CN107697065A - Automatic parking control method for general parking scene - Google Patents
Automatic parking control method for general parking scene Download PDFInfo
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- CN107697065A CN107697065A CN201710962411.2A CN201710962411A CN107697065A CN 107697065 A CN107697065 A CN 107697065A CN 201710962411 A CN201710962411 A CN 201710962411A CN 107697065 A CN107697065 A CN 107697065A
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- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000013528 artificial neural network Methods 0.000 claims abstract description 6
- 238000004088 simulation Methods 0.000 claims abstract description 6
- 230000008859 change Effects 0.000 claims description 6
- 230000005484 gravity Effects 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 239000013598 vector Substances 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 5
- 230000009897 systematic effect Effects 0.000 claims description 4
- 230000014509 gene expression Effects 0.000 claims description 3
- 238000003032 molecular docking Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 230000007613 environmental effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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- 230000007935 neutral effect Effects 0.000 description 1
- 230000031068 symbiosis, encompassing mutualism through parasitism Effects 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/06—Automatic manoeuvring for parking
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/141—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces
- G08G1/143—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces inside the vehicles
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
- Feedback Control In General (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides an automatic parking control method for a general parking scene. Firstly, generating control instructions and parking track data sets of different types of vehicles through simulation; secondly, learning simulation data by using a deep neural network algorithm, extracting a general relation between a control instruction and a parking track, and when any parking scene is given and a vehicle with dynamic model parameters is determined, finding a proper parking strategy through training in a few steps, giving the control instruction in the parking scene, and generating a planning track; and finally, performing control feedback according to the deviation of the actual parking track and the planned track, so that the parking track is closest to the ideal track planned by the system.
Description
Technical field
The present invention relates to automatic parking field, more particularly to the automatic parking paths planning method towards general scene.
Background technology
For many drivers, parallel parking is a kind of painful experience, and big city parking space is limited, by vapour
Car drives into narrow space turns into a required skill.Few situations for having stopped car without taking some twists and turns, parking may
Traffic jam, neurolysis and bumper is caused to be hit curved.With the development of automatic parking technology, above mentioned problem has obtained very big
Improvement.Automatic parking technology additionally aids some for solving densely populated city except that can help driver's automatic stopping
Parking and traffic problems.Sometimes, it can stop in small space and be limited by driver's technology.Automatic parking technology can be with
By automobile parking in less space, these spaces are more much smaller than the space that most of drivers oneself can stop.This just makes
Parking stall can be more easily found by obtaining car owner, while the space that the automobile of identical quantity takes is also smaller.
In the prior art, as publication number CN107102642A provides a kind of automatic parking system for pilotless automobile
System, it primarily focuses on automatic parking monitoring sensor-based system, and estimating for track of vehicle is carried out using the detection data of geomagnetic sensor
Calculate.Such as publication number CN106427996A offers a kind of multi-functional park control method and system, it is hindered by obtaining vehicle periphery
Hinder thing information；Automatic parking mode is selected according to vehicle periphery obstacle information or is remotely controlled mode of parking.Such as publication number CN
106043282 A provide a kind of full-automatic parking system and its control method for vehicle, and it is according to the periphery of the vehicle
Environmental information and the running state information of the vehicle cook up parking path, control electric boosting steering system and electronic stability
System and gear box control unit are completed automatically to park according to the parking path.
The related content of " automatic parking " is mainly that the hardware for laying particular emphasis on automated parking system forms, is each in the prior art
Part of module be how to work and each module between communication mode, its most of technology contents is without reference to parking scene
Consider, all have not been able to the parking problem for solving various parking scenes and different berth types.
The content of the invention
In order to solve the above-mentioned technical problem, the present invention provides a kind of automatic parking controlling party towards general parking scene
Method.
Specifically adopt the following technical scheme that：
Method comprises the following steps：
(1) type of vehicle is defined according to car body size, position of centre of gravity, wheel base, front and back wheel rotary inertia；
(2) Track Pick-up is carried out respectively to different type of vehicle：Whole docking process is divided into N number of stage, N number of stage
Control instruction collection be combined into δf N={ δf(1),…,δf(N)}；Specific wagon control instruction comes from K control in each stage
The set S at angle processedδ={ δ1,…,δk, the vehicle of each type can obtain KNIndividual combined result；The time span in each stage
For TN, total time span is T=T1+…+TN；Simulation step length is t, wherein TN=K*t；
(3) according to vehicle final position, drift angle and speed, learn to obtain the control instruction of vehicle by deep neural network
Combination；
(4) according to control instruction set { δ1,δ2,…,δt, with reference to current specific vehicle dynamic model parameter, draw
Vehicle parking track；
(5) feedback control, the final track of parking of generation are carried out according to the deviation of actually park track and planned trajectory.
Preferably, concretely comprising the following steps for vehicle parking track is drawn in step (4)：
1) in forward mode, according to vehicle-state equation of transfer
And
δf=δmax, δr=0,
Obtain
Wherein,For last moment Vehicular system state variable in forward mode,For subsequent time in forward mode
Vehicular system state variable；δfFor front wheel steering angle, δrFor rear-axle steering angle, δmaxFor steering locking angle；
2) in reversing mode, according to vehicle-state equation of transfer
And
δr=0, δf=δmax
Obtain
Wherein,For last moment Vehicular system state variable in reversing mode,For subsequent time in reversing mode
Vehicular system state variable.
Preferably, feedback control concretely comprises the following steps in step (5)：
Feedback control is carried out according to the rotational angular velocity r under the angle β and inertial coodinate system of car speed and the vehicle longitudinal axis：
The state space vectors of original system are expressed as
The change real system of systematic parameter is
The state space vectors of reponse system are expressed as
Wherein, x=[β, r]TFor the state variable of idealized system；X '=[β ', r ']TFor the state variable of real system；A,
B is state constant；U is vehicle corner；K is feedback matrix；
By object functionMinimum, obtaining state feedback controller is
U=-Kx.
Preferably, concretely comprising the following steps for feedback is controlled in step (5)：
Feedback control is carried out according to vehicle location and corner deviation：
By object function
Minimum, obtaining state feedback controller is
U=-Kx
Wherein：
(x ', y ') represents the coordinate of actual path point；
(x, y) represents the coordinate of ideal trajectory point；
The corner of ψ ' expressions actual path point；
ψ represents the corner of ideal trajectory point；
K=[k1, k2] represents feedback matrix.
The present invention has the advantages that：
(1) automatic parking method proposed by the present invention is towards general scene, is looked for by Algorithm Learning and on-site training
To the parking strategy for adapting to current scene, solve the problems, such as that the applicable berth type of current shutdown system is single；
(2) two kinds of track of vehicle control modes based on control theory algorithm are proposed, than simply providing in the prior art
The mode of the correction value of one speed or deflection angle is more accurate.
Brief description of the drawings
Fig. 1 is two kinds of different automobile types simulation track schematic diagrames.
Fig. 2 is neural network structure schematic diagram.
Fig. 3 is planned trajectory schematic diagram.
Fig. 4 is vehicle dynamic model schematic diagram.
Fig. 5 is to carry out feedback control principle schematic diagram to δ _ f according to β and r.
Fig. 6 is that position and corner deviation carry out feedback control principle schematic diagram.
Embodiment
(1) different vehicle type is defined：Different automobile types take on a different character parameter, such as car body size, position of centre of gravity, preceding
Rear axle is away from, front and back wheel rotary inertia etc., and we choose two kinds of vehicles, and A represents car, and B represents SUV.
The different vehicle type parameter example of table 1
(2) emulation generation control instruction and parking trajectory：Track is carried out respectively for two kinds of different type of vehicle A and B
Generation.Whole docking process is divided into N number of stage, the control instruction collection in N number of stage is combined into δf N={ δf(1),…,δf(N)}；Often
Specific wagon control instruction comes from the set S of K pilot angle in the individual stageδ={ δ1,…,δk, therefore, each type
Vehicle can obtain KNIndividual combined result；The time span in each stage is TN, total time span is T=T1+…+TN；Emulation
Step-length is t, TN=K*t.Fig. 1 be two kinds of vehicle simulation track, δf N=-0.6, -0.4, -0.2,0,0.2,0.4,
0.6 }, K=7, N=4, t=0.01s, TN=3s, symbiosis is into 2401 tracks.
(3) neural network learning：In order in the case where learning vehicle final position, drift angle and speed, can determine one
Car is parked in final position by kind control instruction combination exactly to realize, we solve this using deep neural network algorithm
Problem.Fig. 2 is the structural representation of neutral net, is divided into input layer, hidden layer and output layer.Input as the position of vehicle target state
PutWith velocity v, the control instruction set { δ for needs is exported1,δ2,…,δt}。
(4) trajectory planning：In given control instruction set { δ1,δ2,…,δtOn the basis of, with reference to current specific vehicle
Kinetic parameters, provide vehicle parking track.Fig. 3 is the example of a planned trajectory.
Fig. 4 is vehicle dynamic model rough schematic view, there is as follows parameter used in model：
● v=car speeds
● the component of v_x=speed in the horizontal direction
● the component of v_y=speed in the vertical directions
● β=car speed and vehicle longitudinal axis angle
●Rotational angular velocity under=r=inertial coodinate systems
● x=vehicle's center of gravity abscissas
● y=vehicle's center of gravity ordinates
● the side force of F=centers of gravity
●δf(δr)=front-wheel (trailing wheel) steering angle
● θ=vehicle front-wheel and berth long axis direction angle, direction of traffic conversion critical view angle
● ψ=from x-axis to the corner of vehicle major axis
1) forward mode
Vehicle-state equation of transfer is
WhereinFor last moment system state variables,For subsequent time system state variables.
Wherein：
Tire stiffness Coefficient mgV is to vehicle corner controlled quentity controlled variable Cf、CrInfluence coefficient；
Tire stiffness Coefficient mgV is to vehicle corner speed CflfInfluence coefficient；
Tyre rotation inertia IgzTo vehicle corner speed CflfInfluence coefficient；
Tyre rotation inertia is to vehicle corner speed CflfInfluence coefficient；
Influence coefficient of the front-wheel stiffness coefficient to front wheel angle controlled quentity controlled variable；
Influence coefficient of the trailing wheel stiffness coefficient to trailing wheel corner controlled quentity controlled variable；
Influence coefficient of the front tyre rotary inertia to front wheel angle controlled quentity controlled variable；
Influence coefficient of the rear tyre rotary inertia to trailing wheel corner controlled quentity controlled variable；
Cf=μ cf：Front tyre stiffness coefficient when coefficient of road adhesion is μ；
Cr=μ cr：Rear tyre stiffness coefficient when coefficient of road adhesion is μ；
(dry pavement μ=1, wet road surface μ=0.5).
During advance, δf=δmax, δr=0, then：
I.e.：
2) reversing mode
If the model is not influenceed by vehicle forerunner's rear-guard, reversing model can be regarded as regards headstock by the tailstock, still
Advance model so is used, then each parameter is exchanged before and after vehicle：
Wherein：
Therefore：
δ during reversingf *=δr=0, δr *=δf=δmax, therefore
Then：
No matter due to advancing or retreating, β and r are incremental form, so defining identical；But ψ is cumulant, currently
When entering,
ψ*=ψ+r* Δs t；
When reversing, the tailstock is considered as headstock, so
ψ*=ψ+r* Δ t+ π；
Velocity component
v_x*=| v | * cos (β+ψ*)；
v_y*=| v | * sin (β+ψ*)；
Position coordinates
x*=x+vx*Δt；
y*=y+vy*Δt；
Deflection angle
ψ*=ψ+r* Δs t；(advance)
ψ*=ψ+r* Δ t+ π；(reversing).
(5) feedback control：Due to reasons such as vehicle abrasion, environmental changes, the model parameter in actual vehicle operating system
It may be had differences with the model parameter of preferable planning system, it is inclined between actual parking trajectory and planned trajectory so as to cause
Difference.In order to eliminate as much as or reduce this deviation, we carry out feedback control using linear control method to system.
In actual shutdown system, due to the intrinsic parameter of vehicle such as mg、lf(lr)、Cf(Cr) difference, vehicle running orbit meeting
Had differences with the track under idealized system.To eliminate this species diversity, negative feedback control is added to system, to adjust vehicle operation
Track.
The feedback control of track of vehicle regulation is divided into two kinds：First, according to internal system variable quantity β and r deviation to δfEnter
Row feedback control；Second, it is position and angular deviation to δ according to system cumulantfCarry out feedback control.
1) according to β and r to δfCarry out feedback control
It can be seen from vehicle dynamic model, the change of systematic parameter can cause system change amount β and r change, and then
Influence track of vehicle.Feedback principle is as shown in Figure 5.
The state space vectors of original system can be expressed as
Because the change real system of systematic parameter is
According to LQR control methods, we will design a state feedback controller u=-Kx and cause object functionIt is minimum.So, the state space vectors of reponse system can be expressed as
Wherein x=[β, r]T：The state variable of idealized system；
X '=[β ', r ']T：The state variable of real system；
A, B are state constant；
U=δf：Vehicle corner；
K：Feedback matrix K=[k1, k2], circular refer to LQR control algolithms.
Emulation step number is set to 12000 steps in MATLAB discrete systems, step-length 0.001s, is divided into four-stage：
stage1：Step1-step3000, δf=-0.4 (v=-1)/δf=0.4 (v=1)；
stage2：Step3001-step6000, δf=0；
stage3：Step6001-step9000, δf=0.4 (v=-1)/δf=-0.4 (v=1)；
stage4：Step9001-step12000, δf=0.Emulation step number is set to 12000 in MATLAB discrete systems
Step, step-length 0.001s, is divided into four-stage：
stage1：Step1-step3000, δf=-0.4 (v=-1)/δf=0.4 (v=1)；
stage2：Step3001-step6000, δf=0；
stage3：Step6001-step9000, δf=0.4 (v=-1)/δf=-0.4 (v=1)；
stage4：Step9001-step12000, δf=0.
2) feedback control is carried out according to vehicle location and corner deviation, feedback principle is as shown in Figure 6.
On the premise of knowing last moment feedback ideal trajectory point with actual path point, we can find a steering
Angle causes the actual path of vehicle next step closest to the track of path planning, and causes object function
It is minimum.Wherein：
(x ', y ') represents the coordinate of actual path point；
(x, y) represents the coordinate of ideal trajectory point；
The corner of ψ ' expressions actual path point；
ψ represents the corner of ideal trajectory point；
K=[k1, k2] represents feedback matrix, herein k1, k2=1；
According to the limitation of vehicle steering locking angle, δ is takenf∈[-0.70,0.70].Feedback procedure is：
●step1：Original system δf=0.4, reponse system δf=0.4
●step2-step300：Original system δf=0.4, [- 0.70,0.70]
●step301-step600：Original system δf=0, reponse system δf=[- 0.70,0.70]
●step601-step900：Original system δf=-0.4, reponse system δf=[- 0.70,0.70]
●step900-step1200：Original system δf=0, reponse system δf=[- 0.70,0.70].
Claims (4)
1. a kind of automatic parking control method towards general parking scene, it is characterised in that comprise the following steps：
(1) type of vehicle is defined according to car body size, position of centre of gravity, wheel base, front and back wheel rotary inertia；
(2) Track Pick-up is carried out respectively to different type of vehicle：Whole docking process is divided into N number of stage, the control in N number of stage
Instruction set processed is δf N={ δf(1),…,δf(N)}；Specific wagon control instruction comes from K pilot angle in each stage
Set Sδ={ δ1,…,δk, the vehicle of each type can obtain KNIndividual combined result；The time span in each stage is TN,
Total time span is T=T1+…+TN；Simulation step length is t, wherein TN=K*t；
(3) according to vehicle final position, drift angle and speed, learn to obtain the control instruction group of vehicle by deep neural network
Close；
(4) according to control instruction set { δ1,δ2,…,δt, with reference to current specific vehicle dynamic model parameter, draw vehicle
Parking trajectory；
(5) feedback control, the final track of parking of generation are carried out according to the deviation of actually park track and planned trajectory.
A kind of 2. automatic parking control method towards general parking scene as claimed in claim 2, it is characterised in that step
(4) concretely comprising the following steps for vehicle parking track is drawn in：
1) in forward mode, according to vehicle-state equation of transfer
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</msub>
<msub>
<mi>&delta;</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
<mrow>
<mover>
<mi>r</mi>
<mo>&CenterDot;</mo>
</mover>
<mo>=</mo>
<msub>
<mi>a</mi>
<mn>21</mn>
</msub>
<mi>&beta;</mi>
<mo>+</mo>
<msub>
<mi>a</mi>
<mn>22</mn>
</msub>
<mi>r</mi>
<mo>+</mo>
<msub>
<mi>b</mi>
<mn>21</mn>
</msub>
<msub>
<mi>&delta;</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
Wherein,For last moment Vehicular system state variable in forward mode,For subsequent time vehicle system in forward mode
System state variable；δfFor front wheel steering angle, δrFor rear-axle steering angle, δmaxFor steering locking angle；
2) in reversing mode, according to vehicle-state equation of transfer
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msup>
<mover>
<mi>&beta;</mi>
<mo>&CenterDot;</mo>
</mover>
<mo>*</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<mover>
<mi>r</mi>
<mo>&CenterDot;</mo>
</mover>
<mo>*</mo>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>a</mi>
<mn>11</mn>
</msub>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<mn>2</mn>
<mo>-</mo>
<msub>
<mi>a</mi>
<mn>12</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<msub>
<mi>a</mi>
<mn>21</mn>
</msub>
</mrow>
</mtd>
<mtd>
<msub>
<mi>a</mi>
<mn>22</mn>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msup>
<mi>&beta;</mi>
<mo>*</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<mi>r</mi>
<mo>*</mo>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>+</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>b</mi>
<mn>12</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>b</mi>
<mn>11</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>b</mi>
<mn>22</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>b</mi>
<mn>21</mn>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msup>
<msub>
<mi>&delta;</mi>
<mi>f</mi>
</msub>
<mo>*</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<msub>
<mi>&delta;</mi>
<mi>r</mi>
</msub>
<mo>*</mo>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>b</mi>
<mn>12</mn>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>C</mi>
<mi>r</mi>
</msub>
<mrow>
<msub>
<mi>m</mi>
<mi>g</mi>
</msub>
<mi>v</mi>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>b</mi>
<mn>22</mn>
</msub>
<mo>=</mo>
<mo>-</mo>
<mfrac>
<mrow>
<msub>
<mi>C</mi>
<mi>r</mi>
</msub>
<msub>
<mi>l</mi>
<mi>r</mi>
</msub>
</mrow>
<msub>
<mi>I</mi>
<mrow>
<mi>g</mi>
<mi>z</mi>
</mrow>
</msub>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
And
δr=0, δf=δmax
Obtain
<mrow>
<msup>
<mover>
<mi>&beta;</mi>
<mo>&CenterDot;</mo>
</mover>
<mo>*</mo>
</msup>
<mo>=</mo>
<msub>
<mi>a</mi>
<mn>11</mn>
</msub>
<msup>
<mi>&beta;</mi>
<mo>*</mo>
</msup>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mo>-</mo>
<mn>2</mn>
<mo>-</mo>
<msub>
<mi>a</mi>
<mn>12</mn>
</msub>
<mo>)</mo>
</mrow>
<msup>
<mi>r</mi>
<mo>*</mo>
</msup>
<mo>+</mo>
<msub>
<mi>b</mi>
<mn>11</mn>
</msub>
<msub>
<mi>&delta;</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
<mrow>
<msup>
<mover>
<mi>r</mi>
<mo>&CenterDot;</mo>
</mover>
<mo>*</mo>
</msup>
<mo>=</mo>
<mo>-</mo>
<msub>
<mi>a</mi>
<mn>21</mn>
</msub>
<msup>
<mi>&beta;</mi>
<mo>*</mo>
</msup>
<mo>+</mo>
<msub>
<mi>a</mi>
<mn>22</mn>
</msub>
<msup>
<mi>r</mi>
<mo>*</mo>
</msup>
<mo>+</mo>
<msub>
<mi>b</mi>
<mn>21</mn>
</msub>
<msub>
<mi>&delta;</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
Wherein,For last moment Vehicular system state variable in reversing mode,For subsequent time vehicle in reversing mode
System state variables, a11For tire stiffness Coefficient mgInfluence coefficients of the v to vehicle corner controlled quentity controlled variable；a12For tire stiffness coefficient
mgV is to vehicle corner speed CflfInfluence coefficient；a21For tyre rotation inertia IgzTo the influence coefficient of vehicle corner speed；a22
Influence coefficient for tyre rotation inertia to vehicle corner speed；b11For influence of the front-wheel stiffness coefficient to front wheel angle controlled quentity controlled variable
Coefficient；b12Influence coefficient for trailing wheel stiffness coefficient to trailing wheel corner controlled quentity controlled variable；b21It is front tyre rotary inertia to preceding rotation
The influence coefficient of angle controlled quentity controlled variable；b22Influence coefficient for rear tyre rotary inertia to trailing wheel corner controlled quentity controlled variable；CfIt is attached for road surface
Front tyre stiffness coefficient when coefficient is μ；CrRear tyre stiffness coefficient when for coefficient of road adhesion being μ.
A kind of 3. automatic parking control method towards general parking scene as claimed in claim 1, it is characterised in that step
(5) feedback control concretely comprises the following steps in：
Feedback control is carried out according to the rotational angular velocity r under the angle β and inertial coodinate system of car speed and the vehicle longitudinal axis：
The state space vectors of original system are expressed as
<mrow>
<mover>
<mi>x</mi>
<mo>&CenterDot;</mo>
</mover>
<mo>=</mo>
<mi>A</mi>
<mi>x</mi>
<mo>+</mo>
<mi>B</mi>
<mi>u</mi>
</mrow>
The change real system of systematic parameter is
<mrow>
<msup>
<mover>
<mi>x</mi>
<mo>&CenterDot;</mo>
</mover>
<mo>&prime;</mo>
</msup>
<mo>=</mo>
<msup>
<mi>A</mi>
<mo>&prime;</mo>
</msup>
<msup>
<mi>x</mi>
<mo>&prime;</mo>
</msup>
<mo>+</mo>
<msup>
<mi>B</mi>
<mo>&prime;</mo>
</msup>
<mi>u</mi>
</mrow>
The state space vectors of reponse system are expressed as
<mover>
<mrow>
<msup>
<mi>x</mi>
<mo>&prime;</mo>
</msup>
<mo>=</mo>
<msup>
<mi>A</mi>
<mo>&prime;</mo>
</msup>
<msup>
<mi>x</mi>
<mo>&prime;</mo>
</msup>
<mo>+</mo>
<msup>
<mi>B</mi>
<mo>&prime;</mo>
</msup>
<mo>&lsqb;</mo>
<mi>u</mi>
<mo>-</mo>
<mi>K</mi>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mo>&prime;</mo>
</msup>
<mo>-</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mo>&CenterDot;</mo>
</mover>
Wherein, x=[β, r]TFor the state variable of idealized system；X '=[β ', r ']TFor the state variable of real system；A, B are
State constant；U is vehicle corner；K is feedback matrix；
By object functionMinimum, obtaining state feedback controller is
U=-Kx.
A kind of 4. automatic parking control method towards general parking scene as claimed in claim 1, it is characterised in that step
(5) control feedback concretely comprises the following steps in：
Feedback control is carried out according to vehicle location and corner deviation：
By object function
<mrow>
<mi>d</mi>
<mi>i</mi>
<mi>s</mi>
<mo>=</mo>
<mi>k</mi>
<mn>1</mn>
<msqrt>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<msup>
<mi>x</mi>
<mo>&prime;</mo>
</msup>
<mo>-</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msup>
<mi>y</mi>
<mo>&prime;</mo>
</msup>
<mo>-</mo>
<mi>y</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
<mo>+</mo>
<mi>k</mi>
<mn>2</mn>
<mo>|</mo>
<msup>
<mi>&psi;</mi>
<mo>&prime;</mo>
</msup>
<mo>-</mo>
<mi>&psi;</mi>
<mo>|</mo>
</mrow>
Minimum, obtaining state feedback controller is
U=-Kx
Wherein：
(x ', y ') represents the coordinate of actual path point；
(x, y) represents the coordinate of ideal trajectory point；
The corner of ψ ' expressions actual path point；
ψ represents the corner of ideal trajectory point；
K=[k1, k2] represents feedback matrix.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108313051A (en) * | 2018-01-15 | 2018-07-24 | 北京汽车集团有限公司 | It parks method, apparatus, system and storage medium |
| DE102018205062A1 (en) * | 2018-04-04 | 2019-10-10 | Audi Ag | Method for positioning a motor vehicle for performing an inductive charging process |
| CN110517527A (en) * | 2019-08-21 | 2019-11-29 | 同济大学 | Isomerous multi-source wireless sensor network and its it is autonomous nobody park method |
| WO2020094534A1 (en) * | 2018-11-06 | 2020-05-14 | Robert Bosch Gmbh | Method for training an artificial neural network, artificial neural network, use of an artificial neural network, corresponding computer program, machine-readable storage medium and corresponding device |
| CN111859778A (en) * | 2020-06-04 | 2020-10-30 | 北京百度网讯科技有限公司 | Parking model generation method and device, electronic device and storage medium |
| CN113110500A (en) * | 2021-05-10 | 2021-07-13 | 的卢技术有限公司 | Transverse control method for low-speed automatic parking of automatic-driving automobile |
| CN115562038A (en) * | 2022-11-01 | 2023-01-03 | 南栖仙策(南京)科技有限公司 | Early warning method, device, equipment and medium for feedback control system change |
| CN116311878A (en) * | 2022-10-21 | 2023-06-23 | 广州金王科技股份有限公司 | Intelligent parking device control method and control system thereof |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10269497A (en) * | 1997-03-25 | 1998-10-09 | Nissan Motor Co Ltd | Automatic parking device and parked vehicle guiding device |
| CN203047250U (en) * | 2012-11-20 | 2013-07-10 | 长安大学 | Automatic parking device |
| CN103796904A (en) * | 2011-09-21 | 2014-05-14 | 大众汽车有限公司 | Method for classifying parking scenarios for system for parking motor vehicle |
| CN104260722A (en) * | 2014-09-23 | 2015-01-07 | 北京理工大学 | Automatic parking system |
| CN105857306A (en) * | 2016-04-14 | 2016-08-17 | 中国科学院合肥物质科学研究院 | Vehicle autonomous parking path programming method used for multiple parking scenes |
| CN107004350A (en) * | 2014-10-27 | 2017-08-01 | 罗伯特·博世有限公司 | Apparatus and method for running parking lot |
| CN107065567A (en) * | 2017-05-22 | 2017-08-18 | 江南大学 | A kind of automatic stopping control system that control is constrained based on adaptive neural network |
-
2017
- 2017-10-16 CN CN201710962411.2A patent/CN107697065B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10269497A (en) * | 1997-03-25 | 1998-10-09 | Nissan Motor Co Ltd | Automatic parking device and parked vehicle guiding device |
| CN103796904A (en) * | 2011-09-21 | 2014-05-14 | 大众汽车有限公司 | Method for classifying parking scenarios for system for parking motor vehicle |
| CN203047250U (en) * | 2012-11-20 | 2013-07-10 | 长安大学 | Automatic parking device |
| CN104260722A (en) * | 2014-09-23 | 2015-01-07 | 北京理工大学 | Automatic parking system |
| CN107004350A (en) * | 2014-10-27 | 2017-08-01 | 罗伯特·博世有限公司 | Apparatus and method for running parking lot |
| CN105857306A (en) * | 2016-04-14 | 2016-08-17 | 中国科学院合肥物质科学研究院 | Vehicle autonomous parking path programming method used for multiple parking scenes |
| CN107065567A (en) * | 2017-05-22 | 2017-08-18 | 江南大学 | A kind of automatic stopping control system that control is constrained based on adaptive neural network |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108313051A (en) * | 2018-01-15 | 2018-07-24 | 北京汽车集团有限公司 | It parks method, apparatus, system and storage medium |
| DE102018205062A1 (en) * | 2018-04-04 | 2019-10-10 | Audi Ag | Method for positioning a motor vehicle for performing an inductive charging process |
| WO2020094534A1 (en) * | 2018-11-06 | 2020-05-14 | Robert Bosch Gmbh | Method for training an artificial neural network, artificial neural network, use of an artificial neural network, corresponding computer program, machine-readable storage medium and corresponding device |
| CN110517527A (en) * | 2019-08-21 | 2019-11-29 | 同济大学 | Isomerous multi-source wireless sensor network and its it is autonomous nobody park method |
| CN111859778A (en) * | 2020-06-04 | 2020-10-30 | 北京百度网讯科技有限公司 | Parking model generation method and device, electronic device and storage medium |
| US11741690B2 (en) | 2020-06-04 | 2023-08-29 | Apollo Intelligent Driving Technology (Beijing) Co., Ltd. | Method for generating parking model, electronic device, and storage medium |
| CN113110500A (en) * | 2021-05-10 | 2021-07-13 | 的卢技术有限公司 | Transverse control method for low-speed automatic parking of automatic-driving automobile |
| CN116311878A (en) * | 2022-10-21 | 2023-06-23 | 广州金王科技股份有限公司 | Intelligent parking device control method and control system thereof |
| CN115562038A (en) * | 2022-11-01 | 2023-01-03 | 南栖仙策(南京)科技有限公司 | Early warning method, device, equipment and medium for feedback control system change |
| CN115562038B (en) * | 2022-11-01 | 2023-08-29 | 南栖仙策(南京)高新技术有限公司 | Early warning method, device, equipment and medium for feedback control system change |
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