CN116834754A - Transverse and longitudinal cooperative control method for self-adaptive speed regulation of automatic driving vehicle - Google Patents
Transverse and longitudinal cooperative control method for self-adaptive speed regulation of automatic driving vehicle Download PDFInfo
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Abstract
The application provides a transverse and longitudinal cooperative control method for self-adaptive speed regulation of an automatic driving vehicle, which comprises the following steps: building a vehicle model and a tire model; according to the real-time position of the vehicle and the global road information, the longitudinal reference speed of the vehicle is predicted and tracked in real time; and performing transverse and longitudinal cooperative control on the automatic driving vehicle. The application adjusts the longitudinal speed according to the vehicle state parameters and the predicted path information of different road surfaces through the path tracking error and the road curvature so as to solve the real-time high-speed curve path tracking problem.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a transverse and longitudinal cooperative control method for self-adaptive speed regulation of an automatic driving vehicle.
Background
With the continuous improvement of the living standard of people, intelligent transportation is receiving more and more attention, and the automatic driving technology plays an important role in intelligent transportation. Among them, vehicle motion control is a core technology of an automatic driving technology, and ensuring accuracy and stability of vehicle tracking during automatic control is a worth exploring problem.
Vehicles are a complex system with highly non-linear lateral and longitudinal dynamics, and considering the driving stability of high-speed autopilot vehicles, variations in longitudinal speed and dynamics parameters will gradually affect lateral control simultaneously. The variation of the transverse parameters also has an important influence on the longitudinal speed control process. In particular, when the vehicle is operating in a curved condition, it is a considerable aspect of research how to further improve the driving safety of the vehicle.
Disclosure of Invention
Aiming at the defects in the prior art, the application aims to provide a transverse and longitudinal cooperative control method for self-adaptive speed regulation of an automatic driving vehicle, and the longitudinal speed is regulated by aiming at vehicle state parameters and predicted path information of different roads through path tracking errors and road curvature so as to solve the problem of real-time high-speed curve path tracking.
In order to solve the problems, the technical scheme of the application is as follows:
a transverse and longitudinal cooperative control method for self-adaptive speed regulation of an automatic driving vehicle comprises the following steps:
building a vehicle model and a tire model;
according to the real-time position of the vehicle and the global road information, the longitudinal reference speed of the vehicle is predicted and tracked in real time;
and performing transverse and longitudinal cooperative control on the automatic driving vehicle.
Preferably, the vehicle model includes a vehicle yaw dynamics model and a longitudinal dynamics model in consideration of a tracking error.
Preferably, the step of building a vehicle model and a tire model specifically includes:
establishing a yaw dynamics model considering a vehicle tracking error, wherein the vehicle yaw dynamics model considering the tracking error is expressed as:
wherein m is the mass of the vehicle,longitudinal and lateral speeds at the body centroid, respectively, delta being the tire rotation angle, F lf and Flr F is the longitudinal force of the front and rear wheels xf and Fxr For the lateral force of the front and rear wheels +.>For yaw rate, I z For the moment of inertia of the vehicle body about the Z axis, l f 、l r The distances from the center of mass of the vehicle to the front and rear axes, e y and />The lateral position error and the heading angle error of the vehicle are respectively, and kappa is the time-varying curvature on the reference path;
establishing a longitudinal dynamics model of the vehicle, wherein the longitudinal dynamics model is as follows:
wherein r represents the radius of the wheel, ω represents the rotation speed of the single wheel, I w Representing moment of inertia, T, of the wheel b Is the braking torque on the wheel;
for the total power of the motor, the relation between the motor torque and the motor rotation speed is as follows: p=t e ω e The motor model used was set as: in the formula ,Tmax Is the maximum torque of the motor, alpha v Is the opening degree eta of an accelerator pedal r Is the ratio of peak motor power to maximum torque.
Preferably, the step of predicting and tracking the longitudinal reference speed of the vehicle in real time according to the real-time position and the global road information of the vehicle specifically includes: and according to the real-time position of the vehicle and the global road information, predicting the longitudinal speed of the vehicle in real time based on the road curvature and the transverse error obtained by fitting calculation, and designing a longitudinal speed tracker.
Preferably, the step of predicting and tracking the longitudinal reference speed of the vehicle in real time according to the real-time position and the global road information of the vehicle specifically includes:
when the vehicle is in a small curvature road section or a straight road section, the vehicle runs at a stable high-speed state, the longitudinal speed is adjusted in real time according to the size of the transverse error value to reduce the transverse error, and when the vehicle is about to enter a curve road section, the fuzzy controller outputs a reference speed value after the adjustment is reduced;
the method for sectionally fitting road point information by using the Bezier curve is adopted to calculate and process the road information in the current period and the future period of the vehicle, after the sectionalized Bezier curve is obtained, the road curvature is calculated by using a three-point curvature calculating method, and then the road curvature information in the current period and the future period of time can be output in real time, and the information is input into a fuzzy controller to complete the prediction of the curve reference longitudinal speed;
in the adopted longitudinal speed tracking controller, T i and Td Respectively an integral time constant and a differential time constant, k p 、k i and kd The scale factor, the integral factor, and the differential factor, respectively, and therefore, for the control amount variation signal and the controlled object, there are:the longitudinal controller controls the vehicle speed tracking error, and selects a driving or braking strategy according to a calibration test result so as to match the actual speed with the expected speed.
Preferably, the step of performing lateral-longitudinal cooperative control on the automatic driving vehicle specifically includes: in order to reduce the calculation workload and improve the real-time performance, discretizing and linearizing the nonlinear vehicle model to obtain a state space equation of a linear time-varying system; in the design of the transverse path tracking and yaw stability controller, a method based on linear time-varying model predictive control is adopted, and stability constraint is carried out by adopting an envelope curve method, so that cooperative control is carried out.
Preferably, the step of performing lateral-longitudinal cooperative control on the automatic driving vehicle specifically includes:
designing a transverse control strategy;
constructing yaw dynamics constraint;
constructing an objective function;
and a transverse and longitudinal cooperative control strategy.
Preferably, the step of designing the lateral control strategy specifically includes:
the front wheel steering angle delta is set as the control input u of the vehicle 1 Road curvature u 2 Let κ be the additional disturbance variable, the state variable of the vehicle isThe dynamics model of the vehicle is: /> For the established vehicle dynamics model based on tracking error, the state quantity matrix and the input matrix are respectively as follows:
wherein ,/>To be at the system operating point->Is a state of (2).
Defining new state variablesOutput state variable η (k), increment of control input Δu (k) =u 1 (k)-u 1 (k-1) the controller model of the discrete state space is transformed into:
η(k|t)＝H p ζ(k|t)
in the formula ,
after obtaining the new state variable, iterating continuously in the following time sequence to obtain a predicted output expression in a predicted time domain: y (t) =ψ t ζ(t)+Θ t ΔU(t)+Λ t γ(t)
wherein :
Y(t)＝[η(k+1|t) η(k+2|t) … η(k+N c |t) … η(k+N p |t)] T ,
ΔU(t)＝[Δu(k|t) Δu(k+1|t) Δu(k+2|t) … … Δu(k+N c -1|t)] T ,
wherein ,Np To predict the time domain, N c To predict the time domain.
Preferably, the step of constructing yaw dynamics constraint specifically includes:
the constraint equation of the maximum slip angle of the rear wheels is expressed by the vehicle lateral speed and the yaw rate: wherein ,/>Tire slip angle when the lateral force of the rear wheel is saturated;
from the tire linearization model and the vehicle dynamics equation for the small angle hypothesis, the vehicle yaw rate change can be constrained as:solving the formula to obtain envelope constraints of yaw stability;
the envelope-based yaw stability criterion expression is:
-G en (k)-ε en (k)≤H en (k)ζ(k)≤G en (k)+ε em (k).
wherein ,
and />Constraint relaxation values of the rear wheel slip angle, the centroid slip angle and the yaw rate are respectively.
Introducing a relaxation factor, and performing soft constraint processing on the cost function:
wherein sigma is a weight coefficient, and epsilon is a relaxation factor;
according to the yaw dynamics constraint method, the expression of the predicted output is introduced into the formula:
obtaining an objective function to be solved finally, and solving quadratic programming:
|δ f |≤δ f,max
|Δδ f |≤Δδ f,max
wherein the first term of the cost function represents a lateral position error of the vehicle relative to the reference trajectoryAnd heading error, the second term represents a steady change in the required control amount during path tracking, Q ey 、 and RΔu For adjustable weights, different weights are set to adjust the priorities of different targets.
Compared with the prior art, the application has the following advantages:
1. the application introduces a cooperative controller to process the problem of curve path tracking, considers the influence of continuous change of longitudinal speed on transverse path tracking under the curve path, and improves the control precision of transverse tracking.
2. The application tracks and controls the longitudinal speed of the vehicle, can effectively reduce tracking error and improve the safety of vehicle driving.
3. The application only needs to provide global reference path information externally, does not need other road information to realize the prediction of the longitudinal reference speed, and has strong practical applicability.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a transverse and longitudinal cooperative control method for self-adaptive speed regulation of an automatic driving vehicle, which is provided by the embodiment of the application;
FIG. 2 is a diagram of a vehicle dynamics model provided by an embodiment of the present application;
FIG. 3 is a diagram of a fuzzy logic surface according to an embodiment of the present application;
FIG. 4 is a longitudinal control logic diagram provided by an embodiment of the present application;
fig. 5 is a schematic diagram of a coordinated control logic diagram of the horizontal and vertical directions according to an embodiment of the present application.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the application in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present application.
Specifically, the application provides a transverse and longitudinal cooperative control method for self-adaptive speed regulation of an automatic driving vehicle, as shown in fig. 1, comprising the following steps:
s1: building a vehicle model and a tire model;
the vehicle model comprises a vehicle yaw dynamics model and a longitudinal dynamics model, and specifically comprises the following steps:
step 11: establishing a yaw dynamics model considering vehicle tracking errors;
and establishing a vehicle body coordinate system xyz at the center of gravity of the vehicle, wherein the origin of the coordinate system coincides with the center of mass of the vehicle, the x axis coincides with the longitudinal running direction of the vehicle and is parallel to the ground, the y axis coincides with the transverse running direction of the vehicle, and the z axis is perpendicular to the ground. The vehicle dynamics model is shown in fig. 2, and the vehicle yaw dynamics equation is:
wherein m is the mass of the vehicle,longitudinal and lateral speeds at the body centroid, respectively, delta being the tire rotation angle, F lf and Flr F is the longitudinal force of the front and rear wheels xf and Fxr For the lateral force of the front and rear wheels +.>For yaw rate, I z For the moment of inertia of the vehicle body about the Z axis, l f 、l r The distances from the vehicle center of mass to the front and rear axes, respectively.
The tire lateral and longitudinal forces can be expressed as:
in the formula ,Cα For cornering stiffness of tyre, C x For the longitudinal rigidity of the tire, the adhesion coefficient mu between the two tires and the ground and the vertical force F of the tire z Related to; alpha is the tire slip angle; s is S f,r Is the slip ratio of the front and rear tires.
The front and rear tire slip angle and the vehicle center of mass slip angle can be expressed as:
the vehicle tracking error model may be expressed as:
in the formula ,ey Andthe lateral position error and heading angle error of the vehicle, respectively, and κ is the time-varying curvature on the reference path.
Equations 1-6 are converted into the following vehicle dynamics equations:
step 12: establishing a longitudinal dynamics model of the vehicle;
based on the single-wheel vehicle model, the resulting longitudinal dynamics expression from the front wheel driving force and the vehicle resistance is:
wherein ,is the first derivative of the longitudinal speed of the vehicle, F p and Fr Respectively the sum of the driving force and the vehicle resistance, the vehicle driving force F p Is determined by the control quantity input values of the accelerator opening and the brake, F r Is the sum of the resistance caused by the rolling friction of air and road and some other resistance.
For the assumed three-degree-of-freedom yaw dynamics model, the front wheel angles of the left and right wheels are set to be equal, dynamics analysis is carried out on the single wheels, and the single wheel rotation dynamics equation of the wheels at the two sides is unified into an expression:
wherein ,Ffl,fr The corresponding longitudinal forces acting on the left and right wheels,for the total force on each wheel. r represents the wheel radius, ω represents the rotational speed of a single wheel. I k Representing moment of inertia of the wheel, B d Is the damping coefficient.
The above equation can further be written as:
v＝ωr (11)
F p ＝F f (12)
wherein ,Ttf and Tb Representing the drive torque and the brake torque, respectively.
The following longitudinal dynamics equation can be obtained by combining the above equations:
according to the definition of longitudinal dynamics:
for the total power of the motor, the relation between the motor torque and the motor rotation speed is as follows:
P＝T e ω e (15)
the motor model used was set as:
in the formula ,Tmax Is the maximum torque of the motor, alpha v Is the opening degree eta of an accelerator pedal r Is the ratio of peak motor power to maximum torque.
S2: according to the real-time position of the vehicle and the global road information, the longitudinal reference speed of the vehicle is predicted and tracked in real time;
specifically, according to the real-time position of the vehicle and global road information, the longitudinal speed of the vehicle is predicted in real time based on the road curvature and the transverse error obtained by fitting calculation, and the longitudinal speed tracker is designed. When the vehicle passes through a curve at a high speed, the calculated curve value and the transverse error value in the prediction time domain are selected to be used as the input of the fuzzy controller, fuzzy reasoning is carried out, and the reference speed is obtained to be used as the prediction output value of the fuzzy controller.
Step 21: designing a fuzzy rule;
the longitudinal tracking controller is set taking into account the actual steering thinking of the driver over-bending. When the vehicle is in a small curvature road section or a straight road section, the vehicle runs at a stable high-speed state, and simultaneously, the longitudinal speed is adjusted in real time according to the magnitude of the transverse error value to reduce the transverse error. When the vehicle is about to enter the curve section, the fuzzy controller outputs a reference speed value after the adjustment is reduced. Next, the reference longitudinal speed is tracked by the longitudinal controller. In the longitudinal speed curve prediction process, considering the requirements of the steering stability and the passenger comfort of the vehicle, in the design process of the integral fuzzy control rule, the road curvature value is used as the most sensitive input quantity affecting the longitudinal speed, and the reference longitudinal speed is continuously adjusted along with the change of the curvature. In addition, when the vehicle enters a curve at a high speed, the lateral tracking error increases and even jumps. However, as the vehicle travels in a curve, the lateral error gradually decreases. Therefore, in the design of the fuzzy rule, the sensitivity to the influence of the transverse tracking error is considered to be smaller on a path with relatively smaller road curvature; on paths with larger curvatures, the sensitivity to lateral error effects is greater.
In the membership function design, the calculation of the triangular membership function is relatively simple and the response speed is high, so the membership functions of curvature, transverse error and predicted longitudinal speed are set as the triangular membership functions. And setting a fuzzy rule, wherein the inference curved surface of the fuzzy rule base is shown in figure 3.
In the designed fuzzy controller, the input quantity needs to be described in a fuzzy way. For the physical domain of the vehicle speed variation range, it is converted into a discrete domain by a quantization factor. For the dual input single output system described in this specification, the discrete domains of inputs and outputs are set to { -6, -5, -4, -3, -2, -1,0,1,2,3,4,5,6}. And the linguistic variables are defined as { "negative large (NB)", "Negative Medium (NM)", "Negative Small (NS)", "Zero (ZO)", "Positive Small (PS)", "median (PM)", "positive large (PB)" }7 levels. The triangle membership function is relatively simple to calculate and has high response speed, the triangle membership function is selected as the membership function of curvature, transverse error and longitudinal speed, and the centroid method is adopted to deblur. The designed fuzzy rule is as follows:
where Ln indicates that the selected fuzzy rule is the nth strip. The linguistic variables are and Qm . The fuzzy rule is 49 in total. The fuzzy input variable is a predicted road curvature value and a transverse tracking error. v des Indicating the desired speed of the output.
Step 22: the self-adaptive fitting method and calculation of the road curvature;
in order to capture the road curvature information in the prediction time domain in real time, a method of piecewise fitting road point information is adopted to calculate and process the road information in the current and future time of the vehicle. For the smoothing treatment of the reference track, the Bezier curve has the characteristics of relatively simple and convenient and easy smoothing treatment, and the like, and the three-time Bezier curve is selected to treat the reference track. The expression is as follows:
in the initial stage, two endpoints of a reference path are taken as starting and stopping control points of a fitting curve. Then, the intermediate points of the segmented path are fitted through a least square method, and two intermediate control points N2 and N3 of the subsequent cubic Bezier curve are obtained. In the continuous iterative process, calculating the residual error value between the fitting curve and the road point information of the section, and taking the residual error value as a judging standard of whether to perform the sectional fitting calculation or not, thereby achieving the purpose of self-adaptive sectional fitting and completing the fitting of the third-order Bezier curve.
After obtaining the segmented Bezier curve, calculating the curvature of the road by using a three-point curvature calculating method:
wherein x' =x c -x a ,y′＝y c -y a ,x″＝x c +x a -2x b ,y″＝y c +y a -2y b ，ζ＝(x′) 2 +(y′) 2 ,(x a ,y a ),(x b ,y b ),(x c ,y c ) Interpolation points selected for fitting the road segments.
After the path curve of the sectional self-adaptive fitting is obtained, the calculation can be performed in real time, the road curvature information in the current and future time is output, and the information is input into the fuzzy controller to complete the prediction of the curve reference longitudinal speed.
Step 23: longitudinal controller design;
as shown in fig. 4, the actual vehicle speed of the vehicle is input to the upper controller, and the fuzzy controller also outputs the predicted reference speed to the upper controller, and the PID controller of the upper controller calculates the current desired acceleration of the vehicle through the input. The state quantities such as the expected acceleration and the actual vehicle speed are input into the lower controller together, and then the vehicle is determined to be in a driving mode or a braking mode. And finally, calculating the throttle opening and the brake master cylinder pressure value through an inverse longitudinal dynamics model, and inputting the throttle opening and the brake master cylinder pressure value into a controlled vehicle to finish the longitudinal acceleration and deceleration control of the vehicle.
e v Tracking error, i.e. the difference between the desired speed and the actual speed, for the longitudinal vehicle speed:
e v ＝v des -v r (19)
further, there are:
wherein ,vdes Indicating a desired longitudinal speed of the vehicle, v r Indicating the actual speed of the vehicle,indicating the desired longitudinal acceleration +.>Indicating the actual longitudinal acceleration of the vehicle. T (T) k Representing the sampling time of the controller.
In the adopted longitudinal speed tracking PID controller, T i and Td An integration time constant and a differentiation time constant, respectively. k (k) p 、k i and kd The scale factor, the integral factor and the differential factor, respectively. Therefore, for the control amount variation signal and the controlled object, there are:
the longitudinal controller controls the vehicle speed tracking error, and selects a driving or braking strategy according to a calibration test result so as to match the actual speed with the expected speed.
S3: and performing transverse and longitudinal cooperative control on the automatic driving vehicle.
In order to reduce the calculation workload and improve the real-time performance, discretizing and linearizing the nonlinear vehicle model to obtain a state space equation of a linear time-varying system; in the design of the transverse path tracking and yaw stability controller, a method based on linear time-varying model predictive control is adopted, and stability constraint is carried out by adopting an envelope curve method, so that cooperative control is carried out. Specifically, the step S3 includes the steps of:
step 31: designing a transverse control strategy;
in order to reduce the calculated amount and improve the instantaneity, the nonlinear vehicle model is linearized and then discretized, so that a state space equation of a linear time-varying system is obtained. In the design of the transverse path tracking and yaw stability controller, the application adopts a method based on linear time-varying model predictive control and adopts the constraint of an envelope curve method.
The front wheel steering angle delta is set as the control input u of the vehicle 1 Road curvature u 2 Let κ be the additional disturbance variable, the state variable of the vehicle isThe dynamics model of the vehicle is: /> For the established vehicle dynamics model based on tracking error, the state quantity matrix and the input matrix are respectively as follows:
wherein ,/>To be at the system operating point->Is a state of (2).
Defining new state variablesOutput state variable η (k), increment of control input Δu (k) =u 1 (k)-u 1 (k-1) the controller model of the discrete state space is transformed into:
η(k|t)＝H p ζ(k|t)
in the formula ,H p ＝[1 0 0].
after obtaining the new state variable, iterating continuously in the following time sequence to obtain a predicted output expression in a predicted time domain: y (t) =ψ t ζ(t)+Θ t ΔU(t)+Λ t γ(t)
wherein :
Y(t)＝[η(k+1|t) η(k+2|t) … η(k+N c |t) … η(k+N p |t)] T ,
ΔU(t)＝[Δu(k|t) Δu(k+1|t) Δu(k+2|t) … … Δu(k+N c -1|t)] T ,
/>
wherein ,Np To predict the time domain, N c To predict the time domain.
Step 32: constructing yaw dynamics constraint;
in yaw dynamics constraints of high-speed unmanned vehicles, the lateral acceleration of the vehicle is limited by the limiting grip coefficient, considering whether the grip coefficient of the actual road surface and the tire force match. In the vehicle yaw dynamics model, the expression of the vehicle lateral acceleration is:
the yaw rate and nominal centroid slip angle constraints can be expressed as:
wherein mu is the road adhesion coefficient, g is the gravitational acceleration.
When the vehicle sideslips, the vehicle lateral sway is caused to lose stability, and the vehicle typically sideslips at the rear axle. Thus constraining the maximum slip angle for the rear wheels. The constraint equation of the maximum slip angle of the rear wheels can be expressed by the vehicle lateral speed and the yaw rate:
wherein ,is the tire slip angle at which the lateral force of the rear wheel is saturated.
From the tire linearization model and the vehicle dynamics equation for the small angle hypothesis, the vehicle yaw rate change can be constrained as:
taking the smaller value of the data as the constraint extremum of the yaw rate and the lateral rate.
And solving the equation to obtain the envelope constraint of yaw stability. Within the constraint range of the control boundary, yaw stability and slip stability of the vehicle can be ensured. Meanwhile, in consideration of solving the practical problem, a relaxation factor is introduced to properly expand the boundaries of the rear wheel slip angle, the centroid slip angle and the yaw rate, so that the vehicle is allowed to stay briefly in a nonlinear region of the tire. The envelope-based yaw stability criterion expression is:
-G en (k)-ε en (k)≤H en (k)ζ(k)≤G en (k)+ε en (k).
wherein ,
and />Constraint relaxation values of the rear wheel slip angle, the centroid slip angle and the yaw rate are respectively.
Step 33: constructing an objective function;
after the reference path is given, the problem of whether a feasible optimal solution can be found in practical application is considered, so that in order to ensure that the feasible solution can be obtained every time a vehicle transversely tracks the target path, a relaxation factor is required to be introduced, and soft constraint processing is carried out on a cost function:
wherein σ is a weight coefficient and ε is a relaxation factor.
Considering and introducing the yaw dynamics constraint method described in the previous section, introducing the expression of the predicted output into the above expression to obtain a final solved objective function, and solving quadratic programming:
wherein a first term of the cost function represents a lateral position error and a heading error of the vehicle relative to the reference trajectory. The second term represents a smooth change in the required control amount during the path tracking process. The first constraint represents a linear discretized vehicle dynamics model. The following constraints represent envelope constraints of yaw stability of the high-speed running vehicle, constraints of limiting front wheel rotation angle and front wheel rotation angle increment, respectively. Q (Q) ey 、 and RΔu For adjustable weights, different weights are set to adjust the priorities of different targets.
Step 34: a transverse and longitudinal cooperative control strategy;
the integrated control block diagram shown in fig. 5 illustrates the architecture of the cooperative control strategy and the interaction relationship between the different modules, taking into account the influence of the speed of change tracked in the longitudinal controller on the accuracy of the predictive and control model in the lateral control module. The time-varying vehicle speed is regarded as an external disturbance of the control model, acting on the transverse path tracking process, which is regarded as an influence of the kinematic coupling in the coupling control. The actual vehicle dynamics state quantity and the position information of the reference path are input to a lateral controller, which can output a steering wheel angle of the vehicle. A series of road curvature information in the prediction time domain can be obtained to complete the prediction of road curvature. The lateral error of the current position is obtained. The output lateral error value and the predicted road curvature curve are then input into a fuzzy controller to generate a reference longitudinal speed value. Next, the actual longitudinal speed and other state parameters of the controlled vehicle are input to the longitudinal controller and the dynamics model. The reference speed output from the predict and generate speed profile module is input to the longitudinal control module. And finally, performing logic switching of driving and braking through the speed error fed back dynamically by the vehicle, and outputting a corresponding accelerator opening value or brake master cylinder pressure value to complete the tracking of the path and the speed.
In summary, in the present application, the control method based on the MPC control strategy has been used for the lateral control, and the control method based on the PID and the fuzzy prediction has been used for the longitudinal control. The transverse controller is designed according to a vehicle path tracking method by adopting a transverse and longitudinal decoupling strategy. It is noted that the longitudinal movement of the vehicle has a large influence on the lateral movement of the vehicle. Under extreme conditions, proper constraint is selected to ensure the stability of the vehicle, or the longitudinal speed is adjusted to adapt to the running safety requirements under different working conditions. In this regard, the present application solves this problem by employing a strategy for generating a reference longitudinal velocity by means of fuzzy prediction. In the previous path-following control strategy, the longitudinal speed of the vehicle is generally set to a constant value, and is considered constant within a predicted range. The application outputs the predicted curvature curve and the transverse error value in real time by the reference path, and considers the influence of the tracked continuously-changing actual speed in the longitudinal controller on the accuracy of the prediction and control model in the transverse control module. The time-varying speed of the real vehicle is used as the external interference of the control model to act in the transverse path tracking process, and the influence of kinematic coupling in transverse-longitudinal coupling control is considered.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (10)
1. The transverse and longitudinal cooperative control method for self-adaptive speed regulation of the automatic driving vehicle is characterized by comprising the following steps of:
building a vehicle model and a tire model;
according to the real-time position of the vehicle and the global road information, the longitudinal reference speed of the vehicle is predicted and tracked in real time;
and performing transverse and longitudinal cooperative control on the automatic driving vehicle.
2. The method for adaptively controlling the yaw and longitudinal cooperative control of an automatically driven vehicle according to claim 1, wherein the vehicle model includes a vehicle yaw dynamics model and a longitudinal dynamics model taking a tracking error into consideration.
3. The method for adaptively adjusting speed of an autonomous vehicle according to claim 1, wherein said step of building a vehicle model and a tire model comprises:
establishing a yaw dynamics model considering a vehicle tracking error, wherein the vehicle yaw dynamics model considering the tracking error is expressed as:
wherein m is the mass of the vehicle,longitudinal and lateral speeds at the body centroid, respectively, delta being the tire rotation angle, F lf and Flr F is the longitudinal force of the front and rear wheels xf and Fxr For the lateral force of the front and rear wheels +.>For yaw rate, I z For the moment of inertia of the vehicle body about the Z axis, l f 、l r The distances from the center of mass of the vehicle to the front and rear axes, e y and />The lateral position error and the heading angle error of the vehicle are respectively, and kappa is the time-varying curvature on the reference path;
establishing a longitudinal dynamics model of the vehicle, wherein the longitudinal dynamics model is as follows:
wherein r represents the radius of the wheel, ω represents the rotation speed of the single wheel, I w Representing moment of inertia, T, of the wheel b Is the braking torque on the wheel;
for the total power of the motor, the relation between the motor torque and the motor rotation speed is as follows: p=t e ω e The motor model used was set as: in the formula ,Tmax Is the maximum torque of the motor, alpha v Is the opening degree eta of an accelerator pedal r Is the ratio of peak motor power to maximum torque.
4. The method for adaptively adjusting speed of an automatically driven vehicle according to claim 1, wherein the steps of predicting and tracking the longitudinal reference speed of the vehicle in real time according to the real-time position and global road information of the vehicle comprise: and according to the real-time position of the vehicle and the global road information, predicting the longitudinal speed of the vehicle in real time based on the road curvature and the transverse error obtained by fitting calculation, and designing a longitudinal speed tracker.
5. The method for adaptively adjusting speed of an automatically driven vehicle according to claim 1, wherein the steps of predicting and tracking the longitudinal reference speed of the vehicle in real time according to the real-time position and global road information of the vehicle comprise:
when the vehicle is in a small curvature road section or a straight road section, the vehicle runs at a stable high-speed state, the longitudinal speed is adjusted in real time according to the size of the transverse error value to reduce the transverse error, and when the vehicle is about to enter a curve road section, the fuzzy controller outputs a reference speed value after the adjustment is reduced;
the method for sectionally fitting road point information by using the Bezier curve is adopted to calculate and process the road information in the current period and the future period of the vehicle, after the sectionalized Bezier curve is obtained, the road curvature is calculated by using a three-point curvature calculating method, and then the road curvature information in the current period and the future period of time can be output in real time, and the information is input into a fuzzy controller to complete the prediction of the curve reference longitudinal speed;
in the adopted longitudinal speed tracking controller, T i and Td Respectively an integral time constant and a differential time constant, k p 、k i and kd The scale factor, the integral factor, and the differential factor, respectively, and therefore, for the control amount variation signal and the controlled object, there are:the longitudinal controller controls the vehicle speed tracking error, and selects a driving or braking strategy according to a calibration test result so as to match the actual speed with the expected speed.
6. The method for cooperative control of an autonomous vehicle in lateral and longitudinal direction according to claim 1, wherein the step of cooperative control of the autonomous vehicle in lateral and longitudinal direction specifically comprises: in order to reduce the calculation workload and improve the real-time performance, discretizing and linearizing the nonlinear vehicle model to obtain a state space equation of a linear time-varying system; in the design of the transverse path tracking and yaw stability controller, a method based on linear time-varying model predictive control is adopted, and stability constraint is carried out by adopting an envelope curve method, so that cooperative control is carried out.
7. The method for cooperative control of an autonomous vehicle in lateral and longitudinal direction according to claim 1, wherein the step of cooperative control of the autonomous vehicle in lateral and longitudinal direction specifically comprises:
designing a transverse control strategy;
constructing yaw dynamics constraint;
constructing an objective function;
and a transverse and longitudinal cooperative control strategy.
8. The method for adaptively adjusting speed of an autonomous vehicle according to claim 7, wherein the step of designing the lateral control strategy comprises:
the front wheel steering angle delta is set as the control input u of the vehicle 1 Road curvature u 2 Let κ be the additional disturbance variable, the state variable of the vehicle isThe dynamics model of the vehicle is: /> For the established vehicle dynamics model based on tracking error, the state quantity matrix and the input matrix are respectively as follows:
wherein ,/>To be at the system operating point->Is a state of (2).
Defining new state variablesOutput state variable η (k), increment of control input Δu (k) =u 1 (k)-u 1 (k-1) the controller model of the discrete state space is transformed into:
η(k|t)＝H p ζ(k|t)
in the formula ,H p ＝[1 0 0].
after obtaining the new state variable, iterating continuously in the following time sequence to obtain a predicted output expression in a predicted time domain:
wherein ：
Y(t)＝[η(k+1|t) η(k+2|t)…η(k+N c |t)…η(k+N p |t)] T ，
ΔU(t)＝[Δu(k|t) Δu(k+1|t) Δu(k+2|t)……Δu(k+N c -1|t)] T ，
wherein ,Np To predict the time domain, N c To predict the time domain.
9. The method for adaptively adjusting speed of an autonomous vehicle according to claim 7, wherein said step of constructing yaw dynamics constraints comprises:
the constraint equation of the maximum slip angle of the rear wheels is expressed by the vehicle lateral speed and the yaw rate: wherein ,/>Tire slip angle when the lateral force of the rear wheel is saturated;
from the tire linearization model and the vehicle dynamics equation for the small angle hypothesis, the vehicle yaw rate change can be constrained as:solving the formula to obtain envelope constraints of yaw stability;
the envelope-based yaw stability criterion expression is:
-G en (k)-ε en (k)≤H en (k)ζ(k)≤G en (k)+ε en (k).
wherein ,
and />Constraint relaxation values of the rear wheel slip angle, the centroid slip angle and the yaw rate are respectively.
10. The method for adaptively adjusting speed of an autonomous vehicle according to claim 7, wherein said step of constructing an objective function comprises:
introducing a relaxation factor, and performing soft constraint processing on the cost function:
wherein sigma is a weight coefficient, and epsilon is a relaxation factor;
according to the yaw dynamics constraint method, the expression of the predicted output is introduced into the formula:
obtaining an objective function to be solved finally, and solving quadratic programming:
|H en ζ(k)|≤G en +ε en (k)
|δ f |≤δ f，max
|Δδ f |≤Δδ f，max
wherein the first term of the cost function represents the lateral position error and heading error of the vehicle relative to the reference trajectory, the second term represents the steady change of the required control amount during the path tracking process, Q ey 、 and RΔu For adjustable weights, different weights are set to adjust the priorities of different targets.
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