CN107097785B - A kind of intelligent vehicle crosswise joint method that preview distance is adaptive - Google Patents
A kind of intelligent vehicle crosswise joint method that preview distance is adaptive 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 intelligent vehicle crosswise joint methods that preview distance is adaptive.Belong to intelligent vehicle crosswise joint technical field.Crosswise joint method of the present invention comprises the steps of: step 1, establishes ten four-degree-of-freedom dynamics reference model of vehicle.Step 2 designs layer-stepping Lateral Controller structure.Layer-stepping Lateral Controller is divided into upper controller and lower layer's controller, and wherein upper controller is composed in parallel by fuzzy controller and iterative learning controller.Lower layer'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 condition, Lateral Controller compared to the fixed preview distance of tradition has taken into account the control stability and riding comfort of vehicle while guaranteeing that path trace precision is met the requirements.
Description
Technical field
The invention belongs to intelligent vehicle motion control fields, are related to a kind of intelligent vehicle crosswise joint method, in particular to
A kind of preview distance calculation method based on fuzzy theory and iterative learning theory.
Background technique
Intelligent vehicle movement control technology is divided into longitudinally controlled and two class of crosswise joint according to the difference of control target.Its
In, crosswise joint technology is one of the key technology realizing intelligent vehicle and independently travelling.Formula crosswise joint is taken aim in advance with vehicle front
The position and attitude error for taking aim at place in advance is that controller inputs, and changing to reference path has good adaptability.
Emulation and test result show that, in the case where reference path continual curvature changes operating condition, the selection of preview distance is to path
Tracking accuracy, vehicle handling stability and riding comfort have a significant impact.Currently, being taken aim in the design of formula Lateral Controller in advance, lead to
Preview distance is often expressed as to the primary or quadratic function of longitudinal speed.Patent CN103439884A is to fix preview distance design
Intelligent vehicle Lateral Controller.This method only can guarantee crosswise joint precision meet demand, with the increase of longitudinal speed, vehicle
Mass center side acceleration approaches or more than 0.4g, causes to linearize kinetic model description inaccuracy, not only makes crosswise joint
Accuracy decline, vehicle handling stability and riding comfort are deteriorated.
Summary of the invention
In order to overcome the above problem of the existing technology, the present invention needs to propose a kind of intelligence that preview distance is adaptive
Vehicle lateral control method, should make intelligent vehicle realized in 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 crosswise joint method that preview distance is adaptive, packet
Include following steps:
Step 1, the non-linear vehicle dynamic model of 14 freedom degree of vehicle is initially set up as reference model；
Step 2, layer-stepping Lateral Controller is constructed, layer-stepping Lateral Controller is divided into upper controller and lower layer's controller
Two parts；Upper controller is composed in parallel by fuzzy controller and iteration controller, and lower layer's controller is sliding mode controller；
Step 3, the vehicle for the preview kinematics model receiving step 1 established according to vehicle and the geometrical relationship of reference path
The longitudinal velocity v that kinetic model generatesx, side velocity vyWith yaw velocity ω data, calculated in conjunction with reference path curvature ρ
A transverse direction for vehicle, deflection error ε at taking aim in advance, and inputted as sliding mode controller；
Step 4, with eliminate take aim in advance at composition error ELTo control target, 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 obtains
Required sliding mode controller；
Step 5, it is based on real-time car status information: vehicle centroid side drift angle β, yaw velocity ω, taking aim at place's cross in advance
Fuzzy controller is designed to error y, deflection error ε；
Step 6, design iteration controller: design open loop law of learning first, controlled device include 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 obtains and the results added that last moment obtains be sent to memory storage,
It is sent to controlled device simultaneously；
Step 7, adaptive preview distance calculates.
Further, the vehicle dynamic model of the step 1 are as follows:
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 model.
Further, the preview kinematics model of the step 3 are as follows:
The lateral error and deflection error at taking aim in advance, expression formula are calculated according to vehicle and the geometrical relationship of reference path
Are as follows:
In formula: y is a lateral error at taking aim in advance, m；ε is a deflection error at taking aim in advance, rad；R, L distinguishes road curvature half
Diameter and preview distance, m；vx、vyRespectively longitudinal velocity, side velocity.
Further, the sliding mode controller of the step 4 are as follows:
Define comprehensive deviation EL:
In formula: γ is weight coefficient；ymax、ymin、εmax、εminThe respectively maximum of lateral error and deflection error, minimum
Value；
The value of γ 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 derivation, enableLateral direction of car kinetic model is substituted into, before obtaining 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 take aim at place's comprehensive deviation in advance and are positive to the left, be negative to the right, and definition vehicle centroid side acceleration is to the left
It is negative, is positive to the right, define comprehensive deviation and negative mass center lateral deviation acceleration as fuzzy controller input, controller output is to take aim in advance
Compensated distance amount Δ L1；
S3.2, composition error and mass center side acceleration are converted into the fuzzy set of [- 6,6], and the language of fuzzy subset becomes
Amount is { NB, NM, NS, ZE, PS, PM, PB }, and output variable is converted into the fuzzy set of [0,1], linguistic variable be NB, NM, NS,
ZE, PS, PM, PB }, wherein NB, NM, NS, ZE, PS, PM, PB are referred to as negative big, bear, bear it is small, zero, just small, center, just
Greatly；Select trigonometric function as input, the subordinating degree function of output variable, fuzzy logic inference uses Mamdani method, gravity model appoach
As defuzzification；
S3.3, using method of expertise ambiguity in definition rule list, fuzzy control rule obscures sentence by IF-THEN and constitutes:
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 are as follows: with sliding mode controller and vehicle power
Model is controlled device, and to eliminate deflection error as control target, iteration controller output is the preview distance of subsequent time,
PID type open loop iterative learning control law is designed, then preview distance compensation rate are as follows:
In formula: kp、kd、kiRespectively ratio, differential, integral coefficient, εkIt (t) is current time deflection error.
Further, the adaptive preview distance of the step 7 calculates are as follows: by initial preview distance L '=0.5vxWith take aim in advance
Compensated distance amount Δ L1、ΔL2It adds up: L=0.5vx+ΔL1+ΔL2；Wherein vxFor longitudinal velocity.
The invention has the benefit that the transversely layered controller adaptive the invention proposes a kind 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, mass center side acceleration are as the modified reference factor of preview distance.Upper controller combines real-time vehicle shape
State information calculates reasonable preview distance, and lower layer's controller receives the preview distance that upper controller is calculated, realization pair
The accurate tracking of reference path.This Lateral Controller not only ensure that intelligent vehicle path trace precision meet demand, simultaneously
It has taken into account in path tracking procedure, the control stability and riding comfort of vehicle.
Detailed description of the invention
Fig. 1 is crosswise joint system control process schematic diagram；
Fig. 2 is 14 DOFs vehicle dynamics model schematic diagram of vehicle；
Fig. 3 is intelligent vehicle and reference path geometrical relationship schematic diagram；
Fig. 4 is the subordinating degree function schematic diagram of input variable；
Fig. 5 is the subordinating degree function schematic diagram of output variable；
Fig. 6 is iterative learning controller structural schematic diagram；
Specific embodiment
Describe implementation process of the invention in detail below in conjunction with technical solution and attached drawing:
As shown in Figure 1, the crosswise joint system that the present invention refers to includes preview kinematics model, layer-stepping crosswise joint
Device, vehicle dynamic model three parts.Wherein, layer-stepping Lateral Controller is divided into upper controller and lower layer's controller two
Point.Upper controller is composed in parallel by fuzzy controller and iteration controller.Lower layer's controller is sliding mode controller.
The specific workflow of control system is preview kinematics model according to current vehicle longitudinal direction speed vx, lateral speed
vy, yaw velocity ω and reference path curvature ρ lateral error y, deflection error ε at pre- take aim at is calculated.
Upper controller sends initial preview distance L to lower layer's controller first.Lower layer's controller initially to take aim in advance at
Position and attitude error is input, is tracked to reference path.In driving process, fuzzy controller receives real-time vehicle centroid lateral deviation
Angle beta, takes aim at a lateral error y, deflection error ε in advance at yaw velocity ω, and real-time vehicle mass center side acceleration a is calculatedyWith
Composition error EL, and inputted as controller, with preview distance compensation rate Δ L1For controller output.Iteration controller is to eliminate
Deflection error ε is target, is exported as preview distance compensation rate Δ L2.With above-mentioned preview distance compensation rate to current preview distance into
Row amendment is retransmited to lower layer's sliding mode controller, so the circulation above process.
The vehicle dynamic model that is referred in Fig. 1 as shown in Fig. 2, 14 freedom degree simplified model of vehicle is made of four parts,
Respectively sprung mass block, suspension system, stabilizer bar 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 stabilizer bar.
Specific implementation step of the present invention is as follows:
Step 1:
14 freedom degree kinetic model of vehicle is established as reference model.As mass center 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, 1/2 car gage of the vehicle centroid away from axle, m.ω is yaw velocity, rad/
s。vx、vyRespectively longitudinal velocity, side velocity, m/s.θ,β,Respectively pitch angle, 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 vehicle body tie point displacement, tire with
The displacement of suspension tie point, m, kaf、karRespectively front and back stabilizer bar side drift angle stiffness K Nm/rad.FiCIt is lateral for tire
Power is obtained by Dugoff tire model.
Step 2:
Preview kinematics model receives the longitudinal velocity v that vehicle dynamic model generatesx, side velocity vyWith yaw angle speed
ω data are spent, a transverse direction for vehicle, deflection error y, ε at taking aim in advance are calculated in conjunction with reference path curvature ρ, and as lower layer's controller
Input.
The geometrical relationship figure of vehicle and reference path as shown in Figure 3, establishes preview kinematics model, then take aim in advance at it is horizontal
Calculation method to error and deflection error y, ε is as follows:
In formula: y is a lateral error at taking aim in advance, m.ε is a deflection error at taking aim in advance, rad.R, L distinguishes road curvature half
Diameter and preview distance, m.
It will take aim at place's lateral error in advance and after deflection error normalizes, be combined into composition error by certain weight.It is comprehensive
Error ELCalculation method 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:
With eliminate take aim in advance at composition error ELTo control target, lower layer's sliding mode controller is designed.
Define switching function:
In formula: c is constant；
Switching function S derivation is 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 exports, 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, take aim in advance at lateral error
Y, deflection error ε designs upper controller.
Step 4.1:
Driver makes vehicle limited usually using front certain point as target by driver behavior during actual travel
Objects ahead point is reached in time.In order to keep driving procedure safe, comfortable, experienced driver is generally according to vehicle
State and road environment constantly adjust objects ahead point position.
With reference to the above process, control rule is converted by driving experience, fuzzy theory is recycled to be converted into mathematical function, if
Count preview distance Optimizing Fuzzy Controller.
It is known that the composition error E that step 2 refers toLThe path trace precision of vehicle can be represented.Vehicle roll angleMass center
The evaluation indexes such as side drift angle β can measure safety and the comfort of vehicle.Known vehicle roll angle againWith side slip angle β
With vehicle centroid side acceleration ayIt is positively correlated.Therefore selection composition error ELWith mass center side acceleration ayFor fuzzy controller
Input, preview distance compensation rate Δ L1For controller output.It defines mass center side acceleration to be positive to the right, be negative to the left.It will be comprehensive
It closes error and negative mass center side acceleration is converted into the fuzzy set of domain [- 6,6].Fuzzy subset's linguistic variable be NB, NM, NS,
ZE, PS, PM, PB }, wherein NB, NM, NS, ZE, PS, PM, PB are referred to as negative big, bear, bear it is small, 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 variation is excessively sensitive, is unfavorable for the stability of system.Therefore it inputs, the subordinating degree function of output variable is
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 constitutes:
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 method, is sentenced using gravity model appoach as ambiguity solution
Certainly.
An example 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 when composition error is just small, and mass center side acceleration is negative middle, and preview distance is mended
The amount of repaying center.
One fuzzy reasoning table of table
Step 5: being based on Fig. 6 iteration controller structural schematic diagram, designs open loop law of learning.Steps are as follows:
Controlled device as shown in the figure includes lower layer'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 obtains
As a result the results added obtained with last moment is sent to memory storage.It is sent to controlled device simultaneously.
To eliminate deflection error as control target.Open loop PID iterative learning control law is designed, preview distance compensation rate can indicate
Are as follows:
In formula: kp、kd、kiRespectively ratio, differential, integral coefficient, εkIt (t) is current time deflection error.
Step 6: adaptive preview distance calculation method are as follows: by initial preview distance L '=0.5vxIt is compensated with preview distance
Measure Δ L1、ΔL2It adds 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 specific features described in conjunction with this embodiment or example, knot
Structure, material or feature are included at least one embodiment or example of the invention.In the present specification, to above-mentioned term
Schematic representation may not refer to the same embodiment or example.Moreover, specific features, structure, material or the spy of description
Point can be combined in any suitable manner in any one or more of the embodiments or examples.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that: not
A variety of change, modification, replacement and modification can be carried out to these embodiments in the case where being detached from the principle of the present invention and objective, this
The range of invention is defined by the claims and their equivalents.
Claims (7)
1. a kind of intelligent vehicle crosswise joint method that preview distance is adaptive, which comprises the following steps:
Step 1, the non-linear vehicle dynamic model of 14 freedom degree of vehicle is initially set up as reference model；
Step 2, layer-stepping Lateral Controller is constructed, layer-stepping Lateral Controller is divided into upper controller and lower layer's controller two
Point；Upper controller is composed in parallel by fuzzy controller and iteration controller, and lower layer's controller is sliding mode controller；
Step 3, the vehicle power for the preview kinematics model receiving step 1 established according to vehicle and the geometrical relationship of reference path
Learn the longitudinal velocity v that model generatesx, side velocity vyWith yaw velocity ω data, taken aim in advance in conjunction with reference path curvature ρ calculating
Lateral error y, the deflection error ε of vehicle at point, and inputted as sliding mode controller；
Step 4, with eliminate take aim in advance at composition error ELTo control target, switching function S is designed, is substituted and is accorded with using saturation function
Number function designs tendency rate, the derivative and tendency rate of simultaneous switching function, and substitutes into needed for lateral direction of car kinetic model obtains
Sliding mode controller；
Step 5, it is based on real-time car status information: vehicle centroid side drift angle β, yaw velocity ω, taking aim at place's laterally mistake in advance
Poor y, deflection error ε design fuzzy controller；
Step 6, design iteration controller: design open loop law of learning first, controlled device include 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 result that open loop law of learning current time obtains and the results added that last moment obtains are sent to memory storage, simultaneously
It is sent to controlled device；
Step 7, adaptive preview distance calculates.
2. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, which is characterized in that
The vehicle dynamic model of the step 1 are as follows:
In formula: a, b are respectively distance of the vehicle centroid away from axle, m；ω is yaw velocity, rad/s；vx、vyIt is respectively vertical
To speed, side velocity, m/s；IzIt is vehicle around the rotary inertia of z-axis, kg.m2；FiFor the suspension of suspension and vehicle body linking point
Power；FiCFor side force of tire, obtained by Dugoff tire model；M is complete vehicle quality, kg.
3. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, which is characterized in that
The preview kinematics model of the step 3 are as follows:
The lateral error and deflection error at taking aim in advance, expression formula are calculated according to vehicle and the geometrical relationship of reference path are as follows:
In formula: y is a lateral error at taking aim in advance, m；ε is a deflection error at taking aim in advance, rad；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, which is characterized in that
The sliding mode controller of the step 4 are as follows:
Define comprehensive deviation EL:
In formula: γ is weight coefficient；ymax、ymin、εmax、εminThe respectively maximum of lateral error and deflection error, minimum value；
The value of γ is determined by examination survey method；
Define switching function S:
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 derivation, enableLateral 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, which is characterized in that
The design of Fuzzy Controller of the step 5 is as follows:
S3.1, definition take aim at place's comprehensive deviation in advance and are positive to the left, be negative to the right, and definition vehicle centroid side acceleration is to the left
It is negative, be positive to the right, define comprehensive deviation and negative mass center lateral deviation acceleration is that fuzzy controller inputs, controller output for it is pre- take aim at away from
From compensation rate Δ L1；
S3.2, composition error and mass center 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 are converted into the fuzzy set of [0,1], linguistic variable be NB, NM, NS, ZE, PS,
PM, PB }, wherein NB, NM, NS, ZE, PS, PM, PB are referred to as negative big, bear, bear it is small, zero, just small, center is honest；Selection
Trigonometric function is as input, the subordinating degree function of output variable, and fuzzy logic inference uses Mamdani method, and gravity model appoach is as solution
Fuzzy judgment；
S3.3, using method of expertise ambiguity in definition rule list, fuzzy control rule obscures sentence by IF-THEN and constitutes:
R(i): IF y isAND-ay isTHEN ΔL1 is Bi；
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, which is characterized in that
In the step 6, the specific design process of iteration controller are as follows: with sliding mode controller and vehicle dynamic model be controlled pair
As to eliminate deflection error as control target, iteration controller output is the preview distance of subsequent time, designs the open loop of PID type
Iterative learning control law, then preview distance compensation rate are as follows:
In formula: kp、kd、kiRespectively ratio, differential, integral coefficient, εkIt (t) is current time deflection error.
7. a kind of adaptive intelligent vehicle crosswise joint method of preview distance according to claim 1, which is characterized in that
The adaptive preview distance of the step 7 calculates are as follows: by initial preview distance L '=0.5vxWith preview distance compensation rate Δ L1、Δ
L2It adds up: L=0.5vx+ΔL1+ΔL2；Wherein vxFor longitudinal velocity.
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