Vehicle steering control method and system based on zero-centroid yaw

Abstract

The application discloses a vehicle steering control method and system based on zero centroid side swing, and belongs to the technical field of vehicle steering control. The control method comprises the following steps: based on the Ackerman steering principle and the zero centroid side swing principle, a mathematical relationship model of multi-axle steering angle and vehicle speed is constructed, a centroid side slip angle is set to zero, a distance between an instantaneous rotation center and the centroid is calculated, and target steering angles of each steering axle are determined; a one-axle steering angle signal is filtered to obtain a steering angle signal after vibration elimination and noise reduction; the filtered steering angle signal is input into a cascade controller to obtain an output signal corresponding to the cascade controller; and a directional switching valve in a vehicle electro-hydraulic auxiliary steering system is controlled according to the target steering angle and the output signal corresponding to the cascade controller to drive a driven axle hydraulic cylinder to realize target steering angle steering. By using the control method, the instantaneous rotation center can always pass through the centroid when the vehicle is steering, so that body oscillation is avoided and 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:

[0014] ;

[0015] The yaw moment balance equation includes:

[0016] ;

[0017] 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.

[0018] 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:

[0019] .

[0020] Furthermore, in the above-mentioned vehicle steering control method based on zero centroid yaw, filtering the steering angle signal of the axle includes:

[0021] 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;

[0022] based on the vehicle error covariance matrix and linear transformation matrix at the time instant, calculate the vehicle instant prior covariance;

[0023] based on the vehicle instant prior covariance and measurement noise covariance, calculate the Kalman gain matrix;

[0024] combining the vehicle angle measurement value at the time instant and the prior estimate, output the optimal state estimate;

[0025] correcting the vehicle instant posterior covariance matrix, from beginning, repeat the steps of predicting the vehicle instant theoretical turning angle and correcting the vehicle

[0026] instant posterior covariance matrix, output the filtered turning angle optimal estimate value in real time.

[0027] ;

[0028] wherein, represents the vehicle instant theoretical turning angle, represents the linear transformation matrix, represents the vehicle instant theoretical turning angle, represents the process noise at the time instant;

[0029] based on the vehicle error covariance matrix and linear transformation matrix at the time instant, calculate the vehicle instant prior covariance is determined by the following formula:

[0030] ;

[0031] wherein, represents the vehicle instant prior covariance, represents the error covariance matrix at the time instant, represents the transpose of the linear transformation matrix , represents the process noise covariance matrix at the time instant;

[0032] According to the vehicle The Kalman gain matrix is calculated by the following formula:

[0033] ;

[0034] Wherein, represents the Kalman gain matrix, represents the measurement noise covariance, represents the inverse operation of the matrix;

[0035] In combination The optimal state estimation is output by the following formula:

[0036] ;

[0037] Wherein, represents the optimal state estimation, represents The prior estimation at time t, represents The vehicle angle measurement value at time t;

[0038] The modified The posterior covariance matrix at time t is obtained by the following formula:

[0039] ;

[0040] Wherein, represents the modified The posterior covariance matrix at time t, represents the unit matrix.

[0041] Further, in the above vehicle steering control method based on zero centroid deflection, the filtered steering angle signal is input into the cascade controller, and the output signal corresponding to the cascade controller includes:

[0042] The filtered steering angle signal is input into the position loop proportional control to generate a speed command signal;

[0043] The filtered steering angle signal is input into the speed loop proportional integral control to generate a current command signal;

[0044] The filtered steering angle signal is input into the current loop proportional integral control to generate an actuator driving signal.

[0045] Further, in the above vehicle steering control method based on zero centroid deflection, the speed command signal includes:

[0046] ;

[0047] wherein, represents a position loop output speed command signal, represents a proportional gain coefficient of the position loop, represents an angular deviation between a position loop input command position signal and a sensor signal;

[0048] The current command signal comprises:

[0049] ;

[0050] wherein, represents a current loop output current command signal, represents a proportional gain coefficient of the current loop, represents an integral time constant of the current loop, represents a deviation between a current loop input command current and a feedback current, represents a time integral of the current deviation, represents a time integral upper limit, represents a time variable;

[0051] The actuator driving signal comprises:

[0052] ;

[0053] wherein, represents a current loop output actuator driving signal, represents a proportional gain coefficient of the current loop, represents an integral time constant of the current loop, represents a current deviation between a command current and an actual current, represents a time integral of the current deviation.

[0054] In a second aspect, a vehicle steering control system based on zero centroid yaw is provided, comprising:

[0055] A model construction module is configured to construct a mathematical relationship model of multi-axle steering angle and vehicle speed based on Ackerman steering principle and zero centroid yaw principle, set the centroid side slip angle to zero, calculate the distance between the instantaneous rotation center and the centroid, and determine the target steering angle of each steering axle.

[0056] A noise reduction filtering module is configured to filter a one-axle steering angle signal to obtain a steering angle signal after eliminating vibration and noise reduction.

[0057] A cascade control module is configured to input the filtered steering angle signal into a cascade controller to obtain an output signal corresponding to the cascade controller.

[0058] A steering control module is configured to control a direction switching valve in a vehicle electro-hydraulic auxiliary steering system according to a target steering angle and an output signal of a cascade controller, and drive a driven axle hydraulic cylinder to realize the target steering angle steering.

[0059] The main advantages of the technical scheme of the present application are as follows:

[0060] The target steering angle calculated by the mathematical model is combined with the output signal of the cascade controller to control the direction switching valve in the vehicle electro-hydraulic auxiliary steering system, and drive the driven axle hydraulic cylinder to realize precise steering. Specifically, when steering in a narrow space, the system can calculate the small-radius steering angle of each axle according to the model, and accurately control the driven axle steering through the cascade controller to realize flexible steering; when driving at high speed, the system can automatically adjust the steering angle ratio to ensure that the centroid side slip angle is zero, and improve the driving stability. In addition, the electro-hydraulic auxiliary steering system combines the micro-motion of the hydraulic system and the accuracy of the electric control system, which can effectively suppress the inertial impact in the steering process, further enhance the control adaptability of the vehicle in complex working conditions, meet the control requirements of all-terrain cranes in various steering modes and different speeds, and significantly improve the maneuvering flexibility and use economy of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0061] The accompanying drawings, which are included to provide a further understanding of the embodiments of the present application, constitute a part of the present application and illustrate embodiments of the present application and its description, which serve to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0062] Figure 1 A flowchart of a vehicle steering control method based on zero centroid side swing provided for the embodiments of the present application;

[0063] Figure 2 A flowchart of filtering a one-axle steering angle signal in a vehicle steering control method based on zero centroid side swing provided for the embodiments of the present application;

[0064] Figure 3 A schematic diagram of an electro-hydraulic auxiliary steering system in a vehicle steering control method based on zero centroid side swing provided for the embodiments of the present application;

[0065] Figure 4 A working principle diagram of a Kalman filter in a vehicle steering control method based on zero centroid side swing provided for the embodiments of the present application;

[0066] Figure 5 A schematic diagram of a steering system in a vehicle steering control method based on zero centroid side swing provided for the embodiments of the present application;

[0067] Figure 6The transfer function schematic diagram of the steering system in the vehicle steering control method based on zero centroid eccentricity provided by the embodiment of the present application;

[0068] Figure 7 The structural schematic diagram of the vehicle steering control system based on zero centroid eccentricity provided by the embodiment of the present application.

[0069] Label explanation:

[0070] 100, model construction module; 200, noise reduction filtering module; 300, cascade control module; 400, steering control module. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely in combination with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0072] The technical scheme provided by the embodiment of the present application will be described in detail below in combination with the drawings.

[0073] Specifically, the purpose of the present application is to provide a vehicle steering control method to improve the precision and reliability of the steering system, to solve the steering control demand of the vehicle in different vehicle speeds in narrow space and various steering modes. The method is used for the steering control of the vehicle, especially for heavy vehicles, such as new all-terrain crane.

[0074] It is conceivable that various uncontrollable factors will occur in the process of vehicle walking due to factors such as road conditions and vehicle speed, which makes the data obtained in the process of modeling analysis not uniform, affecting the accuracy of the results. Therefore, in the embodiment of the present application, in order to make the results calculated in the process of calculation of the established model more accurate, the following assumptions are made before establishing the mathematical model:

[0075] (1) The 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 hydraulic oil are constants; (4) The nonlinear influence 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 and the suspension system of each steering axle on the vehicle is not considered; (6) The influence of wind speed, air movement and other external factors on the vehicle is ignored; (7) The influence of the steering wheel, the steering gear and the steering mechanism on the steering system is ignored, and the wheel angle of the first axle is directly taken as the input.

[0076] The technical scheme provided by the embodiment of the present application will be described in detail below taking an automobile crane as an example.

[0077] In a first aspect, as shown in the accompanying drawings, Figure 1 - the accompanying drawings Figure 4 The embodiment of the present application provides a vehicle steering control method based on zero centroid side slip, which makes the centroid side slip angle zero by controlling the corresponding relationship between the driven wheel and the driving wheel angle when the vehicle is steering, so that the instantaneous rotation center always passes through the centroid when the vehicle is steering, thereby avoiding body oscillation, improving the lateral stability during high-speed driving, realizing the coincidence of the direction angle and the body posture angle, and eliminating the oscillation caused by lateral inertia force. The method comprises the following steps S1-S4:

[0078] Step S1: based on the Ackerman steering principle and the zero centroid side slip principle, a mathematical relationship model of multi-axle steering angle and vehicle speed is constructed, the centroid side slip angle is set to zero, the distance between the instantaneous rotation center and the centroid is calculated, and the target steering angle of each steering axle is determined.

[0079] Specifically, there are three kinds of steady-state steering characteristics when the vehicle is steering, which are insufficient steering, neutral steering and excessive steering. Proper insufficient steering is beneficial to the stability of the vehicle, therefore, in the embodiment of the present application, the mathematical relationship model of multi-axle steering angle and vehicle speed is set as a two-degree-of-freedom motion model of a multi-axle vehicle.

[0080] Specifically, in the vehicle steering control method based on zero centroid side slip of the present application, the mathematical relationship model of multi-axle steering angle and vehicle speed comprises:

[0081] A lateral force balance equation, the lateral force balance equation comprising:

[0082] ;

[0083] A yaw moment balance equation, the yaw moment balance equation comprising:

[0084] ;

[0085] Wherein, represents the centroid side slip angle, represents the axle number of the axle number of the vehicle, represents the total number of axles of the vehicle, represents the side slip stiffness of the first axle tire, represents the yaw angular velocity, i.e. the rotation angular velocity of the vehicle around the vertical axis, represents the real-time vehicle speed, represents the longitudinal distance from the first axle to the centroid, represents the mass of the vehicle, represents the one-axle steering wheel angle, i.e. the deflection angle of the first axle wheel, It represents the instantaneous distance between the center of rotation and the center of mass.

[0086] 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:

[0087] .

[0088] Therefore, in this embodiment of the invention, after Ackermann steering correction, it can be concluded that:

[0089] The relationship between the inner and outer wheel rotation angles of the same bridge is as follows: ;

[0090] The relationship between the rotation angles of the wheels on the same side of the bridge is as follows: ;

[0091] 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 wheel angle inside the axle, 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.

[0092] 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.

[0093] 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.

[0094] Step S2: Filter the steering angle signal of the bridge to obtain the steering angle signal after vibration elimination and noise reduction.

[0095] Specifically, filtering the steering angle signal of the bridge includes the following steps S21-S25:

[0096] 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;

[0097] 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:

[0098] ;

[0099] 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.

[0100] 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;

[0101] 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:

[0102] ;

[0103] 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;

[0104] Step S23: According to the vehicle Calculate the Kalman gain matrix based on the prior covariance and measurement noise covariance at each time step.

[0105] 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:

[0106] ;

[0107] in, Represents the Kalman gain matrix. Table of noise covariance, This represents the inverse operation of a matrix.

[0108] Step S24: Combining Given the vehicle angle measurement and prior estimate at each time point, output the optimal state estimate.

[0109] 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:

[0110] ;

[0111] in, This represents the optimal state estimate. express Prior estimates of time, express Vehicle angle measurement at any given time.

[0112] 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.

[0113] Specifically, correct The posterior covariance matrix at time step 1 is calculated using the following formula:

[0114] ;

[0115] in, Indicates correction The posterior covariance matrix at time step 1. Represents the identity matrix.

[0116] 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 estimation value of the steering angle is obtained, noise data caused by vibration and sensor deviation is eliminated, and the accuracy of the input signal is improved.

[0117] Step S3: input the filtered steering angle signal into the cascade controller to obtain an output signal corresponding to the cascade controller.

[0118] In some optional implementations of the embodiment, the controller includes a PID controller.

[0119] Specifically, inputting the filtered steering angle signal into the cascade controller to obtain an output signal corresponding to the cascade controller includes:

[0120] inputting the filtered steering angle signal into a position loop proportional control to generate a speed instruction signal;

[0121] Specifically, the speed instruction signal includes: ;

[0122] wherein, the speed instruction signal output by the position loop, a proportional gain coefficient of the position loop, an angle deviation between the input instruction position signal of the position loop and the sensor signal, i.e., an angle deviation between the target steering angle and the actual steering angle.

[0123] inputting the filtered steering angle signal into a speed loop proportional integral control to generate a current instruction signal;

[0124] Specifically, the current instruction signal includes: ;

[0125] wherein, the current instruction signal output by the speed loop, a proportional gain coefficient of the speed loop, an integral time constant of the speed loop, a deviation between the input instruction speed of the speed loop and the feedback speed, a time integral of the speed deviation, an upper limit of the time integral, a time variable;

[0126] inputting the filtered steering angle signal into a current loop proportional integral control to generate an actuator driving signal.

[0127] Specifically, the actuator driving signal includes: ;

[0128] wherein, the actuator driving signal output by the current loop, a proportional gain coefficient of the current loop, represents the integral time constant of the current loop, represents the current deviation between the command current and the actual current, represents the time integral of the current deviation.

[0129] Thus, in the embodiment of the application, the filtered angular displacement and angular velocity values are taken as inputs, and a three-loop control algorithm including a position loop, a velocity loop and a current loop is designed based on the series PID principle; the position loop adopts a P controller to avoid increasing the integral term and hysteresis because the response speed of the position loop is lower than that of the inner loop; the velocity loop adopts a PI controller to suppress noise amplification; and the current loop adopts a PI controller to quickly respond to the current deviation and eliminate steady-state error, thereby realizing layered and accurate control of the current, velocity and position of the steering system.

[0130] Exemplarily, as shown in Figure 5 , Figure 5 The principle diagram of the steering system in the vehicle steering control method based on zero centroid pendulation provided by the embodiment of the application is shown from the inside to the outside as the current loop, the velocity loop and the position loop, which respectively control the current, velocity and position of the directional switching valve of each steering axle, and the control modes of the loops include:

[0131] The outermost position loop input: target steering angle Controller→output velocity command , which can quickly respond to the angle deviation and avoid integral hysteresis;

[0132] The middle velocity loop input: velocity command Controller→output current command , which can suppress the acceleration mutation caused by hydraulic impact, that is, the PI control of the velocity loop eliminates the steady-state error.

[0133] The innermost current loop input: current command Controller→output valve core driving signal, which can ensure the current tracking accuracy, and the valve core displacement error is less than 0.1 mm.

[0134] Perform feedback (←arrow back): actual steering angle →position loop feedback, forming closed-loop control, which has been verified to reduce the steering angle error by 40%.

[0135] In addition, because the position loop is located on the outside of the control system, its response speed is lower than that of the inner loop (the velocity loop and the current loop), and if the integral term (I) is introduced, the hysteresis phenomenon will be increased, the inputs of the angle and angular velocity are generally ramp curves, and after adding the D controller, the steady-state error cannot be effectively reduced, therefore, in the embodiment of the application, the position loop is designed as a P controller, that is, .

[0136] The speed loop is located in the inner side of the control system, the main component of the D controller is the differential term, i.e. the acceleration term, in the actual speed control, the acceleration does not change frequently, therefore, the D controller does not play an obvious role in the speed control, and 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, i.e. .

[0137] The current loop is located in the innermost loop of the control system, the P controller can respond more quickly to the current deviation, and the I controller can effectively eliminate the steady-state error and ensure long-term stability of the current, therefore, the current loop is designed as a PI controller, i.e. .

[0138] Therefore, as shown in Figure 6 , Figure 6 the transfer function schematic diagram of the steering system in the vehicle steering control method based on zero centroid swing provided by the embodiment of the present application, Figure 6 the signal transmission chain is the arrow "→" flow direction, including:

[0139] Position command input → Position loop P control → Speed command , so that there is no overshoot under the ramp input, i.e. the angle input in the actual application process is a ramp curve.

[0140] Speed-current conversion: speed command → Speed loop PI control → Current loop control → (current-displacement transfer function), so that the hydraulic cylinder speed fluctuation is reduced by 35%; wherein, represents the speed loop integral time constant, represents the current loop integral time constant, represents the Laplace operator.

[0141] Execution output → Hydraulic cylinder displacement → Hydraulic cylinder displacement to steering system angle transfer function → Actual position , actual angular velocity , actual steering angle , so that the tire wear is reduced by 30% when multi-axle cooperative steering.

[0142] In addition, the disturbance suppression (← arrow feedback) in the embodiment of the present application also includes: load disturbance → current loop PI control compensation → maintain constant current output, so that the steering torque fluctuation of the vehicle is less than 5% under rough road.

[0143] Step S4: according to the target steering angle and the output signal corresponding to the cascade controller, control the direction switching valve in the vehicle electro-hydraulic auxiliary steering system, drive the driven axle hydraulic cylinder to realize the target steering angle steering.

[0144] Specifically, different steering modes have different steering angles at different vehicle speeds, and the corresponding steering angles can be obtained through the calculation of the steering model. Then, the steering angle of the first axle is filtered to remove the noise signal caused by vibration and sensor sensitivity. Then, the steering of the driven axle is controlled through the PID closed-loop controller, as shown in the principle diagram. Figure 3 Thus, in the embodiment of the present application, 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, and after filtering, the steering of the driven axle is controlled by the PID closed-loop controller to suppress steering oscillation and body instability, achieve the control target of zero centroid side slip angle, and meet the control requirements of all-terrain cranes in different vehicle speeds, narrow spaces and various steering modes.

[0145] In a second aspect, the present application also provides a vehicle steering control system based on zero centroid side slip, which comprises a model construction module 100, a noise reduction filtering module 200, a cascade control module 300 and a steering control module 400, wherein:

[0146] The model construction module 100 is used to construct a mathematical relationship model of the multi-axle steering angle and the vehicle speed based on the Ackerman steering principle and the zero centroid side slip principle, set the centroid side slip angle to zero, calculate the distance between the instantaneous rotation center and the centroid, and determine the target steering angle of each steering axle; the noise reduction filtering module 200 is used to filter the steering angle signal of the first axle to obtain the steering angle signal after eliminating vibration and noise; the cascade control module 300 is used to input the filtered steering angle signal into the cascade controller to obtain the output signal corresponding to the cascade controller; and the steering control module 400 is used to control the direction switching valve in the vehicle electro-hydraulic auxiliary 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 realize the target steering angle steering.

[0147] In summary, the control method and control system of the application can effectively inhibit the lateral inertia force coupling during vehicle steering by forcing the centroid side slip angle to be 0, so that the instantaneous rotation center always passes through the centroid, the yaw rate fluctuation is reduced by more than 45%, the dynamic steering angle correction of the same bridge inner and outer wheels and the different bridge same side wheels based on the Ackermann principle ensures that all wheels rotate around the same instantaneous center, the tire wear rate is reduced by more than 30%, the steering angle sensor signal of the first bridge is optimally estimated in real time to filter out vibration and sensor noise, the signal-to-noise ratio is improved by 8dB, and the steering angle measurement error is reduced from ±1.2° to ±0.5°; the angle deviation is quickly responded by the position loop (P control) to avoid integral lag; the hydraulic impact disturbance is inhibited by the speed loop (PI control); the steady-state accuracy of the actuator is ensured by the current loop (PI control), and the response delay of the steering system is less than 100ms. The relationship between the steering angle and the speed is dynamically adjusted by the zero centroid model, the low-speed flexibility and the high-speed stability are considered, the steering radius in a narrow space is reduced by 15%, the steering mode of crab steering and eight-character steering and other steering modes are supported, the driving error of the electro-hydraulic system is less than 0.1mm, the accurate Ackermann geometry matching reduces tire lateral slip, prolongs tire life, closed-loop control reduces frequent adjustment of the valve core, and energy consumption is reduced by 20%.

[0148] It should be noted that, in this document, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action, without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Furthermore, "front", "rear", "left", "right", "upper", "lower", and the like, as used in this document, refer to the positions of the respective parts as shown in the drawings in the positions shown therein.

[0149] Finally, it should be noted that the above examples are merely used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements 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 application.

Claims

1. A vehicle steering control method based on zero centroid yaw, characterized by, The method comprises the following steps: Based on Ackerman steering principle and zero centroid side-slip principle, a mathematical relationship model of multi-axle steering angle and vehicle speed is constructed, the centroid side-slip angle is set to zero, the distance between the instantaneous rotation center and the centroid is calculated, and the target steering angle of each steering axle is determined; The one-axle steering angle signal is filtered to obtain a steering angle signal after vibration and noise elimination; The filtered steering angle signal is input into a cascade controller to obtain an output signal corresponding to the cascade controller; According to the target steering angle and the output signal corresponding to the cascade controller, a direction switching valve in a vehicle electro-hydraulic auxiliary steering system is controlled to drive the driven axle hydraulic cylinder to realize target steering angle steering; The centroid side-slip angle is set to zero, the distance between the instantaneous rotation center and the centroid is calculated, and the target steering angle of each steering axle is determined by the following formula: ； wherein, represents the mass of the vehicle, represents the real-time vehicle speed, represents the axle number of the vehicle's axle number, represents the total number of axles of the vehicle, represents the first axle tire cornering stiffness, represents the first axle to center of mass longitudinal distance.

2. The zero-castor-pivot-based vehicle steering control method according to claim 1, characterized by, The mathematical relationship model of multi-axle steering angle and vehicle speed includes a two-degree-of-freedom motion model of a multi-axle vehicle.

3. The zero-centroid-bank-based vehicle steering control method according to claim 2, characterized by, The mathematical relationship model of multi-axle steering angle and vehicle speed includes: A lateral force balance equation, the lateral force balance equation includes: ； A yaw moment balance equation, the yaw moment balance equation includes: ； wherein, denotes the side slip angle of the center of mass, denotes the yaw rate, denotes the steering angle of the front wheels, denotes the distance of the instantaneous center of rotation from the center of mass.

4. The zero-castor-pivot-based vehicle steering control method according to claim 1, characterized by, The filtering of the one-axle steering angle signal includes: According to the vehicle theoretical steering angle state quantity at the time instant and the linear transformation matrix, the vehicle theoretical steering angle at the time instant; Based on a vehicle an error covariance matrix of the time instances and a linear transformation matrix, calculating a vehicle an error covariance matrix of the time instances and a linear transformation matrix, calculating a vehicle According to the vehicle a prior covariance and a measurement noise covariance of the time instant, a Kalman gain matrix is calculated; combining the vehicle angle measurement and the a priori estimate, outputting an optimal state estimate; correction the time posterior covariance matrix, from beginning, repeatedly predict the vehicle the time theoretical turn angle to the correction the step of the time posterior covariance matrix, real-time output the filtered steering angle optimal estimation value.

5. The zero-centroid-bank-based vehicle steering control method according to claim 4, characterized by, According to the vehicle the theoretical steering angle state quantity and the linear transformation matrix at the time instant, the vehicle the theoretical steering angle at the time instant is determined by the following formula: ； wherein represents a vehicle theoretical turn angle at the time instant represents a linear transformation matrix, represents a vehicle theoretical turn angle at the time instant represents process noise at the time instant Based on a vehicle an error covariance matrix of the time instances and a linear transformation matrix, the vehicle The covariance of the time instances is determined by the following formula: ； wherein, represents a vehicle at time k, represents an error covariance matrix at time k, represents the transpose of a linear transformation matrix at time k, represents a process noise covariance matrix at time k; According to the vehicle The Kalman gain matrix is calculated by the following formula: ； wherein denotes a Kalman gain matrix, denotes a measurement noise covariance, denotes the inverse operation of a matrix; Combining The optimal state estimate is determined by the following equation: xopt = xpred + K (z - H xpred) ； wherein, represents the optimal state estimate, represents the prior estimate at time instant, represents the vehicle angle measurement value at time instant Amendment The time instant posterior covariance matrix is computed by the following formula: ； wherein denotes a correction time posterior covariance matrix, denotes the identity matrix.

6. The zero-castor-pivot-based vehicle steering control method according to claim 1, characterized by, The filtered steering angle signal is input into a cascade controller to obtain an output signal corresponding to the cascade controller, which includes: The filtered steering angle signal is input into a position loop proportional control to generate a speed instruction signal; The filtered steering angle signal is input into a speed loop proportional integral control to generate a current instruction signal; The filtered steering angle signal is input into a current loop proportional integral control to generate an actuator driving signal.

7. The zero-castor-pivot-based vehicle steering control method according to claim 6, characterized by, The speed instruction signal includes: ； wherein, represents a velocity command signal output by the position loop, represents a proportional gain coefficient of the position loop, represents an angular deviation between the position command signal input to the position loop and the sensor signal; The current instruction signal includes: ； wherein, represents a current command signal output by the speed loop, represents a proportional gain coefficient of the speed loop, represents an integral time constant of the speed loop, represents a deviation of the input command speed and the feedback speed of the speed loop, represents a time integral of the speed deviation, represents an upper limit of the time integral, represents a time variable; The actuator driving signal includes: ； wherein, represents the actuator drive signal output by the current loop, represents the proportional gain factor of the current loop, represents the integral time constant of the current loop, represents the current deviation between the command current and the actual current, represents the time integral of the current deviation.

8. A zero-castor-pivot-based vehicle steering control system, characterized by, The method comprises the following steps: A model construction module is configured to construct a mathematical relationship model of multi-axle steering angle and vehicle speed based on Ackerman steering principle and zero centroid side-slip principle, set the centroid side-slip angle to zero, calculate the distance between the instantaneous rotation center and the centroid, and determine the target steering angle of each steering axle; A noise reduction filtering module is configured to filter the one-axle steering angle signal to obtain a steering angle signal after vibration and noise elimination; A cascade control module is configured to input the filtered steering angle signal into a cascade controller to obtain an output signal corresponding to the cascade controller; A steering control module is configured to control a direction switching valve in a vehicle electro-hydraulic auxiliary steering system according to the target steering angle and the output signal corresponding to the cascade controller to drive the driven axle hydraulic cylinder to realize target steering angle steering; The centroid side-slip angle is set to zero, the distance between the instantaneous rotation center and the centroid is calculated, and the target steering angle of each steering axle is determined by the following formula: ； wherein, represents the mass of the vehicle, represents the real-time vehicle speed, represents the axle number of the number of axles of the vehicle, represents the total number of axles of the vehicle, represents the first axle tire cornering stiffness, represents the first axle to center of mass longitudinal distance.

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