CN109001976B - Double-path cooperative extension transverse control method for automatic driving vehicle - Google Patents

Double-path cooperative extension transverse control method for automatic driving vehicle Download PDF

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CN109001976B
CN109001976B CN201810927572.2A CN201810927572A CN109001976B CN 109001976 B CN109001976 B CN 109001976B CN 201810927572 A CN201810927572 A CN 201810927572A CN 109001976 B CN109001976 B CN 109001976B
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position deviation
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蔡英凤
臧勇
孙晓强
梁军
陈龙
王海
袁朝春
唐斌
李祎承
刘擎超
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Jiangsu University
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Abstract

The invention discloses a double-path cooperative extension transverse control method of an automatic driving vehicle, which comprises the following steps: the method comprises the steps of suggesting a two-degree-of-freedom dynamic model, establishing a trajectory tracking preview error model, extracting characteristics, dividing domain boundaries, calculating a two-way correlation function and controlling system output. The method comprises the steps of respectively selecting transverse position deviation and course deviation as the characteristic quantity of an extension controller, establishing two extension sets, dividing the extension sets into a domain boundary, dividing the two extension sets into three regions, namely a classical domain, an extension domain and a non-domain, calculating two correlation function values through the real-time characteristic quantity of a vehicle-road system, classifying each real-time characteristic state quantity into each region based on the correlation function values, respectively calculating and outputting two front wheel steering angle output values based on the two correlation function values, and finally realizing the coordination output of the two extension controllers through a coordination weight coefficient.

Description

Double-path cooperative extension transverse control method for automatic driving vehicle
Technical Field
The invention belongs to the technology of automatic driving control of automobiles, and particularly relates to a double-path cooperative extension transverse control method of an automatic driving automobile.
Background
In order to meet the requirements of safe, efficient and intelligent traffic development, the automatic driving automobile becomes an important carrier and a main research object thereof, and particularly, the electric automatic driving automobile plays a great role in improving environmental pollution, improving energy utilization rate and solving the problem of traffic congestion. Therefore, the accuracy and safety of lateral vehicle control becomes an important part of autonomous vehicle control under various road conditions.
The automatic driving vehicle is based on a common vehicle platform, a computer, a vision sensor, an automatic control executing mechanism and signal communication equipment are constructed, and the functions of autonomous perception, autonomous decision making and autonomous execution of operation actions to guarantee safe driving are achieved. Common automatic driving vehicles are mostly driven by front wheels, and the transverse control precision of the vehicles and the running safety and stability of the vehicles are ensured by adjusting the angles of the front wheels.
As can be seen from many automatic driving vehicle competition competitions, one of the most main difficulties of the automatic driving vehicle at present is the safety and reliability under high dynamic conditions such as high-speed curve working conditions and the like. Data show that when the vehicle speed exceeds 50km/h and the curvature radius of a tracking path is larger than 75m, the maximum transverse position deviation is difficult to ensure to be smaller than 0.1m, and the phenomenon of instability or runaway of an automatic driving vehicle can occur under some severe working conditions.
There are two main types of lateral control: the system mainly comprises a pre-aiming type reference system and a non-pre-aiming type reference system, wherein the pre-aiming type reference system mainly takes the curvature of a road at the front position of a vehicle as input, and designs a feedback control system robust to vehicle dynamic parameters through various feedback control methods according to the transverse deviation or course deviation between the vehicle and an expected path as a control target, such as a reference system based on a vision sensor such as a radar or a camera. The non-predictive reference system calculates physical quantities describing the vehicle motion, such as vehicle yaw rate, from a vehicle kinematics model based on a desired path in the vicinity of the vehicle, and then designs a feedback control system for tracking.
At present, the prior art does not monitor the real-time state of a vehicle-road system and give controller output based on the real-time state characteristics, so that the tracking control precision is not high under large curvature and time-varying curvature, and the requirement of high-dynamic real-time control of an automatic driving vehicle is difficult to meet.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the defects in the prior art and provides a double-path cooperative extension transverse control method for an automatic driving vehicle.
The technical scheme is as follows: the invention discloses a double-path cooperative extension transverse control method of an automatic driving vehicle, which comprises the following steps of:
(1) suggesting a two-degree-of-freedom dynamic model;
(2) a track tracking preview error model is suggested;
(3) extracting features and dividing domain boundaries;
(4) calculating a two-way correlation function;
(5) and controlling system output.
Further, the two-degree-of-freedom dynamic model in the step (1) is as follows:
the mathematical equation of the two-degree-of-freedom dynamic model of the vehicle can be expressed as:
Figure BDA0001765776910000021
wherein the mass of the whole vehicle is M, and the moment of inertia of the vehicle around the z-axis of the center of mass (CG) is IzThe distances between the front and rear axes and the center of mass are respectively lfAnd lr，vxAnd vyLongitudinal and lateral speeds of the vehicle along the x-axis and y-axis, respectively, beta and r being the centroid yaw angle and yaw rate, respectively, Fyfl、Fyfr、FyrlAnd FyrrThe lateral forces to which the four wheels are subjected are respectively;
Fyf、Fyfthe resulting lateral forces to which the front axle and rear axle tires are subjected, respectively, are denoted Fyf＝Fyfl+Fyfr、Fyr＝Fyrl+FyrrFront wheel corner deltafRegulating the direction of travel, delta, of the vehiclefAs input parameters for a two-degree-of-freedom model of a vehicle, it is assumed here that the longitudinal speed v of the vehicle isxConstant, left and right wheelsAre the same in slip angle, IzIs moment of inertia about the center of mass;
front and rear tire side force Fyf、FyrSide deviation angle alpha of front and rear wheel tiresf、αrThe relationship of (1) is:
Fyf(t)＝cfαf(t) Fyr(t)＝crαr(t) (2)
wherein, cf、crThe cornering stiffness of the front and rear tires is a constant value when the tire works in a linear region;
front and rear tire slip angle alphaf、αrExpressed as:
Figure BDA0001765776910000022
substituting equations (2) and (3) into equation (1) yields the equation:
Figure BDA0001765776910000031
wherein the content of the first and second substances,
Figure BDA0001765776910000032
Figure BDA0001765776910000033
Figure BDA0001765776910000034
it is written in the form of a state space equation:
Figure BDA0001765776910000035
the state quantity x ═ beta, r]TAnd is and
Figure BDA0001765776910000036
further, the trajectory tracking preview error model in the step (2) is as follows:
Figure BDA0001765776910000037
in the formula, evThe transverse distance from the pre-aiming point to the reference track is the deviation of the pre-aiming transverse position; l is the distance from the center of mass CG of the vehicle to the pre-aiming point;
Figure BDA0001765776910000038
the heading angle at the pre-aiming point of the reference track,
Figure BDA0001765776910000039
for the vehicle heading angle, define
Figure BDA00017657769100000310
Is the course deviation; (ii) a
Figure BDA00017657769100000311
R is the road curvature radius for the curvature of the reference trajectory.
Further, the specific process of feature quantity extraction and domain boundary division in the step (3) is as follows:
(3.1) the extension controller selects the lateral position deviation e at the preview pointvDeviation from heading
Figure BDA00017657769100000312
As characteristic quantity, and constructing extension set by deviation and deviation differential of the two
Figure BDA00017657769100000313
And
Figure BDA00017657769100000314
(3.2) aggregating the lateral position deviation extension
Figure BDA00017657769100000315
And heading deviation extension set
Figure BDA00017657769100000316
The division into three regions: a classical domain, an extended domain, and a non-domain; setting a vehicle-road system in a controllable state, an adjustable state and an uncontrollable state; then, defining a two-way extension set domain boundary as:
the classical domain of lateral position deviation is:
Figure BDA00017657769100000317
the extension domain boundary of the transverse position deviation is as follows:
Figure BDA00017657769100000318
the course deviation classical domain boundary is:
Figure BDA00017657769100000319
the course deviation extension domain boundary is as follows:
Figure BDA0001765776910000041
further, the step (4) may set the point according to the horizontal position deviation of the preview point in real time
Figure BDA0001765776910000042
Sum course deviation extension set point
Figure BDA0001765776910000043
And optimum point S0Extension distance | S of (0,0)eS0|、
Figure BDA0001765776910000044
And calculating two correlation functions of classical domain boundary and extension distance of extension domain boundary, namely
Figure BDA0001765776910000045
Figure BDA0001765776910000046
Wherein, the horizontal position deviation at the pre-aiming point
Figure BDA0001765776910000047
And optimum point S0The weighted topology distance of (0,0) is:
Figure BDA0001765776910000048
course deviation
Figure BDA0001765776910000049
And optimum point S0The weighted topology distance of (0,0) is
Figure BDA00017657769100000410
The extension distance of the classical domain boundary of the transverse position deviation is
Figure BDA00017657769100000411
The extension distance of the extension domain boundary of the lateral position deviation is
Figure BDA00017657769100000412
Course deviation classical domain extension distance of
Figure BDA00017657769100000413
The extension distance of the extension domain boundary of course deviation is
Figure BDA00017657769100000414
Further, the output method of the control system in the step (5) is as follows:
(5.1) according to the two-way correlation function, the system characteristic quantity e is measuredvAnd
Figure BDA00017657769100000415
carrying out pattern recognition;
(5.2) adopting a corresponding controller front wheel steering angle output value in a corresponding mode based on the mode identification of the real-time characteristic quantity;
(5.3) based on the characteristic quantity evAnd characteristic amount
Figure BDA00017657769100000416
Obtaining the front wheel steering angle input delta of the vehicle dynamic model by weight coordination addition mode according to the determined front wheel steering angle output valuef。
Further, the mode identification process in the step (5.1) is as follows:
IF Ke(Se)≥0,THEN
Figure BDA0001765776910000051
measure mode Me1；
IF -1≤Ke(Se)<0,THEN
Figure BDA0001765776910000052
Measure mode Me2；
ELSE measurement mode Me3.
And
Figure BDA0001765776910000053
Measure patterns
Figure BDA0001765776910000054
Figure BDA0001765776910000055
Measure patterns
Figure BDA0001765776910000056
ELSE measurement mode
Figure BDA0001765776910000057
Further, the characteristic quantity e in the step (5.2)vThe output value of the front wheel steering angle of the controller in the corresponding mode is as follows:
when the measure pattern is Me1And when the vehicle-road system is in a stable state, the output value of the front wheel steering angle of the controller is as follows: deltae＝-kCMe1ev (19)
Wherein k isCMe1Is a measure pattern Me1Based on the characteristic quantity evFor example, a pole allocation method is adopted to select the state feedback coefficient; front wheel steering angle delta according to vehicle dynamics modelfDeviation e from the horizontal position of the preview pointvTransfer function, by command [ K, r ] in MATLAB]The state feedback coefficient is obtained for rlocfind (num, den), and the parameter is fine-tuned according to the response result on the basis of the state feedback coefficient.
When the measure pattern is Me2When the vehicle-road system is in a slight instability state, the vehicle-road system belongs to an adjustable range, the vehicle-road system is readjusted to a stable state by adding an additional output item of the controller, and the output value of the front wheel steering angle of the controller is as follows:
δe＝-kCMe1ev+kCMe2Ke(Se)[-sgn(ev)] (20)
kCMe2is a measure pattern Me2The additional output term controls a coefficient based on the measure mode Me1The lower control quantity is manually adjusted in a proper amount to ensure that the additional output item can enable the vehicle-road system to return to a stable state.
Wherein the content of the first and second substances,
Figure BDA0001765776910000058
kCMe2Ke(Se)[-sgn(ev)]adding to the controller an output term that incorporates the correlation function value Ke(Se) The correlation function can intuitively reflect the adjusting difficulty of the distance stable area of the vehicle-road system, so that the value of the additional output item of the controller is changed in real time according to the control difficulty through the change of the correlation function value.
When the measure pattern is Me3In time, the vehicle-road model cannot be adjusted to a stable state in time due to large deviation, and in order to ensure the safety of the vehicle, the output value of the front wheel steering angle of the controller is as follows:
δe＝0 (22)
measure mode Me3Should be avoided as much as possible in the control process.
In summary, for the feature quantity evThe output value of the front wheel steering angle of the controller is as follows:
Figure BDA0001765776910000061
solution feature quantity
Figure BDA0001765776910000062
The output value of the front wheel steering angle of the controller is as follows:
Figure BDA0001765776910000063
further, the step (5.3) is based on the above-mentioned characteristic quantity evAnd characteristic amount
Figure BDA0001765776910000064
Obtaining the front wheel steering angle input delta of the vehicle dynamic model by weight coordination addition mode according to the determined front wheel steering angle output valuef。
Figure BDA0001765776910000065
Wherein k iseIs a characteristic quantity evThe controller is used for controlling the output value coordination coefficient of the front wheel steering angle,
Figure BDA0001765776910000066
is a characteristic quantity
Figure BDA0001765776910000067
And the controller outputs a value coordination coefficient of the front wheel steering angle.
Has the advantages that: the invention utilizes a method for expanding the output result of the controller in real time based on deviation change to achieve the effect of ensuring the error in a lower range, thereby being capable of applying an extension control method to a transverse control system of an automatic driving vehicle and ensuring high tracking precision in the motion process of the vehicle. In order to realize the control target, the double-path extension control system is creatively designed, the transverse position deviation and the course deviation are respectively selected as the characteristic quantity of an extension controller, two extension sets are established, the extension sets are divided into domain boundaries, and the whole extension set is divided into three regions, namely a classical domain, an extension domain and a non-domain. And calculating a correlation function value through the real-time characteristic quantity of the vehicle-road system, classifying each real-time characteristic state quantity into each region based on the correlation function value, respectively calculating and outputting two-path front wheel steering angle output values based on the correlation function value, and finally realizing the coordination output of the two-path extensible controller through a coordination weight coefficient.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a two degree of freedom vehicle dynamics model of the present invention;
FIG. 3 is a trajectory tracking preview error model of the present invention;
FIG. 4 is a two-way scalable aggregation zone partition diagram of the present invention;
FIG. 5 is a schematic diagram of a dual-path correlation function calculation module according to the present invention;
FIG. 6 is a diagram of the relationship between the correlation function and the domain boundary of the present invention;
FIG. 7 is a diagram of feasibility simulation verification in an embodiment;
FIG. 8 is a diagram illustrating the tracking results of the trajectory under the double lane-changing condition of the embodiment;
wherein, fig. 8(a) is a schematic diagram of overall trajectory tracking; fig. 8(b) is a partial schematic view of the block in fig. 8 (a).
Detailed Description
The technical solution of the present invention is described in detail below, but the scope of the present invention is not limited to the embodiments.
As shown in fig. 1, the two-way cooperative extension transverse control method for the automatic driving vehicle of the present invention includes the following steps:
(1) suggesting a two-degree-of-freedom dynamic model; as shown in figure 1 of the drawings, in which,
the mathematical equation of the two-degree-of-freedom dynamic model of the vehicle is expressed as follows:
Figure BDA0001765776910000071
wherein the mass of the whole vehicle is M, and the moment of inertia of the vehicle around the z-axis of the center of mass (CG) is IzThe distances between the front and rear axes and the center of mass are respectively lfAnd lr，vxAnd vyLongitudinal and lateral speeds of the vehicle along the x-axis and y-axis, respectively, beta and r being the centroid yaw angle and yaw rate, respectively, Fyfl、Fyfr、FyrlAnd FyrrThe lateral forces to which the four wheels are subjected are respectively;
Fyf、Fyrthe resulting lateral forces to which the front axle and rear axle tires are subjected, respectively, are denoted Fyf＝Fyfl+Fyfr、Fyr＝Fyrl+FyrrFront wheel corner deltafRegulating the direction of travel, delta, of the vehiclefAs input parameters for a two-degree-of-freedom model of a vehicle, it is assumed here that the longitudinal speed v of the vehicle isxIs constant and the slip angles of the left and right wheels are the same, IzIs moment of inertia about the center of mass;
front and rear tire side force Fyf、FyrSide deviation angle alpha of front and rear wheel tiresf、αrThe relationship of (1) is:
Fyf(t)＝cfαf(t) Fyr(t)＝crαr(t) (2)
wherein, cf、crThe cornering stiffness of the front and rear tires is a constant value when the tire works in a linear region;
front and rear tire slip angle alphaf、αrExpressed as:
Figure BDA0001765776910000081
substituting equations (5) and (6) into equation (4) yields the equation:
Figure BDA0001765776910000082
wherein the content of the first and second substances,
Figure BDA0001765776910000083
Figure BDA0001765776910000084
Figure BDA0001765776910000085
it is written in the form of a state space equation:
Figure BDA0001765776910000086
the state quantity x ═ beta, r]TAnd is and
Figure BDA0001765776910000087
(2) a track tracking preview error model is suggested;
as shown in fig. 3, the model is:
Figure BDA0001765776910000088
in the formula, evThe transverse distance from the pre-aiming point to the reference track is the deviation of the pre-aiming transverse position; l is the distance from the center of mass CG of the vehicle to the pre-aiming point;
Figure BDA0001765776910000089
the heading angle at the pre-aiming point of the reference track,
Figure BDA00017657769100000810
for the vehicle heading angle, define
Figure BDA00017657769100000811
Is the course deviation; rho is the curvature of the reference track and is the reciprocal of the road bending radius;
(3) feature extraction and domain boundary division:
(3.1) the extension controller selects the lateral position deviation e at the preview pointvDeviation from heading
Figure BDA00017657769100000812
As characteristic quantities, and constructed by the deviation of the two and the deviation differential
Figure BDA00017657769100000813
And
Figure BDA00017657769100000814
(3.2) As shown in FIG. 4, lateral position deviation extension sets are formed
Figure BDA00017657769100000815
And heading deviation extension set
Figure BDA00017657769100000816
The division into three regions: a classical domain, an extended domain, and a non-domain; setting a vehicle-road system in a controllable state, an adjustable state and an uncontrollable state; then, a two-way extension set domain boundary is defined as follows:
the classical domain of lateral position deviation is:
Figure BDA00017657769100000817
the extension domain boundary of the transverse position deviation is as follows:
Figure BDA00017657769100000818
the course deviation classical domain boundary is:
Figure BDA0001765776910000091
the course deviation extension domain boundary is as follows:
Figure BDA0001765776910000092
(4) calculating a two-way correlation function:
as shown in fig. 5, the extension set point is obtained according to the deviation of the horizontal position of the preview point in real time
Figure BDA0001765776910000093
Sum course deviation extension set point
Figure BDA0001765776910000094
And optimum point S0Extension distance | S of (0,0)eS0|、
Figure BDA0001765776910000095
And calculating two correlation functions of classical domain boundary and extension distance of extension domain boundary, namely
Figure BDA0001765776910000096
Figure BDA0001765776910000097
Wherein, the horizontal position deviation at the pre-aiming point
Figure BDA0001765776910000098
And optimum point S0The weighted topology distance of (0,0) is:
Figure BDA0001765776910000099
course deviation
Figure BDA00017657769100000910
And optimum point S0The weighted topology distance of (0,0) is
Figure BDA00017657769100000911
The extension distance of the classical domain boundary of the transverse position deviation is
Figure BDA00017657769100000912
The extension distance of the extension domain boundary of the lateral position deviation is
Figure BDA00017657769100000913
Course deviation classical domain extension distance of
Figure BDA00017657769100000914
The extension distance of the extension domain boundary of course deviation is
Figure BDA00017657769100000915
(5) And (3) outputting by a control system:
(5.1) As shown in FIG. 6, the system feature quantity e is calculated according to a two-way correlation functionvAnd
Figure BDA00017657769100000916
carrying out pattern recognition;
(5.2) adopting a corresponding controller front wheel steering angle output value in a corresponding mode based on the mode identification of the real-time characteristic quantity;
(5.3) based on the characteristic quantity evAnd characteristic amount
Figure BDA0001765776910000101
Obtaining the front wheel steering angle input delta of the vehicle dynamic model by weight coordination addition mode according to the determined front wheel steering angle output valuef。
Wherein, the mode identification process in the step (5.1) is as follows:
IF Ke(Se)≥0,THEN
Figure BDA0001765776910000102
measure patternsMe1；
IF -1≤Ke(Se)<0,THEN
Figure BDA0001765776910000103
Measure mode Me2；
ELSE measurement mode Me3.
And
Figure BDA0001765776910000104
Measure patterns
Figure BDA0001765776910000105
Figure BDA0001765776910000106
Measure patterns
Figure BDA0001765776910000107
ELSE measurement mode
Figure BDA0001765776910000108
Characteristic quantity e in the above step (5.2)vThe output value of the front wheel steering angle of the controller in the corresponding mode is as follows:
when the measure pattern is Me1And when the vehicle-road system is in a stable state, the output value of the front wheel steering angle of the controller is as follows: deltae＝-kCMe1ev (19)
Wherein k isCMe1Is a measure pattern Me1Based on the characteristic quantity evFor example, a pole allocation method is adopted to select the state feedback coefficient; front wheel steering angle delta according to vehicle dynamics modelfDeviation e from the horizontal position of the preview pointvTransfer function, by command [ K, r ] in MATLAB]The state feedback coefficient is obtained for rlocfind (num, den), and the parameter is fine-tuned according to the response result on the basis of the state feedback coefficient.
When the measure pattern is Me2When the vehicle-road system is in a slight instability state, the vehicle-road system belongs to an adjustable range, the vehicle-road system is readjusted to a stable state by adding an additional output item of the controller, and the output value of the front wheel steering angle of the controller is as follows:
δe＝-kCMe1ev+kCMe2Ke(Se)[-sgn(ev)] (20)
kCMe2is a measure pattern Me2The additional output term controls a coefficient based on the measure mode Me1The lower control quantity is manually adjusted in a proper amount to ensure that the additional output item can enable the vehicle-road system to return to a stable state.
Wherein the content of the first and second substances,
Figure BDA0001765776910000109
kCMe2Ke(Se)[-sgn(ev)]adding to the controller an output term that incorporates the correlation function value Ke(Se) The correlation function can intuitively reflect the adjusting difficulty of the distance stable area of the vehicle-road system, so that the value of the additional output item of the controller is changed in real time according to the control difficulty through the change of the correlation function value.
When the measure pattern is Me3In time, the vehicle-road model cannot be adjusted to a stable state in time due to large deviation, and in order to ensure the safety of the vehicle, the output value of the front wheel steering angle of the controller is as follows:
δe＝0 (22)
measure mode Me3Should be avoided as much as possible in the control process.
In summary, for the feature quantity evThe output value of the front wheel steering angle of the controller is as follows:
Figure BDA0001765776910000111
solution feature quantity
Figure BDA0001765776910000112
The output value of the front wheel steering angle of the controller is as follows:
Figure BDA0001765776910000113
the step (5.3) is based on the above characteristic quantity evAnd characteristic amount
Figure BDA0001765776910000114
Obtaining the front wheel steering angle input delta of the vehicle dynamic model by weight coordination addition mode according to the determined front wheel steering angle output valuef。
Figure BDA0001765776910000115
Wherein k iseIs a characteristic quantity evThe controller is used for controlling the output value coordination coefficient of the front wheel steering angle,
Figure BDA0001765776910000116
is a characteristic quantity
Figure BDA0001765776910000117
And the controller outputs a value coordination coefficient of the front wheel steering angle.
Example (b):
the method is based on MATLAB (Simulink) -Carsim platform to build a feasibility simulation system model, a feasibility simulation verification framework of the two-way cooperative extension transverse control method is shown in figure 7, the effectiveness and the stability of the two-way cooperative extension transverse control system provided by the invention are verified through two working conditions, a two-time lane change path is adopted, and the vehicle speed is selected from 100km/h, 110km/h and 120 km/h. In the control method, the parameter values are set as follows:
the pre-aiming distance L in the track tracking error model is 15 m; road adhesion coefficient μ ═ 1.0; vehicle with wheelsLongitudinal speed v of vehiclexIs a constant; two-way extension controller front wheel turning output coordination coefficient ke0.471 because
Figure BDA0001765776910000118
Therefore, it is not only easy to use
Figure BDA0001765776910000119
For feature sets
Figure BDA00017657769100001110
Its classical domain boundary RecAnd an extension field boundary ReeAre respectively as
Figure BDA0001765776910000121
For feature sets
Figure BDA0001765776910000122
Its classical domain boundary
Figure BDA0001765776910000123
And an extension Domain boundary
Figure BDA0001765776910000124
Are respectively as
Figure BDA0001765776910000125
Determining measure mode M in double-synergy extension control system according to pole-zero allocation methode1And
Figure BDA0001765776910000126
lower, state feedback coefficient kCMe1＝50.12，
Figure BDA0001765776910000127
Measure mode Me2And
Figure BDA0001765776910000128
then, the additional output term controls the coefficient kCMe2＝0.01，
Figure BDA0001765776910000129
As shown in FIG. 8, according to the response result of the double-pass lane-changing working condition under the high-speed working condition, the double-cooperation extension transverse control system has higher tracking accuracy and good control method reliability on the high-speed time-varying curvature road.

Claims (7)

1. A double-path cooperative extension transverse control method of an automatic driving vehicle is characterized by comprising the following steps: the method comprises the following steps:
(1) establishing a two-degree-of-freedom dynamic model;
(2) establishing a track tracking preview error model;
(3) extracting features and dividing domain boundaries;
(4) calculating a two-way correlation function;
(5) controlling system output;
the specific process of feature quantity extraction and domain boundary division in the step (3) is as follows:
(3.1) the extension controller selects the lateral position deviation e at the preview pointvDeviation from heading
Figure FDA0002882058890000011
As characteristic quantity, and constructing extension set by deviation and deviation differential of the two
Figure FDA0002882058890000012
And
Figure FDA0002882058890000013
(3.2) aggregating the lateral position deviation extension
Figure FDA0002882058890000014
And course deviation canRubbing set
Figure FDA0002882058890000015
The division into three regions: a classical domain, an extended domain, and a non-domain; setting a vehicle-road system in a controllable state, an adjustable state and an uncontrollable state; then, defining a two-way extension set domain boundary as:
the classical domain of lateral position deviation is:
Figure FDA0002882058890000016
the extension domain boundary of the transverse position deviation is as follows:
Figure FDA0002882058890000017
the course deviation classical domain boundary is:
Figure FDA0002882058890000018
the course deviation extension domain boundary is as follows:
Figure FDA0002882058890000019
wherein the content of the first and second substances,
Figure FDA00028820588900000110
to account for the differential of the preview lateral position deviation,
Figure FDA00028820588900000111
is the heading deviation differential at the pre-aiming point, evomIs the classical domain boundary of the lateral position deviation,
Figure FDA00028820588900000112
is the differential classical domain of the lateral position deviation, evmIs the extent of the lateral position deviation,
Figure FDA00028820588900000113
is the differential extension domain boundary of the lateral position deviation,
Figure FDA00028820588900000114
is the classical domain boundary of the heading deviation,
Figure FDA00028820588900000115
is the heading deviation differential classical domain boundary,
Figure FDA00028820588900000116
for the heading deviation extension domain boundary,
Figure FDA00028820588900000117
is a heading deviation differential extension field bound, KeFor the correlation degree of the pre-aiming lateral position deviation,
Figure FDA00028820588900000118
for correlation of the horizontal position deviation of the preview, SeIs a coordinate point of the differential of the preview lateral position deviation and the preview lateral position deviation,
Figure FDA0002882058890000021
and the coordinate point is the differential coordinate point of the deviation of the pre-aiming course and the deviation of the pre-aiming course.
2. The two-way cooperative extendable lateral control method of the autonomous vehicle of claim 1, characterized in that: the two-degree-of-freedom dynamic model in the step (1) is as follows:
the mathematical equation of the two-degree-of-freedom dynamic model of the vehicle is expressed as follows:
Figure FDA0002882058890000022
wherein the mass of the whole vehicle is M, and the moment of inertia of the vehicle around the z-axis of the center of mass (CG) is IzThe distances between the front and rear axes and the center of mass are respectively lfAnd lr，vxAnd vyLongitudinal and lateral speeds of the vehicle along the x-axis and y-axis, respectively, beta and r being the centroid yaw angle and yaw rate, respectively, Fyfl、Fyfr、FyrlAnd FyrrThe lateral forces to which the four wheels are subjected are respectively;
Fyf、Fyrthe resulting lateral forces to which the front axle and rear axle tires are subjected, respectively, are denoted Fyf＝Fyfl+Fyfr、Fyr＝Fyrl+FyrrFront wheel corner deltafRegulating the direction of travel, delta, of the vehiclefInput parameters of a two-degree-of-freedom model of the vehicle;
front and rear tire side force Fyf、FyrSide deviation angle alpha of front and rear wheel tiresf、αrThe relationship of (1) is:
Fyf(t)＝cfαf(t)Fyr(t)＝crαr(t) (2)
wherein, cf、crThe cornering stiffness of the front and rear tires is a constant value when the tire works in a linear region;
front and rear tire slip angle alphaf、αrExpressed as:
Figure FDA0002882058890000023
substituting equations (2) and (3) into equation (1) yields the equation:
Figure FDA0002882058890000024
wherein the content of the first and second substances,
Figure FDA0002882058890000025
Figure FDA0002882058890000026
Figure FDA0002882058890000031
it is written in the form of a state space equation:
Figure FDA0002882058890000032
the state quantity x ═ beta, r]TAnd is and
Figure FDA0002882058890000033
3. the two-way cooperative extendable lateral control method of the autonomous vehicle of claim 1, characterized in that: the track tracking preview error model in the step (2) is as follows:
Figure FDA0002882058890000034
in the formula, evThe transverse distance from the pre-aiming point to the reference track is the deviation of the pre-aiming transverse position; l is the distance from the center of mass CG of the vehicle to the pre-aiming point;
Figure FDA0002882058890000035
the heading angle at the pre-aiming point of the reference track,
Figure FDA0002882058890000036
for the vehicle heading angle, define
Figure FDA0002882058890000037
Is the course deviation;
Figure FDA0002882058890000038
r is the road curvature radius for the curvature of the reference trajectory.
4. The two-way cooperative extendable lateral control method of the autonomous vehicle of claim 1, characterized in that: the step (4) can extend the set point according to the horizontal position deviation of the preview point in real time
Figure FDA0002882058890000039
Sum course deviation extension set point
Figure FDA00028820588900000310
And optimum point S0Extension distance | S of (0,0)eS0|、
Figure FDA00028820588900000311
And calculating two correlation functions of classical domain boundary and extension distance of extension domain boundary, namely
Figure FDA00028820588900000312
Figure FDA00028820588900000313
Wherein the transverse position deviation and deviation differential at the preview point
Figure FDA00028820588900000314
And optimum point S0The weighted topology distance of (0,0) is:
Figure FDA00028820588900000315
course deviation
Figure FDA00028820588900000316
And optimum point S0The weighted topology distance of (0,0) is
Figure FDA00028820588900000317
The extension distance of the classical domain boundary of the transverse position deviation is
Figure FDA00028820588900000318
The extension distance of the extension domain boundary of the lateral position deviation is
Figure FDA00028820588900000319
Course deviation classical domain extension distance of
Figure FDA0002882058890000041
The extension distance of the extension domain boundary of course deviation is
Figure FDA0002882058890000042
Wherein the content of the first and second substances,
Figure FDA0002882058890000043
to account for the differential of the preview lateral position deviation,
Figure FDA0002882058890000044
is heading deviation at the pre-aiming pointDifferential, evomIs the classical domain boundary of the lateral position deviation,
Figure FDA0002882058890000045
is the differential classical domain of the lateral position deviation, evmIs the extent of the lateral position deviation,
Figure FDA0002882058890000046
is the differential extension domain boundary of the lateral position deviation,
Figure FDA0002882058890000047
is the classical domain boundary of the heading deviation,
Figure FDA0002882058890000048
is the heading deviation differential classical domain boundary,
Figure FDA0002882058890000049
for the heading deviation extension domain boundary,
Figure FDA00028820588900000410
is a heading deviation differential extension field bound, KeFor the correlation degree of the pre-aiming lateral position deviation,
Figure FDA00028820588900000411
for correlation of the horizontal position deviation of the preview, SeIs a coordinate point of the differential of the preview lateral position deviation and the preview lateral position deviation,
Figure FDA00028820588900000412
and the coordinate point is the differential coordinate point of the deviation of the pre-aiming course and the deviation of the pre-aiming course.
5. The two-way cooperative extendable lateral control method of the autonomous vehicle of claim 1, characterized in that: the output method of the control system in the step (5) comprises the following steps:
(5.1) root of PolygonaceaeAccording to two-way correlation function to system characteristic quantity evAnd
Figure FDA00028820588900000413
carrying out pattern recognition;
(5.2) adopting a corresponding controller front wheel steering angle output value in a corresponding mode based on the mode identification of the real-time characteristic quantity;
(5.3) based on the characteristic quantity evAnd characteristic amount
Figure FDA00028820588900000414
Obtaining the front wheel steering angle input delta of the vehicle dynamic model by weight coordination addition mode according to the determined front wheel steering angle output valuef；
Figure FDA00028820588900000415
Wherein k iseIs a characteristic quantity evThe controller is used for controlling the output value coordination coefficient of the front wheel steering angle,
Figure FDA00028820588900000416
is a characteristic quantity
Figure FDA00028820588900000417
And the controller outputs a value coordination coefficient of the front wheel steering angle.
6. The two-way cooperative extendable lateral control method of the autonomous vehicle of claim 5, characterized in that: the mode identification process in the step (5.1) is as follows:
IF Ke(Se)≥0，THEN
Figure FDA00028820588900000418
measure the mode Me1；
IF -1≤Ke(Se)＜0，THEN
Figure FDA00028820588900000419
Measure the mode Me2；
ELSE measurement mode Me3
And
IF
Figure FDA0002882058890000051
THEN
Figure FDA0002882058890000052
Measure the pattern
Figure FDA0002882058890000053
IF
Figure FDA0002882058890000054
THEN
Figure FDA0002882058890000055
Measure the pattern
Figure FDA0002882058890000056
ELSE measurement mode
Figure FDA0002882058890000057
Wherein R isecFor presaim lateral position deviation classical domain, ReeTo predict the lateral position deviation extension,
Figure FDA0002882058890000058
in order to predict the classical domain of heading deviation,
Figure FDA0002882058890000059
for the predicted heading deviation extension field, KeFor the correlation degree of the pre-aiming lateral position deviation,
Figure FDA00028820588900000510
and the correlation degree of the horizontal position deviation of the preview is obtained.
7. The two-way cooperative extendable lateral control method of the autonomous vehicle of claim 6, characterized in that: characteristic quantity e in the step (5.2)vThe output value of the front wheel steering angle of the controller is as follows:
Figure FDA00028820588900000511
characteristic amount
Figure FDA00028820588900000512
The output value of the front wheel steering angle of the controller is as follows:
Figure FDA00028820588900000513
wherein k isCMe1Is a measure pattern Me1Based on the characteristic quantity evThe state feedback coefficient of (a); k is a radical ofCMe2Is a measure pattern Me2The next additional output term controls the coefficient,
Figure FDA00028820588900000514
kCMe2Ke(Se)[-sgn(ev)]adding an output item to a controller
Wherein M ise1，Me2，Me3The method is a measuring mode of a horizontal position deviation pre-aiming classical domain, an extension domain and a non-domain,
Figure FDA00028820588900000515
the method is a pre-aiming course deviation classical domain, an extension domain and a non-domain measure mode.
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