Three-axle vehicle all-wheel steering control method based on mass estimation

Abstract

The invention discloses a three-axle vehicle all-wheel steering control method based on mass estimation, and the method comprises the steps: building a mass estimation model of a recursive least square method containing a forgetting factor through a longitudinal dynamic model, and transmitting a mass estimation result to an all-wheel steering controller on the basis. The method comprises the following steps: 1, establishing a longitudinal dynamic model of a three-axle vehicle based on a vehicle driving force and a vehicle driving balance equation; 2, establishing a mass estimation algorithm of a recursive least square method containing a forgetting factor based on a longitudinal dynamic model; 3, establishing an all-wheel steering control algorithm of a zero side slip angle based on a two-degree-of-freedom model of the three-axle vehicle and an Ackerman steering principle, and transmitting the estimated mass to an all-wheel steering controller in real time; according to the method, on the basis of all-wheel steering of the three-axle vehicle, mass estimation of the whole vehicle is introduced, interference of mass change of the vehicle on the all-wheel steering control effect is avoided, and the all-wheel steering control effect is improved.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle posture control, and in particular relates to an all-wheel steering control method for a three-axle vehicle based on mass estimation. Background Art

[0002] Due to their longer bodies and multi-axle design, three-axle vehicles require a smaller turning radius to improve maneuverability at low speeds (such as parking and turning on narrow roads), while a larger turning radius is required to ensure stability at higher speeds (such as changing lanes on highways). Traditional three-axle vehicles mostly use front-wheel steering or front-and-rear-wheel steering configurations, but these designs cannot minimize the turning radius, and the central direct drive configuration of the intermediate axle is not conducive to optimizing the vehicle's economic and power performance.

[0003] Vehicle mass is a key factor influencing steering performance. Real-time vehicle mass estimation enables more precise adjustment of steering parameters, thereby optimizing steering control. Furthermore, changes in vehicle mass (such as changes in load) affect the vertical load distribution on the tires, which in turn affects steering performance. Control strategies based on mass estimation can dynamically adjust steering parameters to ensure optimal steering performance under varying vehicle load conditions.

[0004] Existing patents, such as the invention patent with patent number CN202410094765.X, establish a dynamic model of the all-wheel steering system of a multi-axis vehicle, and form an ideal reference model of the center of mass motion of the vehicle during high-speed and low-speed steering based on the dynamic model of the steering system and the steering characteristics, which is used to form an ideal reference trajectory for all-wheel steering, and form an error dynamic model of the lateral motion and yaw motion of the vehicle based on the motion difference between the dynamic model of the steering system and the ideal reference model of the center of mass motion; construct a lateral super-twisting sliding mode control rate based on the error dynamic model of the lateral motion to form a lateral finite-time stable process; construct a yaw super-twisting sliding mode control rate based on the error dynamic model of the yaw motion to form a yaw finite-time stable process; decouple the control data formed by the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate to each axis to form the wheel steering angle of each axis.

[0005] This method relies heavily on the accuracy of the vehicle dynamics model and vehicle parameters. However, the vehicle's status information, such as mass, may change during driving, and the control data needs to be decoupled. During the processing, the data may lose accuracy or be unable to be transmitted in real time. Summary of the Invention

[0006] A three-axle vehicle all-wheel steering control method based on mass estimation, characterized by comprising the following steps:

[0007] S1, based on the vehicle's driving external forces and vehicle's driving balance equations, establish the longitudinal dynamics model of the three-axle vehicle;

[0008] S2, based on the longitudinal dynamics model, a mass estimation algorithm using the recursive least squares method with forgetting factor is established;

[0009] S3, establishing an all-wheel steering control algorithm with zero sideslip angle based on the two-degree-of-freedom model of a three-axle vehicle and the Ackerman steering principle, and then correcting the control accuracy of the all-wheel steering controller in real time through the mass estimation in step S2;

[0010] The specific process of step S1 is:

[0011] When a car accelerates on a slope, the resistance and the driving force of the car are balanced. The car's driving equation is as follows:

[0012]

[0013] Where, As driving force, is the rolling resistance, is the air resistance, is the slope resistance, Acceleration resistance

[0014] The longitudinal dynamic model of the car during driving is:

[0015]

[0016] Where, is the longitudinal driving force of the car, For the quality of the car, is the air density, is the air resistance coefficient, is the road slope, is the rolling resistance coefficient, is the speed of the car;

[0017] The specific process of step S2 is:

[0018] When a car is driving on a longitudinal slope, the longitudinal acceleration sensor measurement value contains slope information and actual acceleration information. The equation constructed based on Newtonian mechanics is shown as follows:

[0019]

[0020] Substituting formula (3) into formula (2) yields:

[0021]

[0022] Combining vehicle dynamics and acceleration signals, the decoupling of mass and slope can be achieved by replacing the road slope with an acceleration signal containing slope information. The mass estimation model is established using the forgetting factor recursive least squares method. Equations (3) and (4) are combined and converted into the least squares format:

[0023]

[0024] Where, is the input quantity, which is an observable data vector; is the system output; are the model parameters to be identified;

[0025] The recursive process is as follows:

[0026]

[0027] Where, for Moment gain; is the covariance matrix; is the forgetting factor. When <1, the influence of historical data on parameter estimation can be reduced; when When >1, the weight of historical data is enhanced;

[0028] The specific process of step S3 is:

[0029] Kinematic analysis of lateral and yaw two-degree-of-freedom of three-axis vehicles:

[0030]

[0031]

[0032] Where, is the lateral force of each axis, is the vehicle rotation angle of each axle, is the vehicle's yaw rate, is the moment of inertia around the z-axis; are the lateral and longitudinal velocities; is the distance from the front, middle and rear axles to the center of mass;

[0033] Small angle assumption:

[0034]

[0035] In the formula is the sideslip angle of the center of mass;

[0036] Cornering force of each wheel:

[0037]

[0038] In the formula is the cornering stiffness of the front, middle and rear wheels, is the side slip angle of the front, center and rear wheels;

[0039] Combining equations (7), (8), (9), and (10), we can obtain:

[0040]

[0041] When the vehicle is in steady state,

[0042]

[0043] When the zero center of mass sideslip angle control strategy is adopted,

[0044]

[0045] Substituting equations (12) and (13) into equation (11), we have

[0046]

[0047] When proportional feedforward control is used, the relationship between the equivalent angles of the center axle, the rear axle, and the front axle of a three-axle vehicle is:

[0048]

[0049] in: is the proportional coefficient of the rear axle and front axle angles;

[0050] The Ackermann steering principle based on three-axle vehicles is

[0051]

[0052] Then there is

[0053]

[0054] In the formula They are the distance from the front axle to the middle axle, and the distance from the middle axle to the rear axle;

[0055] Combining equations (14), (15), (16), and (17), we can get the equivalent wheelbase D as follows:

[0056]

[0057] Thus, it is deduced for

[0058]

[0059]

[0060] The mid-rear axle angle based on the zero center of mass side slip angle strategy is for

[0061]

[0062]

[0063] Compared with the prior art, the advantages of the present invention are:

[0064] 1 This method takes into account the load changes during actual vehicle driving and improves the steering control effect of the all-wheel steering controller through real-time estimation of mass

[0065] 2 The all-wheel controller of this method adopts the proportional feedforward zero-center-of-mass sideslip angle control idea, which improves the stability of the vehicle during driving and the steering angle gain is easy to calculate and adjust. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The present invention will be further described below with reference to the accompanying drawings and examples:

[0067] Figure 1 This is a flow chart of a three-axle vehicle all-wheel steering control method based on mass estimation according to the present invention.

[0068] Figure 2 Schematic diagram of longitudinal force analysis of all-wheel steering control of a three-axle vehicle based on mass estimation according to the present invention;

[0069] Figure 3 A two-degree-of-freedom model of a three-axle vehicle for the three-axle vehicle all-wheel steering control method based on mass estimation according to the present invention;

[0070] Figure 4 This is an Ackerman steering principle diagram of the three-axle vehicle all-wheel steering control method based on mass estimation according to the present invention; DETAILED DESCRIPTION

[0071] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present invention.

[0072] The present invention will be further described below with reference to the accompanying drawings.

[0073] See Figure 1 The present invention provides a three-axle vehicle all-wheel steering control method based on mass estimation, which is characterized by comprising the following steps:

[0074] S1, based on the vehicle's driving external forces and vehicle's driving balance equations, establish the longitudinal dynamics model of the three-axle vehicle;

[0075] S2, based on the longitudinal dynamics model, a mass estimation algorithm using the recursive least squares method with forgetting factor is established;

[0076] S3, establishing an all-wheel steering control algorithm with zero sideslip angle based on the two-degree-of-freedom model of a three-axle vehicle and the Ackerman steering principle, and then correcting the control accuracy of the all-wheel steering controller in real time through the mass estimation in step S2;

[0077] See Figure 2 , the longitudinal forces during vehicle driving specifically include the following:

[0078] When a car accelerates on a slope, the resistance and the driving force of the car are balanced. The car's driving equation is as follows:

[0079]

[0080] Where, As driving force, is the rolling resistance, is the air resistance, is the slope resistance, Acceleration resistance

[0081] The longitudinal dynamic model of the car during driving is:

[0082]

[0083] Where, is the longitudinal driving force of the car, For the quality of the car, is the air density, is the air resistance coefficient, is the road slope, is the rolling resistance coefficient, is the speed of the car;

[0084] See Figure 3 , the quality estimation method of the recursive least squares method with forgetting factor includes the following:

[0085] When a car is driving on a longitudinal slope, the longitudinal acceleration sensor measurement value contains slope information and actual acceleration information. The equation constructed based on Newtonian mechanics is shown as follows:

[0086]

[0087] Substituting formula (3) into formula (2) yields:

[0088]

[0089] Combining vehicle dynamics and acceleration signals, the decoupling of mass and slope can be achieved by replacing the road slope with an acceleration signal containing slope information. The mass estimation model is established using the forgetting factor recursive least squares method. Equations (3) and (4) are combined and converted into the least squares format:

[0090]

[0091] Where, is the input quantity, which is an observable data vector; is the system output; are the model parameters to be identified;

[0092] The recursive process is as follows:

[0093]

[0094] Where, for Moment gain; is the covariance matrix; is the forgetting factor. When <1, the influence of historical data on parameter estimation can be reduced; when When >1, the weight of historical data is enhanced;

[0095] See Figure 3 , Figure 4 , the all-wheel steering control algorithm based on the three-axis vehicle two-degree-of-freedom model and the Ackerman steering principle includes the following:

[0096] Kinematic analysis of lateral and yaw two-degree-of-freedom of three-axis vehicles:

[0097]

[0098]

[0099] Where, is the lateral force of each axis, is the vehicle rotation angle of each axle, is the vehicle's yaw rate, is the moment of inertia around the z-axis; are the lateral and longitudinal velocities; is the distance from the front, middle and rear axles to the center of mass;

[0100] Small angle assumption:

[0101]

[0102] In the formula is the sideslip angle of the center of mass;

[0103] Cornering force of each wheel:

[0104]

[0105] In the formula is the cornering stiffness of the front, middle and rear wheels, is the side slip angle of the front, center and rear wheels;

[0106] Combining equations (7), (8), (9), and (10), we can obtain:

[0107]

[0108] When the vehicle is in steady state,

[0109]

[0110] When the zero center of mass sideslip angle control strategy is adopted,

[0111]

[0112] Substituting equations (12) and (13) into equation (11), we have

[0113]

[0114] When proportional feedforward control is used, the relationship between the equivalent angles of the center axle, the rear axle, and the front axle of a three-axle vehicle is:

[0115]

[0116] in: is the proportional coefficient of the rear axle and front axle angles;

[0117] The Ackermann steering principle based on three-axle vehicles is

[0118]

[0119] Then there is

[0120]

[0121] In the formula They are the distance from the front axle to the middle axle, and the distance from the middle axle to the rear axle;

[0122] Combining equations (14), (15), (16), and (17), we can get the equivalent wheelbase D as follows:

[0123]

[0124] Thus, it is deduced for

[0125]

[0126]

[0127] The mid-rear axle angle based on the zero center of mass side slip angle strategy is for

[0128]

[0129] .

Claims

1. A three-axle vehicle all-wheel steering control method based on mass estimation, characterized in that: The following steps are involved: S1, based on the vehicle's driving external forces and vehicle's driving balance equations, establish the longitudinal dynamics model of the three-axle vehicle; S2, based on the longitudinal dynamics model, a mass estimation algorithm using the recursive least squares method with forgetting factor is established; S3, based on the two-degree-of-freedom model of the three-axle vehicle and the Ackerman steering principle, an all-wheel steering control algorithm with zero center of mass sideslip angle is established, and then the control accuracy of the all-wheel steering controller is corrected in real time through the mass estimation in step S2.

2. The all-wheel steering control method for a three-axle vehicle based on mass estimation according to claim 1, characterized in that: The specific process of step S1 is: When a car accelerates on a slope, the resistance and the driving force of the car are balanced. The car's driving equation is as follows: ， Where, As driving force, is the rolling resistance, is the air resistance, is the slope resistance, Acceleration resistance The longitudinal dynamic model of the car during driving is: ， Where, is the longitudinal driving force of the car, For the quality of the car, is the air density, is the air resistance coefficient, is the road slope, is the rolling resistance coefficient, The speed of the car.

3. The all-wheel steering control method for a three-axle vehicle based on mass estimation according to claim 1, characterized in that: The specific process of step S2 is: When a car is driving on a longitudinal slope, the longitudinal acceleration sensor measurement value contains slope information and actual acceleration information. The equation constructed based on Newtonian mechanics is shown as follows: ， Substituting formula (3) into formula (2) yields: ， Combining vehicle dynamics and acceleration signals, the decoupling of mass and slope can be achieved by replacing the road slope with an acceleration signal containing slope information. The mass estimation model is established using the forgetting factor recursive least squares method. Equations (3) and (4) are combined and converted into the least squares format: ， Where, is the input quantity, which is an observable data vector; is the system output; are the model parameters to be identified; The recursive process is as follows: ， Where, for Moment gain; is the covariance matrix; is the forgetting factor; when When <1, the influence of historical data on parameter estimation can be reduced; when When >1, the weight of historical data is increased.

4. The all-wheel steering control method for a three-axle vehicle based on mass estimation according to claim 1, characterized in that: The specific process of step S3 is: Kinematic analysis of lateral and yaw two-degree-of-freedom of three-axis vehicles: ， Where, is the lateral force of each axis, is the vehicle rotation angle of each axle, is the vehicle's yaw rate, is the moment of inertia around the z-axis; are the lateral and longitudinal velocities; is the distance from the front, middle and rear axles to the center of mass; Small angle assumption: ， In the formula is the sideslip angle of the center of mass; Cornering force of each wheel: ， In the formula is the cornering stiffness of the front, middle and rear wheels, is the side slip angle of the front, center and rear wheels; Combining equations (7), (8), (9), and (10), we can obtain: ， When the vehicle is in steady state, ， When the zero center of mass sideslip angle control strategy is adopted, ， Substituting equations (12) and (13) into equation (11), we have ， When proportional feedforward control is used, the relationship between the equivalent angles of the center axle, the rear axle, and the front axle of a three-axle vehicle is: ， in: is the proportional coefficient of the rear axle and front axle angles; The Ackermann steering principle based on three-axle vehicles is ， Then there is ， In the formula They are the distance from the front axle to the middle axle, and the distance from the middle axle to the rear axle; Combining equations (14), (15), (16), and (17), we can get the equivalent wheelbase D as follows: ， Thus, it is deduced for ， The mid-rear axle angle based on the zero center of mass side slip angle strategy is for 。

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