CN107697065A - Automatic parking control method for general parking scene - Google Patents

Automatic parking control method for general parking scene Download PDF

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Publication number
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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CN107697065B (en
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谭墍元
谢娜
徐春玲
郭伟伟
李颖宏
施翔匀
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North China University of Technology
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North China University of Technology
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT 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
    • B60W30/00Purposes 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
    • B60W30/06Automatic manoeuvring for parking
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/14Traffic control systems for road vehicles indicating individual free spaces in parking areas
    • G08G1/141Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces
    • G08G1/143Traffic 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)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Steering Control In Accordance With Driving Conditions (AREA)
  • Feedback Control In General (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

A kind of automatic parking control method towards general parking scene
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 { δ12,…,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
δfmax, 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, δfmax
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 exported12,…,δt}。
(4) trajectory planning:In given control instruction set { δ12,…,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
●δfr)=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, δfmax, 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 *r=0, δr *fmax, 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 { δ12,…,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
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mover> <mi>&amp;beta;</mi> <mo>&amp;CenterDot;</mo> </mover> </mtd> </mtr> <mtr> <mtd> <mover> <mi>r</mi> <mo>&amp;CenterDot;</mo> </mover> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>a</mi> <mn>11</mn> </msub> </mtd> <mtd> <msub> <mi>a</mi> <mn>12</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>a</mi> <mn>21</mn> </msub> </mtd> <mtd> <msub> <mi>a</mi> <mn>22</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mi>&amp;beta;</mi> </mtd> </mtr> <mtr> <mtd> <mi>r</mi> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>b</mi> <mn>11</mn> </msub> </mtd> <mtd> <msub> <mi>b</mi> <mn>12</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>b</mi> <mn>21</mn> </msub> </mtd> <mtd> <msub> <mi>b</mi> <mn>22</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>&amp;delta;</mi> <mi>f</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&amp;delta;</mi> <mi>r</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mn>11</mn> </msub> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>c</mi> <mi>r</mi> </msub> </mrow> <mrow> <msub> <mi>m</mi> <mi>g</mi> </msub> <mi>v</mi> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mn>12</mn> </msub> <mo>=</mo> <mo>-</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>-</mo> <msub> <mi>c</mi> <mi>r</mi> </msub> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> <mrow> <msub> <mi>m</mi> <mi>g</mi> </msub> <msup> <mi>v</mi> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mn>21</mn> </msub> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>-</mo> <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> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mn>22</mn> </msub> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <msup> <msub> <mi>l</mi> <mi>f</mi> </msub> <mn>2</mn> </msup> <mo>+</mo> <msub> <mi>c</mi> <mi>r</mi> </msub> <msup> <msub> <mi>l</mi> <mi>r</mi> </msub> <mn>2</mn> </msup> </mrow> <mrow> <msub> <mi>I</mi> <mrow> <mi>g</mi> <mi>z</mi> </mrow> </msub> <mi>v</mi> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>b</mi> <mn>11</mn> </msub> <mo>=</mo> <mfrac> <msub> <mi>c</mi> <mi>f</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>21</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <msub> <mi>l</mi> <mi>f</mi> </msub> </mrow> <msub> <mi>I</mi> <mrow> <mi>g</mi> <mi>z</mi> </mrow> </msub> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced>
And
δfmax, δr=0,
Obtain
<mrow> <mover> <mi>&amp;beta;</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>=</mo> <msub> <mi>a</mi> <mn>11</mn> </msub> <mi>&amp;beta;</mi> <mo>+</mo> <msub> <mi>a</mi> <mn>12</mn> </msub> <mi>r</mi> <mo>+</mo> <msub> <mi>b</mi> <mn>11</mn> </msub> <msub> <mi>&amp;delta;</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow>
<mrow> <mover> <mi>r</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>=</mo> <msub> <mi>a</mi> <mn>21</mn> </msub> <mi>&amp;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>&amp;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>&amp;beta;</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>*</mo> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <mover> <mi>r</mi> <mo>&amp;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>&amp;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>&amp;delta;</mi> <mi>f</mi> </msub> <mo>*</mo> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <msub> <mi>&amp;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, δfmax
Obtain
<mrow> <msup> <mover> <mi>&amp;beta;</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>*</mo> </msup> <mo>=</mo> <msub> <mi>a</mi> <mn>11</mn> </msub> <msup> <mi>&amp;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>&amp;delta;</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow>
<mrow> <msup> <mover> <mi>r</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>*</mo> </msup> <mo>=</mo> <mo>-</mo> <msub> <mi>a</mi> <mn>21</mn> </msub> <msup> <mi>&amp;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>&amp;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>&amp;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>&amp;CenterDot;</mo> </mover> <mo>&amp;prime;</mo> </msup> <mo>=</mo> <msup> <mi>A</mi> <mo>&amp;prime;</mo> </msup> <msup> <mi>x</mi> <mo>&amp;prime;</mo> </msup> <mo>+</mo> <msup> <mi>B</mi> <mo>&amp;prime;</mo> </msup> <mi>u</mi> </mrow>
The state space vectors of reponse system are expressed as
<mover> <mrow> <msup> <mi>x</mi> <mo>&amp;prime;</mo> </msup> <mo>=</mo> <msup> <mi>A</mi> <mo>&amp;prime;</mo> </msup> <msup> <mi>x</mi> <mo>&amp;prime;</mo> </msup> <mo>+</mo> <msup> <mi>B</mi> <mo>&amp;prime;</mo> </msup> <mo>&amp;lsqb;</mo> <mi>u</mi> <mo>-</mo> <mi>K</mi> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mo>&amp;prime;</mo> </msup> <mo>-</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mo>&amp;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>&amp;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>&amp;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>&amp;psi;</mi> <mo>&amp;prime;</mo> </msup> <mo>-</mo> <mi>&amp;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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