Vehicle four-wheel steering stability control method and system, medium and program product

Abstract

The invention discloses a vehicle four-wheel steering stability control method and system, a medium and a program product. The method comprises the steps that a Simulink vehicle model and a Trucksim whole vehicle model are obtained; carrying out verification on the Simulink vehicle model and the Tricksim whole vehicle model, and carrying out comparison verification on a real vehicle test result and a Tricksim simulation result; designing an upper-layer LQR controller by taking tracking of an ideal side slip angle and an ideal yaw velocity as a control target; longitudinal control is introduced on the basis of four-wheel steering transverse control, and lateral dynamic accurate control over the vehicle is achieved through a lower-layer torque distribution controller. The method is suitable for small vehicles and special vehicles, the technical problem that a traditional method is limited during low-speed steering and high-speed steering is effectively solved, meanwhile, the tracking performance of the side slip angle and the yaw velocity is optimized to be close to ideal values as much as possible, the torque of vehicle yaw motion is directly controlled in a transverse and longitudinal combined control mode, and the stability of the vehicle yaw motion is improved. Therefore, the vehicle is stabilized or the driving posture is changed, and the operation burden of a driver is relieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to a vehicle four-wheel steering stability control method, system, medium and program product. BACKGROUND

[0002] For a front-wheel steering vehicle, during driving, the driver controls the steering wheel to make the front wheels of the vehicle turn, and the rear wheels do not turn. This front-wheel steering control system is mature in technology and simple in structure.

[0003] Four-wheel steering is the evolution and functional expansion of traditional front-wheel steering, and the correlation between the two is inheritance, development and complementation. For most ordinary family cars, low-cost and reliable front-wheel steering is sufficient; and for luxury cars, performance cars, large trucks or buses, four-wheel steering can significantly improve the handling and safety.

[0004] At present, the research on vehicle handling stability based on four-wheel steering mainly includes front-wheel steering angle-based feedforward control and yaw rate-based feedback control. The core idea of front-wheel steering angle-based feedforward control is to directly calculate the angle by which the rear wheels should be deflected according to the current steering wheel angle (i.e. front-wheel steering angle) and vehicle speed through a predetermined control rule (usually a transfer function or a lookup table), for example: front-wheel steering angle proportional feedforward control, the rear-wheel steering angle controlled thereby has a proportional relationship with the front-wheel steering angle. The core idea of yaw rate-based feedback control is to monitor the actual dynamic response of the vehicle (i.e. yaw rate) in real time through a sensor, and compare it with the expected yaw rate calculated by the ideal model, if there is a deviation (i.e. error) between the two, the system will actively adjust the rear-wheel steering angle to eliminate the deviation, so that the actual performance of the vehicle closely follows the ideal model.

[0005] In the process of implementing the technical solutions of the present application, at least the following technical problems in the prior art have been found: The traditional front-wheel steering control method has reached its limit in control ability after years of development, and its defects are as follows: at low speed, due to the limitation of wheel steering angle, front-wheel steering cannot meet the requirement of high maneuverability; at high speed, only relying on front-wheel steering cannot control the large side slip angle of the vehicle body, the vehicle has poor tracking ability and is prone to side slip and rollover.

[0006] The traditional four-wheel steering method is only controlled based on the front wheel steering angle and the yaw rate, and the improvement effect on the vehicle handling stability is general, especially for the special vehicle which needs to work in the complex and changeable environment such as narrow lane and rugged mountain road, and the traditional four-wheel steering method cannot meet the requirements. Specifically, due to the certain limitations of the traditional front wheel steering angle proportional feedforward control and the yaw rate feedback control, it is difficult to simultaneously realize the ideal tracking effect of the mass side slip angle tending to zero and the yaw rate, that is, the mass side slip angle and the yaw rate cannot simultaneously achieve the ideal effect.

[0007] In summary, the traditional vehicle steering strategy cannot meet the actual use requirements. SUMMARY

[0008] The present application provides a vehicle four-wheel steering stability control method, system, medium and program product, which improves the existing steering control strategy and solves the problem that the traditional vehicle steering strategy cannot meet the actual use requirements.

[0009] In the first aspect, the present application provides a vehicle four-wheel steering stability control method, which is not only suitable for small vehicles, but also suitable for special vehicles which need to work in complex and changeable environment, and the method comprises the following steps: Based on the ideal four-wheel steering dynamics model, an ideal linear two-degree-of-freedom Simulink vehicle model is constructed, and the simulation conditions of the Trucksim software are set to obtain the Trucksim whole vehicle model; The Simulink vehicle model and the Trucksim whole vehicle model are verified, and the real vehicle test results of the special vehicle are compared with the Trucksim simulation results for verification; When the model verification and the comparison verification are passed, the mass side slip angle and the yaw rate output by the Trucksim whole vehicle model are subtracted from the ideal mass side slip angle and the ideal yaw rate respectively, the calculated lateral velocity deviation, yaw rate deviation and lateral displacement deviation are taken as the inputs of the upper LQR controller, the ideal mass side slip angle and the ideal yaw rate are taken as the control targets, and the required rear wheel steering angle and additional yaw moment are output; The rear wheel steering angle is fed back to the Trucksim whole vehicle model to form a closed loop control; The additional yaw moment is taken as the input of the lower torque distribution controller, and is distributed to the four wheels of the vehicle in the form of braking / driving torque, and the longitudinal control is introduced on the basis of the four-wheel steering lateral control to realize the accurate control of the vehicle lateral dynamics.

[0010] Optionally, the motion differential equation set of the two-degree-of-freedom vehicle model of the Simulink vehicle model is constructed, and specifically comprises: , wherein, is a front wheel cornering stiffness, is a rear wheel cornering stiffness, is a distance from a center of mass to a front axle, is a distance from a center of mass to a rear axle, is a cornering angle of a center of mass, is a yaw rate, is a vehicle longitudinal speed, is a front wheel steering angle, is a rear wheel steering angle, is a vehicle weight, is a cornering angle change rate of a center of mass, is a moment of inertia, is a yaw acceleration.

[0011] Optionally, the output rear wheel steering angle is specifically: , wherein, is a rear wheel steering angle, is a state feedback matrix, is a state variable, is a front wheel cornering stiffness, is a rear wheel cornering stiffness, is a yaw rate, is a cornering angle of a center of mass.

[0012] Optionally, the front wheel steering angle is taken as a disturbance term, and the rear wheel steering angle is taken as an input variable , then the system state equation is: wherein, , , , , ; wherein, is a second state matrix, is a state variable, is a first order derivative of the state variable , is a second control sub-matrix 1, is an input variable, is a second control sub-matrix 2, is a front wheel steering angle, is an output variable, is a second output matrix, is a second direct transfer matrix, is a front wheel cornering stiffness, is a rear wheel cornering stiffness, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle, is the vehicle longitudinal speed, is the vehicle mass, is the moment of inertia.

[0013] Optionally, the front and rear wheel braking torque formulas are respectively: , In the formula, is the front wheel braking / driving torque, is the rear wheel braking / driving torque, is the vertical load on the front wheel, is the vertical load on the rear wheel, is the additional yaw moment, is the front wheel track, is the rear wheel track, is the wheel rolling radius.

[0014] Optionally, the control target of the upper LQR controller is quantified as a performance index Take the minimum value, and the calculation formula is as follows: , In the formula, is the performance index, is the state variable, is the state weighting matrix, is the input variable, is the control weighting matrix, is the time variable, indicates that the time variable is infinitely subdivided.

[0015] Optionally, the Simulink vehicle model and the Trucksim whole vehicle model are verified through at least one of an angle step working condition simulation test and a double moving line working condition simulation test; and the real vehicle test results and the Trucksim simulation results are compared and verified through lateral acceleration and yaw angular velocity.

[0016] In a second aspect, the present application provides a computer system, comprising a memory, a processor and a computer program stored on the memory, wherein the processor executes the computer program to realize the steps of the aforementioned vehicle four-wheel steering stability control method.

[0017] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to realize the steps of the aforementioned vehicle four-wheel steering stability control method.

[0018] In a fourth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the aforementioned vehicle four-wheel steering stability control method.

[0019] The one or more technical solutions provided by the present application have at least the following technical effects or advantages: The vehicle four-wheel steering control method designed by the present application is characterized in that, at low speed, the front and rear wheels of the vehicle are deflected in opposite directions to reduce the turning radius and improve the maneuverability of the vehicle; at high speed, the front and rear wheels of the vehicle are deflected in the same direction to improve the steering stability of the vehicle; therefore, the present application can effectively improve the technical problem of the limited maneuverability caused by the wheel angle limitation of the traditional front-wheel steering control method at low speed, and can effectively improve the technical problem of poor tracking ability, easy side slipping and overturning of the vehicle caused by the inability to control the large side slip angle of the vehicle body at high speed of the traditional front-wheel steering control method.

[0020] The vehicle four-wheel steering stability control method provided by the present application improves the traditional vehicle four-wheel steering control strategy, introduces longitudinal control on the basis of four-wheel steering lateral control, and realizes precise control of the longitudinal dynamics of the vehicle by controlling the driving / braking force distribution of the left and right wheels. Specifically, by introducing an additional yaw moment, the limitations of the existing control method are overcome by adopting a combined lateral and longitudinal control method, and by applying an additional yaw moment, a moment is generated to directly control the yaw (rotation around the vertical axis) motion of the vehicle, thereby stabilizing the vehicle or changing its driving posture to reduce the operating burden of the driver. By constructing an upper LQR controller, the tracking performance of the center of mass side slip angle and yaw angular velocity is optimized at the same time, so as to be as close to the ideal value as possible.

[0021] Before designing the steering stability control algorithm, the present application performs model verification, including verification of the Simulink vehicle model and the Trucksim whole vehicle model, and verification of the real vehicle test and the Trucksim simulation results. Both the accuracy and effectiveness of the two-degree-of-freedom Simulink vehicle model are ensured, and the simplified Trucksim whole vehicle model is proved to be reasonable and effective, and it is confirmed that the vehicle steering stability can be researched based on this model.

[0022] The vehicle four-wheel steering control method designed by the application is universal, and is not only suitable for small vehicles, but also suitable for special vehicles that need to work in complex and variable environments such as narrow lanes and rugged mountain roads. Due to the particularity of the driving environment of the special vehicle, the angle of the rear wheel deflection needs to be larger. The data collected by the early-stage real vehicle of the application is derived from the special vehicle, and the flexibility test under low-speed working conditions is carried out, which proves that the application is effective for improving the flexibility of the special vehicle at low speed. In the later control strategy verification link, high-speed working condition simulation is carried out, which proves that the application is effective for improving the steering stability of the special vehicle at high speed. Therefore, the application can not only improve the flexibility at low speed, but also enhance the stability at high speed. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 It is a flowchart of a vehicle four-wheel steering stability control method in the application; Figure 2 It is a control process schematic diagram of a vehicle four-wheel steering stability control method in the application; Figure 3 It is a four-wheel steering dynamics relationship schematic diagram in the application; Figure 4a It is a comparison diagram of the results of real vehicle test and Trucksim simulation of lateral acceleration under low-speed 30km / h working condition; Figure 4b It is a comparison diagram of the results of real vehicle test and Trucksim simulation of lateral acceleration under low-speed 35km / h working condition; Figure 4c It is a comparison diagram of the results of real vehicle test and Trucksim simulation of yaw angular velocity under low-speed 30km / h working condition; Figure 4d It is a comparison diagram of the results of real vehicle test and Trucksim simulation of yaw angular velocity under low-speed 35km / h working condition; Figure 5a It is a simulation result schematic diagram of the center of mass side slip angle under high-speed 90km / h working condition in feedforward control, feedback control and the technical scheme of the application; Figure 5b It is a simulation result schematic diagram of the yaw angular velocity under high-speed 90km / h working condition in feedforward control, feedback control and the technical scheme of the application. DETAILED DESCRIPTION

[0024] The application provides a vehicle four-wheel steering stability control method, system, medium and program product, which improves the existing steering control strategy and solves the problem that the traditional vehicle steering strategy cannot meet the actual use requirements.

[0025] Firstly, the terms appearing in the specification will be explained and described.

[0026] Simulink refers to the Simulink environment of MATLAB, which can model, simulate and analyze the dynamic system of vehicle four-wheel steering. The Simulink vehicle model of the present application refers to an ideal linear two-degree-of-freedom model, which is a simplified vehicle model, so it needs to be verified for effectiveness. The verification steps include: according to the motion differential equation of the linear two-degree-of-freedom automobile model, the first derivative of the mass center side slip angle and the yaw angular velocity (i.e. the yaw angular acceleration) is solved, the state matrix is expressed and solved in the form of a spatial state equation, the model is built in the Simulink environment, and the simulation results are output.

[0027] Trucksim is an engineering software dedicated to vehicle dynamics simulation, widely used in the research and development, testing and optimization of commercial vehicles such as trucks, buses and trailers. It uses high-precision mathematical models to simulate the dynamic response of the whole vehicle and its subsystems under various working conditions. The core of Trucksim is its high-fidelity parameterized model based on multi-body dynamics, users build virtual vehicles by defining a large number of detailed physical parameters, rather than creating a 3D visual model. The Trucksim whole vehicle model of the present application is also a simplified vehicle model, which is more complex than the Simulink vehicle model. Since the present application focuses on the four-wheel steering control system and ignores other systems such as suspension, the test results (i.e. Trucksim simulation results) also need to be compared with the real vehicle test results to verify their reference to the real vehicle. The verification steps include: joint simulation based on Simulink and Trucksim, using the Trucksim whole vehicle model instead of the two-degree-of-freedom Simulink model, comparing the degree of agreement of the mass center side slip angle and the yaw angular velocity of the two models to verify the accuracy of the model.

[0028] LQR (Linear Quadratic Regulator, Linear Quadratic Regulator) is a core idea that when the system state deviates from the expected value, an optimal control command is automatically calculated to minimize the total cost of the entire system. The control objective of LQR in the present application is to track the ideal mass center side slip angle and yaw angular velocity, and to minimize the total cost accumulated over time, rather than just the cost at a certain time, so the LQR in the present application is a dynamic system evolution optimal control method.

[0029] The angle step working condition, also known as "steering wheel step input test" or "variant of the snake-shaped pole test", is mainly used to evaluate the transient response characteristics and stability of the vehicle. The core is that the driver turns the steering wheel at an extremely fast, almost instantaneous speed by a fixed angle, and keeps the angle unchanged, then observes the response of the vehicle.

[0030] The double lane change test, also known as the "elk test" or "obstacle avoidance test", is used to comprehensively evaluate the comprehensive handling stability, ESP system efficiency and path tracking capability of a vehicle in extreme conditions. It is a more complex and more realistic comprehensive test than the angle step test. The core of the test is that the driver first turns in one direction to avoid obstacles, and then quickly turns back to the original lane to simulate two consecutive lane changes.

[0031] For better understanding, the following will be combined with the description of the drawings and specific embodiments. Obviously, the embodiments described in the present application are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0032] As shown in Figure 1 and Figure 2 , the overall inventive concept of the present application is as follows: Based on the ideal four-wheel steering dynamics model, an ideal linear two-degree-of-freedom model, i.e. a Simulink vehicle model, is built in the Simulink environment. The simulation conditions of the Trucksim software are set to obtain the Trucksim whole vehicle model. Then, simulation tests are respectively performed on the Simulink vehicle model and the Trucksim whole vehicle model, which can be at least one of angle step test and double lane change test, for example, only double lane change test can be performed to compare the center of mass side slip angle and yaw rate of the front wheel steering and four-wheel steering vehicles, and analyze the influence of four-wheel steering on vehicle stability. At the same time, the test results of the real vehicle are compared and verified with the Trucksim simulation results, and the center of mass side slip angle and yaw rate are output by controlling the front wheel and rear wheel of the vehicle respectively, to observe the stability and flexibility of the vehicle at high and low speed. Next, the intermediate state variables are calculated, i.e. the center of mass side slip angle and yaw rate output by the Trucksim whole vehicle model are respectively subtracted from the ideal center of mass side slip angle and ideal yaw rate, to calculate the lateral velocity deviation, yaw rate deviation and lateral displacement deviation. Finally, taking tracking the ideal center of mass side slip angle and ideal yaw rate as the control target, a vehicle rear wheel steering angle controller based on LQR control is built as an upper LQR controller, and the aforementioned calculation results are taken as the input of the upper LQR controller, and the required rear wheel steering angle and additional yaw moment , the required rear wheel steering angle is fed back to the Trucksim whole vehicle model. The additional yaw moment is input to the lower torque distribution controller, and the longitudinal control is introduced on the basis of the four-wheel steering lateral control, so as to realize precise control of the lateral dynamics of the vehicle by controlling the rotational speed and brake force distribution of the left and right wheels.

[0033] Please continue to refer to Figure 2 The specific implementation process of the present application includes steps S10 to S40.

[0034] S10: Establish an ideal four-wheel steering dynamics model.

[0035] According to Figure 3 The four-wheel steering dynamics relationship shown in the formula is obtained: , In the formula, is the front tire cornering force, is the rear tire cornering force, is the front wheel steering angle, is the rear wheel steering angle, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle, is the vehicle weight, is the lateral acceleration, is the moment of inertia, is the yaw rate The yaw angular acceleration obtained by taking the first derivative of the yaw rate.

[0036] Since the tire steering angle value is very small and according to the linear relationship between the tire cornering force and the tire cornering stiffness, the simplified formula is obtained: , In the formula, is the front wheel cornering stiffness, is the rear wheel cornering stiffness, is the front wheel cornering angle, is the rear wheel cornering angle.

[0037] From the definition of the coordinate system shown in Figure 3 The front and rear wheel cornering angles are respectively: , In the formula, is the center of mass cornering angle, is the angle between the front wheel speed direction and the axle, is the angle between the rear wheel speed direction and the axle, is the vehicle longitudinal speed.

[0038] Substitute the front and rear wheel cornering angles into the above simplified formula to obtain the motion differential equation set of the two-degree-of-freedom vehicle model as follows: , In the formula, is the rate of change of the center of mass cornering angle.

[0039] state variable input variable output variable The above equation group is converted into a state equation form as follows: , According to the above equation group and the state equation form, the state matrixes are obtained as follows: , , , , In the formula, is a first state matrix, is a first control matrix, is a first output matrix, is a first direct transfer matrix.

[0040] Step 20: Simulink vehicle model and Trucksim whole vehicle model verification are performed, and real vehicle test results and Trucksim simulation results are compared and verified.

[0041] A two-degree-of-freedom vehicle model is built in the Simulink environment. In order to verify the accuracy and effectiveness of the model, a whole vehicle model of the target special vehicle is further established in Trucksim, which is used as a reference for comparison and verification. The results show that the four-wheel steering characteristics of the two models are highly consistent, which effectively proves the effectiveness and accuracy of the two models built in the application, and proves that they can be used to study the handling stability problem of four-wheel steering of special vehicles.

[0042] Since the simulation model indeed plays an extremely important role in theoretical research and technical verification, it can simulate various different road conditions and conditions, and preliminarily prove the effectiveness and feasibility of the control algorithm under ideal conditions. However, the simulation environment is essentially an idealized construction, and its parameters, variables and boundary conditions are often idealized and controllable, and it is difficult to include all the complex situations and uncertain factors encountered by the actual vehicle in the model. Therefore, although the simulation results can prove the rationality of the control algorithm to some extent, in order to truly evaluate its control effect in the actual application scene, it is necessary to apply the corresponding controller to the actual vehicle for field test and verification. According to GB / T6323.1-2014 Organic Chemical Products Test Method Part 1: Water Miscibility Test of Liquid Organic Chemical Products, the vehicle yaw rate and lateral acceleration Real-vehicle test data was collected based on a front wheel steering angle proportional feedforward control algorithm. To verify the accuracy of the Trucksim model based on real-vehicle testing, a comparison chart was created using Matlab plotting tools, comparing the real-vehicle test results with the Trucksim model simulation results. The comparison results are shown below. Figure 4a , Figure 4b , Figure 4c , Figure 4d As shown.

[0043] Figure 4a Lateral acceleration at low speed of 30 km / h A comparison chart of the results from real vehicle testing and Trucksim simulation. Figure 4b Lateral acceleration at low speed of 35 km / h A comparison chart of the results from real vehicle testing and Trucksim simulation. Figure 4c Yaw rate at low speed of 30 km / h A comparison chart of the results from real vehicle testing and Trucksim simulation. Figure 4d Yaw rate at low speed of 35 km / h A comparison chart of the results from real-vehicle testing and Trucksim simulation. Figure 4a , Figure 4b , Figure 4c , Figure 4d It can be seen that the actual vehicle test results and the Trucksim simulation results have a high degree of agreement, proving that the Trucksim vehicle model based on the front wheel steering angle feedforward control design is reasonable and effective, and can be further studied based on other complex algorithms.

[0044] According to the national standard for serpentine road condition testing, the data from the four middle locations in the test environment are selected as the average value for calculation. The calculation formula is as follows: , In the formula, These are the average yaw rate and the average lateral acceleration, respectively. They are the first The maximum value of the yaw rate and the maximum value of the lateral acceleration at each position.

[0045] The results of Trucksim simulation and real vehicle test in the low-speed serpentine test at 30km / h and 35km / h are shown in Table 1 and Table 2, respectively: Table 1 Comparison of 30km / h serpentine test results

[0046] Table 2 Comparison of 35km / h serpentine test results

[0047] From the results of Table 1 and Table 2, it can be seen that the Trucksim simulation has a certain deviation compared with the real vehicle test results, but the deviation range of yaw rate and lateral acceleration is controlled within 20%. In view of the complexity of the whole vehicle and the complexity of the test environment, the Trucksim whole vehicle model constructed can be regarded as a reference for the characteristics of the real vehicle.

[0048] Step S30: The upper LQR controller takes the difference between the ideal two-degree-of-freedom Simulink vehicle model and the Trucksim whole vehicle model as input, and calculates the rear wheel angle of the vehicle through the difference between the ideal lateral velocity, ideal yaw rate and ideal lateral displacement and the actual lateral velocity, actual yaw rate and actual lateral displacement , and then inputs the rear wheel angle into the Trucksim whole vehicle model to form a closed-loop control.

[0049] Optimal control refers to designing the control strategy of a system by optimizing the objective function, so that the control index achieves the ideal control effect. LQR control is a special optimal control method, which is suitable for linear time-invariant systems. The goal of LQR control is to design a linear feedback controller to make the state of the system converge to zero and minimize a quadratic performance index, which is usually the weighted sum of state and control input. LQR control can obtain the optimal linear feedback controller of the system by solving the Riccati equation. As an optimal control strategy, the control goal of LQR is to minimize the error between the state quantity and the ideal value, so the performance index J should be minimized.

[0050] wherein the performance index is calculated as follows: , wherein is the state variable, is the state weighting matrix, is the input variable, is the control weighting matrix, is the time variable, represents infinite subdivision of the time variable .

[0051] The formula of the control rate is: , wherein is the first control matrix, is the co-state variable vector, which has the same dimension as the state variable .

[0052] Let: , where, is the solution of the time-varying Riccati differential equation; where, when the time variable tends to infinity converges to the steady-state value to obtain , is the solution of the algebraic Riccati equation with constant, there exists satisfying the Riccati equation, to obtain the Riccati expression: , where, is the first state matrix, is the second control sub-matrix 1 obtained by adding the rear wheel steering angle on the basis of the first control matrix .

[0053] Then: , obtain the expression of the state feedback of the control rate : , , where, is the state feedback matrix.

[0054] The output rear wheel steering angle is: .

[0055] The front wheel steering angle is taken as the disturbance term, and the rear wheel steering angle is taken as the input variable , then the system state equation is: , where, is the second state matrix, is the second control sub-matrix 1, is the second control sub-matrix 2, is the second output matrix, is the second direct transfer matrix.

[0056] where, compared with , both are unchanged, and are respectively: , , Taking state variables , input variables , output variables , transforming system state equations into state equation expressions , and the state matrixes are respectively: , , , .

[0057] wherein, is a third state matrix, is a third control matrix, is a third output matrix, is a third direct transfer matrix.

[0058] Step 30: Designing a four-wheel steering upper LQR controller with tracking ideal center of mass side slip angle and yaw rate as control targets.

[0059] Control logic of additional yaw moment in upper LQR controller is as follows: Under the front wheel feedforward control mode, the control target is the center of mass side slip angle of the vehicle as close to zero as possible, and the yaw rate is as stable as possible.

[0060] Let the ratio be: , At this time, the lateral acceleration is 0 m / s 2 , and the yaw angular acceleration is also 0 rad / s 2 , and after Laplace transformation, we get: .

[0061] From the above formula, under the front wheel feedforward control mode, after the front wheel steering angle and vehicle parameters are determined, the rear wheel steering angle is only related to the vehicle longitudinal speed .

[0062] Taking the lateral velocity deviation, yaw rate deviation and lateral displacement deviation as the input of the upper LQR controller, outputting additional yaw moment through the upper LQR controller, and then taking the obtained additional yaw moment as the input of the lower torque distribution controller.

[0063] Step 40: Taking additional yaw moment The lower vehicle control model is built as the input of the lower torque distribution controller, and outputs the wheel braking / driving torque to control the vehicle longitudinal direction. The longitudinal force control is introduced on the basis of the four-wheel steering lateral control, and the vehicle longitudinal dynamics are precisely controlled by controlling the rotational speed and brake force distribution of the left and right wheels.

[0064] The brake / driving force distribution control logic of the lower torque distribution controller is as follows: The additional yaw moment calculated by the upper LQR controller , which needs to be reasonably distributed to the four wheels in the form of brake / driving torque to help the vehicle recover to a stable state when it is unstable. The brake / driving torque distribution rule is shown in Table 3, which reflects the relationship between the vehicle steering state and the wheel braking / driving.

[0065] Table 3 Brake / driving torque distribution rule table

[0066] When the vehicle is unstable, the additional yaw moment required to restore stability The formula is: , In the formula, is the front wheel braking / driving force, is the rear wheel braking / driving force, is the front wheel track, is the rear wheel track.

[0067] The formula for the vertical load received by the front and rear wheels is: , In the formula, is the vertical load received by the front wheel, is the vertical load received by the rear wheel.

[0068] The front and rear wheel braking / driving forces are distributed according to the size ratio of the vertical load received by the front and rear wheels, and the required additional yaw moment In the formula, the front and rear wheel braking / driving forces are obtained as: , In the formula, is the front wheel braking / driving force, is the rear wheel braking / driving force.

[0069] Substitute the front and rear wheel braking / driving forces into the torque formula , the front and rear wheel braking torque formulas are obtained as: , In the formula, For front wheel braking / driving torque, For rear wheel braking / driving torque, This is the rolling radius of the wheel.

[0070] Vehicle and control algorithm models were built using Trucksim and Simulink, and the control algorithm was verified under double lane change conditions. With a road adhesion coefficient set to 0.85, the vehicle stability evaluation index was verified at a high speed of 90 km / h, and the combined control effect of four-wheel steering control and direct yaw moment control and its impact on vehicle stability were analyzed. After repeated simulation tests, the state weighting matrix was determined. and control weighting matrix They are respectively: .

[0071] Figure 5a and Figure 5b These are the sideslip angles of the center of gravity under high-speed driving conditions of 90 km / h. and yaw rate The simulation results of feedforward control, feedback control, and this technical solution are shown in the diagram.

[0072] Depend on Figure 5a It can be seen that the centroid side slip angle during feedforward control The absolute value of the maximum value is 1.61 deg, and the centroid sideslip angle during feedback control. The absolute value of the maximum value is 0.89 degrees. The method in this invention, i.e., the technical solution of this invention, controls the centroid sideslip angle. The absolute value of the maximum value is 0.47 deg. Compared with the feedforward control, the centroid sideslip angle of this technical solution is reduced by 71%, and compared with the feedback control, the centroid sideslip angle is reduced by 47%. This proves that the control effect of this technical solution is good and can improve the vehicle's tracking ability to a certain extent.

[0073] Depend on Figure 5b It can be seen that the yaw rate of the vehicle during feedforward control is... The absolute value of the maximum value is 12.94 deg / s, which is the vehicle's yaw rate during feedback control. The absolute value of the maximum value is 12.61 deg / s. The yaw rate of this technical solution is... The absolute values ​​of the maximum values ​​are all 8.42 deg / s. Compared with the feedforward control yaw rate, this technical solution is superior. The absolute value of the maximum value decreased by 35%, compared to the feedback control yaw rate. The absolute value of the maximum value decreased by 33%, proving that this technical solution provides better vehicle handling stability and helps reduce the risk of sideslip and loss of control.

[0074] In a second aspect, the present application provides a computer system comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to implement the steps of the above-mentioned vehicle four-wheel steering stability control method.

[0075] The memory can be volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of illustration, and not limitation, nonvolatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which acts as external cache. By way of illustration and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The memory of the present application is intended to include, without being limited to, these and any other suitable types of memory.

[0076] The processor can be an integrated circuit chip having a processing capability of signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of hardware in the processor or the instruction in the form of software. The processor mentioned above can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. Each method, step and logic block diagram disclosed in the present application can be implemented or executed. The general processor can be a microprocessor or the processor can be any conventional processor. The steps of the method disclosed in the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method.

[0077] The method steps of the present application can be implemented by hardware, software, firmware, middleware, microcode or their combination. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for executing functions described in the present application or their combination.

[0078] For software implementation, the functions can be implemented by executing function modules (such as processes, functions, etc.). The software code can be stored in the memory and executed by the processor. The memory can be implemented in the processor or outside the processor.

[0079] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the above-mentioned vehicle four-wheel steering stability control method.

[0080] The computer storage medium can include a U disk, a mobile hard disk, a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and the like.

[0081] In a fourth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the above-mentioned vehicle four-wheel steering stability control method.

[0082] The computer program product specifically refers to a data signal, a data signal embodied in a carrier wave, or a computer readable storage medium.

[0083] It should be noted that the technical solutions disclosed in the present application can be combined arbitrarily without conflict.

[0084] Obviously, various modifications and variations of the present application can be made by those skilled in the art without departing from the spirit and scope of the application. Thus, it should be understood that, if these modifications and variations fall within the scope of the claims of the present application and their equivalents, the present application also includes these modifications and variations.

Claims

1. A method for controlling the four-wheel steering stability of a vehicle, characterized in that, The method is applicable not only to small vehicles but also to special vehicles that need to operate in complex and changing environments. The method includes: Based on the ideal four-wheel steering dynamics model, an ideal linear two-degree-of-freedom Simulink vehicle model is constructed, and the simulation conditions of Trucksim software are set to obtain the Trucksim whole vehicle model. Verification was performed using the Simulink vehicle model and the Trucksim vehicle model, and the real vehicle test results of the special vehicle were compared and verified with the Trucksim simulation results. After the model verification and comparison verification are passed, the sideslip angle and yaw rate output by the Trucksim vehicle model are subtracted from the ideal sideslip angle and ideal yaw rate, respectively. The calculated lateral velocity deviation, yaw rate deviation and lateral displacement deviation are used as inputs to the upper-level LQR controller. The controller aims to track the ideal sideslip angle and ideal yaw rate and outputs the required rear wheel steering angle and additional yaw moment. The rear wheel steering angle is fed back to the Trucksim vehicle model to form a closed-loop control; The additional yaw moment is used as the input to the lower-level torque distribution controller and distributed to the four wheels of the vehicle in the form of braking / driving torque. Based on the four-wheel steering lateral control, longitudinal control is introduced to achieve precise control of the vehicle's lateral dynamics.

2. The method as described in claim 1, characterized in that, The system of differential equations of motion for a two-degree-of-freedom vehicle model in Simulink is as follows: ， In the formula, For the front wheel lateral stiffness, For rear wheel lateral stiffness, The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. The sideslip angle is the angle of the center of mass. The yaw rate is angular velocity. For the longitudinal speed of the vehicle, For the front wheel steering angle, For the rear wheel steering angle, For vehicle weight, The rate of change of the centroid sideslip angle. For rotational inertia, This is the yaw acceleration.

3. The method as described in claim 1, characterized in that, The output rear wheel steering angle is as follows: ， In the formula, For the rear wheel steering angle, For the state feedback matrix, For state variables, For the front wheel lateral stiffness, For rear wheel lateral stiffness, The yaw rate is angular velocity. It is the centroid sideslip angle.

4. The method as described in claim 1, characterized in that, Turn the front wheel corner As a distractor, the rear wheel steering angle as input variables The system state equation is then: ,in, , , , , ; In the formula, This is the second state matrix. For state variables, For state variables Find the first derivative. For the second control submatrix 1, For input variables, For the second control submatrix 2, For the front wheel steering angle, For output variables, This is the second output matrix. This is the second direct transfer matrix. For the front wheel lateral stiffness, For rear wheel lateral stiffness, The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. For the longitudinal speed of the vehicle, For vehicle weight, Let be the moment of inertia.

5. The method as described in claim 1, characterized in that, The formulas for the braking torque of the front and rear wheels are as follows: ， In the formula, For front wheel braking / driving torque, For rear wheel braking / driving torque, This refers to the vertical load on the front wheels. The vertical load on the rear wheel. To add yaw moment, The front wheel track. The rear wheel track. This is the wheel's rolling radius.

6. The method as described in claim 1, characterized in that, The control objective of the upper-level LQR controller is quantified into performance indicators. The minimum value is calculated using the following formula: ， In the formula, For performance indicators, For state variables, The state weighting matrix, For input variables, To control the weighting matrix, It is a time variable. Represents the time variable Subdivide it infinitely.

7. The method as described in claim 1, characterized in that, The Simulink vehicle model and the Trucksim vehicle model were verified by at least one of the following simulation tests: angular step condition simulation test and double lane change condition simulation test; the real vehicle test results and the Trucksim simulation results were compared and verified by lateral acceleration and yaw rate.

8. A computer system comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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