above virtual track train trajectory tracking method, the present disclosure also provides a virtual track train trajectory tracking device. Figure 4 The device is described in detail.

[0131] Figure 4 The structural block diagram of the virtual track train trajectory tracking device according to an embodiment of the present disclosure is schematically shown.

[0132] like Figure 4 As shown, the virtual track train trajectory tracking device of this embodiment includes a model building module 410, an information conversion module 420, a control amount determination module 430 and a trajectory tracking module 440.

[0133] The model building module 410 is used to build a dynamic model based on the energy conversion strategy, the initial state information of the train and the initial control information, and use the dynamic model to convert the initial state information to obtain the target state information of the train in the vehicle coordinate system, wherein the initial state information represents the state information of the train in the global coordinate system, the target state information includes the velocity components of the actual point on the train in multiple dimensions, and the energy conversion strategy is used to convert the initial kinetic energy information and initial potential energy information of the train into the initial state information, and the train includes multiple carriages. In one embodiment, the model building module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0134] The information conversion module 420 is used to determine the reference position information corresponding to the virtual track based on the actual position information of the train, wherein the reference position information represents the position information obtained by projecting the actual position information of the train onto the virtual track, including reference angle information, angle deviation information, and reference point information, and the angle deviation information represents the yaw angle deviation between multiple cars in the vehicle coordinate system. In one embodiment, the information conversion module 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0135] The control amount determination module 430 is used to determine the optimal control amount of the train based on the objective function and the reference position information, wherein the objective function is obtained based on the angle deviation information and the lateral deviation information, and the lateral deviation information represents the distance between the actual point and the reference point. In one embodiment, the control amount determination module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0136] The trajectory tracking module 440 is used to track the trajectory of the train using the optimal control amount. In one embodiment, the trajectory tracking module 440 can be used to perform the operation S240 described above, which will not be described in detail here.

[0137] According to the embodiments of the present disclosure, the model building module 410, the information conversion module 420, the control amount determination module 430 and the trajectory tracking module 440 in the virtual track train trajectory tracking device are used. The dynamic model fully considers the dynamic characteristics of the vehicle and the real-time changes in the curvature of the virtual track, and makes a tracking response in advance, thereby improving the tracking accuracy of the train. Since the energy conversion strategy can simplify the initial kinetic energy information and initial potential energy information of the train to obtain a dynamic model, the complexity of the model prediction is reduced, and the consumption of system computing resources is reduced. The reference position information is obtained by real-time conversion based on the actual point information, which realizes the real-time linear optimization of the nonlinear optimization problem, further improving the computing efficiency and the control accuracy of the train trajectory tracking.

[0138] According to an embodiment of the present disclosure, the initial state information includes a state matrix and an initial state quantity, the carriages include a head carriage, a middle carriage and a tail carriage, and the actual point position includes a first point position of the head carriage; the device also includes: a state matrix determination module and an initial state quantity determination module. The state matrix determination module is used to determine the state matrix based on the coordinate information of the first point position and the first yaw angles of multiple carriages, and the state matrix represents the state information of the train in the global coordinate system; the initial state quantity determination module is used to determine the initial state quantity based on the state matrix, the instantaneous rate of change information of the state matrix and the longitudinal driving torque of the train.

[0139] According to an embodiment of the present disclosure, the head car, the middle car and the rear car each include multiple transverse axes; the device also includes: an initial control information determination module for determining initial control information based on the transverse axis yaw angles of the multiple transverse axes.

[0140] According to an embodiment of the present disclosure, the model building module 410 includes: an information determination submodule, a summing submodule and a power model building submodule. The information determination submodule is used to determine the mass-related information and force-related information of the train using the state matrix and the energy conversion strategy; the summing submodule is used to sum the mass-related information and the force-related information to obtain energy information; and the power model building submodule is used to build a power model based on the mass-related information, the force-related information and the energy information.

[0141] According to an embodiment of the present disclosure, the device further includes: a second yaw angle acquisition module and a yaw angle as module. The second yaw angle acquisition module is used to acquire the second yaw angle of the head compartment in the vehicle coordinate system; the yaw angle as module is used to use the difference between the first yaw angle and the second yaw angle as the yaw angle of the middle compartment or the rear compartment in the vehicle coordinate system.

[0142] According to an embodiment of the present disclosure, the device further includes: a velocity component determination module, configured to determine the velocity component based on the second yaw angle and the coordinate information of the first point.

[0143] According to an embodiment of the present disclosure, the device further includes: an information conversion module, configured to convert initial orbit information of the virtual orbit into orbit parameter information based on a preset conversion strategy, wherein the initial orbit information is represented as a longitude and latitude sequence of the virtual orbit.

[0144] According to an embodiment of the present disclosure, the actual points include a first point, a second point between the head car and the middle car, a third point between the middle car and the rear car, and a fourth point of the rear car; the device also includes: angle information as a module, used to use the yaw angle between the first point and the second point, the yaw angle between the second point and the third point, and the yaw angle between the third point and the fourth point as reference angle information.

[0145] According to an embodiment of the present disclosure, the device further includes: a first reference point information determination module and a lateral deviation information determination module. The first reference point information determination module is used to determine the reference position information of the first point as the first reference point information; the lateral deviation information determination module is used to determine the lateral deviation information based on the difference between the actual position information of the first point and the first reference point information.

[0146] According to an embodiment of the present disclosure, the information conversion module 420 includes: a moving speed determination submodule and a moving speed determination submodule. The moving speed determination submodule is used to determine the moving speed of the train based on the reference curvature, lateral deviation information, angle deviation information and speed component of the train; and the reference position information determination submodule is used to determine the reference position information based on the moving speed and the time corresponding to the moving speed.

[0147] According to an embodiment of the present disclosure, any multiple modules of the model building module 410, the information conversion module 420, the control amount determination module 430 and the trajectory tracking module 440 can be combined in one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the model building module 410, the information conversion module 420, the control amount determination module 430 and the trajectory tracking module 440 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware and firmware or by a suitable combination of any of them. Alternatively, at least one of the model building module 410 , the information conversion module 420 , the control amount determination module 430 , and the trajectory tracking module 440 may be at least partially implemented as a computer program module, which may perform a corresponding function when executed.

[0148] According to an embodiment of the present disclosure, the present disclosure provides a train, comprising: a carriage; a steering actuator, used to track the trajectory of the train according to an optimal control amount; and a virtual track train trajectory tracking device as described above.

[0149] Figure 5 A block diagram of an electronic device suitable for implementing a virtual track train trajectory tracking method according to an embodiment of the present disclosure is schematically shown.

[0150] like Figure 5 As shown, the electronic device according to the embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0151] In RAM 503, various programs and data required for the operation of the electronic device are stored. The processor 501, ROM 502 and RAM 503 are connected to each other via a bus 504. The processor 501 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 502 and / or RAM 503. It should be noted that the program can also be stored in one or more memories other than ROM 502 and RAM 503. The processor 501 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0152] According to an embodiment of the present disclosure, the electronic device may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The electronic device may further include one or more of the following components connected to the input / output (I / O) interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 508 including a hard disk, etc.; and a communication portion 509 including a network interface card such as a LAN card, a modem, etc. The communication portion 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage portion 508 as needed.

[0153] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0154] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 502 and / or RAM 503 described above and / or one or more memories other than ROM 502 and RAM 503.

[0155] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the virtual track train trajectory tracking method provided by the embodiment of the present disclosure.

[0156] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 501. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0157] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 509, and / or installed from the removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0158] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0159] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0160] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0161] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0162] The embodiments of the present disclosure are described above. However, these embodiments are only for the purpose of illustration and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A virtual track train trajectory tracking method, comprising: A dynamic model is constructed based on an energy conversion strategy, initial state information of the train and initial control information, and the initial state information of the train is converted into target state information of the train in a vehicle coordinate system by using the dynamic model, wherein the initial state information represents state information of the train in a global coordinate system, the target state information includes velocity components of actual points on the train in multiple dimensions, the energy conversion strategy represents the relationship between initial kinetic energy information and initial potential energy information of the train and the initial state information, and the train includes multiple carriages; Determine reference position information corresponding to a virtual track based on the actual position information of the train, wherein the reference position information represents position information obtained by projecting the actual position information of the train onto the virtual track, including reference angle information, angle deviation information, and reference point position information, and the angle deviation information represents a yaw angle deviation between the plurality of carriages in the vehicle coordinate system; Determining an optimal control amount of the train based on an objective function and the reference position information, wherein the objective function is obtained based on the angle deviation information and the lateral deviation information, and the lateral deviation information represents the distance between the actual point position and the reference point position; The optimal control amount is used to track the trajectory of the train.

2. The tracking method according to claim 1, wherein the initial state information includes a state matrix and an initial state quantity, the carriages include a head carriage, a middle carriage and a tail carriage, and the actual position includes a first position of the head carriage; The method further comprises: Determine the state matrix based on the coordinate information of the first point and the first yaw angles of the plurality of carriages, wherein the state matrix represents the state information of the train in the global coordinate system; The initial state quantity is determined based on the state matrix, the instantaneous rate of change information of the state matrix and the longitudinal driving torque of the train.

3. The tracking method according to claim 2, wherein the head carriage, the middle carriage and the tail carriage each include a plurality of transverse axes; The method further comprises: The initial control information is determined based on a plurality of lateral axis yaw angles of the lateral axes.

4. The tracking method according to claim 2, constructing a power model based on the energy conversion strategy, the initial state information and the initial control information of the train, comprising: Determine the mass-related information and force-related information of the train by using the state matrix and the energy conversion strategy; Adding the mass-related information and the force-related information to obtain energy information; The dynamic model is constructed based on the mass-related information, the force-related information and the energy information.

5. The tracking method according to claim 1, further comprising: Acquire a second yaw angle of the head compartment in the vehicle coordinate system; The difference between the first yaw angle and the second yaw angle is used as the yaw angle of the middle car or the rear car in the vehicle coordinate system.

6. The tracking method according to claim 5, further comprising: The velocity component is determined based on the second yaw angle and the coordinate information of the first point.

7. The tracking method according to claim 1, further comprising: The initial orbit information of the virtual orbit is converted into orbit parameter information based on a preset conversion strategy, wherein the initial orbit information is represented by a longitude and latitude sequence of the virtual orbit.

8. The tracking method according to any one of claims 1 to 7, wherein the actual point position includes the first point position, the second point position between the head carriage and the middle carriage, the third point position between the middle carriage and the rear carriage, and the fourth point position of the rear carriage; The method further comprises: The yaw angle between the first point and the second point, the yaw angle between the second point and the third point, and the yaw angle between the third point and the fourth point are used as the reference angle information.

9. The tracking method according to claim 1, further comprising: Determine the reference position information of the first point as the first reference point information; The lateral deviation information is determined based on a difference between the actual position information of the first point and the first reference point information.

10. The tracking method according to claim 9, determining reference position information corresponding to the virtual track based on the actual position information of the train according to the velocity component, comprising: determining a moving speed of the train based on a reference curvature of the train, the lateral deviation information, the angular deviation information, and the speed component; The reference position information is determined based on the moving speed and a time corresponding to the moving speed.

11. A virtual track train track tracking device, comprising: A model building module, used to build a dynamic model based on an energy conversion strategy, initial state information of the train and initial control information, and use the dynamic model to convert the initial state information to obtain target state information of the train in a vehicle coordinate system, wherein the initial state information represents the state information of the train in a global coordinate system, the target state information includes velocity components of actual points on the train in multiple dimensions, the energy conversion strategy is used to convert initial kinetic energy information and initial potential energy information of the train into the initial state information, and the train includes multiple carriages; an information conversion module, configured to determine reference position information corresponding to a virtual track from the actual position information of the train based on the velocity component, wherein the reference position information represents position information obtained by projecting the actual position information of the train onto the virtual track, including reference angle information, angle deviation information, and reference point position information, wherein the angle deviation information represents a yaw angle deviation between the plurality of carriages in the vehicle coordinate system; A control amount determination module, used to determine the optimal control amount of the train based on an objective function and the reference position information, wherein the objective function is obtained based on the angle deviation information and the lateral deviation information, and the lateral deviation information represents the distance between the actual point position and the reference point position; A trajectory tracking module is used to track the trajectory of the train using the optimal control amount.

12. A train comprising: car; A steering actuator, used for tracking the trajectory of the train according to an optimal control amount; A virtual track train trajectory tracking device as claimed in claim 11.

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