Four-wheel electric vehicle steering stability control method and system based on fuzzy reasoning

Abstract

The invention discloses a four-wheel electric vehicle steering stability control method and system based on fuzzy reasoning, and relates to the technical field of automobile engineering.The method comprises the steps that a two-degree-of-freedom dynamic model of four-wheel electric vehicle steering is established; the front wheel turning angle of the four-wheel electric vehicle is obtained in real time, the rear wheel turning angle is calculated according to the steering ratio, and then the actual value of the yaw velocity is calculated through a two-degree-of-freedom kinetic model; the error and the error change rate of the actual value and the target value of the yaw velocity are obtained, the error and the error change rate are input into a fuzzy compensation controller, the front and rear wheel rotation angle compensation amount is calculated according to a preset fuzzy rule base and a membership function, and the compensated front and rear wheel rotation angle is obtained; and controlling a servo steering motor to output a PWM (Pulse-Width Modulation) signal through PID (Proportion Integration Differentiation) closed-loop control of a steering angle ring, a rotating speed ring and a current ring in sequence based on the compensated front and rear wheel steering angles, and driving wheels to complete steering adjustment. The steering control stability and safety of the four-wheel electric vehicle can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of automotive engineering technology, and in particular to a method and system for controlling the steering stability of a four-wheeled electric vehicle based on fuzzy reasoning. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Under the dual pressures of global energy shortages and environmental issues, new energy vehicles have gradually become a research focus. Among them, four-wheel steering electric vehicles have attracted much attention due to their advantages such as environmental friendliness, flexible handling, and ease of digital control. They can reduce the vehicle's center of gravity sideslip angle to near zero, significantly improving the vehicle's dynamic response to steering input and ensuring stability at high speeds or in crosswinds. In low-speed scenarios, the front and rear wheels can reverse steering, reducing the turning radius by about 20%, facilitating driving and parking in narrow streets, and also enhancing the responsiveness and accuracy of steering operations when towing.

[0004] However, in practical applications, especially in autonomous driving scenarios, the vehicle's steering mode—with the front wheels as the active steering wheels and the rear wheels either passively driven or swaying with the vehicle's inertia—is prone to understeer or oversteer. This can lead to skidding or fishtailing during high-speed cornering, affecting driving stability and safety. Currently, steering control strategies often employ PID (Proportional-Integral-Derivative) controllers, using closed-loop control to bring the steering angle close to the target value. However, in reality, during vehicle steering, handling stability is primarily affected by yaw rate and sideslip angle. To improve stability, it's necessary to minimize fluctuations in these two parameters while ensuring rapid response and stable control error. However, PID control often exhibits significant overshoot, failing to guarantee driving stability. Therefore, existing technologies cannot precisely and dynamically adjust the front and rear wheel angles based on real-time vehicle motion and the external environment, making it difficult to establish accurate and effective control models and select appropriate control algorithms for rapid and stable control. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, this invention provides a steering stability control method and system for four-wheeled electric vehicles based on fuzzy inference. This method solves the problem of instability such as sideslip and fishtailing that easily occur in existing four-wheeled electric vehicles. By optimizing the front and rear wheel steering angle compensation control strategy, it improves the speed of vehicle yaw rate and center of gravity sideslip angle response, reduces overshoot, and enhances handling stability and safety.

[0006] In a first aspect, the present invention proposes a steering stability control method for four-wheeled electric vehicles based on fuzzy reasoning.

[0007] A steering stability control method for four-wheeled electric vehicles based on fuzzy reasoning, comprising: Establish a two-degree-of-freedom dynamic model for the steering of a four-wheeled electric vehicle; The front wheel steering angle of a four-wheeled electric vehicle is acquired in real time, the rear wheel steering angle is calculated based on the steering ratio, and the actual value of the yaw rate is calculated using a two-degree-of-freedom dynamic model. The error and rate of change of the actual yaw rate between the target value are obtained. The error and rate of change of the error are input into the fuzzy compensation controller. The front and rear wheel angle compensation is calculated according to the preset fuzzy rule library and membership function to obtain the compensated front and rear wheel angles. Based on the compensated front and rear wheel angles, the servo steering motor outputs PWM signals through sequential PID closed-loop control of the angle loop, speed loop, and current loop, driving the wheels to complete the steering adjustment.

[0008] A further technical solution is to establish a two-degree-of-freedom dynamic model that only considers the vehicle's yaw and lateral motion, as follows: ; ; In the above formula, This refers to the front wheel lateral stiffness. Rear wheel lateral stiffness; It is the centroid sideslip angle; This refers to the yaw rate; This is the front axle distance; This is the rear axle distance; The steering angle of the front wheels; The rear wheel steering angle; Total mass; For the velocity of the center of mass in The components of the axis; Based on the two-degree-of-freedom dynamic model, through mathematical operations and transformations, the transfer function with the front wheel rotation angle as input and the vehicle angular velocity and center-of-gravity sideslip angle as output is obtained as follows: ; ; In the above formula, For the Laplace operator, 、 、 、 、 、 、 、 、 、 It is an intermediate quantity calculated based on vehicle parameters. Let Z be the moment of inertia about the Z-axis. This represents the longitudinal component of the center-of-mass velocity.

[0009] A further technical solution involves inputting the error and error change rate into a fuzzy compensation controller. Based on a preset fuzzy rule base and membership function, the compensation amount for the front and rear wheel steering angles is calculated, resulting in optimized front and rear wheel steering angles, including: The precise values ​​of the yaw rate error and the rate of change of the error are mapped to the fuzzy domain through a quantization factor, and then the fuzzy level and fuzzy membership degree of the mapped value are calculated based on the triangular membership function. Based on the fuzzy membership degree of the input variable, a preset fuzzy rule library is matched. The trigger strength of each rule is determined by taking the minimum value, and the output of all trigger rules is integrated by taking the maximum value to obtain a fuzzy set of compensation quantities. The centroid method is used to convert the fuzzy set of compensation amounts into precise values. That is, the final front and rear wheel steering angle compensation amounts are output by weighted calculation of the fuzzy level center value and the corresponding membership degree.

[0010] In a further technical solution, in the input processing of the fuzzy compensation controller, the actual range of the yaw rate error is limited to ±2 rad / s, and the actual range of the error change rate is limited to ±5 rad / s. The actual precise values ​​of the error and the error change rate are converted to the fuzzy domain by a quantization factor of 0.2. The fuzzy variable levels for the error and the rate of change of error include nine levels: negative large NB, negative medium NM, negative small NS, zero negative NZ, zero ZO, zero positive PZ, positive small PS, positive medium PM, and positive large PB. Each level is represented by a triangular membership function.

[0011] In a further technical solution, the output of the fuzzy controller is the front and rear wheel angle compensation amount. The domain of discourse of the front and rear wheel angle compensation amount is set to ±0.1745 radians. The fuzzy variable levels include seven levels: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB, which correspond to compensation values ​​from maximum negative compensation to maximum positive compensation. The tire steering angle is defined as follows: counterclockwise rotation is positive, and clockwise rotation is negative.

[0012] A further technical solution is that the fuzzy rule base is: if E is A and ECG is B, then U is C; Where E is the error, EC is the error change rate, and U is the front and rear wheel steering angle compensation amount; The core rules of the fuzzy rule base are: If the actual value of the yaw rate is greater than the target value and the rate of change of error shows an increasing trend, then reduce the front and rear wheel steering angles; If the actual value of the yaw rate is less than the target value or the error rate of change shows a decreasing trend, then increase the front and rear wheel steering angles; The angle adjustment range is dynamically adapted through fuzzy inference.

[0013] A further technical solution involves a three-loop PID closed-loop control process comprising an angle loop, a speed loop, and a current loop, as follows: Using the compensated front and rear wheel steering angles as the target steering angles, the reference speed is calculated based on the target and actual steering angles. Then, the reference current is calculated based on the reference and actual speeds. Finally, based on the deviation between the reference and actual currents, the current PID controller calculates and outputs a PWM signal to control the steering motor to perform steering angle compensation and drive the wheels to complete the steering adjustment.

[0014] Secondly, the present invention provides a steering stability control system for four-wheeled electric vehicles based on fuzzy reasoning. A four-wheel electric vehicle steering stability control system based on fuzzy reasoning includes: The model building module is used to establish a two-degree-of-freedom dynamic model of the steering of a four-wheeled electric vehicle; The data acquisition module is used to acquire the front wheel steering angle of the four-wheel electric vehicle in real time, calculate the rear wheel steering angle based on the steering ratio, and then use a two-degree-of-freedom dynamic model to calculate the actual value of the yaw rate. The steering angle compensation module is used to obtain the error and error change rate between the actual value and the target value of the yaw rate. The error and error change rate are input to the fuzzy compensation controller, which calculates the front and rear wheel steering angle compensation amount according to the preset fuzzy rule library and membership function, and obtains the compensated front and rear wheel steering angle. The steering control module is used to control the servo steering motor to output PWM signals based on the compensated front and rear wheel steering angles, and drive the wheels to complete the steering adjustment through PID closed-loop control of the angle loop, speed loop, and current loop in sequence.

[0015] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-described fuzzy inference-based steering stability control method for four-wheeled electric vehicles when executing the executable instructions stored in the memory.

[0016] Fourthly, the present invention also provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-described fuzzy inference-based four-wheel electric vehicle steering stability control method.

[0017] Fifthly, the present invention also provides a computer program product comprising executable instructions stored in a computer-readable storage medium; wherein, when the processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned fuzzy inference-based four-wheel electric vehicle steering stability control method is implemented.

[0018] The above one or more technical solutions have the following beneficial effects: 1. This invention proposes a steering stability control method and system for four-wheel electric vehicles based on fuzzy inference. By using a front and rear wheel steering control system under fuzzy inference, the yaw rate deviation caused by dynamics can be effectively reduced, the yaw rate response can be improved, and the overshoot can be reduced. This allows the vehicle to reach the target yaw rate more quickly and stably during steering, thereby enhancing the vehicle's handling stability and solving the problem of unstable phenomena such as sideslip and fishtailing that easily occur in existing four-wheel steering electric vehicles.

[0019] 2. Compared with traditional feedforward proportional steering control four-wheel steering systems, the fuzzy inference system proposed in this invention can effectively reduce the settling time and overshoot of the center of gravity sideslip angle response when the vehicle is turning, enabling the vehicle to maintain a better driving trajectory during turning, reducing the risk of sideslip, and further improving the vehicle's handling stability and safety. This fuzzy algorithm-based control method overcomes various limitations in existing technologies, such as understeering or oversteering, poor rear wheel steering angle follow-up, and limited front wheel steering margin, achieving optimization of the traditional feedforward proportional steering control strategy and providing a more effective solution for the steering control of four-wheel steering electric vehicles.

[0020] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0022] Figure 1 This is a flowchart of a four-wheeled electric vehicle steering stability control method based on fuzzy reasoning proposed in an embodiment of the present invention; Figure 2 This is a schematic diagram of the two-degree-of-freedom dynamic model constructed in an embodiment of the present invention; Figure 3 This is a block diagram of the joint compensation control of front and rear wheel steering angles based on fuzzy reasoning in an embodiment of the present invention; Figure 4This is a schematic diagram of the fuzzy variable levels of error in an embodiment of the present invention; Figure 5 This is a schematic diagram of the fuzzy variable levels of the error change rate in an embodiment of the present invention; Figure 6 This is a schematic diagram of the fuzzy variable levels for the front and rear wheel compensation steering angles in an embodiment of the present invention; Figure 7 This is a schematic diagram of the fuzzy control rules for front wheel steering angle compensation in an embodiment of the present invention; Figure 8 This is a schematic diagram of the fuzzy control rules for rear wheel steering angle compensation in an embodiment of the present invention; Figure 9 This is a diagram of a servo motor system architecture using three closed-loop controls in an embodiment of the present invention. Detailed Implementation

[0023] It should be noted that the following detailed descriptions are exemplary and are intended only to describe specific embodiments and to provide further explanation of the invention, and are not intended to limit the scope of exemplary embodiments of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0024] Example 1 To address the problem that existing technologies cannot effectively balance the steering performance of vehicles under different operating conditions, and cannot simultaneously meet the dynamic response requirements of yaw rate and center of gravity sideslip angle, leading to instability phenomena such as sideslip and fishtailing during cornering, this embodiment proposes a steering stability control method for four-wheeled electric vehicles based on fuzzy inference. This method establishes a two-degree-of-freedom dynamic model to accurately describe the relationship between the vehicle's yaw and lateral motion; designs a fuzzy steering compensation controller to dynamically adjust the front and rear wheel steering angle compensation based on the error and rate of change of yaw rate; and combines a three-closed-loop servo motor system with feedback of steering angle, speed, and current to perform steering compensation. This control method can improve the yaw rate response speed, reduce the fluctuation of center of gravity sideslip angle, effectively solve the problem of sideslip and fishtailing during cornering, and improve handling stability and safety.

[0025] The four-wheel electric vehicle steering stability control method based on fuzzy reasoning proposed in this embodiment can achieve four-wheel steering control, as follows: A two-degree-of-freedom dynamic model was established, simplifying the vehicle to a model that only considers yaw and lateral motion. The dynamic equations and transfer function equations were derived to describe the dynamic relationship between the front wheel steering angle input and the yaw rate and the center of gravity sideslip angle output. A fuzzy compensation controller is established. Based on the error and rate of change of the actual yaw rate output by the above model and the error between the target value and the actual yaw rate, the target steering angle of the front and rear wheels is calculated by fuzzy inference through the membership function and rules of the fuzzy variables. Servo motor control employs a three-loop closed-loop system consisting of an angle loop, a speed loop, and a current loop. The compensated target angle is used as input, and the servo motor is driven to precisely execute wheel steering through closed-loop feedback and PID control.

[0026] The following content provides a more detailed description of the four-wheeled electric vehicle steering stability control method proposed in this embodiment.

[0027] like Figure 1 As shown, the method specifically includes the following steps: Step S1: Establish a two-degree-of-freedom dynamic model for the steering of a four-wheeled electric vehicle.

[0028] Specifically, such as Figure 2 As shown, in constructing a two-degree-of-freedom dynamic model, to analyze the motion state of a car during turning, it is simplified to a two-degree-of-freedom dynamic model, that is, only the yaw and lateral motion of the vehicle are considered. An xy rectangular coordinate system describing the car's motion is established, thereby deriving the equations of the two-degree-of-freedom vehicle dynamic model, which are: ; ; In the above formula, This refers to the front wheel lateral stiffness. Rear wheel lateral stiffness; It is the centroid sideslip angle; This refers to the yaw rate; This is the front axle distance; This is the rear axle distance; The steering angle of the front wheels; The rear wheel steering angle; Total mass; For the velocity of the center of mass in The components of the axis.

[0029] Based on the above formulas and parameters, through mathematical operations and transformations, the transfer function, with the front wheel steering angle as input and the vehicle angular velocity and center of gravity sideslip angle as outputs, is obtained as follows: ; ; In the above formula, s For the Laplace operator, , , , , , , , , , It is an intermediate quantity calculated based on vehicle parameters, and its specific calculation formula is as follows: ; ; ; ; ; ; ; ; ; ; In the above formula, m is the total mass; a is the front axle distance; b is the rear axle distance; This refers to the front wheel lateral stiffness. Rear wheel lateral stiffness; L is the moment of inertia about the Z-axis; L is the total wheelbase. This represents the longitudinal component of the center-of-mass velocity.

[0030] Furthermore, considering that during vehicle steering, it is necessary to minimize the fluctuations of yaw rate and sideslip angle while ensuring rapid response and stable control error to guarantee vehicle handling stability, traditional PID control often exhibits significant overshoot in this application. Therefore, this embodiment introduces a joint compensation control method for front and rear wheel steering angles based on fuzzy inference to optimize steering stability. Figure 3 As shown, the method is as follows: Step S2: Obtain the front wheel steering angle of the four-wheel electric vehicle in real time, calculate the rear wheel steering angle based on the steering ratio, and then use the two-degree-of-freedom dynamic model to calculate the actual value of the yaw rate.

[0031] Specifically, firstly, the rear wheel angle corresponding to the front wheel angle is calculated based on the steering ratio, and the target yaw rate is calculated based on the preset steady-state yaw rate gain. Based on the transfer function constructed in step S1 above, which takes the front wheel angle as input and the vehicle angular velocity and center of gravity sideslip angle as output, the front wheel angle of the four-wheel electric vehicle acquired in real time is substituted into the transfer function to calculate the actual yaw rate (i.e., the actual value of the yaw rate).

[0032] Step S3: Obtain the error and error change rate between the actual value and the target value of the yaw rate. Input the error and error change rate into the fuzzy compensation controller. Calculate the front and rear wheel rotation angle compensation amount according to the preset fuzzy rule library and membership function to obtain the compensated front and rear wheel rotation angles.

[0033] Specifically, based on the actual and target values ​​of the yaw rate obtained through step S2, the error and its rate of change between the actual and target values ​​are calculated. The error and the rate of change are input to the fuzzy compensation controller. The front and rear wheel angle compensation amounts are calculated according to the preset fuzzy rule library and membership function. Then, combined with the current front / rear wheel angles, the compensated front wheel angle and rear wheel angle are calculated.

[0034] In this embodiment, a fuzzy compensation controller is used to calculate the front and rear wheel steering angle compensation amounts based on a preset fuzzy rule base and membership functions. The calculation steps are as follows: Step S3.1: Map the precise values ​​of the yaw rate error and the rate of change of error to the fuzzy domain through a quantization factor, and then calculate the fuzzy level and fuzzy membership degree of the mapped value based on the triangular membership function.

[0035] Specifically, in the input processing of the fuzzy compensation controller, the yaw rate error E and the error change rate EC are used as inputs. The former reflects the deviation between the actual yaw rate and the target value, and the latter reflects the trend of the deviation. The actual range of the yaw rate error is limited to ±2 rad / s, and the actual range of the error change rate is limited to ±5 rad / s. The actual precise values ​​of the error and the error change rate are converted to the fuzzy domain by a quantization factor of 0.2.

[0036] Furthermore, such as Figure 4 As shown, the fuzzy variable levels of the error are defined from negative largest to positive largest as follows: negative largest NB, negative medium NM, negative smallest NS, zero negative NZ, zero ZO, zero positive PZ, positive smallest PS, positive medium PM, and positive largest PB, totaling 9 levels. Each level is represented by a triangular membership function; similarly, as... Figure 5 As shown, the fuzzy variable levels of the error change rate are defined from negative to positive as above.

[0037] Based on this, the fuzzy level and fuzzy membership degree of the mapped value are calculated using the triangular membership function. Specifically, taking the error mapping value E'=-4.5 as an example, its corresponding fuzzy level and membership degree are as follows: Belonging to "Negative Medium NM": The universe of discourse of NM is [-6,-3], and the membership function formula is μ_NM=(x+6) / 3, where x is the mapped value. Substituting this, we get μ_NM=(-4.5+6) / 3=0.5; Belonging to "Negative Small NS": The universe of discourse of NS is [-3,0], and the membership function formula is μ_NS=(3+x) / 3. Substituting this, we get μ_NS=(3-4.5) / 3=-0.5. Since the membership degree cannot be negative, it is taken as 0. Therefore, E'=-4.5 has a membership degree of 0.5 for NM, and a membership degree of 0 for other levels.

[0038] Step S3.2: Based on the fuzzy membership degree of the input variable, match the preset fuzzy rule library, determine the trigger strength of each rule by taking the minimum value, and then integrate the outputs of all trigger rules by taking the maximum value to obtain the fuzzy set of compensation amount.

[0039] Specifically, such as Figure 7 and Figure 8 The fuzzy control rules for front wheel steering angle compensation and rear wheel steering angle compensation shown are illustrated. The first row represents the error level, and the first column represents the error change rate level. This preset fuzzy rule base is actually based on the form "If (E is A) and (EC is B) then (U is C)", that is, if E is A and EC is B, then U is C, where E is the error, EC is the error change rate, and U is the front and rear wheel steering angle compensation amount. The core rule of this fuzzy rule is: If the actual value of the yaw rate is greater than the target value and the rate of change of error shows an increasing trend, it indicates that it has a tendency to continue to increase. Therefore, the front and rear wheel steering angles should be reduced to suppress the increase of the yaw rate. If the actual value of the yaw rate is less than the target value or the error rate of change shows a decreasing trend, it indicates that it has a continuing decreasing trend. Therefore, increase the front and rear wheel steering angles to accelerate the yaw rate towards the target value. In addition, the adjustment range of the turning angle is not a fixed value, but is dynamically adapted through fuzzy reasoning based on the specific numerical values, positive and negative signs and their combinations of error E and error change rate EC.

[0040] For example, according to the preset rule base, if the error is negative medium and the error change rate is negative small, then the compensation amount is positive small. At the same time, based on the calculation results of the previous step, for each rule corresponding to E and EC, the trigger strength of each rule is first determined by taking the minimum value. For example, the minimum value of the membership degree of E and EC is taken, that is, the trigger strength μ_min (0.5,0.6)=0.5. Then, the output of all trigger rules is integrated by taking the maximum value to obtain μ_max=0.5. At this time, the output U corresponding to the rule is positive small PS, and the trigger strength is 0.5.

[0041] Step S3.3: Using the centroid method, the fuzzy set of compensation amounts is converted into precise values. That is, the final front and rear wheel steering angle compensation amounts are output by weighted calculation of the fuzzy level center value and the corresponding membership degree.

[0042] Specifically, the output of the fuzzy controller is the front and rear wheel angle compensation amount, and the front and rear wheel angle compensation amount also adopts the following... Figure 6 The triangular function shown has a domain of ±0.1745 radians. The fuzzy variable levels, from negative to positive, are defined as follows: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB, totaling 7 levels. These correspond to compensation values ​​from maximum negative compensation to maximum positive compensation. The sign of the tire steering angle is defined as follows: counterclockwise rotation is positive, and clockwise rotation is negative.

[0043] In this embodiment, the center values ​​of each fuzzy variable level of the compensation amount are defined, and the compensation amount is accurately calculated using the centroid method, wherein the formula for the centroid method is: ; Substitute the center value and trigger strength of the corresponding level of U obtained in the previous step into the equation, such as... =0.0581rad, where only the PS level has a membership degree of 0.5, and the other levels have 0. The final output is the front and rear wheel yaw angle compensation amount, which controls the servo motor to increase the front and rear wheel yaw angle by 0.0581 rad to reduce the deviation between the actual yaw rate and the target value.

[0044] Step S4: Based on the compensated front and rear wheel angles, the servo steering motor outputs a PWM signal through PID closed-loop control of the angle loop, speed loop, and current loop in sequence, driving the wheels to complete the steering adjustment.

[0045] In this embodiment, the compensated front and rear wheel steering angles are input into a two-degree-of-freedom model to calculate the yaw rate, thereby achieving dynamic compensation and adjustment of the wheel steering angle. Specifically, as shown... Figure 9As shown, from the outside in, they are the angle loop, speed loop, and current loop. The three-loop PID closed-loop control process of the angle loop, speed loop, and current loop is as follows: taking the compensated front and rear wheel angles as the target angles, the reference speed is calculated based on the target angle and the actual angle (obtained by the encoder). Then, the reference current is calculated based on the reference speed and the actual speed (obtained by the sensor). Finally, based on the deviation between the reference current and the actual current (collected by the motor current sensor), the current PID controller calculates and outputs a PWM signal to control the steering motor to perform angle compensation and drive the wheels to complete the steering adjustment.

[0046] Example 2 This embodiment proposes a four-wheel electric vehicle steering stability control system based on fuzzy reasoning, including: The model building module is used to establish a two-degree-of-freedom dynamic model of the steering of a four-wheeled electric vehicle; The data acquisition module is used to acquire the front wheel steering angle of the four-wheel electric vehicle in real time, calculate the rear wheel steering angle based on the steering ratio, and then use a two-degree-of-freedom dynamic model to calculate the actual value of the yaw rate. The steering angle compensation module is used to obtain the error and error change rate between the actual value and the target value of the yaw rate. The error and error change rate are input to the fuzzy compensation controller, which calculates the front and rear wheel steering angle compensation amount according to the preset fuzzy rule library and membership function, and obtains the compensated front and rear wheel steering angle. The steering control module is used to control the servo steering motor to output PWM signals based on the compensated front and rear wheel steering angles, and drive the wheels to complete the steering adjustment through PID closed-loop control of the angle loop, speed loop, and current loop in sequence.

[0047] Example 3 This embodiment provides an electronic device, including: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the method provided in this embodiment.

[0048] Example 4 This embodiment also provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, will cause the processor to execute the method described above in this embodiment.

[0049] Example 5 This embodiment provides a computer program product including executable instructions, which are computer instructions; the executable instructions are stored in a computer-readable storage medium. When the processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the electronic device performs the method described in this embodiment.

[0050] The steps and methods involved in Embodiments 2 to 5 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0051] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0052] The above description is only a preferred embodiment of the present invention. Although the specific implementation of the present invention has been described in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative effort are still within the scope of protection of the present invention.

Claims

1. A steering stability control method for a four-wheeled electric vehicle based on fuzzy reasoning, characterized in that, include: A two-degree-of-freedom dynamic model for the steering of a four-wheeled electric vehicle is established; The front wheel steering angle of a four-wheeled electric vehicle is acquired in real time, the rear wheel steering angle is calculated based on the steering ratio, and the actual value of the yaw rate is calculated using a two-degree-of-freedom dynamic model. The error and rate of change of the actual yaw rate between the target value are obtained. The error and rate of change of the error are input into the fuzzy compensation controller. The front and rear wheel angle compensation is calculated according to the preset fuzzy rule library and membership function to obtain the compensated front and rear wheel angles. Based on the compensated front and rear wheel angles, the servo steering motor outputs a PWM signal through sequential PID closed-loop control via angle loop, speed loop, and current loop, driving the wheels to complete the steering adjustment.

2. The steering stability control method for four-wheeled electric vehicles based on fuzzy reasoning as described in claim 1, characterized in that, A two-degree-of-freedom dynamic model considering only the vehicle's yaw and lateral motion is established as follows: ； ； In the above formula, This refers to the front wheel lateral stiffness. Rear wheel lateral stiffness; It is the centroid sideslip angle; This refers to the yaw rate; This is the front axle distance; This is the rear axle distance; The steering angle of the front wheels; The rear wheel steering angle; Total mass; For the velocity of the center of mass in The components of the axis; Based on the two-degree-of-freedom dynamic model, through mathematical operations and transformations, the transfer function with the front wheel rotation angle as input and the vehicle angular velocity and center-of-gravity sideslip angle as output is obtained as follows: ； ； In the above formula, For the Laplace operator, 、 、 、 、 、 、 、 、 、 It is an intermediate quantity calculated based on vehicle parameters. Let Z be the moment of inertia about the Z-axis. This represents the longitudinal component of the center-of-mass velocity.

3. The steering stability control method for four-wheeled electric vehicles based on fuzzy reasoning as described in claim 1, characterized in that, The error and error change rate are input into the fuzzy compensation controller, which calculates the front and rear wheel angle compensation amounts based on the preset fuzzy rule base and membership function, resulting in the optimized front and rear wheel angles, including: The precise values ​​of the yaw rate error and the rate of change of the error are mapped to the fuzzy domain through a quantization factor, and then the fuzzy level and fuzzy membership degree of the mapped value are calculated based on the triangular membership function. Based on the fuzzy membership degree of the input variable, a preset fuzzy rule library is matched. The trigger strength of each rule is determined by taking the minimum value, and the output of all trigger rules is integrated by taking the maximum value to obtain a fuzzy set of compensation quantities. The centroid method is used to convert the fuzzy set of compensation amounts into precise values. That is, the final front and rear wheel steering angle compensation amounts are output by weighted calculation of the fuzzy level center value and the corresponding membership degree.

4. The steering stability control method for four-wheeled electric vehicles based on fuzzy reasoning as described in claim 3, characterized in that, In the input processing of the fuzzy compensation controller, the actual range of the yaw rate error is limited to ±2 rad / s, and the actual range of the error change rate is limited to ±5 rad / s. The actual precise values ​​of the error and the error change rate are converted to the fuzzy domain by a quantization factor of 0.

2. The fuzzy variable levels for the error and the rate of change of error include nine levels: negative large NB, negative medium NM, negative small NS, zero negative NZ, zero ZO, zero positive PZ, positive small PS, positive medium PM, and positive large PB. Each level is represented by a triangular membership function. The output of the fuzzy controller is the front and rear wheel angle compensation amount. The domain of discourse for the front and rear wheel angle compensation amount is set to ±0.1745 radians. The fuzzy variable levels include seven levels: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB, which correspond to compensation values ​​from the maximum negative compensation to the maximum positive compensation. The tire steering angle is defined as follows: counterclockwise rotation is positive, and clockwise rotation is negative.

5. The four-wheel electric vehicle steering stability control method based on fuzzy reasoning as described in claim 3, characterized in that, The fuzzy rule base is as follows: if E is A and ECG is B, then U is C; Where E is the error, EC is the error change rate, and U is the front and rear wheel steering angle compensation amount; The core rules of the fuzzy rule base are: If the actual value of the yaw rate is greater than the target value and the rate of change of error shows an increasing trend, then reduce the front and rear wheel steering angles; If the actual value of the yaw rate is less than the target value or the error rate of change shows a decreasing trend, then increase the front and rear wheel steering angles; The angle adjustment range is dynamically adapted through fuzzy inference.

6. The steering stability control method for four-wheeled electric vehicles based on fuzzy reasoning as described in claim 1, characterized in that, The three-loop PID closed-loop control process, consisting of the angle loop, speed loop, and current loop, is as follows: Using the compensated front and rear wheel steering angles as the target steering angles, the reference speed is calculated based on the target and actual steering angles. Then, the reference current is calculated based on the reference and actual speeds. Finally, based on the deviation between the reference and actual currents, the current PID controller calculates and outputs a PWM signal to control the steering motor to perform steering angle compensation and drive the wheels to complete the steering adjustment.

7. A four-wheel electric vehicle steering stability control system based on fuzzy reasoning, characterized in that, include: The model building module is used to establish a two-degree-of-freedom dynamic model of the steering of a four-wheeled electric vehicle; The data acquisition module is used to acquire the front wheel steering angle of the four-wheel electric vehicle in real time, calculate the rear wheel steering angle based on the steering ratio, and then use a two-degree-of-freedom dynamic model to calculate the actual value of the yaw rate. The steering angle compensation module is used to obtain the error and error change rate between the actual value and the target value of the yaw rate. The error and error change rate are input to the fuzzy compensation controller, which calculates the front and rear wheel steering angle compensation amount according to the preset fuzzy rule library and membership function, and obtains the compensated front and rear wheel steering angle. The steering control module is used to control the servo steering motor to output PWM signals based on the compensated front and rear wheel steering angles, and drive the wheels to complete the steering adjustment through PID closed-loop control of the angle loop, speed loop, and current loop in sequence.

8. An electronic device, characterized in that, include: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the fuzzy inference-based steering stability control method for four-wheeled electric vehicles as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The device stores executable instructions that, when executed by a processor, implement the fuzzy inference-based steering stability control method for four-wheeled electric vehicles as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, it implements the fuzzy inference-based steering stability control method for four-wheeled electric vehicles as described in any one of claims 1-6.

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