Control method, apparatus and device

Abstract

This invention provides a control method, apparatus, and device, comprising: acquiring steering ratio handle data, wheel angle data, speed data, and IMU data of a vehicle; calculating a first steering angle based on the steering ratio handle data; calculating a second steering angle of the vehicle based on the wheel angle data, speed data, and IMU data; calculating a control signal for the vehicle based on the first and second steering angles; and controlling the vehicle to perform corresponding actions based on the control signal. This achieves multi-variable coupling and improves the coordination between wheels.

Description

Technical Field

[0001] This invention relates to the field of information processing, and more specifically to a control method, apparatus, and device. Background Technology

[0002] With the continuous development of the engineering construction field, the demand for all-terrain excavators is increasing. All-terrain excavators need to operate in various complex terrain conditions, such as mountains, swamps, and snowfields, which places extremely high demands on their steering performance. Traditional excavator steering methods, such as front-wheel steering or rear-wheel steering, have large turning radii and poor maneuverability in narrow spaces or complex terrains, failing to meet actual operational needs. Four-wheel independent steering technology provides a new solution to these problems. Through independent steering of the four wheels, all-terrain excavators can achieve smaller turning radii, and even turn on the spot, greatly improving their mobility and operational efficiency in complex terrains. For example, the ability to achieve full four-wheel steering makes them more suitable for operation in complex geographical environments such as forests and swamps. However, achieving precise control of four-wheel independent steering faces many technical challenges. For example, all-terrain excavators generally have independent four-wheel steering mechanisms, unlike automobiles and wheeled excavators which have linkage steering mechanisms. The steering control of four-wheel independent steering mechanisms faces many problems, such as: (1) Multivariable coupling problem: In a four-wheel independent steering system, the steering angle, speed and other variables of each wheel are coupled with each other. There is currently no effective way to decouple these variables and solve the multivariable coupling problem, which leads to poor coordination between wheels and problems such as inconsistent steering and vehicle body vibration. Summary of the Invention

[0003] In view of this, the present invention aims to provide a control method, device and equipment to solve the problem of multivariate coupling in the precise control of four-wheel independent steering in the prior art, which leads to poor coordination between wheels and problems such as inconsistent steering and vehicle body vibration.

[0004] This invention provides a control method, the method comprising: Acquire vehicle steering ratio lever data, wheel angle data, speed data, and IMU data; The first steering angle is calculated based on the steering ratio handle data; The second steering angle of the vehicle is calculated based on the wheel angle data, speed data, and IMU data. The control signal of the vehicle is calculated based on the first steering angle and the second steering angle; Based on the control signal, the vehicle is controlled to perform corresponding actions.

[0005] In one possible embodiment, the first steering angle includes the front wheel steering angle and the rear wheel steering angle, and the calculation of the first steering angle based on the steering ratio handle data includes: The turning radius is calculated based on the steering ratio handle data and the vehicle mode. The front wheel steering angle of the vehicle is calculated based on the steering radius. The rear wheel steering angle of the vehicle is calculated based on the vehicle's kinematics and steering requirements.

[0006] In one possible embodiment, the IMU data includes accelerometer data and gyroscope data, and the calculation of the vehicle's second steering angle based on the wheel angle data, speed data, and IMU data includes: Based on the accelerometer data, the pitch angle and roll angle are calculated. The pitch angle, roll angle, and gyroscope data are fused to obtain the vehicle's attitude angle; The second steering angle of the vehicle is calculated based on the attitude angle, wheel angle data, speed data, and IMU data.

[0007] In one possible embodiment, calculating the second steering angle of the vehicle based on the attitude angle, wheel angle data, speed data, and IMU data includes: Based on the wheel angle data, speed data, and IMU data, an observation vector is constructed; Based on the observation vector, the observation equation is constructed. The second steering angle is calculated based on the observation equation and the attitude angle.

[0008] In one possible embodiment, calculating the vehicle control signal based on the first steering angle and the second steering angle includes: Calculate the angle difference between the first steering angle and the second steering angle; The control signal for the vehicle is calculated based on the angle difference.

[0009] In one possible embodiment, controlling the vehicle to perform a corresponding action based on the control signal includes: Based on the control signal, the current magnitude or switching frequency of the vehicle's steering actuator solenoid valve is controlled; The opening degree of the vehicle's regulating solenoid valve is controlled according to the magnitude of the current or the switching frequency of the solenoid valve, thereby controlling the action of the vehicle's steering actuator.

[0010] In one possible embodiment, after acquiring the vehicle's steering ratio handle data, wheel angle data, speed data, and IMU data, the method further includes: The wheel angle data and speed data are filtered using a first-order low-pass filter to obtain filtered wheel angle data and speed data.

[0011] In one possible embodiment, the IMU data includes accelerometer data and gyroscope data. After acquiring the vehicle's steering ratio handle data, wheel angle data, speed data, and IMU data, the method further includes: Acquire the first zero bias value of the accelerometer data and the second zero bias value of the gyroscope data; Based on the first and second zero bias values, zero bias compensation is performed on the accelerometer data and gyroscope data to obtain zero bias compensated accelerometer data and gyroscope data.

[0012] In a second aspect, the present invention provides a control device, the device comprising: The acquisition module is used to acquire vehicle steering ratio handle data, wheel angle data, speed data, and IMU data; The first calculation module is used to calculate the first steering angle based on the steering ratio handle data; The second calculation module is used to calculate the second steering angle of the vehicle based on the wheel angle data, speed data, and IMU data. The third calculation module is used to calculate the control signal of the vehicle based on the first steering angle and the second steering angle; The control module is used to control the vehicle to perform corresponding actions based on the control signal.

[0013] Thirdly, the present invention provides an electronic device, the device comprising: a memory and a processor; the memory being used to store related program code; the processor being used to call the program code to execute the control method described in any of the implementations of the first aspect.

[0014] Fourthly, the present invention provides a computer-readable storage medium for storing a computer program for executing the control method described in any implementation of the first aspect.

[0015] Fifthly, the present invention provides a computer program product comprising a computer program / instruction, wherein the computer program / instruction, when executed by a processor, implements the control method described in any of the implementations of the first aspect.

[0016] In the above implementation of the present invention, the vehicle's steering ratio handle data, wheel angle data, speed data, and IMU data are acquired; a first steering angle is calculated based on the steering ratio handle data; a second steering angle is calculated based on the wheel angle data, speed data, and IMU data; a control signal for the vehicle is calculated based on the first and second steering angles; and the vehicle is controlled to perform corresponding actions based on the control signal. This achieves multivariate coupling and improves the coordination between wheels. Attached Figure Description

[0017] Figure 1 A flowchart of a control method provided in an embodiment of the present invention.

[0018] Figure 2 Another flowchart of a control method provided in an embodiment of the present invention.

[0019] Figure 3 A schematic diagram of a control device provided in an embodiment of the present invention.

[0020] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] One embodiment of the present invention provides a control method that acquires vehicle steering ratio handle data, wheel angle data, speed data, and IMU data; calculates a first steering angle based on the steering ratio handle data; calculates a second steering angle based on the wheel angle data, speed data, and IMU data; calculates a control signal for the vehicle based on the first and second steering angles; and controls the vehicle to perform corresponding actions based on the control signal. This achieves multi-variable coupling and improves the coordination between wheels.

[0023] Please see Figure 1 In one exemplary embodiment, a control method is provided, applied to the control scenario of an all-terrain excavator. The method may include the following steps: S101: Acquires vehicle steering ratio handle data, wheel angle data, speed data, and IMU data.

[0024] Specifically, the vehicle's steering ratio lever data is acquired. The vehicle can be an all-terrain excavator, typically with an independent four-wheel steering mechanism. The normalized steering ratio lever data *i* can be read using a steering ratio lever acquisition device on the vehicle, where the data range is [0,1], where 0 indicates no steering requirement and 1 indicates maximum steering requirement. Wheel angle and speed data are also acquired. This can be done in real-time using angle and speed sensors installed on each wheel. Hall effect angle sensors and photoelectric encoders can be used for speed measurement, with a sampling frequency of 100Hz. IMU data, including accelerometer and gyroscope data, is also acquired. This can be done using an IMU (Inertial Measurement Unit) installed on the vehicle. This multi-dimensional data acquisition allows for subsequent vehicle control, avoiding inaccurate control caused by relying on single-dimensional data.

[0025] After acquiring steering ratio handle data, wheel angle data, speed data, and IMU data, further preprocessing can be performed on these data to reduce data noise and improve their accuracy.

[0026] Specifically, the preprocessing of wheel angle and speed data includes: The wheel angle data and speed data are filtered using a first-order low-pass filter to obtain filtered wheel angle data and speed data.

[0027] In the specific implementation process, a first-order low-pass filter is used to process the wheel angle and speed data. The transfer function of the first-order low-pass filter is G(s) = 1 / (Ts+1), where T is the time constant. Based on actual testing and noise characteristics, T = 0.1s can be selected. Low-pass filtering removes high-frequency noise from the wheel angle and speed data, such as noise caused by electromagnetic interference, making the wheel angle and speed data smoother and thus improving the accuracy of the calculated control signal.

[0028] Specifically, the preprocessing of IMU data includes: Acquire the first zero bias value of the accelerometer data and the second zero bias value of the gyroscope data; Based on the first and second zero bias values, zero bias compensation is performed on the accelerometer data and gyroscope data to obtain zero bias compensated accelerometer data and gyroscope data.

[0029] In practice, the IMU undergoes zero-bias calibration before leaving the factory or during vehicle startup initialization. Under static conditions, the initial output data of the IMU is acquired, and the zero-bias values ​​ba and bg of the accelerometer and gyroscope are calculated. In actual use, zero-bias compensation is applied to the read IMU data. Specifically, the compensated accelerometer measurement value a_{meas} is a = a_meas - ba, and the compensated gyroscope measurement value ω_meas is ω = ω_meas - bg. This reduces the integral drift of the IMU data and improves the convergence and stability of sensor fusion.

[0030] S102: Calculate the first steering angle based on the steering ratio handle data.

[0031] In this embodiment, the first steering angle can be calculated based on the steering ratio handle data. The first steering angle includes the steering angles of the four wheels of the all-terrain excavator, which may include two front wheels and two rear wheels.

[0032] Specifically, the step of calculating the first steering angle based on the steering ratio handle data may include: The turning radius is calculated based on the steering ratio handle data and the vehicle mode. The front wheel steering angle of the vehicle is calculated based on the steering radius. The rear wheel steering angle of the vehicle is calculated based on the vehicle's kinematics and steering requirements.

[0033] In practice, user steering mode commands, such as normal steering mode, low-speed steering mode, and high-speed steering mode, can be obtained through the vehicle's human-machine interface or control bus. Different modes correspond to different steering characteristic parameters. Based on the steering ratio handle data and the vehicle's mode, the steering radius is calculated, and the steering radius R is obtained through interpolation using a pre-set interpolation table. For example, in normal steering mode, if the minimum steering radius R_{min} = 2m is set, then the steering radius R = R_{min} / i (when i is not equal to 0). When i = 0, R is infinite, indicating straight-line driving. The interpolation table stores the correspondence between steering ratio handle data and steering radius under different steering modes, and a continuous and smooth steering radius value can be obtained through interpolation. The interpolation table is stored in the factory-preset area of ​​the vehicle's or controller's non-volatile memory (such as EEPROM or Flash). Based on the steering radius, the front wheel steering angle of the vehicle is calculated, and based on the vehicle's kinematics and steering requirements, the rear wheel steering angle is calculated. Specifically, the front wheel steering angles can be calculated using a four-wheel Ackerman model. The core formula of the four-wheel Ackerman model is cotα–cotβ = B / L, where cotα is the steering angle of the outer front wheel, cotβ is the steering angle of the inner front wheel, B is the track width, and L is the wheelbase. Based on the calculated steering radius R, combined with the vehicle's track width B and wheelbase L, the target steering angles α_{target} and β_{target} of the front wheels are calculated. For the rear wheels, in a four-wheel independent steering system, based on the vehicle's kinematics and steering requirements, the target steering angles γ_target1 and γ_target2 of the rear wheels are calculated, thus obtaining the target angles for all four wheels, i.e., the first steering angles. The optimal solutions for the steering angles of all four wheels can be solved simultaneously without comparing the angles between wheels, ensuring steering coordination and consistency of the targets.

[0034] S103: Calculate the second steering angle of the vehicle based on the wheel angle data, speed data, and IMU data.

[0035] In this embodiment, the second steering angle of the vehicle, i.e. the actual steering angle of the vehicle, is calculated from the wheel angle data, speed data, and IMU data.

[0036] Specifically, the step of calculating the second steering angle of the vehicle based on the wheel angle data, speed data, and IMU data may include: Based on the accelerometer data, the pitch angle and roll angle are calculated. The pitch angle, roll angle, and gyroscope data are fused to obtain the vehicle's attitude angle; The second steering angle of the vehicle is calculated based on the attitude angle, wheel angle data, speed data, and IMU data.

[0037] In this embodiment, the pitch angle and roll angle are calculated based on accelerometer data. Specifically, Based on the accelerometer data obtained, a_x, a_y, a_z, the magnitude of the gravity vector is calculated as g_mag = sqrt(a_x² + a_y² + a_z²). The data in the three dimensions are then normalized according to the magnitude of the gravity vector: specifically, g_x = a_x / g_mag, g_y = a_y / g_mag, and g_z = a_z / g_mag.

[0038] Calculate the roll angle (Roll, φ) based on the normalized data: roll_rad = atan2(g_y, g_z), roll_deg = roll_rad × 180 / π. Calculate the pitch angle (Pitch, θ) based on the normalized data: pitch_rad = atan2(-g_x, sqrt(g_y² + g_z²)), pitch_deg = pitch_rad × 180 / π. Adjust the calculated pitch and roll angles within a range of ±180°. Specifically, if roll_deg > 180, roll_deg -= 360; if roll_deg < -180, roll_deg += 360. Adjust the pitch angle using the same method.

[0039] The vehicle's attitude angle is obtained by fusing pitch angle, roll angle, and gyroscope data. Specifically, let pitch angle and roll angle be θ_a, and gyroscope data be θ_g. The fusion formula for complementary filtering is θ = α·θ_g + (1-α)·θ_a, where α is the fusion coefficient, and α = 0.95 is selected based on the actual dynamic characteristics. Through complementary filtering, the high accuracy of the accelerometer under static or low dynamic conditions and the fast response characteristics of the gyroscope under dynamic conditions are combined to obtain a more accurate vehicle attitude angle θ, providing a foundation for subsequent Kalman filtering.

[0040] The steps for calculating the second steering angle of the vehicle based on the attitude angle, wheel angle data, speed data, and IMU data include: Based on the wheel angle data, speed data, and IMU data, an observation vector is constructed; Based on the observation vector, the observation equation is constructed. The second steering angle is calculated based on the observation equation and the attitude angle, wheel angle data, and speed data.

[0041] In the specific implementation process, based on the attitude angle, wheel angle data, speed data, and second steering angle, the state vector X = [θ ω v δ1 δ2 δ3 δ4]^T is defined, where θ is the attitude angle, ω is the wheel angle data, v is the speed data, and δ1 δ2 δ3 δ4 are the actual angles of the four wheels, i.e., the second steering angle to be calculated. The state equation is defined as X_k = F·X_(k-1) + G·u_k + w_k, where F is the state transition matrix, which can be derived from the standard model in vehicle dynamics and control. G is the control input matrix, which can be found in the actuator specification. u_k is the control input, the target steering angle command sent by the controller to the four steering actuators, i.e., u_k = [δ1_cmd, δ2_cmd, δ3_cmd, δ4_cmd]^T. w_k is the process noise, representing the sensor error. It can also be assumed to be Gaussian white noise with a mean of 0, and its covariance matrix R needs to be set according to the sensor manual or experimental calibration. The observation equation is defined as Z_k = H·X_k + v_k, where Z_k is the observation vector, which consists of wheel angle data, speed data, and IMU data. The second steering angle can be calculated by inputting the corresponding parameters into the observation equation.

[0042] S104: Calculate the control signal of the vehicle based on the first steering angle and the second steering angle.

[0043] In this embodiment, after calculating the first steering angle and the second steering angle, the vehicle control signal can be calculated based on the first steering angle and the second steering angle.

[0044] Specifically, the step of calculating the vehicle's control signal based on the first steering angle and the second steering angle may include: Calculate the angle difference between the first steering angle and the second steering angle; The control signal for the vehicle is calculated based on the angle difference.

[0045] In the specific implementation process, the first steering angle and the second steering angle are subtracted to obtain the angle difference. This angle difference is then input into the vehicle's controller output formula to obtain the control signal. The output formula of the PID controller is: u = 10e + 0.5∫edt + 2(de / dt), where K_p is the proportional coefficient, K_i is the integral coefficient, and K_d is the derivative coefficient. The specific values ​​can be determined through debugging based on the actual steering characteristics of the vehicle.

[0046] S105: Based on the control signal, control the vehicle to perform the corresponding action.

[0047] In practice, control signals are used to control the movement of the vehicle's steering actuator, making the actual wheel angle approach the target angle, thus achieving independent steering control of all four wheels.

[0048] The steps of controlling the vehicle to perform corresponding actions based on the control signal include: Based on the control signal, the current magnitude or switching frequency of the vehicle's steering actuator solenoid valve is controlled; The opening degree of the vehicle's regulating solenoid valve is controlled according to the magnitude of the current or the switching frequency of the solenoid valve, thereby controlling the action of the vehicle's steering actuator.

[0049] In practice, after receiving the control signal, the current magnitude or switching frequency of the vehicle's steering actuator solenoid valve is controlled according to the control signal; based on the current magnitude or switching frequency of the solenoid valve, the opening degree of the vehicle's regulating solenoid valve is controlled, thereby controlling the action of the vehicle's steering actuator. For example, the control signal can be sent to the vehicle's control unit. After receiving the control signal, the control unit parses the control signal to obtain a basic target current value I. base The vehicle's drive circuit is based on I base Output a constant current or slowly varying current signal to the proportional solenoid valve. base The flow rate is proportional to the displacement of the solenoid valve core, thus linearly controlling the flow into the steering power cylinder and ultimately the movement of the vehicle's steering actuator.

[0050] Based on the method provided in the above embodiments, the vehicle's steering ratio handle data, wheel angle data, speed data, and IMU data are acquired; a first steering angle is calculated based on the steering ratio handle data; a second steering angle is calculated based on the wheel angle data, speed data, and IMU data; a control signal for the vehicle is calculated based on the first and second steering angles; and the vehicle is controlled to perform corresponding actions based on the control signal. This achieves multi-variable coupling and improves the coordination between wheels.

[0051] Please refer to Figure 2 This illustrates a flow chart of an embodiment of the control method of the present invention. The control method includes the following steps: S201, Obtain the vehicle's normalized steering ratio lever data; Specifically, the vehicle's steering ratio lever data is acquired. This can be done using a steering ratio lever acquisition device on the vehicle to read normalized steering ratio lever data i, where the steering ratio lever data ranges from [0,1], where 0 indicates no steering requirement and 1 indicates maximum steering requirement.

[0052] S202, Read user steering mode command; User steering mode commands, such as normal steering mode, low-speed steering mode, and high-speed steering mode, can be obtained through the vehicle's human-machine interface or control bus. Different modes correspond to different steering characteristic parameters.

[0053] S203, the steering radius is calculated based on the normalized steering ratio handle data and the user's steering mode command; For example, in normal steering mode, if the minimum steering radius R_{min} = 2m is set, then the steering radius R = R_{min} / i (when i is not equal to 0). When i = 0, R is infinite, indicating straight-line driving. The interpolation table stores the correspondence between steering ratio handle data and steering radius in different steering modes. Continuous and smooth steering radius values ​​can be obtained through interpolation. The interpolation table is stored in the factory preset area of ​​the vehicle or controller's non-volatile memory (such as EEPROM, Flash).

[0054] S204, the first steering angle of the vehicle is calculated based on the steering radius; Specifically, the front wheel steering angles can be calculated using a four-wheel Ackerman model. The core formula of the four-wheel Ackerman model is cotα–cotβ = B / L, where cotα is the outer front wheel steering angle, cotβ is the inner front wheel steering angle, B is the track width, and L is the wheelbase. Based on the calculated steering radius R, combined with the vehicle's track width B and wheelbase L, the target steering angles α_{target} and β_{target} of the front wheels are calculated. For the rear wheels, in a four-wheel independent steering system, based on the vehicle's kinematics and steering requirements, the target steering angles γ_target1 and γ_target2 of the rear wheels are calculated, thus obtaining the target angles of all four wheels, i.e., the first steering angles.

[0055] S205, reads wheel angle data and speed data; Specifically, the wheel angle and speed data can be read in real time using angle and speed sensors installed on each wheel.

[0056] S206, perform low-pass filtering on the read wheel angle data and speed data; A first-order low-pass filter is used to perform low-pass filtering on the wheel angle and speed data. The transfer function of the first-order low-pass filter is G(s) = 1 / (Ts+1), where T is the time constant. Based on actual testing and noise characteristics, T = 0.1s can be selected. Low-pass filtering removes high-frequency noise from the wheel angle and speed data, such as noise caused by electromagnetic interference, making the wheel angle and speed data smoother and facilitating subsequent processing.

[0057] S207, read IMU data; Specifically, IMU (Inertial Measurement Unit) data can be read using an IMU installed on the vehicle.

[0058] S208 performs zero-value compensation processing on the IMU data; Before the IMU leaves the factory or during vehicle startup initialization, zero-bias calibration is performed on the IMU. Under static conditions, the initial output data of the IMU is acquired, and the zero-bias values ​​ba and bg of the accelerometer and gyroscope are calculated. In actual use, zero-bias compensation is performed on the read IMU data. Specifically, the compensated accelerometer measurement value a_{meas} is a = a_meas - ba, and the compensated gyroscope measurement value ω_meas is ω = ω_meas - bg.

[0059] S209, the second steering angle of the vehicle is calculated based on the processed wheel angle data, speed data and IMU data; Based on the accelerometer data, the pitch and roll angles are calculated. Specifically, based on the accelerometer data a_x, a_y, and a_z, the magnitude of the gravity vector g_mag = sqrt(a_x² + a_y² + a_z²) is calculated. The data in the three dimensions are then normalized according to the magnitude of the gravity vector: g_x = a_x / g_mag, g_y = a_y / g_mag, and g_z = a_z / g_mag.

[0060] Calculate the roll angle (Roll, φ) based on the normalized data: roll_rad = atan2(g_y, g_z), roll_deg = roll_rad × 180 / π. Calculate the pitch angle (Pitch, θ) based on the normalized data: pitch_rad = atan2(-g_x, sqrt(g_y² + g_z²)), pitch_deg = pitch_rad × 180 / π. Adjust the calculated pitch and roll angles within a range of ±180°. Specifically, if roll_deg > 180, roll_deg -= 360; if roll_deg < -180, roll_deg += 360. Adjust the pitch angle using the same method.

[0061] The vehicle's attitude angle is obtained by fusing pitch angle, roll angle, and gyroscope data. Specifically, let pitch angle and roll angle be θ_a, and gyroscope data be θ_g. The fusion formula for complementary filtering is θ = α·θ_g + (1-α)·θ_a, where α is the fusion coefficient, and α = 0.95 is selected based on the actual dynamic characteristics. Through complementary filtering, the high accuracy of the accelerometer under static or low dynamic conditions and the fast response characteristics of the gyroscope under dynamic conditions are combined to obtain a more accurate vehicle attitude angle θ, providing a foundation for subsequent Kalman filtering.

[0062] The second steering angle of the vehicle is calculated based on the attitude angle, wheel angle data, speed data, and IMU data.

[0063] S210, calculate the difference between the first steering angle and the second steering angle, and calculate the control signal based on the difference; The first steering angle and the second steering angle are subtracted to obtain the angle difference. This angle difference is then input into the vehicle's controller output formula to obtain the control signal.

[0064] S211, according to the control signal, directs the solenoid valve to perform the rotation.

[0065] Based on the above method embodiments, this invention also provides a control device. See [link to relevant documentation]. Figure 3 The diagram shown is a schematic diagram of a control device provided in an embodiment of the present invention.

[0066] The device 300 includes: The acquisition module 301 is used to acquire vehicle steering ratio handle data, wheel angle data, speed data and IMU data; The first calculation module 302 is used to calculate the first steering angle based on the steering ratio handle data; The second calculation module 303 is used to calculate the second steering angle of the vehicle based on the wheel angle data, speed data and IMU data; The third calculation module 304 is used to calculate the control signal of the vehicle based on the first steering angle and the second steering angle; The control module 305 is used to control the vehicle to perform corresponding actions based on the control signal.

[0067] In one possible implementation, the first steering angle includes a front wheel steering angle and a rear wheel steering angle, and the calculation of the first steering angle based on the steering ratio handle data includes: The turning radius is calculated based on the steering ratio handle data and the vehicle mode. The front wheel steering angle of the vehicle is calculated based on the steering radius. The rear wheel steering angle of the vehicle is calculated based on the vehicle's kinematics and steering requirements.

[0068] In one possible implementation, the IMU data includes accelerometer data and gyroscope data, and the calculation of the vehicle's second steering angle based on the wheel angle data, speed data, and IMU data includes: Based on the accelerometer data, the pitch angle and roll angle are calculated. The pitch angle, roll angle, and gyroscope data are fused to obtain the vehicle's attitude angle; The second steering angle of the vehicle is calculated based on the attitude angle, wheel angle data, speed data, and IMU data.

[0069] In one possible implementation, calculating the second steering angle of the vehicle based on the attitude angle, wheel angle data, speed data, and IMU data includes: Based on the wheel angle data, speed data, and IMU data, an observation vector is constructed; Based on the observation vector, the observation equation is constructed. The second steering angle is calculated based on the observation equation and the attitude angle.

[0070] In one possible implementation, calculating the vehicle control signal based on the first steering angle and the second steering angle includes: Calculate the angle difference between the first steering angle and the second steering angle; The control signal for the vehicle is calculated based on the angle difference.

[0071] In one possible implementation, controlling the vehicle to perform a corresponding action based on the control signal includes: Based on the control signal, the current magnitude or switching frequency of the vehicle's steering actuator solenoid valve is controlled; The opening degree of the vehicle's regulating solenoid valve is controlled according to the magnitude of the current or the switching frequency of the solenoid valve, thereby controlling the action of the vehicle's steering actuator.

[0072] In one possible implementation, after acquiring the vehicle's steering ratio handle data, wheel angle data, speed data, and IMU data, the method further includes: The wheel angle data and speed data are filtered using a first-order low-pass filter to obtain filtered wheel angle data and speed data.

[0073] In one possible implementation, the IMU data includes accelerometer data and gyroscope data. After acquiring the vehicle's steering ratio handle data, wheel angle data, speed data, and IMU data, the method further includes: Acquire the first zero bias value of the accelerometer data and the second zero bias value of the gyroscope data; Based on the first and second zero bias values, zero bias compensation is performed on the accelerometer data and gyroscope data to obtain zero bias compensated accelerometer data and gyroscope data.

[0074] See Figure 4 , Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention.

[0075] The device 400 includes a memory 401 and a processor 402; the memory 401 is used to store relevant program code; the processor 402 is used to call the program code to execute the control method described in the above method embodiments.

[0076] Furthermore, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program for executing the control method described in the above method embodiments.

[0077] This invention also provides a computer program product, which includes a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the control method described in the above method embodiments.

[0078] It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0079] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0080] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. In particular, for system or device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The device embodiments described above are merely illustrative. The units or modules described as separate components may or may not be physically separate. The components shown as units or modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the units or modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0081] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions, and operations that may be implemented according to various embodiments of the invention, including methods, apparatus, and devices. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0082] It should be understood that in this invention, "at least one (item)" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0083] It should also be noted that, in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0084] The steps of the methods or algorithms described in conjunction with the embodiments disclosed in this invention can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A control method, characterized in that, The method includes: Acquire vehicle steering ratio lever data, wheel angle data, speed data, and IMU data; The first steering angle is calculated based on the steering ratio handle data; The second steering angle of the vehicle is calculated based on the wheel angle data, speed data, and IMU data. The control signal of the vehicle is calculated based on the first steering angle and the second steering angle; Based on the control signal, the vehicle is controlled to perform corresponding actions.

2. The method according to claim 1, characterized in that, The first steering angle includes the front wheel steering angle and the rear wheel steering angle. The calculation of the first steering angle based on the steering ratio lever data includes: The turning radius is calculated based on the steering ratio handle data and the vehicle mode. The front wheel steering angle of the vehicle is calculated based on the steering radius. The rear wheel steering angle of the vehicle is calculated based on the vehicle's kinematics and steering requirements.

3. The method according to claim 1, characterized in that, The IMU data includes accelerometer data and gyroscope data. The calculation of the vehicle's second steering angle based on the wheel angle data, speed data, and IMU data includes: Based on the accelerometer data, the pitch angle and roll angle are calculated. The pitch angle, roll angle, and gyroscope data are fused to obtain the vehicle's attitude angle; The second steering angle of the vehicle is calculated based on the attitude angle, wheel angle data, speed data, and IMU data.

4. The method according to claim 3, characterized in that, The calculation of the vehicle's second steering angle based on the attitude angle, wheel angle data, speed data, and IMU data includes: Based on the wheel angle data, speed data, and IMU data, an observation vector is constructed; Based on the observation vector, the observation equation is constructed. The second steering angle is calculated based on the observation equation and the attitude angle, wheel angle data, and speed data.

5. The method according to claim 1, characterized in that, The step of calculating the vehicle's control signal based on the first steering angle and the second steering angle includes: Calculate the angle difference between the first steering angle and the second steering angle; The control signal for the vehicle is calculated based on the angle difference.

6. The method according to claim 1, characterized in that, The step of controlling the vehicle to perform corresponding actions based on the control signal includes: Based on the control signal, the current magnitude or switching frequency of the vehicle's steering actuator solenoid valve is controlled; The opening degree of the vehicle's regulating solenoid valve is controlled according to the magnitude of the current or the switching frequency of the solenoid valve, thereby controlling the action of the vehicle's steering actuator.

7. The method according to claim 1, characterized in that, After acquiring the vehicle's steering ratio handle data, wheel angle data, speed data, and IMU data, the process also includes: The wheel angle data and speed data are filtered using a first-order low-pass filter to obtain filtered wheel angle data and speed data.

8. The method according to claim 1, characterized in that, The IMU data includes accelerometer data and gyroscope data. After acquiring the vehicle's steering ratio handle data, wheel angle data, speed data, and IMU data, the process further includes: Acquire the first zero bias value of the accelerometer data and the second zero bias value of the gyroscope data; Based on the first and second zero bias values, zero bias compensation is performed on the accelerometer data and gyroscope data to obtain zero bias compensated accelerometer data and gyroscope data.

9. A control device, characterized in that, The device includes: The acquisition module is used to acquire vehicle steering ratio handle data, wheel angle data, speed data, and IMU data; The first calculation module is used to calculate the first steering angle based on the steering ratio handle data; The second calculation module is used to calculate the second steering angle of the vehicle based on the wheel angle data, speed data, and IMU data. The third calculation module is used to calculate the control signal of the vehicle based on the first steering angle and the second steering angle; The control module is used to control the vehicle to perform corresponding actions based on the control signal.

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