Vehicle steering control method and system based on zero centroid deflection

Abstract

The invention discloses a vehicle steering control method and system based on zero centroid deflection, and belongs to the technical field of vehicle steering control. The control method comprises the steps that on the basis of the Ackerman steering principle and the zero centroid deflection principle, a mathematical relation model of a multi-axle steering angle and the vehicle speed is built, the centroid side slip angle is set to be zero, and the vehicle speed is set to be zero; the distance between the instantaneous rotation center and the mass center is calculated, and the target steering angle of each steering axle is determined; filtering the first-axle steering angle signal to obtain a steering angle signal after vibration elimination and noise reduction; inputting the filtered steering angle signal into a cascade controller to obtain an output signal corresponding to the cascade controller; and according to the target steering angle and an output signal corresponding to the cascade controller, a direction switching valve in the vehicle electro-hydraulic auxiliary steering system is controlled, and a driven axle hydraulic cylinder is driven to achieve steering at the target steering angle. By the adoption of the control method, the instantaneous rotation center can always pass through the mass center when the vehicle steers, so that vibration of the vehicle body is avoided, and the lateral stability during high-speed driving is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle steering control technology, and in particular to a vehicle steering control method and system based on zero center of mass yaw. Background Technology

[0002] All-terrain truck cranes are high-performance lifting machines that combine the features of truck cranes and off-road cranes. They can be quickly moved and travel long distances like truck cranes, while also having multi-axle drive and all-wheel steering functions. They have multiple steering methods and can turn flexibly in confined spaces. They have a large ground clearance and strong climbing ability, which can meet the requirements for operation on rugged or muddy sites.

[0003] The steering performance of a vehicle directly affects its maneuverability, handling stability, and fuel economy. When a vehicle is turning, due to its large inertia and long moment of inertia, the steering wheels at the rear axle will vibrate, and the vehicle body will become unstable. There is a misalignment between the vehicle's steering angle and its attitude angle, causing body vibration, especially at high speeds where lateral stability is low, posing safety hazards. This also fails to meet the handling requirements of vehicles at different speeds in confined spaces and with multiple steering modes. Summary of the Invention

[0004] To address some or all of the technical problems existing in the prior art, this invention provides a vehicle steering control method and system based on zero center of mass yaw. When the vehicle is steering, by controlling the correspondence between the rotation angles of the driven wheel and the driving wheel, the sideslip angle at the center of mass is made zero, ensuring that the instantaneous rotation center of the vehicle always passes through the center of mass during steering. This avoids vehicle body oscillation, improves lateral stability at high speeds, achieves the coincidence of the steering angle and the vehicle body attitude angle, eliminates oscillations caused by lateral inertial forces, and improves the accuracy and reliability of the steering system. This addresses the operational needs of all-terrain truck cranes at different speeds in confined spaces and with multiple steering modes.

[0005] The technical solution of the present invention is as follows:

[0006] Firstly, a vehicle steering control method based on zero center-of-mass yaw is provided, including:

[0007] Based on the Ackermann steering principle and the zero center of mass yaw principle, a mathematical model of the relationship between multi-axle steering angle and vehicle speed is constructed. The center of mass yaw angle is set to zero. The distance between the instantaneous rotation center and the center of mass is calculated to determine the target steering angle of each steering axle.

[0008] The steering angle signal of the first bridge is filtered to obtain a steering angle signal after vibration elimination and noise reduction.

[0009] The filtered steering angle signal is input into the cascade controller to obtain the corresponding output signal of the cascade controller;

[0010] Based on the target steering angle and the corresponding output signal of the cascade controller, the direction switching valve in the vehicle's electro-hydraulic assisted steering system is controlled to drive the driven axle hydraulic cylinder to achieve steering at the target steering angle.

[0011] Furthermore, in the above-mentioned vehicle steering control method based on zero center of mass yaw, the mathematical relationship model between the multi-axle steering angle and vehicle speed includes a two-degree-of-freedom motion model of the multi-axle vehicle.

[0012] Furthermore, in the above-mentioned vehicle steering control method based on zero center of mass yaw, the mathematical relationship model between the multi-axle steering angle and vehicle speed includes:

[0013] The lateral force equilibrium equations include: ; The yaw moment balance equation includes: ; in, Indicates the centroid sideslip angle. The axle number indicates the number of axles on a vehicle. This indicates the total number of axles on the vehicle. Indicates the first Axle tire lateral stiffness, Indicates yaw rate. Indicates real-time vehicle speed. Indicates the first The longitudinal distance from the bridge to the center of mass. Indicates the mass of the vehicle. Indicates the steering angle of the first axle steering wheel. It represents the instantaneous distance between the center of rotation and the center of mass.

[0014] Furthermore, in the above-mentioned vehicle steering control method based on zero center of mass yaw, the center of mass sideslip angle is set to zero, the distance between the instantaneous rotation center and the center of mass is calculated, and the target steering angle of each steering axle is determined by the following formula: .

[0015] Furthermore, in the above-mentioned vehicle steering control method based on zero centroid yaw, filtering the steering angle signal of the axle includes:

[0016] According to the vehicle Theoretical steering angle state variables and linear transformation matrix at time t, predicting vehicle speed. Theoretical turning angle at a given moment; Based on vehicle Given the error covariance matrix and linear transformation matrix at time points, calculate the vehicle's... Prior covariance at time; According to the vehicle Calculate the Kalman gain matrix based on the prior covariance and measurement noise covariance at each time step. Combination Given the vehicle angle measurement and prior estimate at each time point, output the optimal state estimate. Correction The posterior covariance matrix at time step, from Start by repeatedly predicting vehicles. Time-theoretic turning angle to correction The steps for calculating the posterior covariance matrix at time step are used to output the optimal estimated value of the filtered steering angle in real time.

[0017] Furthermore, in the above-mentioned vehicle steering control method based on zero center of mass yaw, the theoretical steering angle at any given time is predicted and determined by the following formula, based on the theoretical steering angle state variables and the linear transformation matrix of the vehicle at that time: ; in, Indicates vehicle Theoretical turning point of time, Represents a linear transformation matrix. Indicates vehicle The theoretical turning point of time, express Time-matter noise; Based on vehicle Given the error covariance matrix and linear transformation matrix at time points, calculate the vehicle's... The prior covariance at time step is determined by the following formula: ; in, Indicates vehicle Prior covariance at time, express The error covariance matrix at time t. Represents the linear transformation matrix transpose, express The process noise covariance matrix at time step; According to the vehicle The prior covariance and measurement noise covariance at time points are used to calculate the Kalman gain matrix, which is determined by the following formula: ; in, Represents the Kalman gain matrix. Table of noise covariance, This represents the inverse operation of a matrix; Combination The optimal state estimate is calculated using the vehicle angle measurement and prior estimate at each moment, and is determined by the following formula: ; in, This represents the optimal state estimate. express Prior estimates of time, express Vehicle angle measurement at any given time; Correction The posterior covariance matrix at time step 1 is calculated using the following formula: ; in, Indicates correction The posterior covariance matrix at time step 1. Represents the identity matrix.

[0018] Furthermore, in the above-mentioned vehicle steering control method based on zero-centroid yaw, the filtered steering angle signal is input into the cascaded controller, and the output signal of the cascaded controller includes: The filtered steering angle signal is input into the position loop proportional control to generate a speed command signal; The filtered steering angle signal is input into the speed loop proportional-integral control to generate a current command signal; The filtered steering angle signal is input into the current loop proportional-integral control to generate the actuator drive signal.

[0019] Furthermore, in the above-described vehicle steering control method based on zero-center-of-mass yaw, the speed command signal includes: ; in, This indicates the speed command signal output by the position loop. This represents the proportional gain coefficient of the position loop. This indicates the angular deviation between the position loop input command position signal and the sensor signal; The current command signal includes: ; in, This indicates the current command signal output by the speed loop. This represents the proportional gain coefficient of the velocity loop. This represents the integral time constant of the velocity loop. This indicates the deviation between the speed input command speed and the feedback speed of the speed loop. The time integral representing the velocity deviation. Indicates the upper limit of time integration. Represents a time variable; The actuator drive signal includes: ; in, This indicates the actuator drive signal output by the current loop. This represents the proportional gain coefficient of the current loop. This represents the integral time constant of the current loop. This indicates the current deviation between the commanded current and the actual current. The time integral represents the current deviation.

[0020] Secondly, a vehicle steering control system based on zero center of mass yaw is provided, including:

[0021] The model building module is used to construct a mathematical relationship model between the steering angle of multiple axles and the vehicle speed based on the Ackermann steering principle and the zero center of mass yaw principle. The center of mass yaw angle is set to zero, the distance between the instantaneous rotation center and the center of mass is calculated, and the target steering angle of each steering axle is determined.

[0022] A noise reduction and filtering module is used to filter the steering angle signal of a bridge to obtain a steering angle signal after vibration elimination and noise reduction.

[0023] A cascaded control module is used to input the filtered steering angle signal into the cascaded controller to obtain the corresponding output signal of the cascaded controller.

[0024] The steering control module is used to control the direction switching valve in the vehicle's electro-hydraulic assisted steering system according to the target steering angle and the output signal corresponding to the cascade controller, and drive the driven axle hydraulic cylinder to achieve steering at the target steering angle.

[0025] The main advantages of the technical solution of this invention are as follows:

[0026] By combining the target steering angle calculated by the mathematical model with the output signal of the cascaded controller, the direction switching valve in the vehicle's electro-hydraulic assisted steering system is controlled, driving the hydraulic cylinder of the driven axle to achieve precise steering. Specifically, when steering in confined spaces, the system can calculate the small-radius steering angle of each axle based on the model, and precisely control the steering of the driven axle through the cascaded controller to achieve flexible steering. At high speeds, the system can automatically adjust the steering angle ratio to ensure that the sideslip angle is zero, improving driving stability. In addition, the electro-hydraulic assisted steering system combines the micro-motion of the hydraulic system with the precision of the electronic control system, which can effectively suppress inertial impact during steering, further enhancing the vehicle's handling adaptability under complex working conditions, meeting the control requirements of all-terrain cranes for multiple steering modes and different speeds, and significantly improving the overall vehicle's maneuverability and economic efficiency. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and constitute a part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0028] Figure 1 A schematic flowchart of a vehicle steering control method based on zero center of mass yaw provided in an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of the process for filtering the steering angle signal of a single axle in the vehicle steering control method based on zero center of mass yaw provided in an embodiment of the present invention.

[0030] Figure 3 A schematic diagram of the electro-hydraulic assisted steering system in the vehicle steering control method based on zero center of mass yaw provided in an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram illustrating the working principle of the Kalman filter in the vehicle steering control method based on zero centroid yaw provided in an embodiment of the present invention.

[0032] Figure 5 A schematic diagram of the steering system in the vehicle steering control method based on zero center of mass yaw provided in an embodiment of the present invention;

[0033] Figure 6 The schematic diagram of the transfer function of the steering system in the vehicle steering control method based on zero centroid yaw provided in the embodiment of the present invention;

[0034] Figure 7 This is a schematic diagram of a vehicle steering control system based on zero center of mass yaw provided in an embodiment of the present invention.

[0035] Explanation of reference numerals in the attached figures:

[0036] 100. Model building module; 200. Noise reduction and filtering module; 300. Cascade control module; 400. Steering control module. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and 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.

[0038] The technical solutions provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0039] Specifically, the purpose of this invention is to provide a vehicle steering control method to improve the accuracy and reliability of the steering system, thereby addressing the handling requirements of vehicles at different speeds in confined spaces and with multiple steering modes. This method is particularly suitable for heavy-duty vehicles, such as new all-terrain truck cranes.

[0040] It is conceivable that various uncontrollable factors will arise during vehicle operation due to road conditions and speed, leading to inconsistent data acquisition during modeling and analysis, thus affecting the accuracy of the results. Therefore, in this embodiment of the invention, to ensure more accurate calculations by the established model, the following assumptions are made before establishing the mathematical model:

[0041] (1) Pressure loss along the pipeline is not considered; (2) The chassis oil supply system is regarded as a constant pressure oil source, that is, the oil pressure P is constant; (3) The temperature and density of the hydraulic oil are constant; (4) The nonlinear effect of the valve is not considered; (5) The vehicle is assumed to be a rigid structure, and the influence of the left and right tires of each steering axle wheel and the suspension system on the vehicle is not considered; (6) The influence of external factors such as wind speed and air movement on the vehicle is ignored; (7) The influence of the steering wheel, steering gear, steering mechanism and other steering systems on the steering system is ignored, and the steering angle of the first axle wheel is directly used as the input.

[0042] The following uses a truck crane as an example to describe in detail the technical solution provided by the embodiments of the present invention.

[0043] Firstly, as attached Figure 1 -Appendix Figure 4 As shown, this embodiment of the invention provides a vehicle steering control method based on zero center-of-gravity yaw. This method enables the vehicle to achieve a sideslip angle of 0 at the center of gravity by controlling the correspondence between the driven and driving wheel angles during steering. This ensures that the instantaneous rotation center of the vehicle always passes through the center of gravity during steering, thereby avoiding vehicle body oscillation, improving lateral stability at high speeds, achieving the coincidence of the steering angle and the vehicle attitude angle, and eliminating oscillations caused by lateral inertial forces. The method includes the following steps S1-S4:

[0044] Step S1: Based on the Ackermann steering principle and the zero center of mass yaw principle, construct a mathematical relationship model between the multi-axle steering angle and vehicle speed, set the center of mass yaw angle to zero, calculate the distance between the instantaneous rotation center and the center of mass, and determine the target steering angle of each steering axle.

[0045] Specifically, there are three steady-state steering characteristics when a vehicle turns: understeer, neutral steering, and oversteer. Appropriate understeer is beneficial to the stability of the vehicle. Therefore, in this embodiment of the invention, the mathematical relationship model between the multi-axle steering angle and the vehicle speed is set as a two-degree-of-freedom motion model of a multi-axle vehicle.

[0046] Specifically, in the vehicle steering control method based on zero center of mass yaw of the present invention, the mathematical relationship model between the multi-axle steering angle and vehicle speed includes:

[0047] The lateral force equilibrium equations include: ; The yaw moment balance equation includes: ; in, Indicates the centroid sideslip angle. The axle number indicates the number of axles on a vehicle. This indicates the total number of axles on the vehicle. Indicates the first Axle tire lateral stiffness, This represents the yaw rate, which is the angular velocity of the vehicle's rotation about its vertical axis. Indicates real-time vehicle speed. Indicates the first The longitudinal distance from the bridge to the center of mass. Indicates the mass of the vehicle. This indicates the steering angle of the first axle wheel, that is, the deflection angle of the first axle wheel. It represents the instantaneous distance between the center of rotation and the center of mass.

[0048] Specifically, setting the sideslip angle of the center of gravity to zero, the distance between the instantaneous rotation center and the center of gravity is calculated, and the target steering angle of each steering axle is determined by the following formula: .

[0049] Therefore, in this embodiment of the invention, after Ackermann steering correction, it can be concluded that:

[0050] The relationship between the inner and outer wheel rotation angles of the same bridge is as follows: ;

[0051] The relationship between the rotation angles of the wheels on the same side of the bridge is as follows: ;

[0052] in, Indicates the first The steering angle of the outer wheel of the axle, such as the wheel deflection angle on the side furthest from the steering center. Indicates the first The outer wheel of the bridge turns at an angle. Indicates the first The steering angle of the inner wheel of the bridge, such as the wheel deflection angle on the side closer to the steering center. Indicates the first The inner wheel turning angle of the bridge, The geometric parameters representing the steering trapezoid are constants related to the dimensions of the steering linkage mechanism. Indicates the first The longitudinal distance from the bridge to the instantaneous center of rotation. Indicates the first The longitudinal distance from the bridge to the instantaneous center of rotation.

[0053] Therefore, in this embodiment of the invention, it is possible to determine the vehicle's real-time speed. Dynamically calculate the target rotation angle of each bridge to ensure that the center of gravity trajectory is free from sideslip.

[0054] In summary, in this embodiment of the invention, based on the Ackermann principle and combined with the vehicle's two-degree-of-freedom motion model, a mathematical model of the steering angle and vehicle speed of each axle is established. This mathematical model is based on the assumptions that pressure loss along the pipeline is not considered, the chassis oil supply system is a constant pressure oil source, the hydraulic oil temperature and density are constant, the nonlinear effect of the valve is not considered, the vehicle is a rigid structure, and the influence of external factors and steering wheel and other mechanisms on the steering system is ignored. Taking the wheel angle of one axle as input, the relationship between different vehicle speeds and steering angles on the same side and opposite side of the axle is obtained by combining the above formulas, providing a theoretical basis for subsequent steering control.

[0055] Step S2: Filter the steering angle signal of the first bridge to obtain the steering angle signal after vibration elimination and noise reduction.

[0056] Specifically, filtering the steering angle signal of the bridge includes the following steps S21-S25:

[0057] Step S21: According to the vehicle Theoretical steering angle state variables and linear transformation matrix at time t, predicting vehicle speed. Theoretical turning angle at a given moment;

[0058] Specifically, according to the vehicle Theoretical steering angle state variables and linear transformation matrix at time t, predicting vehicle speed. The theoretical rotation angle at any given time is determined by the following formula:

[0059] ;

[0060] in, Indicates vehicle Theoretical turning point of time, Represents a linear transformation matrix. Indicates vehicle The theoretical turning point of time, express Process noise at any given moment.

[0061] Step S22: Based on vehicle Given the error covariance matrix and linear transformation matrix at time points, calculate the vehicle's... Prior covariance at time;

[0062] Specifically, based on vehicle Given the error covariance matrix and linear transformation matrix at time points, calculate the vehicle's... The prior covariance at time step is determined by the following formula:

[0063] ;

[0064] in, Indicates vehicle Prior covariance at time, express The error covariance matrix at time t. Represents the linear transformation matrix transpose, express The process noise covariance matrix at time step;

[0065] Step S23: According to the vehicle Calculate the Kalman gain matrix based on the prior covariance and measurement noise covariance at each time step.

[0066] Specifically, according to the vehicle The prior covariance and measurement noise covariance at time points are used to calculate the Kalman gain matrix, which is determined by the following formula:

[0067] ;

[0068] in, Represents the Kalman gain matrix. Table of noise covariance, This represents the inverse operation of a matrix.

[0069] Step S24: Combining Given the vehicle angle measurement and prior estimate at each time point, output the optimal state estimate.

[0070] Specifically, in combination The optimal state estimate is calculated using the vehicle angle measurement and prior estimate at each moment, and is determined by the following formula:

[0071] ;

[0072] in, This represents the optimal state estimate. express Prior estimates of time, express Vehicle angle measurement at any given time.

[0073] Step S25: Correction The posterior covariance matrix at time step, from Start by repeatedly predicting vehicles. Time-theoretic turning angle to correction The steps for calculating the posterior covariance matrix at time step are used to output the optimal estimated value of the filtered steering angle in real time.

[0074] Specifically, correct The posterior covariance matrix at time step 1 is calculated using the following formula:

[0075] ;

[0076] in, Indicates correction The posterior covariance matrix at time step 1. Represents the identity matrix.

[0077] Therefore, in this embodiment of the invention, a Kalman filter is used to filter the input bridge corner signal. This is achieved through a recursive process of establishing a priori estimation model, updating the prediction covariance, calculating the filter gain, and updating the state estimate and error covariance. Initially, by combining the initial parameters and the angle measurements taken by the angle sensor, we obtain... The optimal estimate of the rotation angle at any given time eliminates noise data caused by vibration and sensor bias, thereby improving the accuracy of the input signal.

[0078] Step S3: Input the filtered steering angle signal into the cascade controller to obtain the corresponding output signal of the cascade controller.

[0079] In some optional implementations of this embodiment, the controller includes a PID controller.

[0080] Specifically, the filtered steering angle signal is input into the cascade controller, and the output signal of the cascade controller includes:

[0081] The filtered steering angle signal is input into the position loop proportional control to generate a speed command signal;

[0082] Specifically, the speed command signal includes: ;

[0083] in, This indicates the speed command signal output by the position loop. This represents the proportional gain coefficient of the position loop. This indicates the angular deviation between the position ring input command position signal and the sensor signal, i.e., the angular deviation between the target rotation angle and the actual rotation angle.

[0084] The filtered steering angle signal is input into the speed loop proportional-integral control to generate a current command signal;

[0085] Specifically, the current command signal includes: ;

[0086] in, This indicates the current command signal output by the speed loop. This represents the proportional gain coefficient of the velocity loop. This represents the integral time constant of the velocity loop. This indicates the deviation between the speed input command speed and the feedback speed of the speed loop. The time integral representing the velocity deviation. Indicates the upper limit of time integration. Represents a time variable;

[0087] The filtered steering angle signal is input into the current loop proportional-integral control to generate the actuator drive signal.

[0088] Specifically, the actuator drive signals include: ;

[0089] in, This indicates the actuator drive signal output by the current loop. This represents the proportional gain coefficient of the current loop. This represents the integral time constant of the current loop. This indicates the current deviation between the commanded current and the actual current. The time integral represents the current deviation.

[0090] Therefore, in this embodiment of the invention, the filtered angular displacement and angular velocity values ​​are used as inputs, and a three-loop control algorithm including a position loop, a speed loop, and a current loop is designed based on the series PID principle. The position loop uses a P controller to avoid increasing hysteresis due to its lower response speed than the inner loop. The speed loop uses a PI controller to suppress noise amplification. The current loop uses a PI controller to quickly respond to current deviations and eliminate steady-state errors, thereby achieving hierarchical and precise control of the steering system's current, speed, and position.

[0091] For example, such as Figure 5 As shown, Figure 5 This is a schematic diagram of the steering system in the vehicle steering control method based on zero center of mass yaw provided in this embodiment of the invention. From the inside out, it consists of a current loop, a speed loop, and a position loop, which control the current, speed, and position of each steering axle direction switching valve, respectively. Their respective control methods include:

[0092] Outermost position loop input: Target rotation angle Controller → Output speed command It can quickly respond to angle deviations and avoid integral lag;

[0093] Mid-level speed loop input: speed command Controller → Output Current Command It can suppress sudden acceleration caused by hydraulic shock, that is, the speed loop PI control eliminates steady-state error.

[0094] Innermost current loop input: Current command The controller outputs a valve core drive signal, ensuring accurate current tracking and a valve core displacement error of <0.1mm.

[0095] Execution feedback (← arrow feedback): Actual turning angle →Position loop feedback forms a closed-loop control, which has been verified to reduce steering angle error by 40%.

[0096] Furthermore, since the position loop is located on the outside of the control system, its response speed is lower than that of the inner loops (velocity loop and current loop). Introducing the integral term (I) would increase hysteresis. The inputs of angle and angular velocity are generally ramp curves; adding a D controller would not effectively reduce steady-state error. Therefore, in this embodiment of the invention, the position loop is designed as a P controller, i.e. .

[0097] The speed loop is located inside the control system. The main component of the D controller is the differential term, i.e., the acceleration term. In actual speed control, the acceleration does not change frequently. Therefore, the D controller does not play a significant role in speed control. Moreover, if there is a noise signal, the D controller will further amplify the noise signal, causing further interference. Therefore, the speed loop is designed as a PI controller. .

[0098] The current loop is located in the innermost loop of the control system. A P controller responds more quickly to current deviations, while an I controller effectively eliminates steady-state errors and ensures long-term current stability. Therefore, the current loop is designed as a PI controller. .

[0099] Therefore, combined Figure 6 As shown, Figure 6 This is a schematic diagram of the transfer function of the steering system in the vehicle steering control method based on zero centroid yaw provided in an embodiment of the present invention. Figure 6 The signal transmission chain in the middle is oriented in the direction of the arrow "→", and includes:

[0100] Position command input →Position loop P control →Speed ​​command This ensures that there is no overshoot under ramp input, meaning that the angle input in actual application is a ramp curve.

[0101] Speed-to-current conversion: speed command →Speed ​​loop PI control →Current loop control → (Current-displacement transfer function) reduces hydraulic cylinder speed fluctuation by 35%; among which, This represents the integral time constant of the velocity loop. This represents the integral time constant of the current loop. This represents the Laplace operator.

[0102] Execution output → Hydraulic cylinder displacement →Transfer function of hydraulic cylinder displacement to steering system angle →Actual location Actual angular velocity Actual turning angle This reduces tire wear by 30% during multi-axle cooperative steering.

[0103] Furthermore, the embodiments of the present invention also include disturbance suppression (←arrow feedback): load disturbance → current loop PI control compensation → maintain constant current output, so that the steering torque fluctuation of the vehicle on rough roads is <5%.

[0104] Step S4: Based on the target steering angle and the output signal corresponding to the cascade controller, control the direction switching valve in the vehicle's electro-hydraulic assisted steering system to drive the driven axle hydraulic cylinder to achieve the target steering angle.

[0105] Specifically, different steering modes have different steering angles at different vehicle speeds. The corresponding steering angles can be obtained through calculation using a steering model. Then, the steering angle of the steering axle is filtered to remove noise signals caused by vibration and sensor sensitivity. Finally, a PID closed-loop controller controls the steering from the steering axle. The schematic diagram is shown below. Figure 3 As shown. Therefore, in this embodiment of the invention, the established all-terrain steering mathematical model is combined with the improved PID closed-loop controller to form a composite control system for driven wheel steering control. According to different steering modes and vehicle speeds, the corresponding steering angle is calculated through the mathematical model. After filtering, the steering of the steering axle is controlled by the PID closed-loop controller to suppress steering oscillations and vehicle instability, achieving the control objective of a zero center of gravity sideslip angle. This meets the operation requirements of all-terrain cranes at different vehicle speeds in confined spaces and with multiple steering modes.

[0106] Secondly, this invention also provides a vehicle steering control system based on zero center of mass yaw, the system comprising: a model building module 100, a noise reduction and filtering module 200, a cascaded control module 300, and a steering control module 400, wherein:

[0107] The model building module 100 is used to construct a mathematical relationship model between the steering angle of multiple axles and vehicle speed based on the Ackermann steering principle and the zero center of mass yaw principle. It sets the center of mass yaw angle to zero, calculates the distance between the instantaneous rotation center and the center of mass, and determines the target steering angle of each steering axle. The noise reduction and filtering module 200 is used to filter the steering angle signal of one axle to obtain a steering angle signal after vibration elimination and noise reduction. The cascade control module 300 is used to input the filtered steering angle signal into the cascade controller to obtain the corresponding output signal of the cascade controller. The steering control module 400 is used to control the direction switching valve in the vehicle's electro-hydraulic assisted steering system according to the target steering angle and the corresponding output signal of the cascade controller, and drive the hydraulic cylinder of the driven axle to achieve the target steering angle.

[0108] In summary, the control method and control system of this invention, by forcing the center of gravity sideslip angle to 0, ensures that the instantaneous rotation center always passes through the center of gravity, effectively suppressing the lateral inertial force coupling during vehicle steering, reducing yaw rate fluctuations by more than 45%. Based on the Ackermann principle, dynamic steering angle correction for inner and outer wheels on the same axle and wheels on the same side of different axles ensures that all wheels rotate around the same instantaneous center, reducing tire wear by more than 30%. Real-time optimal estimation of the steering angle sensor signal for one axle filters out vibration and sensor noise, improving the signal-to-noise ratio by 8dB and reducing the steering angle measurement error from ±1.2° to ±0.5°. The position loop (P control) provides rapid response to angle deviations, avoiding integral lag; the speed loop (PI control) suppresses hydraulic shock disturbances; and the current loop (PI control) ensures the steady-state accuracy of the actuator, resulting in a steering system response delay of <100ms. By dynamically adjusting the relationship between steering angle and vehicle speed using a zero-center-of-gravity model, it balances low-speed agility with high-speed stability, reducing the turning radius in tight spaces by 15%. It supports various steering modes such as crab steering and figure-eight steering, ensuring that the electro-hydraulic system drive error is less than 0.1mm. Precise Ackermann geometry matching reduces tire lateral slippage and extends tire life. Closed-loop control reduces frequent valve core adjustments, resulting in a 20% reduction in energy consumption.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Additionally, the terms "front," "back," "left," "right," "upper," and "lower" in this document refer to the placement shown in the accompanying drawings.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle steering control method based on zero-center-of-mass yaw, characterized in that, include: Based on the Ackermann steering principle and the zero center of mass yaw principle, a mathematical model of the relationship between multi-axle steering angle and vehicle speed is constructed. The center of mass yaw angle is set to zero. The distance between the instantaneous rotation center and the center of mass is calculated to determine the target steering angle of each steering axle. The steering angle signal of the first bridge is filtered to obtain a steering angle signal after vibration elimination and noise reduction. The filtered steering angle signal is input into the cascade controller to obtain the corresponding output signal of the cascade controller; Based on the target steering angle and the corresponding output signal of the cascade controller, the direction switching valve in the vehicle's electro-hydraulic assisted steering system is controlled to drive the driven axle hydraulic cylinder to achieve steering at the target steering angle.

2. The vehicle steering control method based on zero center of mass yaw as described in claim 1, characterized in that, The mathematical model of the relationship between multi-axle steering angle and vehicle speed includes a two-degree-of-freedom motion model for multi-axle vehicles.

3. The vehicle steering control method based on zero center of mass yaw as described in claim 2, characterized in that, The mathematical model relating the multi-bridge steering angle to vehicle speed includes: The lateral force equilibrium equations include: ； The yaw moment balance equation includes: ； in, Indicates the centroid sideslip angle. The axle number indicates the number of axles on a vehicle. This indicates the total number of axles on the vehicle. Indicates the first Axle tire lateral stiffness, Indicates yaw rate. Indicates real-time vehicle speed. Indicates the first The longitudinal distance from the bridge to the center of mass. Indicates the mass of the vehicle. Indicates the steering angle of the first axle steering wheel. It represents the instantaneous distance between the center of rotation and the center of mass.

4. The vehicle steering control method based on zero center of mass yaw as described in claim 3, characterized in that, Setting the sideslip angle of the center of gravity to zero, the distance between the instantaneous rotation center and the center of gravity is calculated, and the target steering angle of each steering axle is determined by the following formula: 。 5. The vehicle steering control method based on zero center of mass yaw as described in claim 1, characterized in that, Filtering the steering angle signal of a single-bridge axle includes: According to the vehicle Theoretical steering angle state variables and linear transformation matrix at time t, predicting vehicle speed. Theoretical turning angle at a given moment; Based on vehicle Given the error covariance matrix and linear transformation matrix at time points, calculate the vehicle's... Prior covariance at time; According to the vehicle Calculate the Kalman gain matrix based on the prior covariance and measurement noise covariance at each time step. Combination Given the vehicle angle measurement and prior estimate at each time point, output the optimal state estimate. Correction The posterior covariance matrix at time step, from Start by repeatedly predicting vehicles. Time-theoretic turning angle to correction The steps for calculating the posterior covariance matrix at time step are used to output the optimal estimated value of the filtered steering angle in real time.

6. The vehicle steering control method based on zero center of mass yaw as described in claim 5, characterized in that, According to the vehicle Theoretical steering angle state variables and linear transformation matrix at time t, predicting vehicle speed. The theoretical rotation angle at any given time is determined by the following formula: ； in, Indicates vehicle Theoretical turning point of time, Represents a linear transformation matrix. Indicates vehicle The theoretical turning point of time, express Time-based process noise; Based on vehicle Given the error covariance matrix and linear transformation matrix at time points, calculate the vehicle's... The prior covariance at time step is determined by the following formula: ； in, Indicates vehicle Prior covariance at time, express The error covariance matrix at time t. Represents the linear transformation matrix transpose, express The process noise covariance matrix at time step; According to the vehicle The prior covariance and measurement noise covariance at time points are used to calculate the Kalman gain matrix, which is determined by the following formula: ； in, Represents the Kalman gain matrix. Table of noise covariance, This represents the inverse operation of a matrix; Combination The optimal state estimate is calculated using the vehicle angle measurement and prior estimate at each moment, and is determined by the following formula: ； in, This represents the optimal state estimate. express Prior estimates of time, express Vehicle angle measurement at any given time; Correction The posterior covariance matrix at time step 1 is calculated using the following formula: ； in, Indicates correction The posterior covariance matrix at time step 1. Represents the identity matrix.

7. The vehicle steering control method based on zero center of mass yaw as described in claim 1, characterized in that, The filtered steering angle signal is input into the cascade controller, and the corresponding output signal of the cascade controller includes: The filtered steering angle signal is input into the position loop proportional control to generate a speed command signal; The filtered steering angle signal is input into the speed loop proportional-integral control to generate a current command signal; The filtered steering angle signal is input into the current loop proportional-integral control to generate the actuator drive signal.

8. The vehicle steering control method based on zero center of mass yaw as described in claim 7, characterized in that, The speed command signal includes: ； in, This indicates the speed command signal output by the position loop. This represents the proportional gain coefficient of the position loop. This indicates the angular deviation between the position loop input command position signal and the sensor signal; The current command signal includes: ； in, This indicates the current command signal output by the speed loop. This represents the proportional gain coefficient of the velocity loop. This represents the integral time constant of the velocity loop. This indicates the deviation between the speed input command speed and the feedback speed of the speed loop. The time integral representing the velocity deviation. Indicates the upper limit of time integration. Represents a time variable; The actuator drive signal includes: ； in, This indicates the actuator drive signal output by the current loop. This represents the proportional gain coefficient of the current loop. This represents the integral time constant of the current loop. This indicates the current deviation between the commanded current and the actual current. The time integral represents the current deviation.

9. A vehicle steering control system based on zero center of mass yaw, characterized in that, include: The model building module is used to construct a mathematical relationship model between the steering angle of multiple axles and the vehicle speed based on the Ackermann steering principle and the zero center of mass yaw principle. The center of mass yaw angle is set to zero, the distance between the instantaneous rotation center and the center of mass is calculated, and the target steering angle of each steering axle is determined. A noise reduction and filtering module is used to filter the steering angle signal of a bridge to obtain a steering angle signal after vibration elimination and noise reduction. A cascaded control module is used to input the filtered steering angle signal into the cascaded controller to obtain the corresponding output signal of the cascaded controller. The steering control module is used to control the direction switching valve in the vehicle's electro-hydraulic assisted steering system according to the target steering angle and the output signal corresponding to the cascade controller, and drive the driven axle hydraulic cylinder to achieve steering at the target steering angle.

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