CN119821442A - Automatic driving control method, electronic device, vehicle and storage medium - Google Patents
Automatic driving control method, electronic device, vehicle and storage medium Download PDFInfo
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Abstract
The application provides an automatic driving control method, electronic equipment, a vehicle and a storage medium. The method comprises the steps of obtaining current state data of a vehicle, wherein the current state data at least comprise a driving mode, a current vehicle speed and a current position of the vehicle, obtaining optimized control parameters by utilizing a linear quadratic regulator optimization module based on the current vehicle speed and the current position and planning parameters corresponding to the automatic driving mode when the driving mode is an automatic driving mode of a specified type, and controlling the motion state of the vehicle by utilizing a PID controller based on the control parameters. Therefore, the automatic driving planning control can be adaptively adjusted along with the change of the running state of the vehicle, and the automatic driving planning control effect is improved.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to an automatic driving control method, electronic equipment, a vehicle and a storage medium.
Background
With the development of automatic driving technology, more and more new energy automobiles apply intelligent driving technology, and the intelligent driving technology comprises intelligent auxiliary cruising and intelligent parking functions. The intelligent driving technology is widely applied to the floor, and a control mode in industry is to control the speed deviation by utilizing PID (Proportional-Integral-Derivative), so that the control mode is mature, occupies small calculation force and is suitable for a low-calculation-force platform. The adjustment of the P proportional parameter, the I integral parameter and the D derivative parameter is mainly determined empirically, for example, PID fixed parameters under different conditions of different vehicle speeds and different situations are formulated through a two-dimensional or three-dimensional table, and the control parameters obtained empirically are relatively fixed, and an optimal planning control effect is not easy to achieve, i.e., the planning control effect of automatic driving needs to be improved.
Disclosure of Invention
In view of the above, an object of an embodiment of the present application is to provide an autopilot control method, an electronic device, a vehicle, and a storage medium, which can improve the planning control effect of autopilot.
In order to achieve the technical purpose, the application adopts the following technical scheme:
In a first aspect, an embodiment of the present application provides an autopilot control method, including:
Acquiring current state data of a vehicle, wherein the current state data at least comprises a driving mode, a current vehicle speed and a current position of the vehicle;
When the driving mode is an automatic driving mode of a specified type, obtaining optimized control parameters by using a linear quadratic regulator optimizing module based on the current vehicle speed, the current position and planning parameters corresponding to the automatic driving mode;
Based on the control parameters, a motion state of the vehicle is controlled by a PID controller, the motion state including a desired acceleration of the vehicle.
With reference to the first aspect, in some optional embodiments, the obtaining, based on the current vehicle speed and the current position, and a planning parameter corresponding to the automatic driving mode, an optimized control parameter using a linear quadratic regulator optimization module includes:
creating a controller model of the vehicle by using a linear quadratic regulator optimization module based on a speed difference of the current vehicle speed and a planned vehicle speed in the planning parameters and a distance difference of the current position and a planned position in the planning parameters;
determining a gain matrix in the controller model based on a preset Richman equation;
and determining the output parameters of the controller model when the cost is minimum based on a preset cost function and the gain matrix, and taking the output parameters as the optimized control parameters.
With reference to the first aspect, in some optional embodiments, the controller model is:
u(t)=-K x(t)
Wherein u (t) refers to an output parameter of the controller model and is used as the control parameter, t refers to time, K refers to the gain matrix, and x (t) comprises the speed difference value and the distance difference value;
K=R-1BTP
r refers to a weight matrix corresponding to the control parameter;
b refers to an output matrix taking the acceleration of the vehicle as an output target;
P refers to a parameter matrix obtained based on the Li-Ka equation;
The licarpa equation is:
P=Q+ATPa-aTPB(R+BTPB)-1BTPA
Wherein Q includes a speed weighting matrix Q v corresponding to the speed difference and a distance weighting matrix Q s corresponding to the distance difference;
A refers to a state matrix representing the state of the vehicle based on the speed difference and the distance difference.
With reference to the first aspect, in some optional embodiments, the determining a gain matrix in the controller model based on a preset licarpa equation includes:
Calculating a P matrix based on the Richman equation;
and inputting the calculated P matrix into a calculation formula K=R -1BT P of the gain matrix K to obtain the gain matrix.
With reference to the first aspect, in some optional embodiments, the determining, based on a preset cost function and the gain matrix, an output parameter of the controller model when the cost is minimum as the optimized control parameter includes:
according to the preset cost function And determining an output parameter u (t) obtained by the controller model based on the gain matrix when the cost J is minimum, and taking the output parameter u (t) as the optimized control parameter.
With reference to the first aspect, in some optional embodiments, before the optimizing module of the linear quadratic regulator, the method further includes:
And when the automatic driving mode is an automatic parking mode, determining a speed weighting matrix and a distance weighting matrix corresponding to the current stopping time distance of the vehicle based on the corresponding relation between the pre-established stopping time distance and the speed weighting matrix and the distance weighting matrix so as to form a Q matrix in the Richa lifting equation, wherein when the stopping time distance is greater than or equal to a preset time distance, the weight of the speed weighting matrix is greater than or equal to the weight of the distance weighting matrix, and when the stopping time distance is less than or equal to the preset time distance, the weight of the speed weighting matrix is less than the weight of the distance weighting matrix.
With reference to the first aspect, in some optional embodiments, the controlling, by a PID controller, a motion state of the vehicle based on the control parameter includes:
Inputting the control parameter and the speed difference value into the PID controller to obtain the expected acceleration output by the PID controller;
Controlling the vehicle to run at the desired acceleration.
In a second aspect, an embodiment of the present application further provides an electronic device, where the electronic device includes a processor and a memory coupled to each other, where the memory stores a computer program, and when the computer program is executed by the processor, causes the electronic device to perform the method described above.
In a third aspect, an embodiment of the present application further provides a vehicle, where the vehicle includes a vehicle body and the electronic device described above.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium having stored therein a computer program which, when run on a computer, causes the computer to perform the above-described method.
The invention adopting the technical scheme has the following advantages:
According to the technical scheme, when the driving mode is an automatic driving mode of a specified type, the optimized control parameters are obtained by utilizing the linear quadratic regulator LQR optimizing module based on the current speed, the current position and the planning parameters corresponding to the automatic driving mode, and then the optimized control parameters are utilized to control the expected acceleration of the vehicle through the PID controller. According to the scheme, the control parameters can be dynamically optimized through the LQR optimization module according to the current state data of the vehicle, and then the acceleration control of the vehicle is carried out by matching with the PID controller, so that the automatic driving planning control can be adaptively adjusted along with the change of the running state of the vehicle, the automatic driving planning control effect is improved, and the problem that the control effect of the vehicle in different running states is poor due to the fact that the control parameters are fixed values can be solved.
Drawings
The application may be further illustrated by means of non-limiting examples given in the accompanying drawings. It is to be understood that the following drawings illustrate only certain embodiments of the application and are therefore not to be considered limiting of its scope, for the person of ordinary skill in the art may admit to other equally relevant drawings without inventive effort.
Fig. 1 is a flow chart of an automatic driving control method according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an LQR-PID controller according to an embodiment of the present application.
Detailed Description
The present application will be described in detail below with reference to the drawings and the specific embodiments, wherein like or similar parts are designated by the same reference numerals throughout the drawings or the description, and implementations not shown or described in the drawings are in a form well known to those of ordinary skill in the art. In the description of the present application, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, the present application further provides an autopilot control method, which may be applied to a vehicle, and may be executed or implemented by an electronic device in the vehicle. The electronic device has a conventional automatic driving function as an electronic control system in a vehicle. The automatic driving control method may include the steps of:
step 110, obtaining current state data of a vehicle, wherein the current state data at least comprises a driving mode, a current vehicle speed and a current position of the vehicle;
step 120, when the driving mode is an automatic driving mode of a specified type, obtaining optimized control parameters by using an LQR (Linear Quadratic Regulator ) optimization module based on the current vehicle speed and the current position and the planning parameters corresponding to the automatic driving mode;
step 130, controlling a motion state of the vehicle by a PID (Proportion Integration Differentiation, proportional integral derivative) controller based on the control parameter, the motion state including a desired acceleration of the vehicle.
The steps of the automatic driving control method will be described in detail as follows:
In step 110, the vehicle may collect various running state data of the vehicle in real time through its own corresponding sensor as current state data. The mode of real-time collection may be collection at a fixed frequency, periodically, or at random intervals, where the frequency of collecting data is not specifically limited, so long as the state data of the vehicle can be collected in real time and updated.
In the present embodiment, the current state data may include, but is not limited to, a driving mode of the vehicle, a current vehicle speed, a current position, and the like. The mode of sensing the driving mode, the current vehicle speed and the current position by the sensor is a conventional mode. If the driving mode of the vehicle is an automatic driving mode, the current state data may further include a planning parameter corresponding to automatic driving of the vehicle. The planning parameters are the vehicle speed and the position calculated by the vehicle based on a conventional regulation algorithm in the automatic driving mode of the specified type.
If the speed difference between the current vehicle speed and the planned vehicle speed is 0 and the distance difference between the current position and the planned position is 0, the current motion state of the vehicle can be maintained to continue running, and at this time, the parameters of the planned position, the planned vehicle speed and the like of the vehicle can be not required to be optimized. If the speed difference between the current vehicle speed and the planned vehicle speed is not 0 and/or the distance difference between the current position and the planned position is not 0, step 120 is continued.
In step 120, the specified type of automatic driving mode is a driving mode requiring dynamic adjustment of the vehicle speed, for example, the specified type of automatic driving mode may be, but is not limited to, an adaptive cruise mode, an automatic parking mode, or the like.
As an optional implementation manner, in step 120, based on the current vehicle speed and the current position, and the planning parameter corresponding to the automatic driving mode, using a linear quadratic regulator optimization module, obtaining an optimized control parameter may include:
creating a controller model of the vehicle by using a linear quadratic regulator optimization module based on a speed difference of the current vehicle speed and a planned vehicle speed in the planning parameters and a distance difference of the current position and a planned position in the planning parameters;
Determining a gain matrix in the controller model based on a preset Li Kadi (Riccati) equation;
and determining the output parameters of the controller model when the cost is minimum based on a preset cost function and the gain matrix, and taking the output parameters as the optimized control parameters.
In this embodiment, the controller model may be:
u(t)=-K x(t)
Wherein u (t) refers to an output parameter of the controller model and is used as the control parameter, t refers to time, K refers to the gain matrix, and x (t) consists of the speed difference value and the distance difference value;
K=R-1BTP
r refers to a weight matrix corresponding to the control parameter;
b refers to an output matrix taking the acceleration of the vehicle as an output target;
P refers to a parameter matrix obtained based on the Li-Ka equation, which is obtained by solving the Li-Ka equation and is used for representing the performance index and the state feedback gain of the automatic driving of the vehicle;
The licarpa equation is:
P=Q+ATPa-aTPB(R+BTPB)-1BTPA
Wherein Q includes a speed weighting matrix Q v corresponding to the speed difference and a distance weighting matrix Q s corresponding to the distance difference;
a refers to a state matrix representing the vehicle state based on the speed difference and the distance difference, i.e., the vehicle state includes the speed difference and the distance difference of the vehicle.
In this embodiment, the Q matrix may weight the difference (or error) of the state variables of the vehicle, affecting the response of the electronic control system to the state error. The matrix Q can be selected to be larger, so that the sensitivity of the electronic control system to the position and speed difference is improved, and the stability of the system is enhanced.
The R matrix may be used to weight the control input u (t), affecting the magnitude of the control input. The values of matrix R may be derived by calibration, for example, smaller values may be selected to allow for greater control inputs, thereby improving the response speed of the vehicle electronic control system.
In this embodiment, determining the gain matrix in the controller model based on the preset licarpa equation may include:
Calculating a P matrix based on the Richman equation;
and inputting the calculated P matrix into a calculation formula K=R -1BT P of the gain matrix K to obtain the gain matrix.
In this embodiment, based on a preset cost function and the gain matrix, determining the output parameter of the controller model when the cost is minimum as the optimized control parameter may include:
according to the preset cost function And determining an output parameter u (t) obtained by the controller model based on the gain matrix when the cost J is minimum, and taking the output parameter u (t) as the optimized control parameter.
In this embodiment, the output parameter u (t) when the cost J is minimum is calculated by using the preset cost function, so that an optimal control parameter can be obtained, thereby being beneficial to realizing an optimal control effect of automatic driving.
As an alternative embodiment, before the step of optimizing the module using the linear quadratic regulator, the method may further comprise:
And when the automatic driving mode is an automatic parking mode, determining a speed weighting matrix and a distance weighting matrix corresponding to the current stopping time distance of the vehicle based on the corresponding relation between the pre-established stopping time distance and the speed weighting matrix and the distance weighting matrix so as to form a Q matrix in the Richa lifting equation, wherein when the stopping time distance is greater than or equal to a preset time distance, the weight of the speed weighting matrix is greater than or equal to the weight of the distance weighting matrix, and when the stopping time distance is less than or equal to the preset time distance, the weight of the speed weighting matrix is less than the weight of the distance weighting matrix.
In the present embodiment, the Q matrix is composed of a matrix Q v affected by the speed difference and a matrix Q s affected by the distance difference. Adjusting the values of the matrices Q v and Q s changes the weight of the state variables (including vehicle speed and position), and the values of the matrices Q v and Q s are determined by the stopping time interval. For example, when the stopping time interval is larger and exceeds the preset time interval, the stopping point of the distance between the vehicle and the vehicle is larger, the larger speed needs to be kept to be quickly approximated, the weight of Q v is larger than that of Q s, when the stopping time interval is small and smaller than the preset time interval, the stopping point of the distance between the vehicle and the vehicle is smaller, the speed of the vehicle needs to be reduced, and the control precision of the stopping position is improved, and the weight of Q s is larger than that of Q v. The preset time interval may be obtained by calibration, which is not specifically limited herein.
In step 130, controlling, by a PID controller, a motion state of the vehicle based on the control parameter, including:
inputting the control parameter and a speed difference value into the PID controller to obtain the expected acceleration output by the PID controller, wherein the speed difference value is the difference value between the current vehicle speed and the planned vehicle speed in the planning parameters;
Controlling the vehicle to run at the desired acceleration.
Understandably, the control parameters obtained by optimizing by the LQR optimizing module are input into the PID controller as the control parameters of the PID controller, and in addition, the input of the PID controller also comprises the current speed difference value. And the control parameter and the speed difference value are calculated by a PID controller to obtain the expected acceleration of the vehicle, and an electronic control system of the vehicle converts the expected acceleration into the wheel end torque to control the power of the vehicle so as to control the vehicle to run at the expected acceleration.
In order to facilitate understanding of the implementation process of the method, the following illustrates the execution flow of the automatic driving control method by taking the driving mode as the automatic parking mode:
The method comprises the steps of firstly, judging the state, namely, receiving an automatic parking function starting state mark by an electronic control system of a vehicle to judge whether the vehicle is in an automatic parking mode, acquiring a target vehicle speed signal and a target distance signal, and judging whether the current vehicle speed of the vehicle is a planned vehicle speed and whether the current position is a planned position. If the vehicle is in the automatic parking mode and the speed difference value and the distance difference value are different and are 0, the second step is entered.
And secondly, parameter optimization, namely inputting the speed difference value and the distance difference value into an LQR optimization module, and calculating an optimal P matrix and a control parameter u (t) required by a current PID controller. The planning position can be used for limiting the size of the P matrix, so that the phenomena of over-acceleration caused by over-large P matrix and insufficient control quantity caused by over-small P matrix are avoided.
For longitudinal speed control of automatic parking, one of the indexes is to reduce the error between the planned vehicle speed and the actual speed, and the cost function is assumed to be J:
and the control parameter u (t) with the minimum cost function is the optimal control parameter.
The control parameters are calculated in the following ways:
u(t)=-K x(t)
The gain matrix k=r -1BT P needs to be iteratively solved by establishing a Riccati equation, so as to obtain an optimal gain matrix K, where the Riccati equation is:
P=Q+ATPa-aTPB(R+BTPB)-1BTPA
Through the second step, the control parameter u (t) required by the current PID controller can be obtained by using the LQR optimization module.
And thirdly, controlling the vehicle speed, namely inputting the control parameter u (t) calculated in the second step into a PID controller, and simultaneously taking the current speed difference value as the input of the PID controller to calculate the current expected acceleration value.
Referring to fig. 2, in this embodiment, the LQR optimization module and the PID controller may form an online optimized LQR-PID controller, which may be deployed as a software functional module in an electronic control system of a vehicle. In the LQR-PID controller, the motion state of the vehicle is changed by the influence of torque, in the acceleration and deceleration process, the LQR optimization module outputs optimized control parameters to the PID controller on line in real time based on a speed difference value e v and a distance difference value e s, the PID controller outputs the expected acceleration of the vehicle based on the control parameters, then the vehicle is controlled to automatically drive based on the expected acceleration, and when the speed difference value and the distance difference value generate new changes, the first step to the third step are continuously repeated to form a closed loop.
Based on the design, the automatic driving control method can be suitable for longitudinal control of automatic parking, can solve the problem that the control parameters of the PID controller cannot reach the optimal value in the longitudinal control of automatic driving parking, can effectively improve the problem that the optimal control of the vehicle cannot be realized in different state scenes due to the fixed parameters of the P matrix, and can enhance the robustness of an electronic control system.
The embodiment of the application provides electronic equipment which is used as an electronic control system in a vehicle and can comprise a processor and a memory. The memory stores a computer program which, when executed by the processor, enables the electronic device to perform the respective steps of the aforementioned automatic driving control method.
In this embodiment, the processor may be an integrated circuit chip with signal processing capability. For example, the processor may be, but is not limited to, an intelligent driving domain controller, a central processing unit (Central Processing Unit, CPU), a Field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or may implement or perform the methods, steps, and logic diagrams disclosed in embodiments of the application.
The memory may be, but is not limited to, random access memory, read only memory, programmable read only memory, erasable programmable read only memory, electrically erasable programmable read only memory, and the like. In this embodiment, the storage module may be used to store planning parameters and the like. Of course, the storage module may also be used to store a program, and the processing module executes the program after receiving the execution instruction.
It should be noted that, for convenience and brevity of description, specific working processes of the electronic device described above may refer to corresponding processes of each step in the foregoing method, and will not be described in detail herein.
The embodiment of the application also provides a vehicle which can comprise a vehicle body and the electronic equipment, and the vehicle adopts the automatic driving control method, so that the automatic driving planning control can be adaptively adjusted along with the change of the running state of the vehicle, and the automatic driving planning control effect is improved.
The embodiment of the application also provides a computer readable storage medium. The computer-readable storage medium has stored therein a computer program which, when run on a computer, causes the computer to execute the automatic driving control method as described in the above embodiments.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented in hardware, or by means of software plus a necessary general hardware platform, and based on this understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disc, a mobile hard disk, etc.), and includes several instructions for causing a computer device (may be a personal computer, an electronic device, or a network device, etc.) to execute the method described in the respective implementation scenario of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed electronic device and method may be implemented in other manners. The above-described electronic device and method embodiments are merely illustrative, for example, of the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. An automatic driving control method, characterized in that the method comprises:
Acquiring current state data of a vehicle, wherein the current state data at least comprises a driving mode, a current vehicle speed and a current position of the vehicle;
When the driving mode is an automatic driving mode of a specified type, obtaining optimized control parameters by using a linear quadratic regulator optimizing module based on the current vehicle speed, the current position and planning parameters corresponding to the automatic driving mode;
Based on the control parameters, a motion state of the vehicle is controlled by a PID controller, the motion state including a desired acceleration of the vehicle.
2. The method of claim 1, wherein the deriving the optimized control parameters using a linear quadratic regulator optimization module based on the current vehicle speed and the current position, and a planning parameter corresponding to the autonomous driving mode, comprises:
creating a controller model of the vehicle by using a linear quadratic regulator optimization module based on a speed difference of the current vehicle speed and a planned vehicle speed in the planning parameters and a distance difference of the current position and a planned position in the planning parameters;
determining a gain matrix in the controller model based on a preset Richman equation;
and determining the output parameters of the controller model when the cost is minimum based on a preset cost function and the gain matrix, and taking the output parameters as the optimized control parameters.
3. The method of claim 2, wherein the controller model is:
u(t)=-K x(t)
Wherein u (t) refers to an output parameter of the controller model and is used as the control parameter, t refers to time, K refers to the gain matrix, and x (t) comprises the speed difference value and the distance difference value;
K=R-1BTP
r refers to a weight matrix corresponding to the control parameter;
b refers to an output matrix taking the acceleration of the vehicle as an output target;
P refers to a parameter matrix obtained based on the Li-Ka equation;
The licarpa equation is:
P=Q+ATPa-aTPB(R+BTPB)-1BTPA
Wherein Q includes a speed weighting matrix Q v corresponding to the speed difference and a distance weighting matrix Q s corresponding to the distance difference;
a refers to a state matrix representing the state of the vehicle based on the speed difference and the distance difference.
4. A method according to claim 3, wherein said determining a gain matrix in said controller model based on a preset licarpa's equation comprises:
Calculating a P matrix based on the Richman equation;
and inputting the calculated P matrix into a calculation formula K=R -1BT P of the gain matrix K to obtain the gain matrix.
5. A method according to claim 3, wherein said determining, based on a preset cost function and the gain matrix, the output parameter of the controller model at which the cost is minimal as the optimized control parameter comprises:
according to the preset cost function And determining an output parameter u (t) obtained by the controller model based on the gain matrix when the cost J is minimum, and taking the output parameter u (t) as the optimized control parameter.
6. A method according to claim 3, wherein prior to said optimizing the module with the linear quadratic regulator to obtain the optimized control parameters, the method further comprises:
And when the automatic driving mode is an automatic parking mode, determining a speed weighting matrix and a distance weighting matrix corresponding to the current stopping time distance of the vehicle based on the corresponding relation between the pre-established stopping time distance and the speed weighting matrix and the distance weighting matrix so as to form a Q matrix in the Richa lifting equation, wherein when the stopping time distance is greater than or equal to a preset time distance, the weight of the speed weighting matrix is greater than or equal to the weight of the distance weighting matrix, and when the stopping time distance is less than or equal to the preset time distance, the weight of the speed weighting matrix is less than the weight of the distance weighting matrix.
7. The method according to claim 1, wherein the controlling the motion state of the vehicle by a PID controller based on the control parameter comprises:
inputting the control parameter and a speed difference value into the PID controller to obtain the expected acceleration output by the PID controller, wherein the speed difference value is the difference value between the current vehicle speed and the planned vehicle speed in the planning parameters;
Controlling the vehicle to run at the desired acceleration.
8. An electronic device comprising a processor and a memory coupled to each other, the memory storing a computer program that, when executed by the processor, causes the electronic device to perform the method of any of claims 1-7.
9. A vehicle comprising a vehicle body and the electronic device of claim 8.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when run on a computer, causes the computer to perform the method according to any of claims 1-7.
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| CN114348003A (en) * | 2021-12-27 | 2022-04-15 | 珠海广通汽车有限公司 | Coupling control method, device, equipment and storage medium for intelligent driving vehicle |
| CN118529052A (en) * | 2024-05-16 | 2024-08-23 | 三一集团有限公司 | Vehicle speed control method of vehicle hub, vehicle-mounted industrial computer and vehicle speed control system |
| CN119270855A (en) * | 2024-09-30 | 2025-01-07 | 长安大学 | Omnidirectional motion chassis device and trajectory tracking control method thereof |
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