Multi-joint trackless rubber-tyred train control method, device, equipment and medium
By using the LQR tracking prediction model and optimizing the objective function, the latency problem of multi-articulated trackless rubber-tired trains in automatic driving was solved, achieving higher precision and real-time control effects.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-28
- Publication Date
- 2026-03-27
AI Technical Summary
In motion control, multi-articulated trackless rubber-tired trains are difficult to achieve precise trajectory tracking control due to delays, especially in the response of automatic driving motion actuators, where communication delays and actuator delays exist.
The LQR tracking prediction model is adopted. By acquiring the steering angle data of the front and rear wheels, a vehicle kinematic model is established, an actuator delay model is determined, and the actuator delay is corrected by optimizing the objective function. The LQR tracking prediction model is then constructed to obtain the optimal control quantity.
This improves the control precision and real-time performance of multi-articulated trackless rubber-tired trains, ensuring accurate and stable train operation.
Smart Images
Figure CN120178721B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of train control technology, and in particular to a multi-articulated trackless rubber-tyred train control method, device, equipment and medium. BACKGROUND
[0002] The movement of the multi-articulated trackless rubber-tyred train is related to multiple axles, and the kinematic model needs to consider this relationship in the embodiment of the present application. Secondly, the train will have a delay phenomenon in the response process of the automatic driving motion executor, which includes the communication delay time from the vehicle main controller issuing an instruction to the executor receiving the instruction, the delay time of the executor itself in the response process, and the communication delay time of the executor feedback state to the vehicle main controller and receiving the state, etc., so that it is difficult to achieve precise trajectory tracking control of the vehicle in the motion control process. SUMMARY
[0003] The main purpose of the embodiment of the present application is to provide a multi-articulated trackless rubber-tyred train control method, device, equipment and medium, which improves the control accuracy of the multi-articulated trackless rubber-tyred train.
[0004] One aspect of the present application provides a multi-articulated trackless rubber-tyred train control method, comprising:
[0005] Obtaining front and rear wheel steering angle data of a target multi-articulated trackless rubber-tyred train, the front and rear wheel steering angle data including front wheel steering angle data and rear wheel steering angle data;
[0006] Analyzing the front and rear wheel steering angle data using an LQR tracking prediction model to obtain a control amount of the target multi-articulated trackless rubber-tyred train;
[0007] According to the control amount, performing control processing of the target train through a steering executor;
[0008] The LQR tracking prediction model is obtained by the following steps:
[0009] According to the front and rear wheel steering angle data, a front and rear wheel steering relationship model of the target multi-articulated trackless rubber-tyred train is determined through a kinematic model;
[0010] According to the line information, the kinematic model and the front and rear wheel steering angle data, a first executor delay model is determined;
[0011] According to the response delay of the executor and the tracking route information of the future preset time period, the first executor delay model is corrected to obtain a second executor delay model;
[0012] Taking the minimum control amount and the minimum state amount as the optimization target, the second executor delay model is optimized to obtain a target adjustment model.
[0013] determining the LQR tracking prediction model according to the curvature information through the target adjustment model.
[0014] According to the multi-joint trackless rubber-tyred train control method, the front-rear wheel steering relationship model of the target multi-joint trackless rubber-tyred train is determined through a kinematic model according to the front-rear wheel steering angle data, and the method comprises the following steps.
[0015] The vehicle kinematic model is determined with the vehicle front axle center of the target multi-joint trackless rubber-tyred train as follows:
[0016]
[0017] wherein, is the vehicle front axle center coordinate, is the yaw angle, is the front wheel steering angle, is the rear wheel steering angle, L is the wheelbase, v is the vehicle speed.
[0018] The vehicle front-rear wheel steering relationship model is determined according to the front wheel steering angle data and the rear wheel steering angle data as follows:
[0019]
[0020] wherein, is a constant obtained by fitting, is time.
[0021] According to the multi-joint trackless rubber-tyred train control method, a first actuator delay model is determined according to the track information, the kinematic model and the front-rear wheel steering angle data, and the method comprises the following steps.
[0022] According to the front wheel steering angle data, the rear wheel steering angle data and the curvature, the following is obtained through kinematic analysis:
[0023]
[0024] wherein, is the front wheel steering angle data of the reference waypoint, is the rear wheel steering angle data of the reference waypoint, wherein:
[0025]
[0026]
[0027] wherein, is the dead zone steering angle;
[0028] The first actuator delay model is determined according to the reference point is:
[0029]
[0030] wherein
[0031]
[0032] wherein
[0033]
[0034] wherein denotes a first order Taylor expansion of the vehicle kinematic model at the reference waypoint, are the lateral, longitudinal, yaw angle of the reference waypoint position and the front wheel steering angle of the reference waypoint, respectively, is .
[0035] According to the multi-articulated trackless rubber-tyred train control method, wherein the first actuator delay model is corrected according to the response delay of the actuator and the tracking route information of a future preset time period to obtain a second actuator delay model, comprising:
[0036] The first order inertia link and the pure time delay link of the response model of the actuator are:
[0037]
[0038] The inertia time constant and the pure time delay constant of the target multi-articulated trackless rubber-tyred train are obtained through data acquisition. is the command of the wheel steering angle at time t, is the reference wheel steering angle at time t, wherein the first order inertia link represents the response delay of the actuator, and wherein the pure time delay link represents the tracking route information of a future preset time period.
[0039] According to the first actuator delay model, the first order inertia link and the pure time delay link, the second actuator delay model obtained is:
[0040]
[0041] wherein
[0042]
[0043]
[0044]
[0045]
[0046]
[0047] in For the discretized time interval, Represents state variables. This indicates that a first-order inertial element and a pure time-delay element are added to the delay model of the first actuator.
[0048] According to the aforementioned control method for multi-articulated trackless rubber-tired trains, the second actuator delay model is optimized using the minimum control quantity and minimum state quantity as optimization objectives to obtain a target adjustment model, including:
[0049] The objective function, determined by minimizing the control quantity and the minimum state quantity, is as follows:
[0050]
[0051] in
[0052]
[0053] in,
[0054] The objective function is used to optimize the second actuator delay model to obtain the target adjustment model.
[0055] According to the aforementioned multi-articulated trackless rubber-tired train control method, the LQR tracking prediction model is determined based on curvature information using the target adjustment model, including:
[0056] Based on the curvature information of the target multi-articulated trackless rubber-tired train at the current moment and the curvature information of the future preset time period, the LQR tracking prediction model is determined as follows:
[0057]
[0058] in
[0059]
[0060] Indicates according to the first A matrix generated from reference points, where Take 0 at that time.
[0061] According to the aforementioned control method for multi-articulated trackless rubber-tired trains, the front and rear wheel steering angle data are analyzed using an LQR tracking prediction model to obtain the control quantities of the target multi-articulated trackless rubber-tired train, including:
[0062] by For from the first Reference points to the last reference point the value of the optimal objective function, resulting in
[0063]
[0064] wherein is equal to
[0065] Further, according to the control amount calculation formula is:
[0066]
[0067] According to the control amount calculation formula, the control amount is obtained.
[0068] Another aspect of the embodiment of the application provides a multi-articulated trackless rubber-tyred train control device, comprising:
[0069] A first module is configured to acquire front and rear wheel steering angle data of a target multi-articulated trackless rubber-tyred train, wherein the front and rear wheel steering angle data comprises front wheel steering angle data and rear wheel steering angle data.
[0070] A second module is configured to analyze the front and rear wheel steering angle data by using an LQR tracking prediction model, so as to obtain a control amount of the target multi-articulated trackless rubber-tyred train.
[0071] A third module is configured to perform control processing of the target train by using a steering actuator according to the control amount.
[0072] The LQR tracking prediction model is obtained by the following modules:
[0073] A fourth module is configured to determine a front and rear wheel steering relationship model of the target multi-articulated trackless rubber-tyred train by using a kinematics model according to the front and rear wheel steering angle data.
[0074] A fifth module is configured to determine a first actuator delay model according to track information, the kinematics model and the front and rear wheel steering angle data.
[0075] A sixth module is configured to correct the first actuator delay model according to a response delay of an actuator and tracking route information in a future preset time period, so as to obtain a second actuator delay model.
[0076] A seventh module is configured to optimize the second actuator delay model by taking a minimum control amount and a minimum state amount as an optimization target, so as to obtain a target adjustment model.
[0077] An eighth module is configured to determine the LQR tracking prediction model according to the target adjustment model and curvature information.
[0078] Another aspect of the embodiments of the present application provides an electronic device, comprising a processor and a memory;
[0079] The memory is configured to store a program;
[0080] The processor executes the program to implement the method as described above.
[0081] The embodiments of the present application also disclose a computer program product or a computer program, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device can read the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the computer device to perform the method as described above.
[0082] The present application has the advantages that: a triangular function is used to fit a nonlinear model of a front and rear axle steering angle of a vehicle; a parameter identification technology is used to obtain a front axle steering angle response delay model, a nonlinear kinematic model of the vehicle is established, and a linear time-varying model of the vehicle is obtained by linearizing the nonlinear kinematic model; an LQR control strategy of the linear time-varying system is obtained; an optimization objective function is constructed, and the kinematic model is equivalently converted based on the optimization objective function; the control amount for accurate and stable operation of the train is obtained by solving the constructed optimization problem; the optimal control amount of the finite step tracking is obtained by solving the LQR problem of the time-varying system through the iteration algorithm of the dynamic programming, and the control accuracy and the real-time performance of the control are improved. BRIEF DESCRIPTION OF DRAWINGS
[0083] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
[0084] Figure 1 is a control method flowchart of the multi-articulated trackless rubber-tyred train according to the embodiments of the present application.
[0085] Figure 2 is a control device flowchart of the multi-articulated trackless rubber-tyred train according to the embodiments of the present application. DETAILED DESCRIPTION
[0086] Embodiments of the present application are described in detail below with reference to the attached drawings, which show by way of example, embodiments in which the same or similar elements have the same or similar designations. In the following description, suffixes "module", "part" or "unit" used for elements are merely intended for facilitating explanation of the present application and by themselves do not have any special meaning or function. Therefore, "module", "part" or "unit" can be mixedly used. "First", "second", etc. are used only to distinguish technical features for the purpose of explanation and cannot be understood to indicate or imply relative importance or implicitly indicate the number of indicated technical features or implicitly indicate the order of the indicated technical features. In the following description, consecutive numbers of method steps are for the convenience of review and understanding in conjunction with the overall technical solution of the present application and the logical relationship between the steps, and adjusting the implementation order between the steps does not affect the technical effects achieved by the technical solution of the present application. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application and cannot be understood as a limitation of the present application.
[0087] Figure 1 is a control method flowchart of a multi-articulated trackless rubber-tyred train according to an embodiment of the present application. It includes a control step and an LQR tracking model generation step, wherein the control step includes but is not limited to steps S100-S300:
[0088] S100, obtaining front and rear wheel steering angle data of a target multi-articulated trackless rubber-tyred train, the front and rear wheel steering angle data including front wheel steering angle data and rear wheel steering angle data;
[0089] S200, analyzing the front and rear wheel steering angle data using an LQR tracking prediction model to obtain a control amount of the target multi-articulated trackless rubber-tyred train;
[0090] S300, according to the control amount, executing control processing of the target train through a steering actuator.
[0091] The LQR tracking model generation step includes but is not limited to steps S400-S800:
[0092] S400, determining a front and rear wheel steering relationship model of the target multi-articulated trackless rubber-tyred train through a kinematic model according to the front and rear wheel steering angle data.
[0093] In some embodiments, since part of the parameters of the vehicle dynamics model need to be identified and are difficult to obtain, a four-wheel steering vehicle kinematic model is used as a prediction model. In the geodetic coordinate system, is the front axle center coordinate, is the yaw angle, is the front wheel steering angle, is the rear wheel steering angle, L is the wheelbase,v The vehicle speed is v. The vehicle kinematics model is established as follows with the vehicle front axle as the center:
[0094] (1)
[0095] The trigonometric function fitting is performed by collecting the data of the front and rear wheel steering angles (forward, left turn, right turn) so that the collected data can cover the information of each steering angle as much as possible. The established green car vehicle front and rear wheel steering relationship model is
[0096] (2)
[0097] wherein is a constant obtained by fitting.
[0098] S500, according to the line information, the kinematics model and the front and rear wheel steering angle data, determining the first actuator delay model.
[0099] In some embodiments, the curvature is , respectively, the reference front wheel steering angle and the front wheel steering angle, by kinematics analysis, and have the following relationship:
[0100]
[0101] In order to simplify the calculation, the relationship (2) between and is simplified as follows:
[0102]
[0103] wherein is the dead zone steering angle. Finally, the curvature of the reference path is calculated as :
[0104] (3)
[0105] Since the model of formula (1) is a relatively complex nonlinear model, in order to facilitate the analysis of the model, the nonlinear model in formula (1) is first-order Taylor expanded at the reference point as follows:
[0106] (4)
[0107] wherein are the horizontal coordinate, the vertical coordinate, the yaw angle of the reference path position and the front wheel steering angle of the reference path calculated by formula (3) , is .
[0108] For the convenience of analysis and calculation, define
[0109]
[0110] Rewrite (4) as:
[0111] (5)
[0112] S600, according to the response delay of the actuator and the future preset period of the tracking route information, the first actuator delay model is corrected to obtain the second actuator delay model.
[0113] In some embodiments, considering the steering actuator response delay, the following method is used to obtain:
[0114] Generally, the response model of the steering actuator is established as a first-order inertia link and a pure time delay link as follows:
[0115] (6)
[0116] By collecting real vehicle data, and then based on the collected data, the parameter identification method can obtain the parameters , are the inertia time constant and the pure time delay constant, is the command of the wheel steering angle at time t, is the reference wheel steering angle at time t, and (6) is added to the kinematics model (5) to obtain the kinematics model considering the steering actuator response delay:
[0117]
[0118] Define , and rewrite the above model as:
[0119] (7)
[0120] Wherein
[0121]
[0122] Discretize the model (7) about to obtain:
[0123]
[0124] Wherein is the discretized time interval.
[0125] Define as:
[0126]
[0127] The discretized model is obtained:
[0128] (8)
[0129] S700, the second actuator delay model is optimized to the minimum control amount and the minimum state amount as the optimization target, and the target adjustment model is obtained.
[0130] The formula (8) is added to the state amount to obtain:
[0131] (9)
[0132] Wherein the state amount and the control amount of the respective model. However, in order to facilitate the solution, the optimization target function of the optimal control problem is generally established as follows:
[0133]
[0134] Wherein is the state amount and the control amount at k moment, according to the above target function cannot be the front wheel steering angle , because the optimization target is to hope is smaller, which does not conform to the control effect that the embodiment of the application wants, but the embodiment of the application wants the change range of the front and rear steering to be smaller, that is is smaller, because this can ensure the smooth running of the train, so the embodiment of the application needs to take as the control amount, so the model in the formula (9) is adjusted as follows:
[0135] (10)
[0136] Define Rewrite the formula (10) as:
[0137]
[0138] Wherein
[0139]
[0140] S800, the LQR tracking prediction model is determined through the target adjustment model according to the curvature information.
[0141] In some embodiments, the above model only considers the curvature information at the current moment, in order to realize more accurate control, the curvature information in the prediction interval is also considered in the vehicle motion model.
[0142] Definition , which is equivalent to (10) by transformation:
[0143] (11)
[0144] The above formula is written in the form of a block:
[0145]
[0146] The LQR optimization problem required by the embodiment of the application is finally constructed:
[0147] (12)
[0148] wherein
[0149]
[0150] denotes a matrix generated according to the first reference point, and the values of different may be different, so this is a time-varying linear system, = 0, it can be seen that the objective of the optimization problem is to minimize the deviation and the amount of steering change, so it can ensure that the control amount obtained can accurately and smoothly complete the tracking task.
[0151] In some embodiments, the optimization problem (12) is solved, and is defined as the value of the optimal objective function from the first reference point to the last reference point , that is:
[0152]
[0153] From the definition, it can be obtained that
[0154] (13)
[0155] When
[0156] (14)
[0157] , the minimum value of is obtained, and (14) is substituted into (13) to obtain
[0158]
[0159] Definition
[0160] (15)
[0161] may be obtained
[0162] (16)
[0163] may be obtained may be obtained may be obtained may be obtained , that is, the control amount required by the embodiment of the application is obtained, and here is equal to .
[0164] Referring to Figure 2 , Figure 2 is a flowchart of a control device of a multi-articulated trackless rubber-tyred train according to the embodiment of the application, and the device includes a first module 210, a second module 220, a third module 230, a fourth module 240, a fifth module 250, a sixth module 260, a seventh module 270 and an eighth module 280.
[0165] The first module is configured to obtain front and rear wheel steering angle data of a target multi-articulated trackless rubber-tyred train, and the front and rear wheel steering angle data includes front wheel steering angle data and rear wheel steering angle data; the second module is configured to analyze the front and rear wheel steering angle data by using an LQR tracking prediction model to obtain a control amount of the target multi-articulated trackless rubber-tyred train; the third module is configured to perform control processing of the target train by using a steering actuator according to the control amount; the LQR tracking prediction model is obtained by the following modules: the fourth module is configured to determine a front and rear wheel steering relationship model of the target multi-articulated trackless rubber-tyred train by using a kinematics model according to the front and rear wheel steering angle data; the fifth module is configured to determine a first actuator delay model according to track information, the kinematics model and the front and rear wheel steering angle data; the sixth module is configured to correct the first actuator delay model according to a response delay of the actuator and tracking route information in a future preset time period to obtain a second actuator delay model; the seventh module is configured to optimize the second actuator delay model by taking a minimum control amount and a minimum state amount as an optimization target to obtain a target adjustment model; and the eighth module is configured to determine the LQR tracking prediction model by using the target adjustment model according to curvature information.
[0166] Exemplarily, under cooperation of the first module to the eighth module in the device, the embodiment device can implement any one of the foregoing multi-articulated trackless rubber-tyred train control methods, that is, obtaining front and rear wheel steering angle data of a target multi-articulated trackless rubber-tyred train, the front and rear wheel steering angle data including front wheel steering angle data and rear wheel steering angle data; analyzing the front and rear wheel steering angle data by using an LQR tracking prediction model to obtain a control quantity of the target multi-articulated trackless rubber-tyred train; and performing control processing of the target train by a steering actuator according to the control quantity. The LQR tracking prediction model is obtained by the following steps: determining a front and rear wheel steering relationship model of the target multi-articulated trackless rubber-tyred train by a kinematics model according to the front and rear wheel steering angle data; determining a first actuator delay model according to line information, the kinematics model and the front and rear wheel steering angle data; correcting the first actuator delay model according to a response delay of the actuator and tracking route information in a future preset time period to obtain a second actuator delay model; optimizing the second actuator delay model to obtain a target adjustment model, with the minimum control quantity and the minimum state quantity as optimization objectives; and determining the LQR tracking prediction model according to curvature information through the target adjustment model. The application has the beneficial effects that: a vehicle front and rear axle steering angle nonlinear model is obtained by using a trigonometric function fitting; a front axle steering angle response delay model is obtained by using a parameter identification technology, a vehicle nonlinear kinematics model is established, and a vehicle linearized time-varying model is obtained by linearizing the vehicle nonlinear kinematics model, an LQR control strategy of the linear time-varying system is obtained, an optimization objective function is constructed, and the kinematics model is equivalently converted based on the optimization objective function, so that the control quantity for accurate and stable operation of the train can be obtained by solving the constructed optimization problem; the LQR problem with the time-varying system is solved by using an iterative algorithm of dynamic programming, and the optimal control quantity of the finite-step tracking is obtained, thereby improving the control precision and the real-time performance of the control.
[0167] The embodiment of the application further provides an electronic device, which comprises a processor and a memory.
[0168] The memory stores a program.
[0169] The processor executes the program to perform the multi-articulated trackless rubber-tyred train control method described above; the electronic device has the function of carrying and running the software system for the multi-articulated trackless rubber-tyred train control provided by the embodiment of the application, for example, a personal computer, a mini computer, a mainframe, a workstation, a network or a distributed computing environment, a separate or integrated computer platform, or communication with a charged particle tool or other imaging device, and the like.
[0170] The embodiment of the application further provides a computer readable storage medium, which stores a program, and the program is executed by a processor to implement the multi-articulated trackless rubber-tyred train control method as described above.
[0171] In some alternative embodiments, the functions / operations described in the block diagrams can not occur in the order presented in the operational illustrations. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality / operations involved. Also, although the embodiments presented in the flow diagrams are shown as a sequence of operations, it is to be understood that the logical flow is merely illustrative of alternative embodiments. The disclosed methods are not limited to the order of operations presented herein. Alternative embodiments can be conceived in which the order of operations is changed and in which sub-operations described as part of a larger operation are independently executed.
[0172] The embodiments of the present application further disclose a computer program product or computer program, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device can read the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the aforementioned multi-articulated trackless rubber-tyred train control method.
[0173] Furthermore, although the present application is described in the context of functional modules, it is to be understood that one or more of the described functions and / or features can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also to be understood that detailed discussion of the actual implementation of each module is unnecessary to an understanding of the present application. Rather, the actual implementation is within the routine skill of engineers familiar with the properties, functions and internal relationships of the various functional modules disclosed in the devices herein. Accordingly, the present application is not limited to purely hardware implementations, but also encompasses software implementations, including firmware, resident software, micro-code, etc. Therefore, the present application, as set forth in the claims, encompasses both hardware and software implementations. It is also to be understood that the disclosed specific concepts are merely illustrative and are not intended to limit the scope of the present application, which is defined by the full scope of the appended claims and equivalents thereof.
[0174] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.
[0175] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or in conjunction with these instructions execution systems, apparatuses, or devices. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport programs for use by an instruction execution system, apparatus, or device, or in conjunction with these instruction execution systems, apparatuses, or devices.
[0176] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CD ROM). In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting, or otherwise processing, if necessary, in other suitable ways, to be electronically obtained, and then stored in the computer memory.
[0177] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware which is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, can be used: a combination of discrete logic circuits having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having logic gates, field programmable gate arrays (FPGA), or others.
[0178] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in an appropriate manner.
[0179] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and alterations can be made hereto without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
[0180] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the described embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and these equivalent modifications or substitutions are included in the scope defined by the claims of the present application.
Claims
1. A control method for a multi-articulated trackless rubber-tired train, characterized in that, include: Acquire the front and rear wheel steering angle data of the target multi-articulated trackless rubber-tired train, wherein the front and rear wheel steering angle data includes front wheel steering angle data and rear wheel steering angle data; The LQR tracking prediction model is used to analyze the steering angle data of the front and rear wheels to obtain the control quantity of the target multi-articular trackless rubber-tired train; Based on the control quantity, the control adjustment of the target train is performed via the steering actuator; The LQR tracking prediction model is obtained through the following steps: Based on the steering angle data of the front and rear wheels, the steering relationship model of the front and rear wheels of the target multi-articular trackless rubber-tired train is determined by kinematic model; Based on the route information, the kinematic model, and the front and rear wheel steering angle data, the first actuator delay model is determined; The first actuator delay model is modified based on the actuator's response delay and the tracking route information for a future preset time period to obtain the second actuator delay model; For the second actuator delay model, the optimization objective is to use the minimum control quantity and the minimum state quantity as optimization objectives to optimize the second actuator delay model and obtain the target adjustment model; The LQR tracking prediction model is determined based on curvature information using the target adjustment model.
2. The control method for multi-articulated trackless rubber-tired trains according to claim 1, characterized in that, The process of determining the front and rear wheel steering relationship model of the target multi-articular trackless rubber-tired train based on the front and rear wheel steering angle data and using a kinematic model includes: The vehicle kinematic model is determined by the front axle center of the target multi-articulated trackless rubber-tired train as follows: in, Here are the coordinates of the vehicle's front axle center. For the horizontal swing angle, This refers to the front wheel steering angle. Rear wheel steering angle L Wheelbase v For vehicle speed; Based on the front wheel steering angle data and the rear wheel steering angle data, the vehicle's front and rear wheel steering relationship model is determined as follows: in, The constant obtained by fitting, For time.
3. The control method for multi-articulated trackless rubber-tired trains according to claim 2, characterized in that, The step of determining the first actuator delay model based on the route information, the kinematic model, and the front and rear wheel steering angle data includes: Based on the front wheel steering angle data, rear wheel steering angle data, and curvature, kinematic analysis yielded the following: in, The front wheel steering angle data is used as a reference point. Rear wheel steering angle data for reference road points, Let be the curvature, where: in, This is a dead zone corner; Determine the first actuator delay model based on the reference point. for: in in This represents the first-order Taylor expansion of the vehicle kinematics model at the reference road point. These represent the x-coordinate, y-coordinate, yaw angle, and front wheel steering angle of the reference road point, respectively. for .
4. The control method for multi-articulated trackless rubber-tired trains according to claim 3, characterized in that, The process of modifying the first actuator delay model based on the actuator's response delay and the tracking path information over a future preset time period to obtain a second actuator delay model includes: The first-order inertial element and the pure time-delay element for establishing the response model of the actuator are as follows: Data acquisition was performed on the target multi-articulated trackless rubber-tired train to obtain the inertial time constant. and pure time delay constant , The command for the wheel steering angle at time t. Let t be the wheel steering angle referenced at time t, where the first-order inertial element represents the actuator response delay, and the pure time delay element represents the tracking route information for a future preset time period. Based on the first actuator delay model, the first-order inertial element, and the pure time-delay element, the second actuator delay model is obtained as follows: in in For the discretized time interval, Represents state variables. This indicates that a first-order inertial element and a pure time-delay element are added to the delay model of the first actuator.
5. The control method for multi-articulated trackless rubber-tired trains according to claim 4, characterized in that, The second actuator delay model is optimized with minimum control quantity and minimum state quantity as optimization objectives to obtain a target adjustment model, including: The objective function, determined by minimizing the control quantity and the minimum state quantity, is as follows: in The objective function is used to optimize the second actuator delay model to obtain the target adjustment model.
6. The control method for multi-articulated trackless rubber-tired trains according to claim 1, characterized in that, The step of determining the LQR tracking prediction model based on curvature information using the target adjustment model includes: Based on the curvature information of the target multi-articulated trackless rubber-tired train at the current moment and the curvature information of the future preset time period, the LQR tracking prediction model is determined as follows: in Indicates according to the first A matrix generated from reference points, where When the time is 0; the aforementioned For the extended state weight matrix; the This is the state weight matrix, used to weight the state deviations; the The control weight matrix is used to weight changes in the control quantity; The expanded state vector contains the original 4 states and N additional predicted states.
7. The control method for multi-articulated trackless rubber-tired trains according to claim 6, characterized in that, The analysis of the front and rear wheel steering angle data using the LQR tracking prediction model yields the control quantities for the target multi-articular trackless rubber-tired train, including: by For from the first Reference points To the last reference point The value of the optimal objective function is obtained as follows: in Equal to Furthermore, the formula for calculating the control quantity is as follows: The control quantity is obtained according to the control quantity calculation formula.
8. A control device for a multi-articulated trackless rubber-tired train, characterized in that, include: The first module is used to acquire the front and rear wheel steering angle data of the target multi-articulated trackless rubber-tired train, wherein the front and rear wheel steering angle data includes front wheel steering angle data and rear wheel steering angle data; The second module is used to analyze the steering angle data of the front and rear wheels using the LQR tracking prediction model to obtain the control quantity of the target multi-articulated trackless rubber-tired train. The third module is used to perform control processing of the target train through the steering actuator according to the control quantity; The LQR tracking prediction model is obtained through the following modules: The fourth module is used to determine the steering relationship model of the front and rear wheels of the target multi-articulated trackless rubber-tired train based on the steering angle data of the front and rear wheels through a kinematic model. The fifth module is used to determine the first actuator delay model based on the route information, the kinematic model, and the front and rear wheel steering angle data; The sixth module is used to modify the first actuator delay model based on the actuator's response delay and the tracking route information for a future preset time period to obtain the second actuator delay model. The seventh module is used to optimize the second actuator delay model with the minimum control quantity and the minimum state quantity as optimization objectives, and to obtain the target adjustment model. The eighth module is used to determine the LQR tracking prediction model based on curvature information through the target adjustment model.
9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the control method for a multi-articulated trackless rubber-tired train as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the control method for a multi-articulated trackless rubber-tired train as described in any one of claims 1-7.