CN113009829A - Longitudinal and transverse coupling control method for intelligent internet motorcade - Google Patents

Longitudinal and transverse coupling control method for intelligent internet motorcade Download PDF

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CN113009829A
CN113009829A CN202110214816.4A CN202110214816A CN113009829A CN 113009829 A CN113009829 A CN 113009829A CN 202110214816 A CN202110214816 A CN 202110214816A CN 113009829 A CN113009829 A CN 113009829A
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CN113009829B (en
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黄晋
杨泽宇
张蔚
宫新乐
胡展溢
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Tsinghua University
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Abstract

The application discloses a longitudinal and transverse coupling control method for an intelligent networked fleet, which is suitable for member vehicles in the intelligent networked fleet and comprises the following steps: step 1, constructing a longitudinal and transverse coupling dynamic model of a member vehicle, and defining virtual control quantity according to the longitudinal and transverse coupling dynamic model; step 2, constructing a driving error model of the member vehicle according to the self-vehicle state information, the front-vehicle information and the rear-vehicle information of the member vehicle and the longitudinal-transverse kinematic relationship of the queue; step 3, calculating a control parameter group of the member vehicle by combining a longitudinal and transverse coupling dynamic model and a driving error model according to the self-vehicle state information and the front and rear vehicle information of the member vehicle; and 4, calculating the steering angle of the tire and the longitudinal control force of the vehicle according to the control parameter set and the defined virtual control quantity. Through the technical scheme in the application, the reliable following of the vehicle to the equation constraint of the designed vehicle state is ensured, the control target of the longitudinal and transverse directions of the queue is indirectly realized, and the more accurate longitudinal and transverse control of the queue is realized.

Description

Longitudinal and transverse coupling control method for intelligent internet motorcade
Technical Field
The application relates to the technical field of automatic driving, in particular to a longitudinal and transverse coupling control method for an intelligent internet motorcade.
Background
The intelligent networked vehicle formation can greatly improve the fuel economy and the driving safety of the system, wherein reliable longitudinal and transverse automatic control is the basis of safe operation of a motorcade, and extensive research in the industrial and academic fields is obtained.
The current mainstream solution is to decouple the longitudinal control and the transverse control of the fleet, and design a longitudinal cooperative controller and a transverse path tracking (or lane keeping) controller separately. The longitudinal cooperative controller realizes the adjustment of the inter-vehicle distance in the motorcade, and ensures the stability of the inter-vehicle distance error and the non-amplification of the inter-vehicle distance error in the process of spreading the inter-vehicle distance error to the tail of the motorcade (queue stability); the latter lateral controller enables reliable following of a given path by the vehicle.
However, the longitudinal and lateral dynamics of the vehicle are mutually influenced, namely, the lateral steering operation influences the longitudinal acceleration of the train, and the longitudinal acceleration and deceleration operation also influences the lateral acceleration and the yaw acceleration. The mutual influence is more obvious on a low-attachment road surface or a vehicle rapid acceleration and deceleration working condition.
Therefore, in the prior art, the longitudinal and lateral decoupling control scheme cannot take the longitudinal and lateral dynamic coupling relationship of the vehicle into consideration, and particularly on some curved lanes, the decoupling control scheme inevitably limits the improvement of the precision of the longitudinal and lateral control of the fleet, and even brings the phenomenon of system buffeting.
Disclosure of Invention
The purpose of this application lies in: according to the kinematic relationship, a target of the longitudinal and transverse control of the queue is modeled into equality constraint on the vehicle state, and then the reliable following of the vehicle to the design equality constraint is ensured through the design constraint tracking controller, so that the longitudinal and transverse control target of the queue is indirectly realized, and the more accurate longitudinal and transverse control of the queue is realized.
The technical scheme of the application is as follows: the method is suitable for member vehicles in the intelligent networked fleet and comprises the following steps: step 1, constructing a longitudinal and transverse coupling dynamic model of a member vehicle, and defining virtual control quantity according to the longitudinal and transverse coupling dynamic model; step 2, according to the self-vehicle state information and the front-and-back vehicle information of the member vehicles and the longitudinal and transverse directions of the queueConstructing a driving error model of the member vehicle according to the kinematic relationship, wherein the driving error model at least comprises a longitudinal driving error model and a transverse driving error model; step 3, calculating a control parameter group of the member vehicle by combining a longitudinal and transverse coupling dynamic model and a driving error model according to the self-vehicle state information and the front and rear vehicle information of the member vehicle; step 4, calculating the steering angle delta of the tire according to the control parameter set and the defined virtual control quantityf，iAnd a vehicle longitudinal control force Fx，i。
In any one of the above technical solutions, further, the queue longitudinal-lateral kinematic relationship at least includes a queue longitudinal-lateral kinematic relationship and a vehicle lateral-kinematic relationship step 2, which specifically includes: step 21, constructing a longitudinal driving error model according to the own vehicle state information, the information of the front and rear vehicles and the longitudinal kinematic relationship of the queue of the member vehicle, wherein the longitudinal driving error model at least comprises a vehicle spacing error and a derivative of the vehicle spacing error; and step 22, constructing a transverse driving error model according to the self-vehicle state information and the expected path information of the member vehicle and the transverse kinematic relationship of the vehicle.
In any one of the above technical solutions, further, the control parameter set at least includes a first parameter set and a second parameter set, and step 3 specifically includes:
step 31, converting the longitudinal and transverse coupling dynamic model into a second-order linear model, wherein the calculation formula of the second-order linear model is as follows:
Figure BDA0002952789590000021
in the formula
Figure BDA0002952789590000022
Is a system state vector, Ui＝[u1，i，u2，i]TTo control the vector, xi，yiAnd
Figure BDA0002952789590000023
respectively, longitudinal displacement, transverse displacement and yaw angle, u1，iAnd u2，iRespectively, a first and a second virtual control quantity, Mi∈R3 ×3，Hi∈R3，Bi∈R3×2Respectively an inertia matrix, a parameter matrix and an input matrix;
step 32, calculating a first parameter set by combining a second-order linear model according to the self-vehicle state information of the member vehicle;
step 33, establishing a vehicle state equality constraint according to the driving error model, wherein when the vehicle state equality constraint is strictly established, a control target of the intelligent internet fleet is realized, and a first order form and a second order form of the vehicle state equality constraint are respectively as follows:
Figure BDA0002952789590000031
Figure BDA0002952789590000032
in the formula, ciAs an intermediate parameter, AiIs a first parameter matrix, biIs a second parameter matrix;
and step 34, calculating a second parameter set according to the driving error model, the own vehicle state information and the front and rear vehicle information by combining the vehicle state equation constraint.
In any of the above technical solutions, further, the second parameter set at least includes: first parameter matrix AiA second parameter matrix biConstrained following error betaiWherein the first parameter matrix AiA second parameter matrix biConstrained following error betaiThe calculation formula of (2) is as follows:
Figure BDA0002952789590000033
Figure BDA0002952789590000034
Figure BDA0002952789590000035
Figure BDA0002952789590000036
in the formula, ciIs an intermediate parameter, qi、ηi、ωiIs a constant parameter and takes a value greater than 0, ey，iIs a transverse position error,
Figure BDA0002952789590000037
Error of orientation angle, ep，iFor lateral position error at the preview point, cR，iIs the curvature of the path at the closest point on the path, Δ yiIs the increase in lateral position error at the pre-aim point, L, caused by path changesiIs the pre-aiming distance.
In any one of the above technical solutions, further, the first parameter set at least includes: an inertia matrix, a parameter matrix, an input matrix, wherein the inertia matrix MiParameter matrix HiInput matrix BiThe calculation formula of (2) is as follows in sequence:
Figure BDA0002952789590000041
Figure BDA0002952789590000042
in the formula, miRepresenting the vehicle mass; i isz，iRepresenting the moment of inertia of the vehicle about an axis perpendicular to the ground at its center of mass,
Figure BDA0002952789590000043
is the vehicle lateral velocity;
Figure BDA0002952789590000044
is the vehicle longitudinal speed; c. Cx，iRepresenting a wind resistance coefficient, and g representing a gravity acceleration; f. ofR，iDenotes the coefficient of rolling resistance,/f，iIs the distance of the center of mass of the vehicle from its front axle,/r，iThe distance of the vehicle's center of mass from its rear axle; cf，iFront wheel cornering stiffness; cr，iIn order to the rear wheel side cornering stiffness,
Figure BDA0002952789590000045
is the yaw rate.
In any one of the above technical solutions, further, step 4 specifically includes:
step 41, according to the control parameter set, to the expected virtual control quantity
Figure BDA0002952789590000046
A calculation is performed in which, among other things,
Figure BDA0002952789590000047
and
Figure BDA0002952789590000048
are respectively the first virtual control quantity u1，iAnd a second virtual control amount u2，iThe calculation formula of the desired virtual controlled variable is:
Figure BDA0002952789590000049
Figure BDA00029527895900000410
Figure BDA00029527895900000411
in the formula, κi> 0 is a control parameter, Pi∈R2×2For a given positive definite parameter matrix, p1，iFor feedforward control of quantity, p2，iIs a feedback control quantity;
and step 42, combining the defined virtual control quantity, and calculating the tire steering angle delta according to the calculated expected virtual control quantityf，iAnd a vehicle longitudinal control force Fx，i，
Wherein, when Fx，iWhen the longitudinal control force F is more than or equal to 0x，iAs a driving force, when Fx，i< 0, vehicle longitudinal control force Fx，iIs the braking force.
The beneficial effect of this application is:
(1) according to the technical scheme, the coupling relation between the longitudinal dynamics and the transverse dynamics of the vehicles is considered, and the coupled longitudinal controller and the coupled transverse controller are established, so that member vehicles in the intelligent internet fleet can be on a curved lane, and more accurate queue longitudinal and transverse control is realized.
(2) The decoupling method and the decoupling device realize the decoupling of the vehicle dynamic model and the motion control task, and further do not need to establish a complex error dynamic model. Under this framework, the coupled longitudinal and transverse vehicle coupling dynamics model is established, which can be time-varying and highly nonlinear, and the vehicle kinematics model for the equality constraint design can be nonlinear. Therefore, the vehicle dynamics model involved in the application is more accurate, and better vehicle following control performance can be certainly realized based on the more accurate model.
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The advantages of the above and/or additional aspects of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow diagram of a method for intelligent networked fleet longitudinal and lateral coupling control according to one embodiment of the present application;
FIG. 2 is a fleet schematic according to one embodiment of the present application;
FIG. 3 is a schematic view of an ith member vehicle path tracking kinematics according to one embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present application can be more clearly understood, the present application will be described in further detail with reference to the accompanying drawings and detailed description. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited by the specific embodiments disclosed below.
As shown in fig. 1, the embodiment provides a method for controlling longitudinal and lateral coupling of an intelligent internet fleet, which is suitable for member vehicles in the intelligent internet fleet and is not suitable for pilot vehicles. The member cars are numbered 1, 2, 1, i, N in sequence. The control method in the embodiment mainly comprises the following steps: and establishing the vehicle state equality constraint capable of directly ensuring the control target according to the longitudinal and transverse kinematic relationship of the queue. And establishing a vehicle longitudinal and transverse coupling dynamic model, and designing a constraint following controller by combining the established vehicle state equation constraint, wherein a corresponding control method is operated on the constraint following controller so as to control the vehicle. The designed constraint following controller strictly ensures that the vehicle state meets the constructed vehicle state equality constraint, and indirectly ensures the control target of the queue.
It is equipped with on-vehicle sensing equipment and car communication equipment to set for all member's cars in the motorcade, and wherein, on-vehicle sensing equipment can measure from car status information, includes: vehicle lateral velocity vy，iLongitudinal speed v of the vehiclex，iLongitudinal acceleration of vehicle
Figure BDA0002952789590000061
Yaw rate
Figure BDA0002952789590000062
Path curvature c at the nearest point of the desired pathR，iThe distance d between the vehicle and the front vehiclei(ii) a The vehicle-to-vehicle communication device can acquire front and rear vehicle information, including: longitudinal speed v of the front vehiclex，i-1Longitudinal direction of front vehicleAcceleration in direction of direction
Figure BDA0002952789590000063
Measured vehicle-to-vehicle distance error e from the rear vehicle to the front vehiclei+1Calculated by the rear car
Figure BDA0002952789590000064
To be received
Figure BDA0002952789590000065
Now, taking the ith member vehicle in the vehicle fleet as an example, a method for controlling the longitudinal and transverse coupling of the intelligent internet fleet in the embodiment is described, which specifically includes:
step 1, constructing a longitudinal and transverse coupling dynamic model of a member vehicle, and defining virtual control quantity according to the longitudinal and transverse coupling dynamic model of the member vehicle;
the setting can be through modes such as experiment or reading vehicle design file, obtains the parameter information of self of vehicle, includes at least: vehicle mass mjMoment of inertia I of the vehicle about an axis perpendicular to the ground at its centre of massz，iWind resistance coefficient cx，iAcceleration of gravity g, rolling resistance coefficient fR，iDistance l between the center of mass of the vehicle and the front axle thereoff，iDistance l of the vehicle's center of mass from its rear axler，iFront wheel side cornering stiffness Cf，iRear wheel side cornering stiffness Cr，i。
By the transverse displacement y of the ith vehicleiAngle of orientation
Figure BDA0002952789590000066
And a longitudinal displacement xiThe tire steering angle delta of the front wheel steering system as the system statef，iAnd vehicle longitudinal driving or braking force Fx，iAnd (vehicle longitudinal control force) is input as a control quantity, and a longitudinal and transverse coupling dynamic model of the ith vehicle is established. To characterize the mapping between the system state quantity change and the control quantity input.
When F is presentx，iWhen the force is more than or equal to 0, the longitudinal stress F of the vehiclex，iAs a driving force, when Fx，iWhen less than 0, the vehicle is longitudinally stressed Fx，iIs the braking force.
The calculation formula of the longitudinal and transverse coupling dynamic model of the ith member vehicle is as follows:
Figure BDA0002952789590000071
Figure BDA0002952789590000072
Figure BDA0002952789590000073
wherein, the subscript i represents the ith member vehicle in the queue; m isiRepresenting a vehicle mass of an ith member vehicle; i isz，iRepresenting the moment of inertia of the vehicle about an axis perpendicular to the ground at its center of mass;
Figure BDA0002952789590000074
is the vehicle lateral velocity;
Figure BDA0002952789590000075
is the vehicle longitudinal speed; c. Cx，iRepresenting a wind resistance coefficient; g represents the gravitational acceleration; f. ofR，iRepresents a rolling resistance coefficient; lf，iThe distance of the center of mass of the vehicle from the front wheel axle of the vehicle; lr，iThe distance of the vehicle's center of mass from its rear axle; cf，iFront wheel cornering stiffness; cr，iIs rear wheel cornering stiffness.
The virtual control quantity in the embodiment is set according to a calculation formula of a longitudinal-transverse coupling dynamic model of the ith member vehicle, and the corresponding definition formula is as follows:
Figure BDA0002952789590000076
wherein u is1，iAnd u2，iThe purpose of defining the virtual control quantities for the first and second virtual control quantities is to reduce the design difficulty of the controller, and when designing the subsequent controller, the expected values of the two virtual control quantities are firstly obtained, and then the control quantity actually applied to the vehicle is reversely obtained according to the definition formula of the virtual control quantities, and the method comprises the following steps: tire steering angle δ of front wheel steering systemf，iAnd vehicle longitudinal driving or braking force, i.e. vehicle longitudinal control force Fx，i。
And 2, constructing a driving error model of the member vehicle according to the self-vehicle state information, the front-vehicle information and the rear-vehicle information of the member vehicle and the queue longitudinal and transverse kinematic relationship, wherein the driving error model at least comprises a longitudinal driving error model and a transverse driving error model. Step 2, specifically comprising:
and step 21, the queue longitudinal and transverse kinematic relationship at least comprises a queue longitudinal kinematic relationship and a vehicle transverse kinematic relationship. According to the self-vehicle state information, the front-vehicle information and the rear-vehicle information of the member vehicles and the longitudinal kinematic relationship of the queue, constructing a longitudinal driving error model, specifically, calculating the inter-vehicle distance error and the derivative of the inter-vehicle distance error;
as shown in FIG. 2, set xiIs the longitudinal displacement of the i-th member vehicle, diIs the inter-vehicle distance between the ith member vehicle and the vehicle in front of the member vehicle, li-1Is the length of the i-1 st member vehicle, and V2V is the vehicle-to-vehicle communication.
Acquiring front vehicle information through vehicle-to-vehicle communication equipment: longitudinal speed v of the front vehiclex，i-1Longitudinal acceleration of the front vehicle
Figure BDA00029527895900000813
Thus, the actual inter-vehicle distance of the ith vehicle from its preceding vehicle can be expressed as:
di＝xi-1-xi-li-1，
wherein li-1For the body length of the i-1 st vehicle, the desired inter-vehicle distance is a fixed value d0，iThen the inter-vehicle distance error and its first and second derivatives can be expressed as:
ei＝xi-1-xi-li-1-d0，i
Figure BDA0002952789590000081
Figure BDA0002952789590000082
by using the vehicle-vehicle communication equipment, the calculated inter-vehicle distance error e of the rear vehicle can be obtainedi+1And its first derivative
Figure BDA0002952789590000083
Second derivative of
Figure BDA0002952789590000084
And step 22, constructing a transverse driving error model according to the self-vehicle state information and the expected path information of the member vehicle and the transverse kinematic relationship of the vehicle. The model at least comprises: lateral position error, orientation angle error, lateral position error at the home position, and the corresponding derivatives of these errors.
Specifically, as shown in FIG. 3, xi-0-yiCoordinates with the center of gravity as the origin for the ith member vehicle,
Figure BDA0002952789590000085
error of orientation angle for i-th member vehicle, ey，iError in lateral position of the i-th member vehicle, LiIs the pre-range of the ith member vehicle, ep，iFor the transverse position error at the preview point, Δ y, of the ith member vehicleiThe position error increment of the ith member vehicle at the preview point is caused by the path change.
The self-vehicle state information of the member vehicle at least comprises: vehicle lateral velocity vy，iLongitudinal speed v of the vehiclex，iLongitudinal acceleration of vehicle
Figure BDA0002952789590000086
Yaw rate
Figure BDA0002952789590000087
The transverse driving error at least comprises: lateral position error ey，iError of orientation angle
Figure BDA0002952789590000088
Lateral position error e at the preview pointp，i，
Lateral position error ey，iThe formula for calculating the derivative of (c) is:
Figure BDA0002952789590000089
Figure BDA00029527895900000810
in the formula (I), the compound is shown in the specification,
Figure BDA00029527895900000811
as a lateral position error ey，iThe first derivative of (a) is,
Figure BDA00029527895900000812
the second derivative of the lateral position error.
Error of orientation angle
Figure BDA0002952789590000091
Expressed as:
Figure BDA0002952789590000092
wherein the content of the first and second substances,
Figure BDA0002952789590000093
is the desired orientation angle, determined by the angle of the closest point tangent to the path.
From the kinematic relationship, the desired yaw rate can be expressed as
Figure BDA0002952789590000094
Figure BDA0002952789590000095
Wherein, cR，iIs the curvature of the path at the closest point on the path.
Therefore, an error of the orientation angle
Figure BDA0002952789590000096
The formula for calculating the derivative of (c) is:
Figure BDA0002952789590000097
Figure BDA0002952789590000098
in the formula (I), the compound is shown in the specification,
Figure BDA0002952789590000099
as an error of the orientation angle
Figure BDA00029527895900000910
The first derivative of (a) is,
Figure BDA00029527895900000911
as an error of the orientation angle
Figure BDA00029527895900000912
The second derivative of (a).
According to the transverse kinematic relation of the vehicle, the transverse position error e at the pre-aiming pointp，iCan be expressed as:
Figure BDA00029527895900000913
wherein, Δ yiIs the lateral position at the point of preview caused by the path changeError increment; l isiIs the pre-aiming distance.
It should be noted that, in actual control, the desired path is usually represented by a series of coordinate points, ep，iUsually directly calculated by coordinates, and delta y is not involved in the actual control processiThe measurement of (2).
Therefore, the lateral position error e at the preview pointp，iThe formula for calculating the derivative of (c) is:
Figure BDA00029527895900000914
Figure BDA00029527895900000915
in the formula (I), the compound is shown in the specification,
Figure BDA00029527895900000916
as a lateral position error e at the preview pointp，iThe first derivative of (a) is,
Figure BDA00029527895900000917
as a lateral position error e at the preview pointp，iThe second derivative of (a).
And 3, calculating a control parameter group of the member vehicle by combining a longitudinal and transverse coupling dynamic model and a driving error model according to the self-vehicle state information and the front and rear vehicle information of the member vehicle, wherein the control parameter group at least comprises a first parameter group and a second parameter group. The step 3 specifically includes:
step 31, converting the longitudinal and transverse coupling dynamic model into a second-order linear model;
specifically, a system state vector is defined as
Figure BDA00029527895900000918
Control vector is Ui＝[u1，i，u2，i]T，u1，iAnd u2，iThe first and second virtual control quantities are provided.
Therefore, the above dynamic model can be organized into the following second order linear model:
Figure BDA0002952789590000101
in the formula Mi∈R3×3，Hi∈R3，Bi∈R3×2Respectively an inertia matrix, a parameter matrix and an input matrix.
And 32, calculating a first parameter set by combining a second-order linear model according to the self-vehicle state information of the member vehicle, wherein the first parameter set at least comprises: an inertia matrix, a parameter matrix, an input matrix, wherein the inertia matrix MiParameter matrix HiInput matrix BiThe calculation formula of (2) is as follows in sequence:
Figure BDA0002952789590000102
Figure BDA0002952789590000103
in the formula, MiIs an inertia matrix, HiAs a parameter matrix, BiFor the input matrix, miRepresenting the vehicle mass; i isz，iRepresenting the moment of inertia of the vehicle about an axis perpendicular to the ground at its center of mass;
Figure BDA0002952789590000104
is the vehicle lateral velocity;
Figure BDA0002952789590000105
is the vehicle longitudinal speed; c. Cx，iRepresenting a wind resistance coefficient; g represents the gravitational acceleration; f. ofR，iRepresents a rolling resistance coefficient; lf，iThe distance of the center of mass of the vehicle from the front wheel axle of the vehicle; lr，iThe distance of the vehicle's center of mass from its rear axle; cf，iFront wheel cornering stiffness; cr，iIs a rear partThe wheel-side cornering stiffness of the vehicle,
Figure BDA0002952789590000106
is the yaw rate.
And step 33, establishing vehicle state equality constraint according to the driving error model, wherein when the vehicle state equality constraint is strictly established, the control target of the intelligent internet fleet is realized.
In this embodiment, taking a conventional control target as an example, the following of the member vehicle is controlled, and the conventional control target is set as:
(1) path tracking stability: the lateral path tracking error can converge to 0 for all vehicles in the fleet, i.e.
Figure BDA0002952789590000111
Wherein e isp，iIs the lateral position error of the ith member vehicle at the preview point.
(2) Stability in the queue: for all vehicles in the queue, the inter-vehicle distance error can converge to 0, limt→∞ei(t) ═ 0, i ═ 1, 2.., N, where e isiAnd (4) the error between the actual inter-vehicle distance of the ith member vehicle and the expected inter-vehicle distance of the ith member vehicle.
(3) Queue stability: the vehicle distance error is attenuated (gradually reduced) in the process of transmitting to the tail of the team, namely | eN(t)|＜|eN-1(t)|＜…＜|e1(t)|＝0。
Transfer function of error
Figure BDA0002952789590000112
For any i 2, 3, when N is established, queue stability is guaranteed, wherein E is equal toi(s) is the inter-vehicle distance error ei(t) Laplace transform.
And establishing vehicle state equality constraint capable of ensuring the control target for the vehicle state according to the driving error model, and converting the path following control task into a servo constraint control task of the vehicle. Furthermore, the control targets of the queue can be ensured by controlling the vehicle to reliably follow the equality constraints of the vehicle states.
The following vehicle state equation constraints are established for the vehicles in the fleet, with the vehicle state equation constraints for the 1 st, 2., N-1 st vehicles being:
Figure BDA0002952789590000113
wherein, i is 1, 2., N-1,
the vehicle state equation for the nth vehicle (i.e., when i equals N) is constrained as:
Figure BDA0002952789590000114
wherein e isiIn order to be the error of the distance between the vehicles,
Figure BDA0002952789590000115
is its first derivative, ep，iThe lateral position error at the preview point for the ith member vehicle,
Figure BDA0002952789590000116
for their corresponding first derivatives, qi＞0，ηi＞0，ωi> 0 are constant parameters.
The following conclusions are reached:
conclusion 1: if the vehicle state equation constraint is strictly met, the queue path tracking stability is ensured, and the stability in the queue is ensured.
Conclusion 2: if the above equation is constrained, the parameter selection satisfies 0 < qi＜1，ηi＝ηi+1If 1, 2, N-1 is true for any i, queue stability is guaranteed.
Therefore, in conjunction with the driving error model in step 2, the above-mentioned vehicle state equation constraints can be converted into:
Figure BDA0002952789590000121
in the formula, ciIs an intermediate parameter. Derivation of the above transformed equation with respect to time yields a second order form of the vehicle state equation constraint:
Figure BDA0002952789590000122
in the formula, biIs a second parameter matrix.
And step 34, calculating a second parameter set by combining the vehicle state equation constraint according to the driving error model, the vehicle state information and the front and rear vehicle information, wherein the second parameter set at least comprises: first parameter matrix AiA second parameter matrix biConstrained following error betaiFirst parameter matrix AiA second parameter matrix biConstrained following error betaiThe calculation formula of (2) is as follows in sequence:
Figure BDA0002952789590000123
Figure BDA0002952789590000124
Figure BDA0002952789590000125
Figure BDA0002952789590000126
in the formula (I), the compound is shown in the specification,
Figure BDA0002952789590000127
in order to orient the angular error,
Figure BDA0002952789590000128
for the first derivative thereof,
Figure BDA0002952789590000129
as its second derivative, LiFor a pre-aiming distance, cR，iCurvature of the path as the closest point on the path, vx，iVehicle longitudinal speed of i-th vehicle, eiIn order to be the error of the distance between the vehicles,
Figure BDA0002952789590000131
for the first derivative thereof,
Figure BDA0002952789590000132
as its second derivative, Δ yiTo account for the incremental position error of the ith member vehicle at the home point caused by the path change, ep，iFor the lateral position error of the ith member vehicle at the preview point,
Figure BDA0002952789590000133
for the first derivative thereof,
Figure BDA0002952789590000134
for the purpose of its second derivative, the first derivative,
Figure BDA0002952789590000135
is the longitudinal acceleration of the front vehicle, vy，iAs the lateral speed of the vehicle,
Figure BDA0002952789590000136
as the yaw rate,
Figure BDA0002952789590000137
for longitudinal acceleration of the vehicle, XiIs a vector of the states of the system,
Figure BDA0002952789590000138
is its first derivative, t is a time parameter, ciIs an intermediate parameter, qi、ηi、ωiIs a constant parameter and takes a value greater than 0.
Step 4, calculating the steering angle delta of the tire according to the control parameter set and the defined virtual control quantityf，iAnd vehicle longitudinal driving or braking force, i.e. vehicle longitudinal control force Fx，iThe method comprises the following specific steps:
step 41, according to the control parameter set, to the expected virtual control quantity
Figure BDA0002952789590000139
A calculation is performed in which, among other things,
Figure BDA00029527895900001310
and
Figure BDA00029527895900001311
are respectively the first virtual control quantity u1，iAnd a second virtual control amount u2，iThe specific calculation formula of the first and second expected values of (1) is as follows:
Figure BDA00029527895900001312
Figure BDA00029527895900001313
Figure BDA00029527895900001314
in the formula, κi> 0 is a control parameter, Pi∈R2×2For a given positive definite parameter matrix, p1，iFor feedforward control of quantities and p2，iIs a feedback control amount. p is a radical of1，iVirtual control quantity required for satisfying vehicle state equality constraint for member vehicles; p is a radical of2，iCan further control the constraint following error betaiApproaching 0. Based on the Lyapunov stability theory, the method can prove that: in the virtual control quantity UiUnder the action of the controller, the member vehicles can strictly follow the equation constraint of the designed vehicle state, and the control target of the queue is indirectly realized.
And step 42, combining the defined virtual control quantity, and calculating the tire steering angle delta according to the expected virtual control quantity obtained by calculationf，iAnd a vehicle longitudinal control force (driving force or braking force) Fx，iThe specific calculation formula is as follows:
Figure BDA0002952789590000141
Figure BDA0002952789590000142
Figure BDA0002952789590000143
in the formula, alpha1，i、α2，i、α3，iIs an intermediate variable.
By the above calculation, the tire steering angle δ of the front wheel steering system is calculatedf，iInput steer-by-wire system, if Fx，iGreater than 0, and driving force is the longitudinal driving force F of the vehiclex，iAn input drive-by-wire system; if Fx，i< 0, and braking force F in the longitudinal direction of the vehicle as braking forcex，iInput brake-by-wire system.
With the vehicle-to-vehicle communication device, the state of the current member vehicle can be transmitted to the neighboring vehicle so that it completes the follow-up control. If the ith member vehicle is in the state vx，i、
Figure BDA0002952789590000144
Packaging and sending to the (i + 1) th member vehicle; calculating the vehicle distance error and the first and second derivatives e thereofi、
Figure BDA0002952789590000145
And
Figure BDA0002952789590000146
and packaging and sending the data to the i-1 st member vehicle.
For the vehicle N at the tail of the queue, only e obtained by calculation is neededN、
Figure BDA0002952789590000147
And
Figure BDA0002952789590000148
and packaging and sending the data to the vehicle N-1.
The technical scheme of the application is described in detail in the above with reference to the accompanying drawings, and the application provides a longitudinal and transverse coupling control method for an intelligent networked fleet, which is suitable for member vehicles in the intelligent networked fleet, and comprises the following steps: step 1, constructing a longitudinal and transverse coupling dynamic model of a member vehicle, and defining virtual control quantity according to the longitudinal and transverse coupling dynamic model; step 2, constructing a driving error model of the member vehicle according to the self-vehicle state information, the front-vehicle information and the rear-vehicle information of the member vehicle and the longitudinal-transverse kinematic relationship of the queue; step 3, calculating a control parameter group of the member vehicle by combining a longitudinal and transverse coupling dynamic model and a driving error model according to the self-vehicle state information and the front and rear vehicle information of the member vehicle; and 4, calculating the steering angle of the tire and the longitudinal control force of the vehicle according to the control parameter set and the defined virtual control quantity. Through the technical scheme in the application, the reliable following of the vehicle to the equation constraint of the designed vehicle state is ensured, the control target of the longitudinal and transverse directions of the queue is indirectly realized, and the more accurate longitudinal and transverse control of the queue is realized.
The steps in the present application may be sequentially adjusted, combined, and subtracted according to actual requirements.
The units in the device can be merged, divided and deleted according to actual requirements.
Although the present application has been disclosed in detail with reference to the accompanying drawings, it is to be understood that such description is merely illustrative and not restrictive of the application of the present application. The scope of the present application is defined by the appended claims and may include various modifications, adaptations, and equivalents of the invention without departing from the scope and spirit of the application.

Claims (6)

1. A method for controlling longitudinal and transverse coupling of an intelligent networked fleet is suitable for member vehicles in the intelligent networked fleet, and comprises the following steps:
step 1, constructing a longitudinal and transverse coupling dynamic model of the member vehicle, and defining a virtual control quantity according to the longitudinal and transverse coupling dynamic model;
step 2, constructing a driving error model of the member vehicle according to the self-vehicle state information, the front-vehicle information and the rear-vehicle information of the member vehicle and the queue longitudinal and transverse kinematic relationship, wherein the driving error model at least comprises a longitudinal driving error model and a transverse driving error model;
step 3, calculating a control parameter group of the member vehicle by combining the longitudinal and transverse coupling dynamic model and the driving error model according to the self-vehicle state information and the front and rear vehicle information of the member vehicle;
step 4, calculating the steering angle delta of the tire according to the control parameter set and the defined virtual control quantityf,iAnd a vehicle longitudinal control force Fx,i。
2. The method according to claim 1, wherein the queue longitudinal-lateral kinematic relationship at least comprises a queue longitudinal-lateral kinematic relationship and a vehicle lateral kinematic relationship, and the step 2 specifically comprises:
step 21, constructing the longitudinal driving error model according to the own vehicle state information, the information of the front and rear vehicles of the member vehicle and the longitudinal kinematic relationship of the queue, wherein the longitudinal driving error model at least comprises a vehicle distance error and a derivative of the vehicle distance error;
and step 22, constructing the transverse driving error model according to the self-vehicle state information and the expected path information of the member vehicle and the transverse kinematic relationship of the vehicle.
3. The method according to claim 1, wherein the control parameter sets at least include a first parameter set and a second parameter set, and the step 3 specifically includes:
step 31, converting the longitudinal and transverse coupling dynamic model into a second-order linear model, wherein a calculation formula of the second-order linear model is as follows:
Figure FDA0002952789580000011
in the formula
Figure FDA0002952789580000012
Is a system state vector, Ui＝[u1,i,u2,i]TTo control the vector, xi,yiAnd
Figure FDA0002952789580000013
respectively, longitudinal displacement, transverse displacement and yaw angle, u1,iAnd u2,iRespectively, a first and a second virtual control quantity, Mi∈R3×3，Hi∈R3，Bi∈R3×2Respectively an inertia matrix, a parameter matrix and an input matrix;
step 32, calculating the first parameter set by combining the second-order linear model according to the self-vehicle state information of the member vehicle;
step 33, establishing a vehicle state equality constraint according to the driving error model, wherein when the vehicle state equality constraint is strictly established, a control target of the intelligent internet fleet is realized, and a first order form and a second order form of the vehicle state equality constraint are respectively:
Figure FDA0002952789580000021
Figure FDA0002952789580000022
in the formula, ciAs an intermediate parameter, AiIs a first parameter matrix, biIs a second parameter matrix;
and step 34, calculating the second parameter set according to the driving error model, the own vehicle state information and the front and rear vehicle information by combining the vehicle state equation constraint.
4. The method of claim 3, wherein the second set of parameters comprises at least: the first parameter matrix AiThe second parameter matrix biConstrained following error betaiWherein the first parameter matrix AiThe second parameter matrix biThe constraint following error betaiThe calculation formula of (2) is as follows:
Figure FDA0002952789580000023
Figure FDA0002952789580000024
Figure FDA0002952789580000025
Figure FDA0002952789580000026
in the formula, ciIs an intermediate parameter, qi、ηi、ωiIs a constant parameter and takes a value greater than 0, ey,iIs a transverse position error,
Figure FDA0002952789580000027
Error of orientation angle, ep,iIs the transverse position at the preview pointError, cR,iIs the curvature of the path at the closest point on the path, Δ yiIs the increase in lateral position error at the pre-aim point, L, caused by path changesiIs the pre-aiming distance.
5. The method of claim 3, wherein the first set of parameters comprises at least: an inertia matrix, a parameter matrix, an input matrix, wherein the inertia matrix MiThe parameter matrix HiThe input matrix BiThe calculation formula of (2) is as follows in sequence:
Figure FDA0002952789580000031
Figure FDA0002952789580000032
in the formula, miRepresenting the vehicle mass; i isz,iRepresenting the moment of inertia of the vehicle about an axis perpendicular to the ground at its center of mass,
Figure FDA0002952789580000033
is the vehicle lateral velocity;
Figure FDA0002952789580000034
is the vehicle longitudinal speed; c. Cx,iRepresenting a wind resistance coefficient, and g representing a gravity acceleration; f. ofR,iDenotes the coefficient of rolling resistance,/f,iIs the distance of the center of mass of the vehicle from its front axle,/r,iThe distance of the vehicle's center of mass from its rear axle; cf,iFront wheel cornering stiffness; cr,iIn order to the rear wheel side cornering stiffness,
Figure FDA0002952789580000035
is the yaw rate.
6. The method for controlling the tandem intelligent vehicle fleet longitudinal and transverse coupling according to any one of claims 1 to 5, wherein the step 4 specifically comprises:
step 41, according to the control parameter set, aiming at the expected virtual control quantity
Figure FDA0002952789580000036
A calculation is performed in which, among other things,
Figure FDA0002952789580000037
and
Figure FDA0002952789580000038
are respectively the first virtual control quantity u1,iAnd the second virtual control amount u2,iThe calculation formula of the desired virtual control amount is:
Figure FDA0002952789580000039
Figure FDA00029527895800000310
Figure FDA00029527895800000311
in the formula, κi>0 is a control parameter, Pi∈R2×2For a given positive definite parameter matrix, p1,iFor feedforward control of quantity, p2,iIs a feedback control quantity;
and 42, combining the defined virtual control quantity, and calculating the tire steering angle delta according to the calculated expected virtual control quantityf,iAnd said vehicle longitudinal control force Fx,i，
Wherein, when Fx,iWhen the longitudinal control force F is more than or equal to 0x,iAs a driving force, the driving force is,when F is presentx,i<At 0, the vehicle longitudinal control force Fx,iIs the braking force.
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