CN114896694B - Path tracking control method based on two-point pre-aiming - Google Patents
Path tracking control method based on two-point pre-aiming Download PDFInfo
- Publication number
- CN114896694B CN114896694B CN202210533627.8A CN202210533627A CN114896694B CN 114896694 B CN114896694 B CN 114896694B CN 202210533627 A CN202210533627 A CN 202210533627A CN 114896694 B CN114896694 B CN 114896694B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- point
- aiming
- steering wheel
- wheel angle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000033001 locomotion Effects 0.000 claims abstract description 16
- 230000001133 acceleration Effects 0.000 claims description 39
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 7
- 230000008878 coupling Effects 0.000 claims description 7
- 238000010168 coupling process Methods 0.000 claims description 7
- 238000005859 coupling reaction Methods 0.000 claims description 7
- 230000000694 effects Effects 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 4
- 238000005096 rolling process Methods 0.000 claims description 4
- 230000002411 adverse Effects 0.000 claims description 3
- 238000006073 displacement reaction Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001808 coupling effect Effects 0.000 description 1
- 230000000994 depressogenic effect Effects 0.000 description 1
- 230000001627 detrimental effect Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
Abstract
A path tracking control method based on two-point pre-aiming relates to the technical field of vehicle path tracking and is used for solving the problem that an existing driver model is low in control precision because the relation between transverse and longitudinal movements of a vehicle is not considered. The technical key points of the invention include: firstly, considering the transverse distance error and the angle deviation of two pre-aiming points, establishing a transverse tracking control model with the two pre-aiming points so as to control the steering wheel angle of a vehicle; secondly, a longitudinal tracking control model is established to control the running speed of the vehicle; again, the lateral and longitudinal movement of the vehicle is coupled. The invention has high adaptability, control precision, tracking performance and steering portability, and can be applied to path tracking control of unmanned vehicles.
Description
Technical Field
The invention relates to the technical field of vehicle path tracking, in particular to a path tracking control method based on two-point pre-aiming.
Background
Path tracking is a key technology in developing autonomous vehicles and is a central problem for controllers. By controlling parameters such as steering wheel angle, accelerator pedal opening, brake pedal opening and the like, the controller can control the transverse and longitudinal movement of the vehicle, and ensure that the vehicle runs on an ideal path.
In order to ensure accurate path tracking, it is necessary to use an efficient control model that simulates the handling of the vehicle by a skilled driver, a driver model being an important aspect of achieving path tracking, and of great importance to the study of "man-car-road" closed loop systems. Since the mid 20 th century, many students have studied driver models, which can be divided into a compensation tracking model and a pre-aiming tracking model. The compensation tracking model has been proposed earlier as the PID model proposed by Iguchi and the cross-server model proposed by McRure, however, these models are not suitable for high speed driving because they may cause oscillations. No model exists today that can take into account both the remote pre-aiming point and the vehicle's deviation information and the coupling between the vehicle's longitudinal and transverse movements.
Disclosure of Invention
In view of the above problems, the invention provides a path tracking control method based on two-point pre-aiming, which is used for solving the problem that the control accuracy is low because the relation between the transverse and longitudinal movements of a vehicle is not considered in the existing driver model.
A path tracking control method based on two-point pre-aiming comprises the following steps:
Step one, a transverse tracking control model with two pre-aiming points is established based on the angle of a driver so as to control the steering wheel angle of a vehicle; wherein the pre-aiming point comprises a far point and a near point;
step two, a longitudinal tracking control model is established to control the running speed of the vehicle;
And thirdly, coupling the transverse tracking control model and the longitudinal tracking control model to track the path of the unmanned vehicle.
Further, in the first step, the near point is located at the center of the front axle of the vehicle, and the far point is determined by the current vehicle speed v and the preset pre-aiming time T p: at a pretightening distance l d along the straight running direction of the vehicle with the center of mass of the vehicle as a starting point, the pretightening distance l d is defined as:
Where v m denotes the minimum vehicle speed.
Further, the specific steps of the first step include:
step one, for the far point, adopting an optimal curvature model, setting that the vehicle reaches the far point through an arc path track, wherein the track has an optimal curvature of 1/R; calculating to obtain a curvature radius R according to the far point transverse distance deviation y e, the pretightening distance l d and a coordinate transformation equation; calculating according to the curvature radius R to obtain a first steering wheel angle delta 1; the far point transverse distance deviation y e is the projection of the distance between the origin of the vehicle coordinate system at the initial position and the mass center when the vehicle reaches the far point on the y axis of the vehicle coordinate system at the initial position;
According to the heading angle error of the far point And calculating a preset aiming time T p to obtain a second steering wheel angle delta 2;
Step two, calculating to obtain the yaw acceleration alpha 3 by adopting a sliding mode control method for the near point; integrating the yaw acceleration alpha 3 in time to obtain a third steering wheel angle delta 3;
Step one, weighting the first steering wheel angle δ 1, the second steering wheel angle δ 2 and the third steering wheel angle δ 3, namely:
δ＝w1·δ1+w2·δ2+w3·δ3
Wherein w 1、w2 and w 3 are the weights of δ 1,δ2 and δ 3, respectively; the final steering wheel angle delta is obtained.
Further, the specific process of the step one by one is as follows: the vehicle reaches a far point through an arc path track, the vehicle coordinate system rotates a centroid sideslip angle beta, and a curvature radius R is obtained according to a geometric relation and a coordinate transformation equation, and the calculation formula is as follows:
Wherein, l' d＝cosβ·ld+sinβ·ye,y′e＝-sinβ·ld+cosβ·ye;
According to a steady-state steering motion formula, calculating a first yaw rate omega 1 for obtaining a far point:
The first steering wheel angle δ 1 is:
δ1＝ω1·Y(S)
wherein Y (S) represents a transfer function of steering wheel angle to yaw rate;
δ2＝ω2·Y(S)
Where ω 2 represents the second yaw rate,
Further, in the first step, a sliding mode control method is adopted, and first, a third yaw rate ω 3 of the near point is obtained:
Where η and k represent the proximity parameters; sgn (S) represents a sign function;
S represents a sliding surface switching function of the lateral deviation of the mass center of the vehicle; lambda 1>0,λ2 >0 each represents a slip film face coefficient; y eg represents the lateral deviation at the centroid; Representing a near point heading angle deviation;
the third yaw rate ω 3 is corrected in consideration of the time and tracking accuracy required for the vehicle to eliminate the near point error, and the corrected third yaw rate is expressed as yaw rate acceleration α 3:
Wherein t represents the time required for the vehicle to eliminate the near point error; epsilon represents the use for eliminating direct application Correction parameters for adverse effects.
Further, the longitudinal tracking control model in the second step controls the vehicle running speed according to the following process:
calculating and obtaining a pretightening speed v d according to a curvature radius R d of a far point of the pretightening point in real time;
Uniformly accelerating the vehicle in a preset aiming time T p, and calculating to obtain preset aiming acceleration a d according to a preset aiming speed v d, a current speed v and a preset aiming time T p;
Calculating and obtaining an acceleration threshold according to vehicle type parameters and a vehicle dynamics formula, and outputting the opening of an accelerator pedal when the pre-aiming acceleration a d is more than 0; outputting a brake pedal opening when the pre-aiming acceleration a d <0 and the acceleration threshold is exceeded; otherwise, the opening outputs of the accelerator pedal and the brake pedal are zero.
Further, in the second step, the calculation formula of the pretightening vehicle speed v d is as follows:
Wherein a ymax represents a preset maximum lateral acceleration; r m denotes a preset maximum radius of curvature.
Further, in the second step, the acceleration threshold calculation formula is:
Wherein G is the weight of the vehicle; f is the rolling resistance coefficient; m represents a vehicle mass; c D is the air resistance coefficient; a is the windward area.
Further, in the third step, the lateral tracking control model and the longitudinal tracking control model are coupled as follows: the steering wheel angle is converted into the vehicle steering radius by considering the vehicle speed change and the vehicle steering radius, and the yaw rate gain is expressed as a function of the vehicle speed and the steering radius by adopting a polynomial fitting method, and then the yaw rate gain G ω is expressed as:
Gω＝c00+(c11·v+c12·r)+(c21·v2+c22·v·r+c23·r2)+(c31·v3+c32·v2·r+c33·v·r2+c34·r3)+(c41·v4+c42·v3·r+c43·v2·r2+c44·v·r3+c45·r4)
wherein, C ij, i=0, 1, 2,3, 4,j =0, 1, 2,3, 4,5 are fitting parameters; r denotes the steering radius.
The beneficial technical effects of the invention are as follows:
the invention provides a path tracking control method based on driver pre-aiming, which is used for controlling the transverse and longitudinal movement of a vehicle. Firstly, considering the transverse distance error and the angle deviation of two pre-aiming points, establishing a transverse tracking control model with the two pre-aiming points; secondly, a longitudinal tracking model is established, the speed of the vehicle is controlled according to the road information of the far point, and the control target is the opening degree of the automobile brake and the accelerator pedal; subsequently, the lateral and longitudinal movements of the vehicle are coupled. The invention has high adaptability and control precision.
Drawings
The invention may be better understood by reference to the following description taken in conjunction with the accompanying drawings, which are included to provide a further illustration of the preferred embodiments of the invention and to explain the principles and advantages of the invention, together with the detailed description below.
FIG. 1 is a two degree of freedom vehicle dynamics model;
FIG. 2 is a control block diagram of a path tracking control method based on two-point pre-aiming according to an embodiment of the present invention;
FIG. 3 is a control block diagram of a lateral tracking model in an embodiment of the invention;
FIG. 4 is another control schematic of a lateral tracking model in an embodiment of the invention;
FIG. 5 is a schematic diagram of the pre-aiming point far point heading angle error input of a transverse tracking model in an embodiment of the invention;
FIG. 6 is a schematic diagram of far point optimal steering radius acquisition of a lateral tracking model in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a near point vehicle motion model control of a lateral tracking model in an embodiment of the invention;
FIG. 8 is a control block diagram of a longitudinal tracking model in an embodiment of the invention;
FIG. 9 is a schematic diagram of calculation of lateral acceleration of rollover in a longitudinal tracking model in accordance with an embodiment of the present invention;
FIG. 10 is an exemplary plot of a fitted curve with a 150m far point optimal steering radius after coupling a lateral tracking control model and a longitudinal tracking control model in an embodiment of the present invention;
FIG. 11 is an exemplary graph of a G omega-v-R curve after coupling a lateral tracking control model and a longitudinal tracking control model in an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, exemplary embodiments or examples of the present invention will be described below with reference to the accompanying drawings. It is apparent that the described embodiments or examples are only implementations or examples of a part of the invention, not all. All other embodiments or examples, which may be made by one of ordinary skill in the art without undue burden, are intended to be within the scope of the present invention based on the embodiments or examples herein.
Vehicles are highly complex nonlinear systems that, for ease of modeling and analysis, first employ a linear two-degree-of-freedom vehicle model to analyze vehicle transient responses as follows.
In order to detect the movement of the vehicle on level ground, it is assumed that the vehicle is not slipping in the driving mode, the influence of the steering system is ignored and the front wheel angle is directly taken as input. When the vehicle runs on a horizontal road surface, the origin of the vehicle coordinate system coincides with the mass center of the vehicle, and a dynamic vehicle coordinate system is established. At this time, the mass distribution parameter (such as moment of inertia) of the vehicle is constant for the dynamic coordinate system fixed on the vehicle. Therefore, by decomposing the absolute acceleration, absolute angular acceleration, external force and external moment of the vehicle centroid on the axis of the vehicle coordinate system, a dynamic model of the whole vehicle can be established, as shown in fig. 1.
According to the established kinetic model, the lateral acceleration a y at the centroid is:
wherein ω represents a vehicle yaw rate; v denotes the vehicle centroid speed;
from the geometrical relationship:
wherein, beta represents the centroid sideslip angle of the vehicle, namely the centroid sideslip angle; l f denotes the vehicle front wheelbase; delta represents the front wheel rotation angle of the vehicle; alpha f represents the front wheel slip angle;
Wherein, l r represents the rear wheelbase of the vehicle; alpha r represents the rear wheel slip angle;
from the mechanical properties of the tire:
Fyf＝K1·αf (4)
Wherein F yf represents a front wheel side bias force; k 1 denotes the effective cornering stiffness of the front wheels of the vehicle;
Fyr＝K2·αr (5)
Wherein F yr represents a front wheel side bias force; k 2 denotes the effective cornering stiffness of the rear wheel.
The force balance and the moment balance of the vehicle are as follows:
Wherein m is the mass of the whole vehicle; i Z represents the moment of inertia of the automobile around the Z axis; the yaw acceleration of the vehicle is indicated.
According to this model, the differential equation of the automobile system is as follows:
the equation is transformed through Laplace transformation, and the initial condition is set to be zero, so that a transfer function of yaw rate to steering wheel rotation angle is obtained:
wherein K is a stability factor; g ω is yaw rate steady-state gain;
And then deduce:
Wherein eta is a mass distribution coefficient; c 1/C2 is the front/rear wheel yaw coefficient; ρ is the radius of inertia about the Z axis; l represents the wheelbase of the vehicle.
The transmission ratio of the steering wheel angle to the front wheel angle is i; therefore, the steady-state gain of yaw rate and steering wheel angle is:
G′ω＝Gω/i (16)
A transfer function of steering wheel angle versus yaw rate is obtained, as well as steady-state gain and correction elements. By omitting the quadratic term of the transfer function by taking the car as a low-pass filter, the transfer function of the steering wheel angle to the yaw rate can be obtained as follows:
The embodiment of the invention provides a path tracking control method based on two-point pre-aiming, which specifically comprises the following steps as shown in fig. 2:
Step one, a transverse tracking control model with two pre-aiming points is established based on the angle of a driver so as to control the steering wheel angle of a vehicle; wherein the pre-aiming point comprises a far point and a near point. A control block diagram of the lateral tracking model is shown in fig. 3.
According to an embodiment of the invention, the lateral tracking control model comprises road input, control quantity, correction links, vehicles, output and control decisions. As shown in fig. 4, the road input includes four pieces of information: distant point lateral distance deviation y e, distant point course angle errorNear point lateral distance deviation y en, near point heading angle error/>The far point lateral distance deviation y e is the projection of the distance between the origin of the vehicle coordinate system at the starting position and the centroid of the vehicle when reaching the far point on the y axis of the vehicle coordinate system at the starting position.
The center of the front axle of the vehicle is selected as a near point, and a far point is determined according to the pre-aiming time and the vehicle speed, specifically, the center of mass of the vehicle is taken as a starting point, and the pre-aiming distance l d along the straight running direction of the vehicle is taken as the far point. The pretighted distance l d is defined as:
Where v m is the minimum vehicle speed and v m =18 km/h. The control model targets are as follows:
(1) After the pre-sight time Tp, the vehicle position coincides with the far point.
(2) After the pretightening time Tp, the vehicle traveling direction coincides with the road traveling direction at a distance.
(3) The vehicle runs on the road center line, i.e., y en →0.
(4) The vehicle running smoothly, i.e
First, a steering wheel angle corresponding to a far point is acquired.
The lateral control actuator is a steering wheel and the input to the steering wheel may be an angle input or a force input. In order to ensure an effective comparison, an effective model is obtained, using an angle input. Because of different functions of far and near point control, different processing methods are adopted for information provided by a control technology: for the far point, an optimal curvature model is used: assuming that the vehicle reaches a far point through an arc-shaped path, and the track has an optimal curvature of 1/R, an ideal yaw rate omega can be determined according to R; the steering wheel angle is then obtained by correcting ω as shown in fig. 5.
In order to determine the far point lateral distance deviation y e, an optimal steering radius, i.e., an optimal radius of curvature R, should first be obtained. As shown in fig. 6, the optimal steering radius is calculated regardless of the vehicle centroid slip angle. Assuming that the instantaneous center of the vehicle is located on the y-axis of the vehicle coordinate system xoy, the distance between the instantaneous center and the x-axis of the vehicle coordinate system xoy is the transverse distance deviation y e, and the distance between the instantaneous center and the y-axis is the pretightening distance l d. And introducing a centroid sideslip angle beta, and rotating the original coordinate system xoy by beta to obtain a new coordinate system x' oy. On the basis of x 'oy', coordinates l d 'and y e' of a pre-aiming far point are calculated, an ideal turning radius R is obtained according to a geometric relation, and an ideal steering wheel corner is finally obtained according to a steady steering movement formula.
Specifically, it is available from a rotation coordinate equation (i.e., coordinate transformation equation):
Then:
l′d＝cosβ·ld+sinβ·ye
y′e＝-sinβ·ld+cosβ·ye
the ideal turning radius, namely the optimal turning radius R, is obtained according to the geometric relation:
next, R is substituted according to the formula of the steady-state steering motion, to obtain a first yaw rate ω 1:
The ideal first steering wheel angle δ 1 can be obtained by the following formula:
δ1＝ω1·Y(S) (22)
the ideal yaw rate (i.e., the second yaw rate) ω 2 is as follows:
wherein T p is the pretightening time. Thus, there are:
δ2＝ω2·Y(S) (24)
In the far point heading control, although a desired heading can be achieved after T p, a large lateral deviation may occur, which is not allowable, and thus the angle deviation information can only be used as a supplement to the pretightening distance offset.
Then, the steering wheel angle corresponding to the near point is acquired.
The near point control compensates for steering wheel angle based on the far point control, and regards the road as a straight line. Assuming that the longitudinal speed is sufficiently high, the transverse speed is negligible. Because of this assumption, the front wheel center of the automobile does not have any yaw angular velocity relative to its centroid, and thus the velocities of the front wheel center and centroid are the same. In this way, a motion model of the vehicle near point as shown in fig. 7 can be obtained. The observed quantity, i.e. the lateral deviation of the front axle, has to be converted into a lateral deviation of the centre of gravity from the road surface, which is the control variable. According to the geometrical relationship shown in fig. 7:
wherein y eg represents the lateral deviation at the centroid;
the rate of change of the lateral position error between the centroid and the ideal path centerline is as follows:
The angular deviation of the near point is related to the distance deviation and can be represented by the first derivative of the distance deviation. Therefore, these two factors are not studied separately, but are added together to the sliding surface. In order to establish a sliding surface with a laterally offset centroid, the switching function S is as follows:
wherein lambda 1 >0 and lambda 2 >0 are synovial face coefficients.
In order to reduce system buffeting, the slip film approach rate is designed as follows:
where η and k represent the proximity parameters; η >0, k >0.sgn (S) represents a sign function,
The switching function formula is as follows:
from formulae (26) and (27):
The combination of formulas (28) - (31) can be obtained:
Where ω 3 represents the third yaw rate.
However, the yaw rate derived from the equation (27) does not take into account the time required for the vehicle to cancel the near-point error, and therefore the vehicle must cancel the near-point error in a very small time. This may lead to a considerable yaw rate, which is detrimental to the stability of the vehicle. In addition, although this can reduce the near point error, the error cannot be completely eliminated. In addition, if there is no parameter correction, directly useThe tracking accuracy cannot be improved. Therefore, a parameter must be considered to counteract the effect and improve the control accuracy of the model.
Taking into account the time and the time required to correct the vehicle errorCorrection of the values, after introducing the vehicle reaction time, the equation must be modified, with the result that the yaw acceleration α 3:
Wherein t represents the time required for the vehicle to eliminate the near point error; epsilon is used for eliminating direct application Correction parameters for adverse effects.
After substituting α 3 into equation (17), the time needs to be integrated to obtain the near-point compensation steering wheel angle. Here, the influence of the vehicle speed on the near point control is ignored, and Y (S) is regarded as a constant.
Near point control is not suitable for complex road and steering wheel angle frequent changing conditions. Therefore, it is necessary to determine whether or not the near point control is enabled by considering the stability of the steering wheel angle.
Wherein K sw is a steering wheel angular velocity threshold, K sw =30 rad/s; delta (t) is the steering wheel angle at time t, if the above condition is satisfied, δ 3 =0; in other words, when the steering wheel angle change is large, the near-point control is not performed; otherwise, the value of δ 3 is determined by equation (34) and near point control is enabled.
Finally, the total steering wheel angle is:
δ＝w1·δ1+w2·δ2+w3·δ3 (36)
where w 1,w2 and w 3 are the weights of δ 1,δ2 and δ 3, respectively, and w 1＝1,w2＝0.15,w3 =1.
And step two, a longitudinal tracking control model is established to control the running speed of the vehicle. A control block diagram of the longitudinal tracking model is shown in fig. 8.
According to an embodiment of the present invention, the longitudinal pretightening must convert road information into speed information at an initial stage. Road information is analyzed from the aspects of driving safety, driving speed, traffic rules and the like, and driving safety and speed are mainly considered. Specifically, the running speed of the vehicle on the straight road is set to 36km/h. The following analysis is made on the running safety of the vehicle in consideration of rollover and sideslip.
1) Side turning: assuming a flat road surface, the gradient is zero. The calculation of the lateral acceleration is shown in fig. 9.
Balanced by torque:
Wherein h is centroid height; b is the track width.
2) Sideslip: when the vehicle performs steady-state circular motion, the lateral force balance and the moment balance are considered, and the following formula is obtained:
Fyf+Fyr＝m·a (39)
lf·Fyf＝lr·Fyr (40)
Thus, there are:
In order to determine the pre-aiming speed, the road curvature at the far point must be considered, which has a great effect on rollover and sideslip. Specifically, the pre-sighting speed can be obtained from the following equation:
Wherein v d is the pretarget speed; a ymax is the maximum lateral acceleration, a ymax＝1m/s2;Rd is the road radius of the pretightening point; r m is the maximum road radius of the pretightening point, R m =100 m.
The purpose of the longitudinal control is to achieve a pre-aiming speed after a pre-aiming time. Assuming that the vehicle accelerates uniformly within the pre-sight time T p, the pre-sight acceleration may be defined as follows:
wherein a d is the pre-aiming acceleration; v is the current vehicle speed.
The longitudinal pre-aiming control has two actuators: an accelerator pedal and a brake pedal provide positive and negative acceleration, respectively. When the pre-acceleration is positive, the accelerator pedal must be stepped on; when the pre-acceleration is negative, the operation must be decided according to circumstances. Reducing the opening of the accelerator pedal when the brake pedal is not depressed may also provide a negative acceleration to the vehicle. Therefore, it is necessary to calculate the threshold value of the vehicle switching actuator, that is, the acceleration when no actuator is executed, as a basis for evaluating whether the brake pedal must be actuated. When the gradient is not considered, the resistance of the vehicle includes rolling resistance and air resistance. According to the vehicle dynamics formula,
Wherein G is the weight of the vehicle; f is the rolling resistance coefficient; m represents a vehicle mass; c D is the air resistance coefficient; a is the windward area.
When the vehicle model parameters are introduced in the above equation, the obtained acceleration may be used as a threshold for pedal actuation. In other words, if a d <0 and a d -a >0, then no pedal actuation is required, i.e.: outputting the opening degree of an accelerator pedal when the pre-aiming acceleration a d is more than 0; outputting a brake pedal opening when the pre-aiming acceleration a d is less than 0 and exceeds an acceleration threshold value; otherwise, the opening outputs of the accelerator pedal and the brake pedal are zero.
And thirdly, coupling the transverse tracking control model and the longitudinal tracking control model, and establishing a comprehensive tracking control model so as to track the path of the unmanned vehicle.
According to an embodiment of the invention, the longitudinal and transverse movements of the vehicle are mutually influenced during normal driving. Therefore, in the driver model, a certain coupling relation exists between the speed model and the direction model, and the influence of the speed model on the direction model is far greater than that of the direction model. Therefore, it is necessary to introduce longitudinal speed feedback in the direction control driver model to more accurately reflect the dynamics of the intelligent vehicle.
According to equation (12), the steady-state gain of the yaw rate is closely related to the speed, and therefore, the steady-state gain of the yaw rate calculated using the two-degree-of-freedom vehicle model is only one reference. By constructing an annular road with equal radius, the vehicle can run at different speeds, and by determining the yaw rate and the steering wheel angle, the yaw rate gain-speed curve is obtained. In order to determine the yaw rate gain versus speed profile, the vehicle is considered to be traveling at different speeds on an established endless road. Yaw rate and steering wheel angle may then be measured. Further, the vehicle model parameters affect the yaw rate gain versus speed. The hatchback type B class car is taken as a study object. Fig. 10 shows a curve for a road radius of 150 m.
Fitting the curve with a cubic polynomial yields:
Gω＝3·10-7·v3-10-4·v2+0.0087v-0.0179 (45)
When the parameters of the vehicle model are identified, according to repeated simulation researches, the G ω is influenced by not only the speed of the vehicle but also the steering angle of the steering wheel. By converting the steering wheel angle to the vehicle steering radius, steady-state yaw rate gains at different speeds at different radii of curvature can be obtained according to the method described above, as shown in fig. 11.
The steering wheel angle is converted into the vehicle steering radius by considering the vehicle speed change and the vehicle steering radius, and the yaw rate gain is expressed as a function of the vehicle speed and the steering radius by adopting a polynomial fitting method, and then the yaw rate gain G ω is expressed as:
Gω＝c00+(c11·v+c12·r)+(c21·v2+c22·v·r+c23·r2)+(c31·v3+c32·v2·r+c33·v·r2+c34·r3)+(c41·v4+c42·v3·r+c43·v2·r2+c44·v·r3+c45·r4) (46)
wherein, C ij, i=0, 1, 2,3, 4,j =0, 1, 2,3, 4,5 are fitting parameters; r denotes the steering radius.
For example, a fourth order polynomial that fits to the vehicle speed and steering radius is then expressed as:
Gω＝-0.1285+0.01617·v-1.012·10-3·r-2.298·10-4·v2-1.003·10-5·v·r+1.26·10-5·r2+1.115·10-6·v3+4.49·10-7·v2·r-1.417·10-7·v·r2-2.934·10-8·r3-2.821·10-9·v4+1.425·10-10·v3·r-1.013·10-9·v2·r2+4.801·10-10·v·r3 (47)
The invention takes a driver preview model as a core, and provides a path tracking model for simulating the manipulation behavior of a real driver in a 'man-vehicle-road' closed loop system. A steering wheel angle and longitudinal speed control model is built, the coupling effect between the models is analyzed, and a comprehensive control model is built. Even under the complex road condition, the comprehensive longitudinal and transverse tracking control provided by the invention can achieve high precision, and has good tracking performance and steering portability.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.
Claims (7)
1. A path tracking control method based on two-point pre-aiming is characterized by comprising the following steps:
Step one, a transverse tracking control model with two pre-aiming points is established based on the angle of a driver so as to control the steering wheel angle of a vehicle; wherein the pre-aiming point comprises a far point and a near point; the method specifically comprises the following steps:
step one, for the far point, adopting an optimal curvature model, setting that the vehicle reaches the far point through an arc path track, wherein the track has an optimal curvature of 1/R; calculating to obtain a curvature radius R according to the far point transverse distance deviation y e, the pretightening distance l d and a coordinate transformation equation; calculating according to the curvature radius R to obtain a first steering wheel angle delta 1; the far point transverse distance deviation y e is the projection of the distance between the origin of the vehicle coordinate system at the initial position and the mass center when the vehicle reaches the far point on the y axis of the vehicle coordinate system at the initial position;
According to the heading angle error of the far point And calculating a preset aiming time T p to obtain a second steering wheel angle delta 2;
Step two, calculating to obtain the yaw acceleration alpha 3 by adopting a sliding mode control method for the near point; integrating the yaw acceleration alpha 3 in time to obtain a third steering wheel angle delta 3;
Step one, weighting the first steering wheel angle δ 1, the second steering wheel angle δ 2 and the third steering wheel angle δ 3, namely:
δ＝w1·δ1+w2·δ2+w3·δ3
Wherein w 1、w2 and w 3 are the weights of δ 1,δ2 and δ 3, respectively; obtaining the final steering wheel angle delta;
step two, a longitudinal tracking control model is established to control the running speed of the vehicle; the longitudinal tracking control model controls the vehicle running speed according to the following process:
calculating and obtaining a pretightening speed v d according to a curvature radius R d of a far point of the pretightening point in real time;
Uniformly accelerating the vehicle in a preset aiming time T p, and calculating to obtain preset aiming acceleration a d according to a preset aiming speed v d, a current speed v and a preset aiming time T p;
calculating and obtaining an acceleration threshold according to vehicle type parameters and a vehicle dynamics formula, and outputting the opening of an accelerator pedal when the pre-aiming acceleration a d is more than 0; outputting a brake pedal opening when the pre-aiming acceleration a d <0 and the acceleration threshold is exceeded; otherwise, the opening outputs of the accelerator pedal and the brake pedal are zero;
And thirdly, coupling the transverse tracking control model and the longitudinal tracking control model to track the path of the unmanned vehicle.
2. The method according to claim 1, wherein in the first step, the near point is located at the center of the front axle of the vehicle, and the far point is determined by the current vehicle speed v and a preset aiming time T p: at a pretightening distance l d along the straight running direction of the vehicle with the center of mass of the vehicle as a starting point, the pretightening distance l d is defined as:
Where v m denotes the minimum vehicle speed.
3. The path tracking control method based on two-point pretightening according to claim 1, wherein the specific process of the step one is: the vehicle reaches a far point through an arc path track, the vehicle coordinate system rotates a centroid sideslip angle beta, and a curvature radius R is obtained according to a geometric relation and a coordinate transformation equation, and the calculation formula is as follows:
Wherein, l' d＝cosβ·ld+sinβ·ye,y′e＝-sinβ·ld+cosβ·ye;
According to a steady-state steering motion formula, calculating a first yaw rate omega 1 for obtaining a far point:
The first steering wheel angle δ 1 is:
δ1＝ω1·Y(S)
wherein Y (S) represents a transfer function of steering wheel angle to yaw rate;
δ2＝ω2·Y(S)
Where ω 2 represents the second yaw rate,
4. The method for controlling path tracking based on two-point pre-aiming as claimed in claim 3, wherein in the first step, a sliding mode control method is adopted, and the third yaw rate ω 3 of the near point is obtained first:
Wherein, DEG C and k represent the proximity parameters; sgn (S) represents a sign function; where S represents a sliding surface switching function of lateral displacement of the vehicle centroid; lambda 1>0,λ2 >0 each represents a slip film face coefficient; y eg represents the lateral deviation at the centroid; Representing a near point heading angle deviation;
the third yaw rate ω 3 is corrected in consideration of the time and tracking accuracy required for the vehicle to eliminate the near point error, and the corrected third yaw rate is expressed as yaw rate acceleration α 3:
Wherein t represents the time required for the vehicle to eliminate the near point error; epsilon represents the use for eliminating direct application Correction parameters for adverse effects.
5. The method for controlling path tracking based on two-point pretightening according to claim 4, wherein in the second step, the pretightening speed v d has a calculation formula as follows:
Wherein a ymax represents a preset maximum lateral acceleration; r m denotes a preset maximum radius of curvature.
6. The method for controlling path tracking based on two-point pretightening according to claim 5, wherein in the second step, the acceleration threshold calculation formula is:
Wherein G is the weight of the vehicle; f is the rolling resistance coefficient; m represents a vehicle mass; c D is the air resistance coefficient; a is the windward area.
7. The method for controlling path tracking based on two-point pretightening according to claim 6, wherein in the third step, the lateral tracking control model and the longitudinal tracking control model are coupled as follows: the steering wheel angle is converted into the vehicle steering radius by considering the vehicle speed change and the vehicle steering radius, and the yaw rate gain is expressed as a function of the vehicle speed and the steering radius by adopting a polynomial fitting method, and then the yaw rate gain G ω is expressed as:
Gω＝c00+(c11·v+c12·r)+(c21·v2+c22·v·r+c23·r2)+(c31·v3+c32·v2·r+c33·v·r2+c34·r3)+(c41·v4+c42·v3·r+c43·v2·r2+c44·v·r3+c45·r4).
wherein, C ij, i=0, 1, 2,3, 4,j =0, 1, 2,3, 4,5 are fitting parameters; r denotes the steering radius.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210533627.8A CN114896694B (en) | 2022-05-17 | 2022-05-17 | Path tracking control method based on two-point pre-aiming |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210533627.8A CN114896694B (en) | 2022-05-17 | 2022-05-17 | Path tracking control method based on two-point pre-aiming |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114896694A CN114896694A (en) | 2022-08-12 |
| CN114896694B true CN114896694B (en) | 2024-06-04 |
Family
ID=82724682
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210533627.8A Active CN114896694B (en) | 2022-05-17 | 2022-05-17 | Path tracking control method based on two-point pre-aiming |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114896694B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115476881B (en) * | 2022-10-20 | 2024-06-04 | 重庆长安汽车股份有限公司 | Vehicle track tracking control method, device, equipment and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109318905A (en) * | 2018-08-22 | 2019-02-12 | 江苏大学 | A kind of intelligent automobile path trace mixing control method |
| CN111703417A (en) * | 2020-06-24 | 2020-09-25 | 湖北汽车工业学院 | High-low speed unified preview sliding mode driving control method and control system |
| CN112141101A (en) * | 2020-09-29 | 2020-12-29 | 合肥工业大学 | Method and system for pre-aiming safety path based on CNN and LSTM |
| CN113320542A (en) * | 2021-06-24 | 2021-08-31 | 厦门大学 | Tracking control method for automatic driving vehicle |
| WO2021238747A1 (en) * | 2020-05-26 | 2021-12-02 | 三一专用汽车有限责任公司 | Method and apparatus for controlling lateral motion of self-driving vehicle, and self-driving vehicle |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11906625B2 (en) * | 2018-01-08 | 2024-02-20 | The Regents Of The University Of California | Surround vehicle tracking and motion prediction |
-
2022
- 2022-05-17 CN CN202210533627.8A patent/CN114896694B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109318905A (en) * | 2018-08-22 | 2019-02-12 | 江苏大学 | A kind of intelligent automobile path trace mixing control method |
| WO2021238747A1 (en) * | 2020-05-26 | 2021-12-02 | 三一专用汽车有限责任公司 | Method and apparatus for controlling lateral motion of self-driving vehicle, and self-driving vehicle |
| CN111703417A (en) * | 2020-06-24 | 2020-09-25 | 湖北汽车工业学院 | High-low speed unified preview sliding mode driving control method and control system |
| CN112141101A (en) * | 2020-09-29 | 2020-12-29 | 合肥工业大学 | Method and system for pre-aiming safety path based on CNN and LSTM |
| CN113320542A (en) * | 2021-06-24 | 2021-08-31 | 厦门大学 | Tracking control method for automatic driving vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114896694A (en) | 2022-08-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107415939B (en) | Steering stability control method for distributed driving electric automobile | |
| CN112092815B (en) | Vehicle track changing tracking control method based on model prediction | |
| CN112519882B (en) | Vehicle reference track tracking method and system | |
| CN111258323A (en) | Intelligent vehicle trajectory planning and tracking combined control method | |
| CN111103798B (en) | AGV path tracking method based on inversion sliding mode control | |
| CN107831761A (en) | A kind of path tracking control method of intelligent vehicle | |
| CN107132761B (en) | Design method of electric steering engine adopting pure fuzzy and fuzzy PID composite control | |
| CN111142534B (en) | Intelligent vehicle transverse and longitudinal comprehensive track tracking method and control system | |
| Németh et al. | Nonlinear analysis and control of a variable-geometry suspension system | |
| US20230001935A1 (en) | Controlling motion of a vehicle | |
| CN114896694B (en) | Path tracking control method based on two-point pre-aiming | |
| CN112109732A (en) | Intelligent driving self-adaptive curve pre-aiming method | |
| CN111845755B (en) | Method for estimating longitudinal speed of vehicle | |
| CN111679575A (en) | Intelligent automobile trajectory tracking controller based on robust model predictive control and construction method thereof | |
| CN112606843A (en) | Intelligent vehicle path tracking control method based on Lyapunov-MPC technology | |
| CN114148403B (en) | Multi-working-condition stability control method for wire-controlled steering system | |
| Weber et al. | Modeling and Control for Dynamic Drifting Trajectories | |
| Sahoo et al. | Design and development of a heading angle controller for an unmanned ground vehicle | |
| Liikanen et al. | Path-following controller for 4wds hydraulic heavy-duty field robots with nonlinear internal dynamics | |
| CN115167135A (en) | Feedback and model feedforward cascade unmanned vehicle self-tendency optimal position and posture control system | |
| CN116165943A (en) | Active safety control method for vehicle under drift limit working condition | |
| CN114740845A (en) | Vehicle tracking control method based on immersion and invariant manifold | |
| CN114179818A (en) | Intelligent automobile transverse control method based on adaptive preview time and sliding mode control | |
| CN115214715A (en) | Lateral control method and control device for automatic driving of vehicle and vehicle | |
| CN114148411A (en) | Drift control method of wheel type unmanned platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |