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

Abstract

The application discloses a vehicle four-wheel steering stability control method, system, medium and program product, and the method comprises the following steps: obtaining a Simulink vehicle model and a Trucksim whole vehicle model; performing Simulink vehicle model and Trucksim whole vehicle model verification, and comparing and verifying real vehicle test results and Trucksim simulation results; designing an upper LQR controller with tracking ideal mass center side slip angle and ideal yaw rate as control targets; introducing longitudinal control on the basis of four-wheel steering lateral control, and realizing accurate control of vehicle lateral dynamics through a lower torque distribution controller. The application is suitable for small vehicles and special vehicles, effectively improves the technical problems of the traditional method in low-speed steering and high-speed steering, optimizes the tracking performance of the mass center side slip angle and the yaw rate, makes the tracking performance as close to the ideal value as possible, adopts a lateral and longitudinal combined control mode to directly control the moment of vehicle yaw motion, thereby stabilizing the vehicle or changing the driving posture of the vehicle, and reducing the operation burden of the driver.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to methods, systems, media, and program products for controlling the four-wheel steering stability of vehicles. Background Technology

[0002] In front-wheel steering vehicles, the driver manipulates the steering wheel to steer the front wheels while the rear wheels remain stationary. This type of front-wheel steering control system is technologically mature and has a simple structure.

[0003] Four-wheel steering is an evolution and functional expansion of traditional front-wheel steering; the two are related in terms of inheritance, development, and complementarity. For most ordinary family cars, inexpensive, reliable, and durable front-wheel steering is sufficient; while for luxury cars, performance cars, large trucks, or buses, four-wheel steering can bring significant improvements in handling and safety.

[0004] Current research on vehicle handling stability based on four-wheel steering mainly focuses on feedforward control based on front wheel steering angle and feedback control based on yaw rate. Feedforward control based on front wheel steering angle calculates the rear wheel deflection angle directly using a predetermined control rule (usually a transfer function or lookup table) based on the current steering wheel angle (front wheel angle) and vehicle speed. For example, in proportional feedforward control, the controlled rear wheel angle is proportional to the front wheel angle. Feedback control based on yaw rate, on the other hand, monitors the vehicle's actual dynamic response (yaw rate) in real time using sensors and compares it with the desired yaw rate calculated by an ideal model. If a deviation exists (i.e., error), the system actively adjusts the rear wheel steering angle to eliminate this deviation, ensuring the vehicle's actual performance closely follows the ideal model.

[0005] In the process of implementing the technical solution of this invention, at least the following technical problems were found in the prior art:

[0006] Traditional front-wheel steering control methods have reached their limit after years of development. Their shortcomings are that at low speeds, due to the limitation of wheel angle, front-wheel steering is difficult to achieve the requirements of high maneuverability; at high speeds, relying solely on front-wheel steering cannot control the large sideslip angle of the vehicle body, resulting in poor vehicle tracking ability and a tendency to skid and roll over.

[0007] Traditional four-wheel steering methods, which rely solely on front wheel steering angle and yaw rate control, generally offer limited improvement in vehicle handling stability. This is especially true for specialized vehicles operating in complex and unpredictable environments such as narrow alleyways and rugged mountain roads, where traditional methods are insufficient. Specifically, traditional front wheel steering angle proportional feedforward control and yaw rate feedback control have limitations, making it difficult to simultaneously achieve a near-zero sideslip angle and good tracking of the ideal yaw rate. In other words, it cannot simultaneously achieve the ideal results for both sideslip angle and yaw rate.

[0008] In summary, traditional vehicle steering strategies cannot meet actual usage needs. Summary of the Invention

[0009] This invention provides a method, system, medium, and program product for controlling the four-wheel steering stability of a vehicle, which improves existing steering control strategies and solves the problem that traditional vehicle steering strategies cannot meet actual usage requirements.

[0010] In a first aspect, the present invention provides a method for controlling the four-wheel steering stability of a vehicle, applicable not only to small vehicles but also to special vehicles that need to operate in complex and changing environments. The method includes:

[0011] 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.

[0012] 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.

[0013] 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.

[0014] The rear wheel steering angle is fed back to the Trucksim vehicle model to form a closed-loop control;

[0015] 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.

[0016] Optionally, construct the set of differential equations of motion for the two-degree-of-freedom vehicle model in Simulink, specifically:

[0017] ,

[0018] 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.

[0019] Optional, the output rear wheel steering angle, specifically:

[0020] ,

[0021] 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.

[0022] Optionally, adjust the front wheel angle. As a distractor, the rear wheel steering angle as input variables The system state equation is then:

[0023] ,in, , , , , ;

[0024] 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.

[0025] Optionally, the formulas for the braking torque of the front and rear wheels are as follows:

[0026] ,

[0027] 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.

[0028] Optionally, the control objective of the upper-level LQR controller can be quantified into performance indicators. The minimum value is calculated using the following formula:

[0029] ,

[0030] 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.

[0031] Optionally, the Simulink vehicle model and the Trucksim vehicle model can be verified by at least one of the following: angular step condition simulation test and double lane change condition simulation test; the real vehicle test results and the Trusim simulation results can be compared and verified by lateral acceleration and yaw rate.

[0032] In a second aspect, the present invention provides a computer system including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the aforementioned vehicle four-wheel steering stability control method.

[0033] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned vehicle four-wheel steering stability control method.

[0034] Fourthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aforementioned vehicle four-wheel steering stability control method.

[0035] One or more technical solutions provided by this invention have at least the following technical effects or advantages:

[0036] The four-wheel steering control method designed in this invention causes the front and rear wheels to deflect in opposite directions at low speeds, reducing the turning radius and improving vehicle maneuverability; at high speeds, the front and rear wheels deflect in the same direction, improving vehicle handling stability. Therefore, this invention can effectively improve the technical problem of limited maneuverability caused by the wheel angle limitation of traditional front-wheel steering control methods at low speeds, and can also effectively improve the technical problem of poor vehicle tracking ability and easy sideslip and rollover caused by the inability to control the large body slip angle at high speeds.

[0037] This invention provides a vehicle four-wheel steering stability control method that improves upon traditional four-wheel steering control strategies. It introduces longitudinal control on top of lateral control, achieving precise longitudinal dynamic control of the vehicle by controlling the distribution of drive / braking force between the left and right wheels. Specifically, by introducing an additional yaw moment and employing a combined lateral and longitudinal control approach, it overcomes the limitations of existing control methods. This additional yaw moment generates a torque that directly controls the vehicle's yaw (rotation around the vertical axis), thereby stabilizing the vehicle or changing its driving posture to reduce the driver's workload. The constructed upper-level LQR controller aims to simultaneously optimize the tracking performance of the center of gravity sideslip angle and yaw rate, bringing them as close as possible to their ideal values.

[0038] Before designing the handling stability control algorithm, this invention first performs model verification, including verification of the Simulink vehicle model and the Trucksim whole vehicle model, as well as verification of real vehicle testing and Trucksim simulation results. This ensures the accuracy and effectiveness of the two-degree-of-freedom Simulink vehicle model and also proves the rationality and effectiveness of the simplified Trucksim whole vehicle model, demonstrating that this model can be used to study vehicle handling stability.

[0039] The four-wheel steering control method designed in this invention is universal and applicable not only to small vehicles but also to special vehicles that need to operate in complex and variable environments such as narrow alleys and rugged mountain roads. Due to the special driving environment of special vehicles, a larger rear wheel deflection angle is required. The initial real-vehicle data collected for this invention came from special vehicles, and low-speed maneuverability tests were conducted, proving the effectiveness of this invention in improving the maneuverability of special vehicles at low speeds. In the later control strategy verification stage, high-speed simulations were performed, proving the effectiveness of this invention in improving the handling stability of special vehicles at high speeds. Therefore, this invention can improve both low-speed maneuverability and high-speed stability. Attached Figure Description

[0040] Figure 1 This is a simplified flowchart of a vehicle four-wheel steering stability control method according to the present invention;

[0041] Figure 2 This is a schematic diagram of the control process of a vehicle four-wheel steering stability control method according to the present invention;

[0042] Figure 3 This is a schematic diagram of the four-wheel steering dynamics relationship in this invention;

[0043] Figure 4a A comparison chart showing the results of real-vehicle testing and Trucksim simulation of lateral acceleration at a low speed of 30 km / h.

[0044] Figure 4b A comparison chart showing the results of real-vehicle testing and Trucksim simulation of lateral acceleration at a low speed of 35 km / h;

[0045] Figure 4c The figure shows a comparison between the real vehicle test and Trucksim simulation results of the yaw rate under low speed condition of 30km / h.

[0046] Figure 4d The figure shows a comparison between the real vehicle test and Trucksim simulation results for the yaw rate at a low speed of 35 km / h.

[0047] Figure 5a This is a schematic diagram showing the simulation results of the centroid sideslip angle under feedforward control, feedback control, and the present technical solution at a high speed of 90km / h.

[0048] Figure 5b This is a schematic diagram showing the simulation results of yaw rate under high-speed 90km / h conditions in feedforward control, feedback control, and the present technical solution. Detailed Implementation

[0049] This invention provides a method, system, medium, and program product for controlling the four-wheel steering stability of a vehicle, which improves existing steering control strategies and solves the problem that traditional vehicle steering strategies cannot meet actual usage requirements.

[0050] First, the terms appearing in the instruction manual will be explained.

[0051] Simulink refers to the Simulink environment within MATLAB, which enables modeling, simulation, and analysis of the dynamic systems of four-wheel steering vehicles. The Simulink vehicle model of this invention is an ideal linear two-degree-of-freedom model, a simplified vehicle model, and therefore requires validity verification. The verification steps include: solving for the first derivatives (i.e., yaw acceleration) of the sideslip angle and yaw rate based on the motion differential equations of the linear two-degree-of-freedom vehicle model; expressing the state in the form of spatial state equations and solving for the state matrix; building the model in the Simulink environment; and outputting the simulation results.

[0052] Trucksim is an engineering software specifically designed for automotive dynamics simulation, widely used in the research, testing, and optimization of commercial vehicles such as trucks, buses, and trailers. It employs high-precision mathematical models to simulate the dynamic response of the entire vehicle and its subsystems under various operating conditions. The core of Trucksim lies in its high-fidelity parametric model based on multibody dynamics, allowing users to build virtual vehicles by defining numerous detailed physical parameters, rather than creating a 3D visual model. The Trucksim vehicle model used in this invention is a simplified model, more complex than the Simulink vehicle model. Since this invention focuses on the four-wheel steering control system and ignores other systems such as the suspension, it is necessary to compare its test results (i.e., Trucksim simulation results) with real-vehicle test results to verify its reference value for real vehicles. The verification steps include: based on Simulink and Trucksim co-simulation, replacing the two-degree-of-freedom Simulink model with the Trucksim vehicle model, and comparing the degree of agreement between the two models' sideslip angle and yaw rate to verify the model's accuracy.

[0053] The core idea of ​​LQR (Linear Quadratic Regulator) is to automatically calculate an optimal control command when the system state deviates from the desired value, thereby minimizing the total cost of the entire system. In this invention, the control objective of LQR is to track the ideal sideslip angle and yaw rate, minimizing the total cost accumulated over time, rather than just the cost at a single moment. Therefore, LQR in this invention is an optimal control method for handling the time-varying evolution of dynamic systems.

[0054] The step test, also known as the "steering wheel step input test" or a variant of the "snake-like pole test," is primarily used to evaluate a vehicle's transient response characteristics and stability. Its core principle is that the driver rapidly, almost instantaneously, turns the steering wheel to a fixed angle and holds that angle, then observes the vehicle's reaction.

[0055] The double lane change test, also known as the "moose test" or "obstacle avoidance test," is used to comprehensively evaluate a vehicle's overall handling stability, ESP system effectiveness, and path tracking ability under extreme conditions. It is a more complex and realistic test than the step test. Its core is that the driver first steers in one direction to avoid an obstacle, and then quickly reverses the steering wheel to return to the original lane, simulating two consecutive lane change maneuvers.

[0056] To better understand, a detailed description will be provided below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described in this invention are only a part of the embodiments of this invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0057] like Figure 1 and Figure 2 As shown, the overall inventive concept of this invention is as follows:

[0058] Based on an 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 Trucksim software are set to obtain a Trucksim vehicle model. Then, simulation tests are conducted on both the Simulink vehicle model and the Trucksim vehicle model, specifically at least one of the following: angular step simulation test and double lane change simulation test. For example, only the double lane change simulation test can be performed to compare the sideslip angle and yaw rate of front-wheel steering and four-wheel steering vehicles, analyzing the impact of four-wheel steering on vehicle stability. Simultaneously, the results of real-vehicle tests are compared and verified with the Trucksim simulation results. By controlling the front and rear wheels of the vehicle separately, the sideslip angle and yaw rate are output to observe the vehicle's stability and agility at high and low speeds. Next, intermediate state variables are calculated, i.e., the differences between the sideslip angle and yaw rate output by the Trucksim vehicle model and the ideal sideslip angle and ideal yaw rate are calculated to obtain the lateral velocity deviation, yaw rate deviation, and lateral displacement deviation. Finally, with the ideal centroid sideslip angle and ideal yaw rate as the control objectives, a vehicle rear wheel steering angle controller based on LQR control is built as the upper-level LQR controller. The aforementioned calculation results are used as the input to the upper-level LQR controller, and the required rear wheel steering angle is output. and additional yaw moment Required rear wheel steering angle Feedback is sent to the Trucksim full vehicle model. Additional yaw moment is applied. The input is fed into the lower-level torque distribution controller, which introduces longitudinal control on the basis of four-wheel steering lateral control. By controlling the speed of the left and right wheels and the distribution of braking force, precise control of the vehicle's lateral dynamics is achieved.

[0059] Please continue to refer to this. Figure 2 The specific implementation process of the present invention includes steps S10 to S40.

[0060] S10: Establish an ideal four-wheel steering dynamics model.

[0061] according to Figure 3 The four-wheel steering dynamics relationship shown is used to obtain the following formula:

[0062] ,

[0063] In the formula, This refers to the lateral force of the front tires. For the lateral force of the rear tire, For the front wheel steering angle, For the rear wheel steering angle, The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. For vehicle weight, It is lateral acceleration. For rotational inertia, For the yaw rate Find the yaw acceleration obtained from the first derivative.

[0064] Since the tire steering angle is very small and based on the linear relationship between tire lateral force and tire lateral stiffness, a simplified formula is obtained:

[0065] ,

[0066] In the formula, For the front wheel lateral stiffness, For rear wheel lateral stiffness, The front wheel slip angle, This refers to the rear wheel slip angle.

[0067] Depend on Figure 3 According to the coordinate system shown, the front and rear wheel slip angles are respectively:

[0068] ,

[0069] In the formula, The sideslip angle is the angle of the center of mass. The direction of front wheel speed is... Angle between axes, Rear wheel speed direction and Angle between axes, This represents the vehicle's longitudinal speed.

[0070] Substituting the front and rear wheel slip angles into the simplified formula above, we obtain the following set of differential equations of motion for the two-degree-of-freedom vehicle model:

[0071] ,

[0072] In the formula, This represents the rate of change of the centroid's sideslip angle.

[0073] Take state variables Input variables Output variables The above system of equations can be transformed into state equations as follows:

[0074] ,

[0075] Based on the above system of equations and the form of the state equations, the state matrices are obtained as follows:

[0076] , , , ,

[0077] In the formula, This is the first state matrix. This is the first control matrix. This is the first output matrix. This is the first direct transfer matrix.

[0078] Step 20: Verify the Simulink vehicle model and the Trucksim vehicle model, and compare the real vehicle test results with the Trucksim simulation results.

[0079] A two-degree-of-freedom vehicle model was built in the Simulink environment. To verify the accuracy and effectiveness of this model, a full-vehicle model of the target special vehicle was further built in Trucksim and used as a reference for comparison and verification. The results show that the four-wheel steering characteristics exhibited by the two models are highly consistent, strongly confirming the effectiveness and accuracy of the two models constructed in this invention, and proving that they can be used to study the handling stability problem of four-wheel steering of special vehicles.

[0080] Simulation models play a crucial role in theoretical research and technical verification, simulating various road conditions and scenarios to initially demonstrate the effectiveness and feasibility of control algorithms under ideal conditions. However, simulation environments are inherently idealized constructs; their parameters, variables, and boundary conditions are often idealized and controllable, making it difficult to incorporate all the complexities and uncertainties encountered by actual vehicles. Therefore, while simulation results can confirm the rationality of the control algorithm to some extent, to truly evaluate its control effect in real-world applications, it is necessary to apply the corresponding controller to actual vehicles for field testing and verification. Using GB / T6323.1-2014 "Test Methods for Organic Chemical Products Part 1: Water Miscibility Test of Liquid Organic Chemical Products," this invention conducted real-world vehicle yaw rate tests at 30 km / h and 35 km / h serpentine road conditions. 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.

[0081] 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.

[0082] 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:

[0083] ,

[0084] 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.

[0085] 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:

[0086] Table 1 Comparison of 30km / h serpentine test results

[0087]

[0088] Table 2 Comparison of 35km / h serpentine test results

[0089]

[0090] The results in Tables 1 and 2 show that the Trucksim simulation results deviate from the actual vehicle test results to some extent, but the deviations in yaw rate and lateral acceleration are both controlled within 20%. Given the complexity of the vehicle and the test environment, the constructed Trucksim vehicle model can be used as a reference for the characteristics of the actual vehicle.

[0091] Step S30: The upper-level LQR controller takes the difference between the ideal two-degree-of-freedom Simulink vehicle model and the Trucksim vehicle model as input, and calculates the rear wheel steering angle of the vehicle by 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. Then turn the rear wheel angle The data is input into the Trucksim vehicle model to form a closed-loop control.

[0092] Optimal control refers to designing a system's control strategy by optimizing the objective function to achieve the desired control performance. Linear QR (LQR) control is a special type of optimal control method, applicable to linear time-invariant systems. The goal of LQR control is to design a linear feedback controller that converges the system state to zero and minimizes a quadratic performance index, typically a weighted sum of the state and control inputs. LQR control obtains the optimal linear feedback controller by solving the Riccati equations. As an optimal control strategy, LQR aims to minimize the error between the state variables and the ideal values; therefore, the performance index J should be minimized.

[0093] Among them, performance indicators The calculation formula is as follows:

[0094] ,

[0095] In the formula, 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.

[0096] Control rate The formula is:

[0097] ,

[0098] In the formula, This is the first control matrix. The vector of costate variables has the same dimensions as the state variables. same.

[0099] make:

[0100] ,

[0101] In the formula, The solution is the time-varying Riccati differential equation;

[0102] Among them, when the time variable When it approaches infinity Converging to the steady-state value , The solution to the algebraic Riccati equation with constants exists. Satisfying the Riccati equation, we obtain the Riccati expression:

[0103] ,

[0104] In the formula, This is the first state matrix. In the first control matrix The second control sub-matrix 1 is obtained by adding the rear wheel steering angle to the original matrix.

[0105] but:

[0106] ,

[0107] Gain control rate The expression for state feedback:

[0108] ,

[0109] ,

[0110] In the formula, This is the state feedback matrix.

[0111] The output rear wheel angle is obtained. for:

[0112] .

[0113] Turn the front wheel corner As a distractor, the rear wheel steering angle as input variables The system state equation is then:

[0114] ,

[0115] In the formula, This is the second state matrix. For the second control submatrix 1, For the second control submatrix 2, This is the second output matrix. This is the second direct transfer matrix.

[0116] Among them, with compared to, None of them have changed, but They are respectively:

[0117] , ,

[0118] Take state variables Input variables Output variables Transform the system state equations into state equation expressions. The state matrices are as follows:

[0119] , , , .

[0120] in, This is the third state matrix. This is the third control matrix. This is the third output matrix. This is the third direct transfer matrix.

[0121] Step 30: Design the upper-level LQR controller for four-wheel steering with the control objective of tracking the ideal center of gravity sideslip angle and yaw rate.

[0122] Additional yaw moment in the upper-level LQR controller The control logic is as follows:

[0123] In front-wheel feedforward control, the control target is the vehicle's sideslip angle. Ideally zero, and the yaw rate. Stabilize the situation as soon as possible.

[0124] Let the ratio be for:

[0125] ,

[0126] At this moment, lateral acceleration 0 m / s 2 yaw acceleration Also 0 rad / s 2 After Laplace transform, we get:

[0127] .

[0128] From the above formula, it can be seen that under the front wheel feedforward control mode, at the front wheel steering angle... After the vehicle parameters are determined, the rear wheel steering angle Only related to the longitudinal speed of the vehicle related.

[0129] Lateral velocity deviation, yaw rate deviation, and lateral displacement deviation are used as inputs to the upper-level LQR controller, which outputs an additional yaw moment. Then the resulting additional yaw moment As input to the lower-level torque distribution controller.

[0130] Step 40: Apply additional yaw moment The lower-level vehicle control model is built by using the torque distribution controller as input, and the wheel braking / driving torque is output for longitudinal vehicle control. Based on the four-wheel steering lateral control, longitudinal force control is introduced, and precise longitudinal dynamic control of the vehicle is achieved by controlling the speed of the left and right wheels and the distribution of braking force.

[0131] The braking / driving force distribution control logic of the lower-level torque distribution controller is as follows:

[0132] Additional yaw moment calculated by the upper-level LQR controller Braking / driving torque needs to be rationally distributed to the four wheels in the form of braking / driving torque to help the vehicle regain stability in the event of instability. The braking / driving torque distribution rules are shown in Table 3, which reflects the relationship between the vehicle's steering state and wheel braking / driving.

[0133] Table 3 Braking / Driving Torque Distribution Rules

[0134]

[0135] When a vehicle becomes unstable, the additional yaw moment required to restore stability is significant. The formula is:

[0136] ,

[0137] In the formula, For front wheel braking / driving force, For rear wheel braking / driving force, The front wheel track. This refers to the rear wheel track.

[0138] The formula for the vertical load on the front and rear wheels is:

[0139] ,

[0140] In the formula, This refers to the vertical load on the front wheels. This refers to the vertical load on the rear wheel.

[0141] Distribute the braking / driving force between the front and rear wheels according to the ratio of the vertical loads on them, and substitute the required additional yaw moment. The formula yields the braking / driving forces for the front and rear wheels as follows:

[0142] ,

[0143] In the formula, For front wheel braking / driving force, This is the braking / driving force for the rear wheels.

[0144] Substitute the braking / driving forces of the front and rear wheels into the torque formula. The formulas for obtaining the braking torque of the front and rear wheels are as follows:

[0145] ,

[0146] In the formula, For front wheel braking / driving torque, For rear wheel braking / driving torque, This is the wheel's rolling radius.

[0147] 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:

[0148] .

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In a second aspect, the present invention provides a computer system including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the aforementioned vehicle four-wheel steering stability control method.

[0153] The memory can be volatile or non-volatile, or a combination of both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory of this invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0154] The processor can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the processor's hardware or by software instructions. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0155] The method steps of this invention can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. 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 performing the functions described in this application, or combinations thereof.

[0156] Software implementation can be achieved by executing functional modules (such as procedures, functions, etc.). Software code can be stored in memory and executed by the processor. Memory can be implemented in the processor or outside the processor.

[0157] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned vehicle four-wheel steering stability control method.

[0158] Computer storage media can include various media that can store program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0159] Fourthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aforementioned vehicle four-wheel steering stability control method.

[0160] Specifically, computer program products include: data signals, data signals embodied in a carrier wave, or computer-readable storage media.

[0161] It should be noted that the technical solutions described in this invention can be combined arbitrarily without conflict.

[0162] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, then this invention 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 control target is to track the ideal sideslip angle and ideal yaw rate, and the required rear wheel steering angle and additional yaw moment are output. 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. For the upper-level LQR controller, the state variables are taken. Output variables Turn the front wheel angle As a distractor, the rear wheel steering angle as input variables The system state equation is then: ,in, , , , , ; In the formula, The sideslip angle is the angle of the center of mass. The yaw rate is angular velocity. It is lateral acceleration. 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, It is the moment of inertia; Take the state variable again Input variables Output variables Transform the system state equations into state equation expressions. The state matrices are as follows: ， ， ， ， In the formula, For lateral velocity, This is lateral displacement. For the front wheel steering angle, For the rear wheel steering angle, This is the third state matrix. This is the third control matrix. This is the third output matrix. This is the third direct transfer matrix.

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, and State feedback matrix The elements in The yaw rate is angular velocity. It is the centroid sideslip angle.

4. 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 rolling radius of the wheel.

5. 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.

6. 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.

7. 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 1-6.

8. 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 1-6.

9. 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 1-6.

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