A virtual kingpin optimization and control method for a by-wire chassis corner module

Abstract

This invention discloses a virtual kingpin optimization and control method for a steerable chassis angle module. The method includes: applying design conditions to trigger the virtual kingpin to generate coupled motion; analyzing the hardpoint architecture to obtain the kinematic and elastic kinematic characteristics of the kingpin; measuring wheel alignment parameters; establishing differential equations to solve the virtual kingpin positioning parameters; optimizing the hardpoint structure to select the optimal value; inputting the control target; calculating the steering feedback torque; and executing closed-loop control to output the drive current. This invention improves the accuracy of kingpin kinematic calculation through multi-condition coupled analysis, establishes a quantitative mapping relationship between wheel attitude and kingpin parameters, applies instantaneous axis theory to achieve accurate calculation of kingpin positioning parameters, uses sensitivity analysis and weighted optimization to improve hardpoint optimization efficiency, introduces kingpin dynamic characteristics to improve the accuracy of return torque calculation, and constructs a dual closed-loop control system to enhance response speed and robustness. This effectively improves the ease of operation, driving stability, and steering control accuracy of the steerable chassis angle module.

Description

Technical Field

[0001] This invention relates to an optimized design method for a corner module virtual kingpin, belonging to the technical field of vehicle drive-by-wire chassis systems. Background Technology

[0002] With the rapid development of vehicle electrification and intelligence, and as electric vehicle products have shifted from policy-driven to market-driven and innovation-driven development, the human-centered philosophy has placed higher demands on safety and comfort. Therefore, in recent years, academia and industry have invested significant resources in the research and development of drive-by-wire chassis platforms, which are facing an iterative path from electric chassis platforms to drive-by-wire chassis platforms.

[0003] The drive-by-wire chassis angle module unit integrates drive, braking, steering, and suspension into one unit. To meet the automotive demands for low floors, wide aisles, small turning radii, and large passenger space, the virtual kingpin angle module is gradually becoming a trend in drive-by-wire chassis design. The virtual kingpin is constructed from the suspension and steering multi-link structure of the angle module, and its kinematic and elasto-kinematic characteristics play a decisive role in bus steering control, rollover safety, stability, and ride comfort.

[0004] However, existing technologies still have the following technical shortcomings when dealing with issues related to virtual master sales: 1. Insufficient accuracy in virtual kingpin kinematics calculation and limited precision in positioning parameter calculation methods: During cornering, the virtual kingpin axis moves, exhibiting time-varying vector dynamic coupling characteristics. Traditional methods simplify the kingpin as a fixed axis for geometric calculation, failing to describe its spatial movement and time-varying vector dynamic coupling characteristics during steering, resulting in low accuracy in the calculation of virtual kingpin kinematics and elastic kinematics. Furthermore, when solving core positioning parameters such as kingpin caster angle, kingpin inclination angle, kingpin inclination offset, and kingpin caster trailing distance, geometric projection methods are often used for approximation, lacking differential equation solutions based on instantaneous axis theory. This makes it difficult to accurately capture the instantaneous changes of the kingpin axis during wheel movement, limiting calculation accuracy and failing to obtain kingpin positioning parameters that conform to actual physical laws, thus failing to meet the high-precision control requirements of steerable chassis. 2. Lack of quantitative mapping relationship between wheel attitude and kingpin parameters: Existing technologies typically only measure single wheel angle parameters, failing to systematically establish a quantitative mapping relationship between the six-degree-of-freedom wheel attitude and the spatial position of the virtual kingpin. In particular, the lack of measurement and utilization of ground contact point displacement parameters makes it impossible to directly correlate the tire contact point's movement trajectory during the design process, making it difficult to comprehensively evaluate and optimize the vehicle's ground contact performance and ride comfort. 3. The problem of low efficiency and difficulty in achieving multi-objective coordination in hard point structure optimization design: In the design process of corner module hard point structures, existing technologies lack quantitative analysis methods for the influence weight of hard points, often requiring blind trial and error among numerous hard points, resulting in low optimization efficiency. At the same time, traditional optimization methods are difficult to balance and coordinate optimization among multiple performance objectives such as handling stability and steering ease, failing to ensure the optimal overall performance of the final design scheme. 4. The problem of not considering the dynamic characteristics of the kingpin in steering return torque calculation: Existing steering return torque calculation methods usually use fixed kingpin parameters, failing to explicitly introduce the dynamic variation characteristics of positioning parameters such as kingpin caster trailing distance. Due to the lack of quantitative analysis of the real-time impact of changes in kingpin positioning parameters on return torque, the control algorithm cannot predict the return torque requirements under different working conditions based on the dynamic characteristics of the kingpin, making it difficult to achieve refined steering control. 5. Insufficient Response Speed ​​and Robustness of Steering Control Systems: Traditional steer-by-wire chassis control systems often employ a single-loop control structure, lacking a real-time compensation mechanism for kingpin feedback torque. When internal torque fluctuations are caused by changes in kingpin parameters, the system struggles to correct them promptly, resulting in limited dynamic response speed and control accuracy. Furthermore, existing systems do not accurately model the dynamic characteristics of steering, making them prone to overshoot and oscillations, thus requiring improved robustness.

[0005] To address the aforementioned technical problems, this invention proposes a method for optimizing and controlling the virtual kingpin of a drive-by-wire chassis corner module. By measuring wheel parameters, the virtual kingpin positioning parameters are calculated in real time, and the kingpin positioning parameters and hardpoint space are optimized. The corner module is controlled based on the steering return torque to achieve the goals of improving maneuverability and stability. Summary of the Invention

[0006] The purpose of this invention is to solve the problem that the movement of the virtual kingpin during steering affects the overall vehicle performance and control when the virtual kingpin angle module is applied to a wire-controlled chassis product.

[0007] To achieve the above objectives, the present invention provides a method for optimizing and controlling the virtual kingpin of a drive-by-wire chassis corner module, comprising the following steps: S1: Apply the design conditions to trigger the virtual master pin to generate coupled motion; S2: Analyze the coupled motion of the hardpoint architecture to obtain the kinematic and elastic kinematic properties of the main pin; S3: Measure wheel alignment parameters and quantify wheel changes caused by kingpin movement; S4: Establish the differential equation and solve for the virtual kingpin positioning parameters; S5: Solve the virtual kingpin positioning parameters based on the differential equation established in S4; S6: Optimize the hard point structure and select the optimal value of the main pin positioning parameters; S7: Input control target and set steering parameters; S8: Calculate steering feedback torque and evaluate self-centering characteristics; S9: Executes closed-loop control of the angle module, outputting drive current.

[0008] Furthermore, the design conditions described in step S1 include a single condition or multiple coupled conditions among wheel angle, vertical wheel bounce, lateral force, and longitudinal force.

[0009] Furthermore, step S2 specifically includes: S2.1: Based on the coupled motion of the hard-point architecture, extract the kinematic characteristics of the virtual master pin; S2.2: Based on the coupled motion of the hard-point architecture, extract the elastic kinematic properties of the virtual master pin.

[0010] Furthermore, step S3 specifically includes: S3.1: Measure the changes in toe angle, camber angle, and roll angle; S3.2: Measure the changes in the lateral displacement, longitudinal displacement, and vertical displacement of the wheel center; S3.3: Measure the changes in the lateral displacement, longitudinal displacement, and vertical displacement of the grounding mark.

[0011] Furthermore, step S4 specifically includes: S4.1: Establish the differential equation of the outward tilt angle relative to the toe angle; S4.2: Establish the differential equations for the roll angle and the toe angle; S4.3: Establish calculation models for virtual kingpin inclination angle and virtual kingpin camber angle; S4.4: Using the lateral displacement, longitudinal displacement, and vertical displacement of the wheel center, as well as the lateral, longitudinal, and vertical displacements of the grounding mark, establish the calculation equations for the kingpin inclination offset, kingpin caster trailing distance, kingpin longitudinal offset, and kingpin lateral offset.

[0012] Furthermore, the virtual kingpin positioning parameters mentioned in step S5 include: kingpin inclination angle, kingpin inclination angle, kingpin inclination offset distance, kingpin inclination trailing distance, kingpin longitudinal offset distance, and kingpin lateral offset distance.

[0013] Furthermore, step S6 specifically includes: S6.1: Conduct sensitivity analysis, screen key hard points, and use the single-factor perturbation method to quantify the impact of hard point changes on the main pin characteristics through simulation calculation of the root mean square value. S6.2: Perform optimization processing, use the selected key hard points as design factors, generate multiple sets of hard point coordinate combination schemes, and determine the optimal hard point combination by weighted root mean square sorting method.

[0014] Furthermore, the control target inputs mentioned in step S7 include: steering angle, steering angular velocity, and steering angular acceleration.

[0015] Furthermore, step S8 specifically includes: S8.1: Set the test path, such as a twisted line path or a figure-eight path; S8.2: Establish the lateral force balance equations based on the motion-dynamics of the angular module; S8.3: Identify the center point of the turn; S8.4: Perform kingpin torque calculation and verification, and analyze the influence of kingpin tilt trailing distance variation on the return torque.

[0016] Furthermore, step S9 specifically includes: S9.1: After comparing the control target input with the steering signal feedback, the PID controller outputs the control torque; S9.2: Compare the control torque with the main pin feedback torque, and generate control current through the control law; S9.3: The steering motion is generated by using the control current to drive the angle module; S9.4: Detects steering motion through sensors, generates steering signal feedback, and completes closed-loop control.

[0017] Compared with the prior art, this patent application has the following advantages: 1. This invention significantly improves the accuracy of virtual kingpin kinematics calculation and positioning parameter calculation by combining multi-condition coupled analysis with instantaneous axis theory.

[0018] Specifically, addressing the shortcomings of traditional methods that simplify the kingpin to a fixed axis, this invention applies one or more coupled working conditions from wheel rotation angle, vertical wheel hop, lateral force, and longitudinal force in step S1 to trigger a composite motion of the virtual kingpin. Then, step S2 analyzes the hard-point architecture to accurately reproduce the instantaneous trajectory of the kingpin axis. Based on this, step S4 applies instantaneous axis theory to construct a system of differential equations. First, differential equations are established for the camber angle relative to the toe angle and the roll angle relative to the toe angle, relating the minute changes in wheel posture to the kingpin angle, thereby calculating the virtual kingpin caster and camber angles. Finally, using the wheel center and ground contact displacement data measured in step S3, the kingpin camber offset, kingpin caster trail, kingpin longitudinal offset, and kingpin lateral offset are calculated. This process abandons the static assumptions and approximations of traditional methods, and can accurately capture the instantaneous changes of the kingpin axis during wheel movement, providing an accurate data basis for subsequent positioning parameter calculations, thereby obtaining kingpin positioning parameters that are more in line with actual physical laws, and overcoming the inherent defects of traditional fixed-axis geometric solution algorithms under dynamic working conditions.

[0019] 2. This invention provides multi-dimensional data support for characterizing kingpin characteristics by establishing a quantitative mapping relationship between wheel positioning parameters and virtual kingpin spatial position.

[0020] Specifically, step S3 does not merely measure a single wheel angle, but systematically measures angular parameters including toe angle, camber angle, and roll angle, as well as wheel center displacement parameters including lateral, longitudinal, and vertical displacements of the wheel center, and contact point displacement parameters including lateral, longitudinal, and vertical displacements of the contact patch. These nine wheel parameters constitute a complete description of the wheel's six-degree-of-freedom attitude, transforming the abstract virtual kingpin motion into quantifiable and analyzable engineering data. The introduction of the contact patch displacement allows the design process to directly correlate with the movement trajectory of the tire contact point, providing key technical indicators for optimizing vehicle contact performance and ride comfort.

[0021] 3. This invention achieves efficient, multi-objective optimization design of corner module hard point structure by quantifying the influence of hard points on the characteristics of the main pin and adopting a weighted optimization strategy.

[0022] Specifically, step S6.1 employs a single-factor perturbation method for sensitivity analysis. By calculating the root mean square value of the difference between the simulated curve and the target curve, the influence weight of the coordinate changes of each hard point on the kingpin characteristics is objectively quantified. This allows for the selection of key hard points that play a decisive role in performance indicators as design variables, avoiding blind trial and error among numerous hard points and significantly improving optimization efficiency. Step S6.2 further uses the selected key hard points as design factors to generate multiple combination schemes for batch simulation. Differentiated weighting coefficients are assigned to multiple performance indicators such as kingpin caster trail and camber angle. The optimal hard point combination is determined by calculating the weighted root mean square ranking value. This method can balance and synergistically optimize multiple performance objectives such as handling stability and steering ease, ensuring the optimal overall performance of the final design scheme.

[0023] 4. In the process of calculating the steering return torque, this invention explicitly incorporates the dynamic variation characteristics of the kingpin caster trailing distance, thereby improving the accuracy of torque calculation.

[0024] Specifically, step S8 does not use fixed kingpin parameters when calculating the steering feedback torque. First, through steps S8.1 to S8.3, a standard test path is set, a lateral force balance equation is established, and the turning center is identified, constructing accurate vehicle kinematic and dynamic boundaries. Based on this, step S8.4, calculating the kingpin torque, uses the change in kingpin caster trailing distance as a key input, comprehensively analyzing the yaw torque, gravitational torque, and torque generated by lateral forces, thereby quantifying the real-time impact of changes in kingpin positioning parameters on the return torque. This allows the control algorithm to predict the return torque demand under different operating conditions based on the dynamic characteristics of the kingpin, providing accurate torque feedforward information for achieving refined steering control.

[0025] 5. This invention constructs a dual closed-loop control system with the kingpin feedback torque as the inner loop and the steering signal as the outer loop, which significantly improves the dynamic response speed and control accuracy of the steerable chassis angle module.

[0026] Specifically, step S9.1 first compares the target steering parameters with the steering signal feedback using a PID controller, outputting an initial control torque. Its innovation lies in step S9.2, which introduces the kingpin feedback torque as an inner-loop control quantity. The control torque output by the PID controller is compared with the real-time calculated kingpin feedback torque, and a precise control current is generated using a control law that includes reduction ratio, proportional, and derivative coefficients. This dual-closed-loop structure allows the system to not only correct based on the final steering result but also compensate in real-time for internal torque fluctuations caused by changes in kingpin parameters, significantly enhancing the system's robustness. Step S9.3 describes the execution process of the angle module using steering motion equations that include moment of inertia, steering damping, and steering stiffness, ensuring accurate modeling of the steering system's dynamic characteristics, effectively suppressing system overshoot and oscillations, and ultimately achieving high-precision following control of steering angle, angular velocity, and angular acceleration. Attached Figure Description

[0027] Figure 1 This is a flowchart of the virtual kingpin optimization method for the wire-controlled chassis corner module of the present invention.

[0028] Figure 2 The present invention relates to an angle module torque control method based on virtual master pin positioning parameters. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0030] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for optimizing and controlling the virtual kingpin of a drive-by-wire chassis corner module. The specific steps are as follows: S1: Apply the design conditions to trigger the virtual master pin to generate coupled motion. Applying a single or multiple coupled working conditions among wheel rotation angle 11, vertical wheel jump 12, lateral force 13, and longitudinal force 14, the virtual kingpin of the drive angle module generates motion.

[0031] S2: Analyze the coupled motion of the hardpoint architecture to obtain the kinematic and elastic kinematic properties of the master pin. S2.1: Coupled motion based on hard point architecture 2, extracting the kinematic characteristics of the virtual master pin 21.

[0032] S2.2: Coupled motion based on hard point architecture 2, extracting the elastic kinematic properties of the virtual master pin 22.

[0033] S3: Measure wheel alignment parameters and quantify wheel changes caused by kingpin movement. S3.1: Measure the changes in toe angle 31, camber angle 32, and roll angle 33; S3.2: Measure the changes in the lateral displacement 34, longitudinal displacement 35, and vertical displacement 36 of the wheel center; S3.3: Measure the changes in the lateral displacement 37, longitudinal displacement 38, and vertical displacement 39 of the grounding imprint.

[0034] S4: Establish differential equations and solve for the virtual kingpin positioning parameters. S4.1: Establish the differential equation of the outward tilt angle 32 relative to the toe angle 31; (1) In the formula, Toe angle of the wheel, Outward tilt angle, Inclination angle, This is the backslope angle.

[0035] S4.2: Establish the differential equations for the roll angle 33 and the toe angle 31. (2) In the formula, This is the wheel rolling angle.

[0036] S4.3: Establish calculation models for virtual kingpin inclination angle 51 and virtual kingpin inclination angle 52.

[0037] (3) S4.4: Using the lateral displacement 34, longitudinal displacement 35, and vertical displacement 36 of the wheel center, as well as the lateral displacement 37, longitudinal displacement 38, and vertical displacement 39 of the grounding mark, establish the equations for the kingpin inclination offset 53, kingpin caster trailing distance 54, kingpin longitudinal offset 55, and kingpin lateral offset 56. (4) S5: Solve the virtual kingpin caster angle 51, kingpin inclination angle 52, kingpin inclination offset 53, kingpin caster trailing distance 54, kingpin longitudinal offset 55, and kingpin lateral offset 56 based on the differential equation established in S4.

[0038] S6: Optimize the hardpoint structure and select the optimal value for the master pin positioning parameters. S6.1: Perform sensitivity analysis to screen key hard points. Using a single-factor perturbation method, based on the initial hard point coordinates, only one hard point's coordinate in a single direction (e.g., X, Y, or Z) is changed each time, with the change set to a small increment (e.g., ±5mm). The coordinates of other hard points and simulation parameters remain unchanged, and suspension K&C simulation is performed. The virtual kingpin variation curve obtained from the simulation is extracted and compared with the target curve. The root mean square (RMS) value of the difference between the two is calculated to quantify the impact of the hard point change on the kingpin characteristics. The smaller the RMS value, the higher the degree of fit between the simulation curve and the target curve after the hard point adjustment, i.e., the higher the sensitivity of the hard point change to the virtual kingpin. The sensitivity calculation formula is as follows: (5) In the formula, The root mean square value, The calculated value is from the perspective of main sales. The target value from the perspective of primary sales.

[0039] S6.2: Perform optimization processing. Using the key hard points identified through sensitivity analysis as design factors, and their adjustable range as factor levels, generate multiple sets of hard point coordinate combination schemes, and perform batch simulation calculations. Calculate the root mean square value of the virtual kingpin positioning parameters and the target curve for each scheme, assign corresponding weighting coefficients to each index, and calculate the weighted root mean square ranking value. By comparing the ranking results of all experimental schemes, determine the optimal hard point combination.

[0040] S7: Input control target, set steering parameters like Figure 2 As shown, the cornering module steering control function is verified. Control input 7 includes steering angle 71, steering angular velocity 72, and steering angular acceleration 73.

[0041] S8: Calculate steering feedback torque and evaluate self-centering characteristics. S8.1: Set the test path, such as a twisted line path or a figure-eight path; S8.2: Based on the motion-dynamics of the angular module, establish the lateral force balance equations; (6) In the formula, For the front axle mass, For rear axle mass, This represents lateral acceleration. The steering angles of the inner and outer wheels of the front axle are respectively... and The lateral forces of the inner and outer wheels of the front axle are respectively , The steering angles of the inner and outer wheels on the rear axle are respectively... and The lateral forces of the inner and outer wheels of the rear axle are respectively , .

[0042] S8.3: Identify the center point of the turn; S8.4: Calculate and verify the kingpin torque, and analyze the impact of changes in kingpin caster trailing distance on the return torque. Kingpin feedback torque. The calculation formula is: ·············(7) In the formula, For the deflection moment, For gravitational torque, This represents the lateral force moment. S9: Executes closed-loop control of the angle module, outputting drive current. S9.1: After comparing the steering angle 71, steering angular velocity 72, and steering angular acceleration 73 in the control target input 7 with the steering signal feedback 95, the PID controller 91 processes the data and outputs the control torque 93. S9.2: Compare the control torque 93 with the main pin feedback torque 92, and generate the control current 94 through the control law; ·············· (8) In the formula, It's the reduction ratio. Motor output torque, It is the proportionality coefficient. These are the differential coefficients. S9.3: The steering motion 86 is generated by using the control current 94 to drive the angle module 85; ········(9) In the formula, It is the moment of inertia. It is steering damping. It is steering stiffness. Virtual main sales change torque, For steering angle, For steering angular velocity, This is the steering angle acceleration.

[0043] S9.4: The steering motion 86 is detected by the sensor, and a steering signal feedback 95 is generated to complete the closed-loop control.

[0044] The technical solutions and technical details disclosed in the embodiments of this invention are merely illustrative of the inventive concept of this invention and do not constitute a limitation on the technical solutions of this invention. Any conventional changes, substitutions, or combinations made to the technical details disclosed in the embodiments of this invention have the same inventive concept as this invention and are within the protection scope of the claims of this invention.

Claims

1. A method for optimizing and controlling the virtual kingpin of a drive-by-wire chassis corner module, characterized in that, Includes the following steps: S1: Apply the design conditions to trigger the virtual master pin to generate coupled motion; S2: Analyze the coupled motion of the hardpoint architecture to obtain the kinematic and elastic kinematic properties of the main pin; S3: Measure wheel alignment parameters and quantify wheel changes caused by kingpin movement; S4: Establish the differential equation and solve for the virtual kingpin positioning parameters; S5: Solve the virtual kingpin positioning parameters based on the differential equation established in S4; S6: Optimize the hard point structure and select the optimal value of the main pin positioning parameters; S7: Input control target and set steering parameters; S8: Calculate steering feedback torque and evaluate self-centering characteristics; S9: Executes closed-loop control of the angle module, outputting drive current.

2. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis corner module according to claim 1, characterized in that, The design conditions described in step S1 include a single condition or multiple coupled conditions among wheel angle, vertical wheel bounce, lateral force, and longitudinal force.

3. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis angle module according to claim 1, characterized in that, Step S2 specifically includes: S2.1: Based on the coupled motion of the hard-point architecture, extract the kinematic characteristics of the virtual master pin; S2.2: Based on the coupled motion of the hard-point architecture, extract the elastic kinematic properties of the virtual master pin.

4. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis angle module according to claim 1, characterized in that, Step S3 specifically includes: S3.1: Measure the changes in toe angle, camber angle, and roll angle; S3.2: Measure the changes in the lateral displacement, longitudinal displacement, and vertical displacement of the wheel center; S3.3: Measure the changes in the lateral displacement, longitudinal displacement, and vertical displacement of the grounding mark.

5. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis angle module according to claim 1, characterized in that, Step S4 specifically includes: S4.1: Establish the differential equation of the outward tilt angle relative to the toe angle; S4.2: Establish the differential equations for the roll angle and the toe angle; S4.3: Establish calculation models for virtual kingpin inclination angle and virtual kingpin camber angle; S4.4: Using the lateral displacement, longitudinal displacement, and vertical displacement of the wheel center, as well as the lateral, longitudinal, and vertical displacements of the grounding mark, establish the calculation equations for the kingpin inclination offset, kingpin caster trailing distance, kingpin longitudinal offset, and kingpin lateral offset.

6. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis corner module according to claim 1, characterized in that, The virtual kingpin positioning parameters mentioned in step S5 include: kingpin caster angle, kingpin inclination angle, kingpin inclination offset distance, kingpin caster trailing distance, kingpin longitudinal offset distance, and kingpin lateral offset distance.

7. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis angle module according to claim 1, characterized in that, Step S6 specifically includes: S6.1: Conduct sensitivity analysis, screen key hard points, and use the single-factor perturbation method to quantify the impact of hard point changes on the main pin characteristics through simulation calculation of the root mean square value. S6.2: Perform optimization processing, use the selected key hard points as design factors, generate multiple sets of hard point coordinate combination schemes, and determine the optimal hard point combination by weighted root mean square sorting method.

8. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis corner module according to claim 1, characterized in that, The control target inputs mentioned in step S7 include: steering angle, steering angular velocity, and steering angular acceleration.

9. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis corner module according to claim 1, characterized in that, Step S8 specifically includes: S8.1: Set the test path; S8.2: Establish the lateral force balance equations based on the motion-dynamics of the angular module; S8.3: Identify the center point of the turn; S8.4: Perform kingpin torque calculation and verification, and analyze the influence of kingpin tilt trailing distance variation on the return torque.

10. The method for optimizing and controlling the virtual kingpin of the drive-by-wire chassis angle module according to claim 1, characterized in that, Step S9 specifically includes: S9.1: After comparing the control target input with the steering signal feedback, the PID controller outputs the control torque; S9.2: Compare the control torque with the main pin feedback torque, and generate control current through the control law; S9.3: The steering motion is generated by using the control current to drive the angle module; S9.4: Detects steering motion through sensors, generates steering signal feedback, and completes closed-loop control.

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