Vehicle drift control method, device, equipment and storage medium
By acquiring the vehicle's current state parameters, calculating the parameter difference, and directly calculating the target control parameters using the state error model, the problem of insufficient vehicle drift control accuracy in the existing technology is solved, and high-precision vehicle drift control is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DONGFENG MOTOR CO LTD DONGFENG NISSAN PASSENGER VEHICLE CO
- Filing Date
- 2023-02-28
- Publication Date
- 2026-07-21
AI Technical Summary
Existing autonomous driving drift control technology cannot accurately control vehicle drift because vehicle parameters are complex and variable, friction is difficult to calibrate in advance, and there are deviations between empirical dynamic models and actual vehicle conditions, resulting in insufficient control precision.
By acquiring the vehicle's current state parameters, calculating the parameter difference, and directly calculating the target control parameters using the vehicle state error model, drift control can be performed directly without calibrating the vehicle's physical parameters or relying on empirical dynamic models.
It improves the precision of vehicle drift control, ensuring that the vehicle can accurately reach the target steady-state drift state.
Smart Images
Figure CN116238479B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle control technology, and in particular to a vehicle drift control method, device, equipment, and storage medium. Background Technology
[0002] Drifting, tail-sliding, and other extreme control techniques can break through the constraints of steady-state control, giving vehicles greater freedom and improving their active safety in conditions such as sideslip, roll, and low road adhesion.
[0003] Current autonomous driving control technology primarily focuses on steady-state vehicle control, where all wheels have good traction, and the vehicle operates on a stable equilibrium surface approximating an ideal Ackermann model. When disturbed, the vehicle tends to automatically return to equilibrium, making control relatively easy. However, in extreme conditions such as sideslip, drifting, or fishtailing, the rear wheels typically slip. To maintain balance, the front wheels need to counter-steer, placing the vehicle on an unstable equilibrium surface. When disturbed, the vehicle tends to deviate further from its equilibrium state, making control more challenging.
[0004] Existing autonomous driving drift control technologies generally require pre-calibrating physical parameters such as the vehicle's center of gravity, moment of inertia, and tire friction curves, and then substituting these parameters into the vehicle dynamics equations to calculate the relationship between control quantities and the vehicle's dynamic response. However, this approach has several drawbacks: 1. Some vehicle parameters are complex and variable, making accurate calibration difficult; 2. Friction varies with ground conditions, making pre-calibration challenging; 3. There are discrepancies between empirical dynamic models and actual vehicle conditions, and even minor calculation errors can cause the vehicle to lose control. These shortcomings prevent current methods from guaranteeing the accuracy of vehicle drift control.
[0005] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0006] The main objective of this invention is to provide a vehicle drift control method, apparatus, device, and storage medium, aiming to solve the technical problem that the prior art cannot accurately control vehicle drift.
[0007] To achieve the above objectives, the present invention provides a vehicle drift control method, which includes the following steps:
[0008] Obtain the vehicle's current status parameters;
[0009] Determine the parameter difference between the current state parameter and the preset state parameter;
[0010] Calculate the target control parameters based on the parameter differences and the vehicle state error model;
[0011] The vehicle is drift-controlled according to the target control parameters.
[0012] Optionally, before calculating the target control parameters based on the parameter difference and the vehicle state error model, the method further includes:
[0013] Obtain the historical parameter information of the vehicle;
[0014] A vehicle state error model corresponding to the vehicle is constructed based on the historical parameter information.
[0015] Optionally, constructing the vehicle state error model corresponding to the vehicle based on the historical parameter information includes:
[0016] Extract historical steady-state parameters and historical steady-state control parameters from the historical parameter information;
[0017] The model parameters are determined based on the historical steady-state parameters and historical steady-state control parameters.
[0018] The vehicle state error model corresponding to the vehicle is constructed based on the model parameters.
[0019] Optionally, the historical steady-state parameters include at least the historical center-of-gravity velocity sideslip angle, historical yaw rate, and historical longitudinal axis velocity; the historical steady-state control parameters include at least the historical steering wheel angle and historical rear wheel drive force; and the extraction of historical steady-state parameters and historical steady-state control parameters from the historical parameter information includes:
[0020] The historical center of gravity velocity, sideslip angle, and historical yaw rate of the vehicle are obtained from the historical parameter information.
[0021] A two-dimensional surface of state parameters is constructed based on several reference state parameters of the vehicle under different steady-state drift states;
[0022] A two-dimensional surface of control parameters is constructed based on several reference control parameters of the vehicle under different steady-state drift states;
[0023] The historical longitudinal velocity, historical steering wheel angle, and historical rear wheel drive force of the vehicle are obtained based on the historical center of mass velocity sideslip angle, the historical yaw rate, the two-dimensional surface of state parameters, and the two-dimensional surface of control parameters.
[0024] Optionally, obtaining the vehicle's historical longitudinal axis velocity, historical steering wheel angle, and historical rear wheel drive force based on the historical center of mass velocity sideslip angle, the historical yaw rate, the two-dimensional surface of state parameters, and the two-dimensional surface of control parameters includes:
[0025] Based on the historical centroid velocity sideslip angle and the historical yaw rate, the historical longitudinal velocity is found from the two-dimensional surface of the state parameters.
[0026] Based on the historical center of gravity velocity sideslip angle and the historical yaw rate, the historical steering wheel angle and historical rear wheel drive force are retrieved from the two-dimensional surface of the control parameters.
[0027] Optionally, before constructing the two-dimensional surface of state parameters based on several reference state parameters of the vehicle in different steady-state drift states, the method further includes:
[0028] The vehicle is controlled from a stationary state to a steady-state drift state using an open-loop drift initiation model;
[0029] When the vehicle is in a steady-state drift state, the open-loop control parameters of the open-loop drift initiation model are adjusted so that the vehicle changes from the current steady-state drift state to other steady-state drift states.
[0030] Optionally, the step of calculating the target control parameters based on the parameter difference and the vehicle state error model includes:
[0031] The parameter difference is input into the vehicle state error model to obtain the correction amount of the control parameters;
[0032] The target control parameter is determined based on the correction amount of the control parameter and the current control parameter.
[0033] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle drift control device, the vehicle drift control device comprising:
[0034] The acquisition module is used to acquire the current status parameters of the vehicle;
[0035] The calculation module is used to determine the parameter difference between the current state parameter and the preset state parameter;
[0036] The calculation module is also used to calculate the target control parameters based on the parameter difference and the vehicle state error model;
[0037] The control module is used to perform drift control on the vehicle according to the target control parameters.
[0038] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle drift control device, which includes: a memory, a processor, and a vehicle drift control program stored in the memory and running on the processor, the vehicle drift control program being configured to implement the vehicle drift control method as described above.
[0039] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a vehicle drift control program, which, when executed by a processor, implements the vehicle drift control method as described above.
[0040] This invention obtains the current state parameters of a vehicle; determines the parameter difference between the current state parameters and preset state parameters; calculates target control parameters based on the parameter difference and a vehicle state error model; and performs drift control on the vehicle based on the target control parameters. By calculating the target control parameters using the current state parameters of the vehicle and the preset state parameters to be achieved, and then directly performing drift control on the vehicle using the target control parameters, this invention improves the accuracy of vehicle drift control by directly calibrating control parameters without calibrating vehicle physical parameters or relying on empirical dynamic models. Attached Figure Description
[0041] Figure 1 This is a schematic diagram of the structure of the vehicle drift control device in the hardware operating environment involved in the embodiments of the present invention;
[0042] Figure 2 This is a flowchart illustrating the first embodiment of the vehicle drift control method of the present invention;
[0043] Figure 3 This is a schematic diagram of a vehicle model in one embodiment of the vehicle drift control method of the present invention;
[0044] Figure 4 This is a flowchart illustrating the second embodiment of the vehicle drift control method of the present invention;
[0045] Figure 5 This is a flowchart of an open-loop drift initiation model in one embodiment of the vehicle drift control method of the present invention;
[0046] Figure 6 This is a schematic diagram of a two-dimensional surface of state parameters in one embodiment of the vehicle drift control method of the present invention;
[0047] Figure 7 This is a two-dimensional surface diagram of a control parameter in one embodiment of the vehicle drift control method of the present invention;
[0048] Figure 8 This is a two-dimensional surface schematic diagram of another control parameter in one embodiment of the vehicle drift control method of the present invention;
[0049] Figure 9 This is a flowchart illustrating the third embodiment of the vehicle drift control method of the present invention;
[0050] Figure 10 This is a structural block diagram of the first embodiment of the vehicle drift control device of the present invention.
[0051] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0052] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.
[0053] Reference Figure 1 , Figure 1 This is a schematic diagram of the vehicle drift control device structure in the hardware operating environment involved in the embodiments of the present invention.
[0054] like Figure 1 As shown, the vehicle drift control device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.
[0055] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the vehicle drift control device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0056] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a vehicle drift control program.
[0057] exist Figure 1In the vehicle drift control device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the vehicle drift control device of the present invention can be set in the vehicle drift control device, and the vehicle drift control device calls the vehicle drift control program stored in the memory 1005 through the processor 1001 and executes the vehicle drift control method provided in the embodiment of the present invention.
[0058] This invention provides a vehicle drift control method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a vehicle drift control method according to the present invention.
[0059] In this embodiment, the vehicle drift control method includes the following steps:
[0060] Step S10: Obtain the current status parameters of the vehicle.
[0061] In this embodiment, the executing entity is the vehicle drift control device, which has functions such as data acquisition, data communication, and program execution. Of course, other devices with similar functions can also be used; this embodiment does not limit this. This embodiment uses a vehicle drift control device as an example for explanation.
[0062] It's important to note that current autonomous driving control technology primarily focuses on steady-state vehicle control, where all wheels have good grip and the vehicle operates on a stable equilibrium surface approximating an ideal Ackermann model. When disturbed, the vehicle tends to automatically return to equilibrium, making control relatively easy. However, in extreme conditions such as sideslip, drifting, or fishtailing, the rear wheels typically slip. To maintain balance, the front wheels need to counter-steer, placing the vehicle on an unstable equilibrium surface. When disturbed, the vehicle tends to deviate further from equilibrium, making control more difficult. Existing autonomous driving drift control technologies generally require pre-calibrating physical parameters such as the vehicle's center of gravity, moment of inertia, and tire friction curves, and then substituting these parameters into the vehicle's dynamics equations to calculate the relationship between control inputs and dynamic response. However, this approach suffers from several drawbacks: some vehicle parameters are complex and variable, making accurate calibration difficult; friction varies with ground conditions, making pre-calibration challenging; and empirical dynamics models deviate from actual vehicle conditions, with even minor calculation errors causing loss of control. These shortcomings prevent current methods from guaranteeing precise drift control.
[0063] To address the aforementioned technical issues, this embodiment obtains the vehicle's current state parameters; determines the parameter difference between the current state parameters and preset state parameters; calculates target control parameters based on the parameter difference and the vehicle state error model; and performs drift control on the vehicle based on the target control parameters. By calculating the target control parameters using the vehicle's current state parameters and the desired preset state parameters, and then directly performing drift control on the vehicle using these target control parameters, the vehicle's physical parameters do not need to be calibrated, and the control parameters are not dependent on empirical dynamic models. This directly calibrates the control parameters, improving the accuracy of vehicle drift control. Specifically, this can be implemented as follows.
[0064] In this implementation, this embodiment does not rely on empirical dynamic models and does not require vehicle physical parameter calibration. When drift control of the vehicle is needed, the current state parameters of the vehicle can be directly obtained. The current state parameters of the vehicle obtained in this embodiment include, but are not limited to, center of mass velocity, sideslip angle, yaw rate, and longitudinal axis velocity. When the vehicle enters the drift equilibrium state, it is a three-degree-of-freedom model. By specifying different drift directions, it can be simplified to a two-degree-of-freedom model, that is, specifying two linearly independent dynamic parameters can determine the vehicle state during all other drifts, such as... Figure 3 As shown. Figure 3 In this context, r represents the vehicle's yaw rate, β represents the vehicle's center of gravity velocity and sideslip angle, V represents the vehicle's longitudinal velocity, Fxr and Fxy represent the vehicle's tire driving force, and Φ represents the vehicle's steering wheel angle.
[0065] In this embodiment, the center of gravity velocity sideslip angle and yaw rate are selected as control variables, and the longitudinal axis velocity is used as the key parameter for observing the vehicle's state. Therefore, the current state of the vehicle can be determined through these parameters. It should be emphasized that when the center of gravity velocity sideslip angle, yaw rate, and longitudinal axis velocity are in a stable state, especially when the longitudinal axis velocity is near the steady-state drift point, the vehicle can be considered to be in a steady-state drift state. It should be noted that the above current state parameters are for illustrative purposes only. In specific processes, other parameters can be selected as the vehicle's state parameters based on different control requirements. This embodiment does not impose any restrictions on this.
[0066] Step S20: Determine the parameter difference between the current state parameter and the preset state parameter.
[0067] In the specific implementation, after obtaining the current state parameters of the vehicle, this embodiment further needs to calculate the parameter difference between the current state parameters and the preset state parameters. The preset state parameters are the state parameters corresponding to the target steady-state drift state. The target steady-state drift state is a pre-selected steady-state drift state that the vehicle needs to achieve. It can be selected according to actual needs, and this embodiment does not impose any restrictions on it.
[0068] In this embodiment, the above process is further illustrated with examples. For instance, if the yaw rate and the center of mass velocity and the sideslip angle do not change in the current state parameters and the preset state parameters, the only difference between the current state of the vehicle and the target steady-state drift state is the vehicle's longitudinal axis velocity. Assuming the vehicle's current longitudinal axis velocity is 11 m / s and the preset longitudinal axis velocity is 10 m / s, the parameter difference at this time can be calculated to be 1 m / s.
[0069] Step S30: Calculate the target control parameters based on the parameter difference and the vehicle state error model.
[0070] In this specific implementation, after calculating the parameter difference, the parameter difference can be substituted into the vehicle state error model for calculation to obtain the target control parameters. Specifically, the parameter difference between the control parameters can be calculated based on the parameter difference between the state parameters, and the target control parameters can be calculated based on the parameter difference between the control parameters.
[0071] Step S40: Perform drift control on the vehicle according to the target control parameters.
[0072] In practical implementation, the control parameters differ depending on the vehicle's steady-state drift state. The correspondence between the control parameters and different steady-state drift states can be found in a pre-constructed two-dimensional surface. After finding the corresponding target control parameters, this embodiment controls the vehicle according to the steering wheel angle and rear wheel drive force included in the target control parameters, thereby enabling the vehicle to reach the target steady-state drift state, which is the desired steady-state drift state.
[0073] This embodiment obtains the vehicle's current state parameters; determines the parameter difference between the current state parameters and preset state parameters; calculates target control parameters based on the parameter difference and the vehicle state error model; and performs drift control on the vehicle based on the target control parameters. By calculating the target control parameters using the vehicle's current state parameters and the preset state parameters to be achieved, and then directly performing drift control on the vehicle using the target control parameters, the vehicle's physical parameters do not need to be calibrated, and the control parameters are not dependent on empirical dynamic models. This directly calibrates the control parameters, improving the accuracy of vehicle drift control.
[0074] refer to Figure 4 , Figure 4 This is a flowchart illustrating a second embodiment of a vehicle drift control method according to the present invention.
[0075] Based on the first embodiment described above, the vehicle drift control method of this embodiment further includes, before step S30:
[0076] Step S301: Obtain the historical parameter information of the vehicle.
[0077] It should be noted that before calculating the target control parameters, this embodiment needs to obtain the vehicle's historical parameter information. For a vehicle with unknown parameters, this embodiment uses an open-loop drift initiation model to control it from a stationary state to a steady-state drift, recording the drift parameters. The flowchart of the open-loop drift initiation model is as follows: Figure 5 As shown, by adjusting the open-loop control parameters of the open-loop drift model, the vehicle can be made to enter different steady-state drift states. Since each steady-state drift state corresponds to a set of drift parameters, multiple sets of drift parameters for the vehicle can be recorded. The recorded drift parameters include at least the longitudinal axis velocity, steering wheel angle, and rear wheel driving force under different drift states, as well as the center of gravity velocity, sideslip angle, and yaw rate. In this embodiment, other parameters can also be recorded or selected for the construction of the vehicle state error model according to actual needs, without limitation.
[0078] Step S302: Construct the vehicle state error model corresponding to the vehicle based on the historical parameter information.
[0079] In specific implementation, after obtaining historical parameter information, this embodiment further extracts historical steady-state parameters and historical steady-state control parameters from the historical parameter information. Specifically, the historical center-of-gravity velocity sideslip angle and historical yaw rate of the vehicle can be obtained from the acquired historical parameter information. Then, multiple sets of parameters recorded in the open-loop drift model under different steady-state drift states, i.e., several reference state parameters and several reference control parameters, are used to construct a two-dimensional surface. The reference state parameters include longitudinal axis velocity, center-of-gravity velocity sideslip angle, and yaw rate. Based on these parameters, a two-dimensional surface of state parameters can be constructed. The reference control parameters include steering wheel angle and rear wheel drive force. Based on these parameters, a two-dimensional surface of control parameters can be constructed. After obtaining the above two parameter surfaces, this embodiment combines the historical center-of-gravity velocity sideslip angle and historical yaw rate obtained from the historical parameter information with the above two parameter surfaces to determine the historical longitudinal axis velocity, historical steering wheel angle, and historical rear wheel drive force.
[0080] The two parametric surfaces obtained in the above process are as follows: Figures 6-8 As shown. (Refer to...) Figure 6 As shown, Figure 6 For state parameter surfaces, Figure 6 In the figure, U_x_low represents the velocity along the vertical axis, yaw_rate_low represents the yaw rate, and bata_low represents the sideslip angle of the center of mass. The coordinate points in the figure represent the vertical velocity, sideslip angle of the center of mass, and yaw rate corresponding to different steady-state drift states. Figure 7 and Figure 8 All are control parameter surfaces. Figure 7 In this context, delta_low represents the steering wheel angle. Figure 8 In this context, Fx_R_low represents the rear wheel drive force.
[0081] Furthermore, after obtaining the historical steady-state parameters and historical steady-state control parameters, this embodiment can determine the model parameters of the vehicle state error model based on these parameters, and then construct the vehicle state error model corresponding to the vehicle based on the model parameters.
[0082] In the specific implementation, after constructing the aforementioned two-dimensional surface of parameters, the coordinate points corresponding to the historical center-of-gravity velocity sideslip angle and historical yaw rate can be found based on the unique correspondence in the two-dimensional surface of state parameters. The coordinate values corresponding to these points contain the historical longitudinal axis velocity, thus yielding the historical longitudinal axis velocity. Similarly, based on the historical center-of-gravity velocity sideslip angle and the historical yaw rate, the historical steering wheel angle and historical rear-wheel drive force can also be found as described above. All state and control parameters are derived based on the historical center-of-gravity velocity sideslip angle and the historical yaw rate. The historical center-of-gravity velocity sideslip angle, historical yaw rate, and historical longitudinal axis velocity obtained above determine the vehicle's steady-state drift state, while the historical steering wheel angle and historical rear-wheel drive force are the control parameters required for this steady-state drift state.
[0083] After obtaining the above parameters, in this embodiment, the above parameters can be substituted into the following equation for solution, for example, d(△X)=A*△X+B*△U, where △X is the parameter difference of the state parameters and △U is the parameter difference of the control parameters. Substituting the above parameters into the equation will solve for A and B. It should be noted that A and B are different in different steady-state drift states, and each steady-state drift state corresponds to a set of A and B.
[0084] This embodiment obtains the historical parameter information of the vehicle, extracts historical steady-state parameters and historical steady-state control parameters from the historical parameter information, determines model parameters based on the historical steady-state parameters and historical steady-state control parameters, and constructs a vehicle state error model corresponding to the vehicle based on the model parameters. For vehicles with unknown parameters, the corresponding vehicle state error model can be accurately constructed in the above way, thereby improving the accuracy of subsequent vehicle drift control.
[0085] refer to Figure 9 , Figure 9 This is a flowchart illustrating a third embodiment of a vehicle drift control method according to the present invention.
[0086] Based on the first and second embodiments described above, a third embodiment of the vehicle drift control method of the present invention is proposed.
[0087] In this embodiment, step S30 specifically includes:
[0088] Step S303: Input the parameter difference into the vehicle state error model to obtain the correction amount of the control parameters.
[0089] In this embodiment, after the vehicle state error model is constructed, a set of model parameters corresponding to the vehicle state error model can be determined according to the target steady-state drift state that the vehicle needs to achieve, so that the correction amount of the control parameters can be calculated.
[0090] Step S304: Determine the target control parameter based on the correction amount of the control parameter and the current control parameter.
[0091] In this embodiment, the vehicle state error model can be a vehicle state error differential equation, such as d(△X)=A*△X+B*△U, where △X is the parameter difference between state parameters, and A and B are a set of fixed constant coefficients determined by the target steady-state drift state. △U can be calculated through the above equation, where △U represents the parameter difference of the control parameters, that is, the parameter difference between the current control parameters of the vehicle and the target control parameters. The target control parameters represent the control parameters required for the vehicle to reach the target steady-state drift state.
[0092] This embodiment obtains the correction amount of the control parameters by inputting the parameter difference into the vehicle state error model; the target control parameters are determined based on the correction amount of the control parameters and the current control parameters; and the final target control parameters are calculated by the model parameters under different steady-state drift states, thereby improving the accuracy of vehicle drift control.
[0093] Furthermore, this embodiment of the invention also proposes a storage medium storing a vehicle drift control program, which, when executed by a processor, implements the steps of the vehicle drift control method described above.
[0094] Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.
[0095] Reference Figure 10 , Figure 10 This is a structural block diagram of the first embodiment of the vehicle drift control device of the present invention.
[0096] like Figure 10 As shown, the vehicle drift control device proposed in this embodiment of the invention includes:
[0097] The acquisition module 10 is used to acquire the current status parameters of the vehicle.
[0098] The calculation module 20 is used to determine the parameter difference between the current state parameter and the preset state parameter.
[0099] The calculation module 20 is also used to calculate the target control parameters based on the parameter difference and the vehicle state error model.
[0100] The control module 30 is used to perform drift control on the vehicle according to the target control parameters.
[0101] This embodiment obtains the vehicle's current state parameters; determines the parameter difference between the current state parameters and preset state parameters; calculates target control parameters based on the parameter difference and the vehicle state error model; and performs drift control on the vehicle based on the target control parameters. By calculating the target control parameters using the vehicle's current state parameters and the preset state parameters to be achieved, and then directly performing drift control on the vehicle using the target control parameters, the vehicle's physical parameters do not need to be calibrated, and the control parameters are not dependent on empirical dynamic models. This directly calibrates the control parameters, improving the accuracy of vehicle drift control.
[0102] In one embodiment, the vehicle drift control device further includes: a construction module;
[0103] The construction module is used to obtain historical parameter information of the vehicle and construct a vehicle state error model corresponding to the vehicle based on the historical parameter information.
[0104] In one embodiment, the construction module is further configured to extract historical steady-state parameters and historical steady-state control parameters from the historical parameter information; determine model parameters based on the historical steady-state parameters and historical steady-state control parameters; and construct a vehicle state error model corresponding to the vehicle based on the model parameters.
[0105] In one embodiment, the historical steady-state parameters include at least the historical center-of-gravity velocity sideslip angle, historical yaw rate, and historical longitudinal axis velocity; the historical steady-state control parameters include at least the historical steering wheel angle and historical rear-wheel drive force. The construction module is further configured to obtain the vehicle's historical center-of-gravity velocity sideslip angle and historical yaw rate from the historical parameter information; construct a two-dimensional surface of state parameters based on several reference state parameters of the vehicle under different steady-state drift states; construct a two-dimensional surface of control parameters based on several reference control parameters of the vehicle under different steady-state drift states; and obtain the vehicle's historical longitudinal axis velocity, historical steering wheel angle, and historical rear-wheel drive force based on the historical center-of-gravity velocity sideslip angle, the historical yaw rate, the two-dimensional surface of state parameters, and the two-dimensional surface of control parameters.
[0106] In one embodiment, the construction module is further configured to find the historical longitudinal axis velocity from the state parameter two-dimensional surface based on the historical center of mass velocity sideslip angle and the historical yaw rate; and to find the historical steering wheel angle and historical rear wheel drive force from the control parameter two-dimensional surface based on the historical center of mass velocity sideslip angle and the historical yaw rate.
[0107] In one embodiment, the control module 30 is further configured to control the vehicle from a stationary state to a steady-state drift state through an open-loop drift initiation model; when the vehicle is in a steady-state drift state, adjust the open-loop control parameters of the open-loop drift initiation model so that the vehicle changes from the current steady-state drift state to another steady-state drift state.
[0108] In one embodiment, the calculation module 20 is further configured to input the parameter difference into the vehicle state error model to obtain the correction amount of the control parameters; and determine the target control parameters based on the correction amount of the control parameters and the current control parameters.
[0109] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.
[0110] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.
[0111] In addition, for technical details not described in detail in this embodiment, please refer to the vehicle drift control method provided in any embodiment of the present invention, which will not be repeated here.
[0112] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0113] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0115] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.
Claims
1. A vehicle drift control method, characterized in that, The vehicle drift control method includes: Obtain the vehicle's current status parameters; Determine the parameter difference between the current state parameter and the preset state parameter; Calculate the target control parameters based on the parameter differences and the vehicle state error model; The vehicle is drift-controlled according to the target control parameters; Before calculating the target control parameters based on the parameter difference and the vehicle state error model, the method further includes: Obtain the historical parameter information of the vehicle; Extracting historical steady-state parameters and historical steady-state control parameters from the historical parameter information, wherein the historical steady-state parameters include at least historical center of gravity speed sideslip angle, historical yaw rate, and historical longitudinal axis velocity, and the historical steady-state control parameters include at least historical steering wheel angle and historical rear wheel drive force, includes: obtaining the vehicle's historical center of gravity speed sideslip angle and historical yaw rate from the historical parameter information; constructing a two-dimensional surface of state parameters based on several reference state parameters of the vehicle in different steady-state drift states; constructing a two-dimensional surface of control parameters based on several reference control parameters of the vehicle in different steady-state drift states; obtaining the vehicle's historical longitudinal axis velocity, historical steering wheel angle, and historical rear wheel drive force based on the historical center of gravity speed sideslip angle, the historical yaw rate, the two-dimensional surface of state parameters, and the two-dimensional surface of control parameters; The model parameters are determined based on the historical steady-state parameters and historical steady-state control parameters. The vehicle state error model corresponding to the vehicle is constructed based on the model parameters.
2. The vehicle drift control method as described in claim 1, characterized in that, The process of obtaining the vehicle's historical longitudinal axis velocity, historical steering wheel angle, and historical rear wheel drive force based on the historical center of mass velocity sideslip angle, the historical yaw rate, the two-dimensional surface of state parameters, and the two-dimensional surface of control parameters includes: Based on the historical centroid velocity sideslip angle and the historical yaw rate, the historical longitudinal velocity is found from the two-dimensional surface of the state parameters. Based on the historical center of gravity velocity sideslip angle and the historical yaw rate, the historical steering wheel angle and historical rear wheel drive force are retrieved from the two-dimensional surface of the control parameters.
3. The vehicle drift control method as described in claim 1, characterized in that, Before constructing the two-dimensional surface of state parameters based on several reference state parameters of the vehicle under different steady-state drift states, the method further includes: The vehicle is controlled from a stationary state to a steady-state drift state using an open-loop drift initiation model; When the vehicle is in a steady-state drift state, the open-loop control parameters of the open-loop drift initiation model are adjusted so that the vehicle changes from the current steady-state drift state to other steady-state drift states.
4. The vehicle drift control method as described in any one of claims 1 to 3, characterized in that, The calculation of the target control parameters based on the parameter difference and the vehicle state error model includes: The parameter difference is input into the vehicle state error model to obtain the correction amount of the control parameters; The target control parameter is determined based on the correction amount of the control parameter and the current control parameter.
5. A vehicle drift control device, characterized in that, The vehicle drift control device includes: The acquisition module is used to acquire the current status parameters of the vehicle; The calculation module is used to determine the parameter difference between the current state parameter and the preset state parameter; The calculation module is also used to calculate the target control parameters based on the parameter difference and the vehicle state error model; The control module is used to perform drift control on the vehicle according to the target control parameters; The vehicle drift control device further includes: a construction module; the construction module is used to acquire historical parameter information of the vehicle; extract historical steady-state parameters and historical steady-state control parameters from the historical parameter information, wherein the historical steady-state parameters include at least historical center of mass velocity sideslip angle, historical yaw rate, and historical longitudinal axis velocity, and the historical steady-state control parameters include at least historical steering wheel angle and historical rear wheel driving force, including: acquiring the historical center of mass velocity sideslip angle and historical yaw rate of the vehicle from the historical parameter information; constructing a two-dimensional surface of state parameters based on several reference state parameters of the vehicle in different steady-state drift states; constructing a two-dimensional surface of control parameters based on several reference control parameters of the vehicle in different steady-state drift states; acquiring the historical longitudinal axis velocity, historical steering wheel angle, and historical rear wheel driving force of the vehicle based on the historical center of mass velocity sideslip angle, the historical yaw rate, the two-dimensional surface of state parameters, and the two-dimensional surface of control parameters; determining model parameters based on the historical steady-state parameters and historical steady-state control parameters; and constructing a vehicle state error model corresponding to the vehicle based on the model parameters.
6. A vehicle drift control device, characterized in that, The vehicle drift control device includes: a memory, a processor, and a vehicle drift control program stored in the memory and running on the processor, the vehicle drift control program being configured to implement the vehicle drift control method as described in any one of claims 1 to 4.
7. A storage medium, characterized in that, The storage medium stores a vehicle drift control program, which, when executed by a processor, implements the vehicle drift control method as described in any one of claims 1 to 4.