CN107097785A - A kind of adaptive intelligent vehicle crosswise joint method of preview distance - Google Patents
A kind of adaptive intelligent vehicle crosswise joint method of preview distance Download PDFInfo
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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, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/10—Path keeping
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
Abstract
The invention discloses a kind of adaptive intelligent vehicle crosswise joint method of preview distance.Belong to intelligent vehicle crosswise joint technical field.Crosswise joint method of the present invention is comprised the steps of：Step one, the four-degree-of-freedom dynamics reference model of vehicle ten is set up.Step 2, designs layer-stepping Lateral Controller structure.Layer-stepping Lateral Controller is divided into top level control device and lower floor's controller, and wherein top level control device is composed in parallel by fuzzy controller and iterative learning controller.Lower floor's controller is based on quasisliding mode Theoretical Design.The adaptive Lateral Controller of preview distance proposed by the present invention, under road curvature consecutive variations operating mode, compared to the Lateral Controller of the fixed preview distance of tradition, while ensureing that path trace precision satisfaction is required, the control stability and riding comfort of vehicle have been taken into account.
Description
Technical field
The invention belongs to intelligent vehicle motion control field, it is related to a kind of intelligent vehicle crosswise joint method, more particularly to
A kind of preview distance computational methods based on fuzzy theory and iterative learning theory.
Background technology
Intelligent vehicle movement control technology is divided into the longitudinally controlled and class of crosswise joint two according to the difference of control targe.Its
In, crosswise joint technology is to realize one of key technology that intelligent vehicle is independently travelled.Formula crosswise joint is taken aim at vehicle front in advance
A pre- position and attitude error at place of taking aim at is controller input, and reference path, which is changed, good adaptability.
Emulation and result of the test show that under reference path continual curvature change operating mode, the selection of preview distance is to path
Tracking accuracy, vehicle handling stability and riding comfort have a significant impact.At present, take aim in the design of formula Lateral Controller, lead in advance
Often by preview distance be expressed as longitudinal speed once or quadratic function.Patent CN103439884A is designed with fixing preview distance
Intelligent vehicle Lateral Controller.This method only can guarantee that crosswise joint precision meets demand, with the increase of longitudinal speed, car
Barycenter side acceleration is approached or more than 0.4g, is caused linearisation kinetic model to describe inaccurate, is not only made crosswise joint
Precise decreasing, vehicle handling stability and riding comfort have deteriorated.
The content of the invention
In order to overcome the above mentioned problem that prior art is present, the present invention needs to propose the intelligence that a kind of preview distance is adaptive
Vehicle lateral control method, should make intelligent vehicle realized in the case of Parameters variation and external interference to path it is accurate with
Track, takes into account vehicle handling stability and riding comfort during tracking again.
To realize above-mentioned target, the technical scheme is that：A kind of adaptive crosswise joint method of preview distance, bag
Include following steps：
Step 1, the non-linear vehicle dynamic model of the free degree of vehicle 14 is initially set up as reference model；
Step 2, layer-stepping Lateral Controller is built, layer-stepping Lateral Controller is divided into top level control device and lower floor's controller
Two parts；Top level control device is composed in parallel by fuzzy controller and iteration controller, and lower floor's controller is sliding mode controller；
Step 3, the vehicle for the preview kinematics model receiving step 1 set up according to vehicle and the geometrical relationship of reference path
The longitudinal velocity v that kinetic model is producedx, side velocity vyWith yaw velocity ω data, calculated with reference to reference path curvature ρ
It is pre- take aim at the transverse direction of vehicle, deflection error ε, and inputted as sliding mode controller；
Step 4, to eliminate composition error E at pre- take aim atLFor control targe, switching function S is designed, is replaced using saturation function
Tendency rate, the derivative and tendency rate of simultaneous switching function are designed for sign function, and substitutes into lateral direction of car kinetic model and is obtained
Required sliding mode controller；
Step 5, based on real-time car status information：Vehicle centroid side drift angle β, yaw velocity ω, pre- take aim at place's horizontal stroke
Fuzzy controller is designed to error y, deflection error ε；
Step 6, design iteration controller：Open loop law of learning is designed first, and controlled device includes sliding mode controller and vehicle
Kinetic model；By vehicle actual travel direction and reference path take aim in advance at the deflection error of tangential direction be open loop law of learning
Input, result that open loop law of learning current time draws sent to memory storage with the results added that last moment draws,
It is sent to controlled device simultaneously；
Step 7, adaptive preview distance is calculated.
Further, the vehicle dynamic model of the step 1 is：
In formula：A, b are respectively distance of the vehicle centroid away from axle, m；ω is yaw velocity, rad/s；vx、vyRespectively
For longitudinal velocity, side velocity, m/s；IzIt is vehicle around the rotary inertia of z-axis, kg.m2；FiFor the outstanding of suspension and vehicle body linking point
Booster；FiCFor side force of tire, obtained by Dugoff tire models.
Further, the preview kinematics model of the step 3 is：
Lateral error and deflection error at pre- take aim at, expression formula are calculated according to vehicle and the geometrical relationship of reference path
For：
In formula：Y is lateral error, m at pre- take aim at；ε is deflection error, rad at pre- take aim at；R, L difference road curvature half
Footpath and preview distance, m；vx、vyRespectively longitudinal velocity, side velocity.
Further, the sliding mode controller of the step 4 is：
Define comprehensive deviation EL：
In formula：γ is weight coefficient；ymax、ymin、εmax、εminThe respectively maximum of lateral error and deflection error, minimum
Value；
γ value is determined by examination survey method；
Define switching function S：
In formula：C is constant；
Exponential approach rate slaw is designed, sign function sgn (S) is replaced with saturation function sat (S):
Slaw=- η sat (S)-kS
In formula：η, k are controller constant；
To switching function S derivations, orderLateral direction of car kinetic model is substituted into, is obtained before sliding mode controller output
Take turns steering angle sigma.
Further, the design of Fuzzy Controller of the step 5 is as follows：
S3.1, definition is pre- to take aim at place's comprehensive deviation to the left just, to be negative to the right, and definition vehicle centroid side acceleration is to the left
It is negative, is inputted just, to define comprehensive deviation and negative barycenter lateral deviation acceleration for fuzzy controller to the right, controller is output as pre- take aim at
Compensated distance amount Δ L1；
S3.2, composition error and barycenter side acceleration are converted into the fuzzy set of [- 6,6], and the language of fuzzy subset becomes
Measure as { NB, NM, NS, ZE, PS, PM, PB }, output variable is converted into the fuzzy set of [0,1], linguistic variable for NB, NM, NS,
ZE, PS, PM, PB }, wherein NB, NM, NS, ZE, PS, PM, PB, which is referred to as, to be born greatly, negative small in bearing, and zero, just small, center, just
Greatly；Select trigonometric function as input, the membership function of output variable, fuzzy logic inference uses Mamdani methods, gravity model appoach
It is used as defuzzification；
S3.3, using method of expertise ambiguity in definition rule list, fuzzy control rule obscures sentence by IF-THEN and constituted：
WhereinFor input variable fuzzy subset's linguistic variable, BiFor output variable fuzzy subset's linguistic variable, i=
1,2 ..., 49 represent the number of fuzzy rule.
Further, in the step 6, the specific design process of iteration controller is：With sliding mode controller and vehicle power
Model is controlled device, and to eliminate deflection error as control targe, iteration controller is output as the preview distance of subsequent time,
PID type open loop iterative learning control laws are designed, then preview distance compensation rate is：
In formula：kp、kd、kiRespectively ratio, differential, integral coefficient, εk(t) it is current time deflection error.
Further, the adaptive preview distance of the step 7 is calculated as：By initial preview distance L '=0.5vxWith taking aim in advance
Compensated distance amount Δ L1、ΔL2Add up：L=0.5vx+ΔL1+ΔL2；Wherein vxFor longitudinal velocity.
Beneficial effects of the present invention are：The present invention proposes a kind of adaptive transversely layered controller of preview distance.No
Conventional Lateral Controller is same as when longitudinal speed is constant, preview distance is definite value.The present invention by the lateral error at taking aim in advance,
Deflection error, barycenter side acceleration as preview distance amendment reference factor.Top level control device combines real-time vehicle shape
State information calculates rational preview distance, and lower floor's controller receives the preview distance that top level control device is calculated, realization pair
The accurate tracking of reference path.This Lateral Controller not only ensure that intelligent vehicle path trace precision meets demand, simultaneously
Take into account in path tracking procedure, the control stability and riding comfort of vehicle.
Brief description of the drawings
Fig. 1 is crosswise joint system control process schematic diagram；
Fig. 2 is the DOFs vehicle dynamics model schematic diagram of vehicle 14；
Fig. 3 is intelligent vehicle and reference path geometrical relationship schematic diagram；
Fig. 4 is the membership function schematic diagram of input variable；
Fig. 5 is the membership function schematic diagram of output variable；
Fig. 6 is iterative learning controller structural representation；
Embodiment
Describe the implementation process of the present invention in detail below in conjunction with technical scheme and accompanying drawing：
As shown in figure 1, the crosswise joint system that the present invention is referred to includes preview kinematics model, layer-stepping crosswise joint
Device, the part of vehicle dynamic model three.Wherein, layer-stepping Lateral Controller is divided into top level control device and lower floor's controller two
Point.Top level control device is composed in parallel by fuzzy controller and iteration controller.Lower floor's controller is sliding mode controller.
The specific workflow of control system is preview kinematics model according to the longitudinal speed v of Current vehiclex, horizontal speed
vy, yaw velocity ω and reference path curvature ρ calculate lateral error y, deflection error ε at pre- take aim at.
Top level control device sends initial preview distance L to lower floor's controller first.Lower floor's controller with initially taking aim in advance
Position and attitude error is input, and reference path is tracked.During traveling, fuzzy controller receives real-time vehicle centroid lateral deviation
Angle beta, yaw velocity ω, it is pre- take aim at a lateral error y, deflection error ε, calculating obtains real-time vehicle barycenter side acceleration ayWith
Composition error EL, and inputted as controller, with preview distance compensation rate Δ L1Exported for controller.Iteration controller is to eliminate
Deflection error ε is target, is output as preview distance compensation rate Δ L2.Current preview distance is entered with above-mentioned preview distance compensation rate
Row amendment, retransmits to lower floor's sliding mode controller, so circulates said process.
The vehicle dynamic model that is referred in Fig. 1 as shown in Fig. 2 the free degree simplified model of vehicle 14 is made up of four parts,
Respectively sprung mass block, suspension system, QS and wheel.Sprung mass block is the simplified model of vehicle body.Suspension system
The simplified model of system includes helical spring and damper.The simplified model of wheel is by equivalent helical spring and unsprung mass block table
Show.Left and right sides unsprung mass block is connected by QS.
Specific implementation step of the present invention is as follows：
Step 1：
The free degree kinetic model of vehicle 14 is set up as reference model.As barycenter side acceleration ayIt is preceding less than 0.4g
When wheel steering angle sigma is smaller, the simplification kinetics equation of reference model is specific as follows：
In formula：
A, b, d are respectively distance of the vehicle centroid away from axle, 1/2 car gage, m.ω is yaw velocity, rad/
s。vx、vyRespectively longitudinal velocity, side velocity, m/s.θ、β、The respectively angle of pitch, side slip angle, vehicle roll angle,
rad。Ix、Iy、IzRespectively vehicle around the rotary inertia of x-axis, vehicle around the rotary inertia of y-axis, vehicle around z-axis rotary inertia,
kg.m2。FiFor the suspension power at suspension and vehicle body tie point, N.zbi、zwiRespectively suspension and the displacement of vehicle body tie point, tire with
Suspension tie point displacement, m, kaf、karRespectively front and rear QS side drift angle stiffness K Nm/rad.FiCIt is lateral for tire
Power, is obtained by Dugoff tire models.
Step 2：
Preview kinematics model receives the longitudinal velocity v that vehicle dynamic model is producedx, side velocity vyWith yaw angle speed
ω data are spent, the transverse direction of vehicle, deflection error y, ε at pre- take aim at are calculated with reference to reference path curvature ρ, and be used as lower floor's controller
Input.
Vehicle and the geometrical relationship figure of reference path as shown in Figure 3, set up preview kinematics model, then take aim in advance at it is horizontal
Computational methods to error and deflection error y, ε are as follows：
In formula：Y is lateral error, m at pre- take aim at.ε is deflection error, rad at pre- take aim at.R, L difference road curvature half
Footpath and preview distance, m.
It pre- will take aim at place's lateral error and after deflection error normalizes, composition error is combined into by certain weight.It is comprehensive
Error ELComputational methods it is as follows：
γ is weight coefficient, γ=0.65 in formula.ymax、ymin、εmax、εminRespectively lateral error and deflection error be most
Greatly, minimum value.
Step 3：
To eliminate composition error E at pre- take aim atLFor control targe, design lower floor sliding mode controller.
Define switching function：
In formula：C is constant；
Switching function S derivations are obtained：
Exponential approach rate is designed, sign function sgn (S) is replaced with saturation function sat (S)：
In formula：η, k are controller constant；
The derivative of simultaneous switching functionWith exponential approach rate slaw, and by the above-mentioned dynamics of vehicle differential equation substitute into, meter
Calculation waits until that controller is exported, i.e. front wheel steering angle δ.
Step 4：
Based on real-time car status information：Vehicle centroid side drift angle β, yaw velocity ω, it is pre- take aim at lateral error
Y, deflection error ε design top level control device.
Step 4.1：
Driver makes vehicle limited generally using front certain point as target by driver behavior during actual travel
Objects ahead point is reached in time.In order to make driving procedure safe, comfortable, experienced driver is generally according to vehicle
State and road environment constantly adjust objects ahead point position.
With reference to said process, driving experience is converted into control rule, recycles fuzzy theory to be converted into mathematical function, if
Count preview distance Optimizing Fuzzy Controller.
, it is known that the composition error E that step 2 is referred toLThe path trace precision of vehicle can be represented.Vehicle roll angleBarycenter
The evaluation indexes such as side drift angle β can weigh the security and comfortableness of vehicle.Vehicle roll angle known to againWith side slip angle β
With vehicle centroid side acceleration ayPositive correlation.Therefore composition error E is selectedLWith barycenter side acceleration ayFor fuzzy controller
Input, preview distance compensation rate Δ L1Exported for controller.It is negative to the left just to define barycenter side acceleration to be to the right.Will be comprehensive
Close error and negative barycenter side acceleration is converted into the fuzzy set of domain [- 6,6].Fuzzy subset's linguistic variable for NB, NM, NS,
ZE, PS, PM, PB }, wherein NB, NM, NS, ZE, PS, PM, PB, which is referred to as, to be born greatly, negative small in bearing, and zero, just small, center, just
Greatly.By preview distance compensation rate Δ L1It is converted into the fuzzy set that domain is [0,1].Fuzzy subset's linguistic variable and input variable phase
Together.Preview distance change is excessively sensitive, is unfavorable for the stability of system.Therefore input, the membership function of output variable are
Trigonometric function and trapezoidal function composition, as shown in Figure 4, Figure 5.
Step 4.2 determines fuzzy control rule using method of expertise.Fuzzy rule is as shown in table 1.Each Fuzzy Control
System rule obscures sentence by following " IF-THEN " and constituted：
WhereinFor input variable fuzzy subset's linguistic variable, BiFor output variable fuzzy subset's linguistic variable.I=
1,2 ..., 49 represent the number of fuzzy rule.Fuzzy logic inference uses Mamdani methods, is sentenced using gravity model appoach as ambiguity solution
Certainly.
One in optional above-mentioned fuzzy reasoning table：
R(12)：IF EL is PS AND -ay is NM THEN ΔL1is PM；
The specific meaning of the fuzzy rule is that preview distance is mended when during composition error is just small, and barycenter side acceleration is negative
The amount of repaying is hit exactly.
The fuzzy reasoning table of table one
Step 5：Based on Fig. 6 iteration controller structural representations, open loop law of learning is designed.Step is as follows：
Controlled device shown in figure includes lower floor's sliding mode controller and vehicle dynamic model.Vehicle actual travel direction and
Reference path take aim in advance at tangential direction deflection error be open loop law of learning input.Open loop law of learning current time draws
As a result the results added drawn with last moment is sent to memory storage.It is sent to controlled device simultaneously.
To eliminate deflection error as control targe.Open loop PID iterative learning control laws are designed, preview distance compensation rate can be represented
For：
In formula：kp、kd、kiRespectively ratio, differential, integral coefficient, εk(t) it is current time deflection error.
Step 6：Adaptively preview distance computational methods are：By initial preview distance L '=0.5vxCompensated with preview distance
Measure Δ L1、ΔL2Add up：
In the description of this specification, reference term " one embodiment ", " some embodiments ", " illustrative examples ",
The description of " example ", " specific example " or " some examples " etc. means to combine specific features, the knot that the embodiment or example are described
Structure, material or feature are contained at least one embodiment of the present invention or example.In this manual, to above-mentioned term
Schematic representation is not necessarily referring to identical embodiment or example.Moreover, specific features, structure, material or the spy of description
Point can in an appropriate manner be combined in any one or more embodiments or example.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
In the case of departing from the principle and objective of the present invention a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The scope of invention is limited by claim and its equivalent.
Claims (7)
1. a kind of adaptive intelligent vehicle crosswise joint method of preview distance, it is characterised in that comprise the following steps：
Step 1, the non-linear vehicle dynamic model of the free degree of vehicle 14 is initially set up as reference model；
Step 2, layer-stepping Lateral Controller is built, layer-stepping Lateral Controller is divided into top level control device and lower floor's controller two
Point；Top level control device is composed in parallel by fuzzy controller and iteration controller, and lower floor's controller is sliding mode controller；
Step 3, the vehicle power for the preview kinematics model receiving step 1 set up according to vehicle and the geometrical relationship of reference path
Learn the longitudinal velocity v that model is producedx, side velocity vyWith yaw velocity ω data, calculate and take aim in advance with reference to reference path curvature ρ
The transverse direction of vehicle, deflection error ε at point, and inputted as sliding mode controller；
Step 4, to eliminate composition error E at pre- take aim atLFor control targe, switching function S is designed, is substituted and accorded with using saturation function
Number function design tendency rate, the derivative and tendency rate of simultaneous switching function, and substitute into needed for lateral direction of car kinetic model obtains
Sliding mode controller；
Step 5, based on real-time car status information：Vehicle centroid side drift angle β, yaw velocity ω, pre- take aim at a place laterally mistake
Poor y, deflection error ε design fuzzy controller；
Step 6, design iteration controller：Open loop law of learning is designed first, and controlled device includes sliding mode controller and vehicle power
Learn model；By vehicle actual travel direction and reference path take aim in advance at the deflection error of tangential direction be the defeated of open loop law of learning
Enter, the results added that the result that open loop law of learning current time draws and last moment draw is sent to memory storage, simultaneously
It is sent to controlled device；
Step 7, adaptive preview distance is calculated.
2. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, it is characterised in that
The vehicle dynamic model of the step 1 is：
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In formula：A, b are respectively distance of the vehicle centroid away from axle, m；ω is yaw velocity, rad/s；vx、vyRespectively indulge
To speed, side velocity, m/s；IzIt is vehicle around the rotary inertia of z-axis, kg.m2；FiFor suspension and the suspension of vehicle body linking point
Power；FiCFor side force of tire, obtained by Dugoff tire models.
3. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, it is characterised in that
The preview kinematics model of the step 3 is：
Lateral error and deflection error at pre- take aim at is calculated according to vehicle and the geometrical relationship of reference path, expression formula is：
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In formula：Y is lateral error, m at pre- take aim at；ε is deflection error, rad at pre- take aim at；R, L distinguish road curvature radius and
Preview distance, m；vx、vyRespectively longitudinal velocity, side velocity.
4. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, it is characterised in that
The sliding mode controller of the step 4 is：
Define comprehensive deviation EL：
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In formula：γ is weight coefficient；ymax、ymin、εmax、εminThe respectively maximum of lateral error and deflection error, minimum value；γ
Value determined by examination survey method；
Define switching function S：
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In formula：C is constant；
Design exponential approach rate slaw：
Slaw=- η sat (S)-kS
In formula：η, k are controller constant；
To switching function S derivations, orderLateral direction of car kinetic model is substituted into, sliding mode controller output front-wheel steer is obtained
Angle δ.
5. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, it is characterised in that
The design of Fuzzy Controller of the step 5 is as follows：
S3.1, definition is pre- to take aim at place's comprehensive deviation to the left just, to be negative to the right, and definition vehicle centroid side acceleration is to the left
It is negative, to the right just, to define comprehensive deviation and negative barycenter lateral deviation acceleration is fuzzy controller input, controller be output as it is pre- take aim at away from
From compensation rate Δ L1；
S3.2, composition error and barycenter side acceleration are converted into the fuzzy set of [- 6,6], and the linguistic variable of fuzzy subset is
{ NB, NM, NS, ZE, PS, PM, PB }, output variable is converted into the fuzzy set of [0,1], linguistic variable for NB, NM, NS, ZE, PS,
PM, PB }, wherein NB, NM, NS, ZE, PS, PM, PB, which is referred to as, to be born greatly, negative small in bearing, and zero, just small, center is honest；Selection
Trigonometric function uses Mamdani methods as input, the membership function of output variable, fuzzy logic inference, and gravity model appoach is used as solution
Fuzzy judgment；
S3.3, using method of expertise ambiguity in definition rule list, fuzzy control rule obscures sentence by IF-THEN and constituted：
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<mi>R</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>:</mo>
<mi>I</mi>
<mi>F</mi>
<mi> </mi>
<mi>y</mi>
<mi> </mi>
<mi>i</mi>
<mi>s</mi>
<mi> </mi>
<msubsup>
<mi>A</mi>
<mn>1</mn>
<mi>i</mi>
</msubsup>
<mi>A</mi>
<mi>N</mi>
<mi>D</mi>
<mo>-</mo>
<msub>
<mi>a</mi>
<mi>y</mi>
</msub>
<mi>i</mi>
<mi>s</mi>
<mi> </mi>
<msubsup>
<mi>A</mi>
<mn>2</mn>
<mi>i</mi>
</msubsup>
<msub>
<mi>THEN&Delta;L</mi>
<mn>1</mn>
</msub>
<mi>i</mi>
<mi>s</mi>
<mi> </mi>
<msup>
<mi>B</mi>
<mi>i</mi>
</msup>
<mo>;</mo>
</mrow>
WhereinFor input variable fuzzy subset's linguistic variable, BiFor output variable fuzzy subset's linguistic variable, i=1,
2 ..., 49 represent the number of fuzzy rule.
6. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, it is characterised in that
In the step 6, the specific design process of iteration controller is：Using sliding mode controller and vehicle dynamic model as controlled pair
As to eliminate deflection error as control targe, iteration controller is output as the preview distance of subsequent time, designs PID type open loops
Iterative learning control law, then preview distance compensation rate be：
<mrow>
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<mi>d</mi>
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In formula：kp、kd、kiRespectively ratio, differential, integral coefficient, εk(t) it is current time deflection error.
7. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, it is characterised in that
The adaptive preview distance of the step 7 is calculated as：By initial preview distance L '=0.5vxWith preview distance compensation rate Δ L1、Δ
L2Add up：L=0.5vx+ΔL1+ΔL2；Wherein vxFor longitudinal velocity.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090157263A1 (en) * | 2007-12-13 | 2009-06-18 | Hyundai Motor Company | System and method for keeping a vehicle in a lane |
| CN102358287A (en) * | 2011-09-05 | 2012-02-22 | 北京航空航天大学 | Trajectory tracking control method used for automatic driving robot of vehicle |
| CN103439884A (en) * | 2013-07-19 | 2013-12-11 | 大连理工大学 | Transversal smart car control method based on vague sliding mode |
| CN104670233A (en) * | 2013-11-28 | 2015-06-03 | 现代摩比斯株式会社 | Method for controlling cornering of vehicle and apparatus thereof |
| CN104960520A (en) * | 2015-07-16 | 2015-10-07 | 北京工业大学 | Preview point determining method based on Pure Pursuit algorithm |
-
2017
- 2017-05-25 CN CN201710378710.1A patent/CN107097785B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090157263A1 (en) * | 2007-12-13 | 2009-06-18 | Hyundai Motor Company | System and method for keeping a vehicle in a lane |
| CN102358287A (en) * | 2011-09-05 | 2012-02-22 | 北京航空航天大学 | Trajectory tracking control method used for automatic driving robot of vehicle |
| CN103439884A (en) * | 2013-07-19 | 2013-12-11 | 大连理工大学 | Transversal smart car control method based on vague sliding mode |
| CN104670233A (en) * | 2013-11-28 | 2015-06-03 | 现代摩比斯株式会社 | Method for controlling cornering of vehicle and apparatus thereof |
| CN104960520A (en) * | 2015-07-16 | 2015-10-07 | 北京工业大学 | Preview point determining method based on Pure Pursuit algorithm |
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