CN109334672A - A kind of intelligent electric automobile path trace and direct yaw moment cooperative control method - Google Patents
A kind of intelligent electric automobile path trace and direct yaw moment cooperative control method Download PDFInfo
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- CN109334672A CN109334672A CN201811319084.XA CN201811319084A CN109334672A CN 109334672 A CN109334672 A CN 109334672A CN 201811319084 A CN201811319084 A CN 201811319084A CN 109334672 A CN109334672 A CN 109334672A
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
- B60W2050/0036—Multiple-track, 3D multi-body vehicle model, e.g. combination of models for vehicle sub-units
Abstract
A kind of intelligent electric automobile path trace and direct yaw moment cooperative control method, are related to electric car.Based on the Multi-sensor Fusions information such as GPS, INS and CCD vision system, the kinetic model of characterization intelligent electric automobile transverse direction behavioral characteristics is established；Establish the intelligent electric automobile transversal sectional multi-model based on velocity partition, Collaborative Control module is predicted with the control minimum control target of input quantity, design intelligent electric automobile path trace and direct yaw moment upper layer multi-model so that intelligent electric automobile driving status is optimal；It designs intelligent electric automobile lower layer and controls distributor, according to practical additional yaw moment Real-time solution except the optimal longitudinal tire force for going out wheel.Effectively overcome the time variation and external disturbance of intelligent electric automobile system model, hence it is evident that improve intelligent electric automobile transverse movement control system performance, reduce costs.
Description
Technical field
The present invention relates to electric cars, assist more particularly to a kind of intelligent electric automobile path trace and direct yaw moment
Same control method.
Background technique
Intelligent electric automobile has outstanding mobility, control flexibility and driving terseness, it is considered to be improves traffic
One of safely, reduce environmental pollution with the effective way of energy consumption, cause the extensive concern of national governments and scientific research institution.
Transverse movement is controlled as one of the basis of intelligent electric automobile and key technology, is always research hotspot, such as
Front-wheel active steering (Active Front-Wheel Steering, AFS) system, path following control (Path Following
Control, PFC) and direct yaw moment control (Direct Yaw Moment Control, DYC).Intelligent electric motor car has
The features such as parameter uncertainty, time randomness and strong nonlinearity, how to design transverse movement control system has deep grind
Study carefully meaning.
The task of path following control (PFC) is to ensure that the intelligent electric automobile accurately specified road planned of tracking in real time
Diameter.(J.Ji, A.Khajepour, W.W.Melek, Y.Huang.Path planning and the tracking for of document 1
vehicle collision avoidance based on model predictive control with
multiconstraints,IEEE Transactions On Vehicular Technology,66(2)(2017)952-
964.) lateral path tracking control system is devised using Model Predictive Control Theory, which can calculate desired front-wheel in real time
Steering angle is to prevent automobile and moving obstacle from colliding.The task of direct yaw moment control (DYC) is by left and right vehicle wheel two
Side wheel due to driving or brake force difference and the additional yaw moment (differential braking) that generates ensures the lateral stability of automobile
Property.(C.Fu, R.Hoseinnezhad, A.B.Hadiashar, R.N.Jazar.Direct the yaw moment of document 2
control for electric and hybrid vehicles with independent motors,
International Journal of Vehicle Design, 69 (1) (2015) 1-24.), it proposes a kind of based on sliding formwork
Theoretical automobile direct yaw moment control method, this method are realized using novel handoff functionality to vehicle expectation yaw angle speed
The tracking of degree and side drift angle.However, research intelligent electric automobile substantially carries out PFC or DYC control system from single task role at present
System design, this single task role design cannot be guaranteed intelligent electric automobile transverse movement control performance total optimization, in consideration of it, this
Invention provides a kind of intelligent electric automobile path trace and cooperate with Multi model Predictive Controllers with direct yaw moment, and realization is intelligently
Electric car path trace, lateral stability and the more performance objective comprehensively controls of comfort.
Summary of the invention
The purpose of the present invention is provide a kind of intelligent electric automobile path trace to solve difficulties in the prior art
With direct yaw moment cooperative control method.
The present invention the following steps are included:
Step 1: based on Multi-sensor Fusions information such as GPS, INS and CCD vision systems, establishing characterization intelligent electric automobile
The kinetic model of lateral behavioral characteristics；
In step 1, described based on Multi-sensor Fusions information such as GPS, INS and CCD vision systems, establish characterization intelligence
The kinetic model of electric car transverse direction behavioral characteristics includes:
(1) yaw velocity is acquired using INS, using GPS gathers longitudinal velocity information and lateral velocity information, designs side
Drift angle estimator；
(2) it establishes using automobile yaw velocity and side drift angle as state variable, is with front wheel angle and additional yaw moment
The intelligent electric automobile horizontal dynamic model of input；
(3) using CCD vision system measurement intelligent electric automobile and expected path relative position information, it is current to establish description
The intelligent electric automobile path trace kinematics model of pose and the error variation of expected pose.
Step 2: establishing the intelligent electric automobile transversal sectional multi-model based on velocity partition, travelled with intelligent electric automobile
State optimization and the control minimum control target of input quantity, design intelligent electric automobile path trace and direct yaw moment upper layer
Multi-model predicts Collaborative Control module；
In step 2, the intelligent electric automobile transversal sectional multi-model of the foundation based on velocity partition, with intelligent electric
Vehicle driving state is optimal and controls the minimum control target of input quantity, designs intelligent electric automobile path trace and direct sideway
Torque upper layer multi-model predicts that the specific method of Collaborative Control module can are as follows:
(1) size based on automobile longitudinal speed constructs intelligent electric automobile transversal sectional time-varying multi-model collection；
(2) when keeping for intelligent electric automobile transversal sectional time-varying continuous time multi-model collection being converted into segmentation using zeroth order
Constant discrete time multi-model collection；
(3) to avoid multi-model switching and causing chattering phenomenon, the velocity ambiguity factor is introduced piecewise linearity time-varying is discrete
Time multi-model collection is normalized；
(4) performance of intelligent electric automobile path trace and direct yaw moment upper layer multi-model prediction Collaborative Control is designed
Target function and constraint condition establish the calculation formula of multi-model prediction optimization problem, solve the optimal intelligence of performance indicator of sening as an envoy to
It can electric car front wheel angle and additional yaw moment input quantity.
Step 3: design intelligent electric automobile lower layer controls distributor, removes according to practical additional yaw moment Real-time solution
The optimal longitudinal tire force of wheel.
In step 3, the design intelligent electric automobile lower layer controls distributor, real-time according to practical additional yaw moment
Solving can except the specific method for the optimal longitudinal tire force for going out wheel are as follows:
(1) it with control distribution deviation and control input consumption energy minimum target, establishes and solves each wheel tyre power most
Optimal cost characteristic index function and constraint condition.
(2) longitudinal force of tire for being assigned to each intelligent electric automobile wheel is calculated using Newton method in real time.
The invention proposes a kind of, and the multi-model based on linear time-varying predicts (LTV-MPC) controller top level control strategy,
Front wheel angle needed for finding out Collaborative Control and additional yaw moment, to effectively overcome time variation and external disturbance characteristic.It builds
The control allocation strategy based on Newton method has been found, has realized the association of the control distribution and redundancy executing agency to additional yaw moment
It adjusts, to realize the multiple target Collaborative Control of intelligent electric automobile path trace and direct yaw moment.
The intelligent electric automobile that the intelligent electric automobile path trace and direct yaw moment cooperative control method use
Path trace and direct yaw moment cooperative control system include data obtaining module, side drift angle estimation module, the upper layer LTV-MPC
Control module, lower layer's control distribution module etc..The lateral dynamics state equation for initially setting up intelligent electric motor car, is then based on view
Feel system, GPS and INS acquisition information establish intelligent electric automobile path following system model, design LTV-MPC top level control mould
Block Real-time solution goes out expected front wheel angle and additional yaw moment, and expected additional yaw moment is controlled distributor by lower layer
It is assigned to the longitudinal force of tire of each tire, to realize that intelligent electric automobile driving status is optimal.
Technical effect and benefit of the invention is: the invention proposes a kind of novel intelligent electric automobile coordinated mechanism with
Track and direct yaw moment multi-model predict cooperative control method, effectively overcome the time variation of intelligent electric automobile system model
And external disturbance, hence it is evident that improve intelligent electric automobile transverse movement control system performance, reduce costs.
Detailed description of the invention
Fig. 1 is the logical box of intelligent electric automobile path trace and direct yaw moment cooperative control system of the present invention
Figure.
Fig. 2 is intelligent electric automobile kinetic model figure of the invention.
Fig. 3 is intelligent electric automobile and path relative position schematic diagram of the invention.
Specific embodiment
Following embodiment will the present invention is further illustrated in conjunction with attached drawing.
As shown in Figure 1, intelligent electric automobile path trace and direct yaw moment Collaborative Control described in the embodiment of the present invention
System acquires intelligent electric automobile running condition information, variation when establishing intelligent electric automobile by multisensor syste first
Section transverse state space multi-model collection derives intelligent electric automobile upper layer multi-model prediction association secondly based on predictive control theory
Then same controller controls distributor Real-time solution by lower layer and goes out each wheel tyre power, so that it is attached to obtain intelligent electric automobile
Add yaw moment, realizes the multiple target Collaborative Control to intelligent electric automobile path trace and direct yaw moment.
Referring to fig. 2 with 3, the specific step of the intelligent electric automobile path trace and direct yaw moment cooperative control method
It is rapid as follows:
Step 1: based on Multi-sensor Fusions information such as GPS, INS and CCD vision systems, establishing characterization intelligent electric automobile
The kinetic model of lateral behavioral characteristics.Its process includes following sub-step:
Step 1.1: yaw velocity is acquired using INSWith the longitudinal velocity information of GPS sampled pointWith lateral speed
Spend informationOn the basis of, obtain the estimated value of side slip angle βIt is as follows:
Step 1.2: intelligent electric automobile horizontal dynamic model is established according to classical mechanics, expression formula is as follows:
Wherein, vxIndicate the longitudinal velocity of vehicle, m indicates the quality of vehicle, IzIndicate that vehicle is used around the Equivalent Rotational of z-axis
Amount, lfAnd lrIndicate fore-and-aft distance of the mass center to front and back wheel shaft, FyfAnd FyrFront and back wheel lateral force of tire is respectively indicated,It indicates
Additional yaw moment.
Step 1.3: according to the relationship between additional yaw moment and each wheel longitudinal force and front wheel angle, establishing vehicle
Additional yaw moment expression formula, it is as follows:
Wherein, Fxfl,Fxfr,Fxrl,FxrrIndicate the longitudinal force of each wheel of vehicle, Fyfl,Fyfr,Fyrl,FyrrIndicate that vehicle is each
The cross force of wheel, δfIndicate front wheel steering angle, lsIndicate the half of wheelspan.
Step 1.4: setting side drift angle β is vehicle course angleIt is as follows with the difference of Vehicular yaw angle ψ:
The proportionate relationship of side force of tire and side drift angle is established, as follows:
Fyf=Cfaf；Fyr=Crar (5)
Wherein, afAnd arRespectively indicate the side drift angle of front wheels and rear wheels, CfAnd CrIt is rigid to respectively indicate front wheels and rear wheels lateral deviation
Degree.
Step 1.5: establishing intelligent electric automobile tyre slip angle afAnd arComputation model:
Step 1.6: being based on formula (2)~formula (6), derive using side drift angle and yaw velocity as the intelligence electricity of state variable
Electrical automobile horizontal dynamic model, as follows:
Step 1.7: intelligent electric automobile and phase are established in lateral deviation and azimuth deviation based on the acquisition of CCD vision system
Hope the computation model of path relative position deviation, as follows:
Wherein, eaIndicate azimuth deviation of the vehicle relative to path, LdFor preview distance, eyIndicate vehicle relative to path
Lateral deviation, ρL(t) curvature of expected path is indicated.
Step 1.8: binding model (7) and (8) obtain having the intelligent electric automobile multiple-input and multiple-output of external disturbance horizontal
It is as follows to state model:
Wherein,
Wherein,The state vector of expression system,The control of expression system is defeated
Enter.Y=[ey ea]TThe measurement of expression system exports, w=[ρL]TIndicate external disturbance.
Step 2: establishing the intelligent electric automobile based on longitudinal velocity subregion and predict time-varying multi-model collection, with intelligent electric vapour
Vehicle cross running state optimization and control input quantity are at least control target, design intelligent electric automobile path trace and directly horizontal
It puts torque upper layer multi-model and predicts Collaborative Control module, process includes following sub-step:
Step 2.1: according to the size of vehicular longitudinal velocity, it is laterally more to make intelligent electric automobile piecewise linearity time-varying (LTV)
Models Sets, as follows:
Wherein, vxhighAnd vxlowRespectively indicate the high speed and low speed of intelligent electric automobile.
Step 2.2: constant continuous time multi-model is converted into when being kept using zeroth order by intelligent electric automobile transversal sectional
Constant discrete time multi-model when segmentation:
Wherein,
Wherein, TsIndicate the control period.
Step 2.3: to avoid chattering phenomenon caused by multi-model switching, it is as follows to introduce the velocity ambiguity factor:
Based on the velocity ambiguity factor, segmentation multimode pattern (11) and formula (12) are normalized, obtain as follows from
Scattered state-space model can indicate as follows:
Wherein,
Step 2.4: design intelligent electric automobile path trace and direct yaw moment upper layer multi-model predict Collaborative Control
Performance index function:
Wherein, NpIndicate estimation range, NcIndicate control range, q1,q2,r1,r2,r3,r4Indicate that weighting coefficient, ε indicate arrow
Measure relaxation factor, Δ δfWithIndicate the increment of control input vector.
Step 2.5: the constraint condition of design intelligent electric automobile control input quantity front wheel angle and additional yaw moment, number
It is as follows to learn expression formula:
δf,min(t)≤δf(k+j|t)≤δf,max(t) (17)
Δδf,min(t)≤Δδf(k+j|t)≤Δδf,max(t) (19)
Wherein, δf,minAnd δf,maxIndicate front steering angle δfBoundary value,WithIndicate additional yaw moment
Boundary value, Δ δf,minWith Δ δf,maxIndicate front steering angle increment Δ δfBoundary value,WithIndicate additional sideway
Torque incrementBoundary value.
Step 2.6: the prediction optimization problem of construction intelligent electric automobile path trace and additional yaw moment Collaborative Control
Calculation formula it is as follows:
Subject to
Step 2.7: can it is expected by solving the best increment that formula (21) are calculated in time t then according to following formula
Steering angle sigmafd(t) and additional yaw momentIt is as follows:
Wherein,WithIndicate the optimal solution of prediction optimization problem (21)~(22).
Step 3 designs intelligent electric automobile lower layer and controls distributor, removes according to practical additional yaw moment Real-time solution
The optimal longitudinal tire force of wheel.
Step 3.1: distribution deviation minimum and control input quantity consumption energy being optimized at least for target with control, establishes and solves
The nonlinear optimal problem of each wheel tyre power, as follows:
Its restrictive condition are as follows:
Fxi,min≤Fxi≤Fxi,max, i=fl, fr, rl, rr (25)
Wherein,
WF=[- cos δfls+sinδflf cosδfls+sinδflf -ls ls]
Fx=[Fxfl Fxfr Fxrl Fxrr]T
Wherein, Q1∈R4×4And Q2∈R1×1Weight positive definite diagonal matrix, c ∈ R4The amount of being biased towards, Fxi,minAnd Fxi,maxTable
Show the minimum value and maximum value of tire longitudinal tire force.
Step 3.2: nonlinear optimal problem (23) being solved using Newton method, obtains be assigned to each tire in real time
Longitudinal force of tire desired value, it is as follows:
Wherein,
Indicate Newton direction.
The above content is combine optimal technical scheme to the present invention done further description, and it cannot be said that invention
Specific implementation is only limitted to these explanations.For general technical staff of the technical field of the invention, the present invention is not being departed from
Design under the premise of, simple deduce and replacement can also be made.
Claims (5)
1. a kind of intelligent electric automobile path trace and direct yaw moment cooperative control method, it is characterised in that including following step
It is rapid:
Step 1: based on Multi-sensor Fusions information such as GPS, INS and CCD vision systems, it is lateral to establish characterization intelligent electric automobile
The kinetic model of behavioral characteristics；
Step 2: the intelligent electric automobile transversal sectional multi-model based on velocity partition is established, with intelligent electric automobile driving status
The optimal and control minimum control target of input quantity, designs intelligent electric automobile path trace and direct yaw moment upper layer multimode
Type predicts Collaborative Control module；
Step 3: design intelligent electric automobile lower layer controls distributor, removes wheel according to practical additional yaw moment Real-time solution
Optimal longitudinal tire force.
2. a kind of intelligent electric automobile path trace and direct yaw moment cooperative control method as described in claim 1, special
Sign is in step 1, described based on Multi-sensor Fusions information such as GPS, INS and CCD vision systems, establishes characterization intelligence electricity
The kinetic model of electrical automobile transverse direction behavioral characteristics includes:
(1) yaw velocity is acquired using INS, using GPS gathers longitudinal velocity information and lateral velocity information, designs side drift angle
Estimator；
(2) it establishes using automobile yaw velocity and side drift angle as state variable, is input with front wheel angle and additional yaw moment
Intelligent electric automobile horizontal dynamic model；
(3) it using CCD vision system measurement intelligent electric automobile and expected path relative position information, establishes and describes current pose
With the intelligent electric automobile path trace kinematics model of the error variation of expected pose.
3. a kind of intelligent electric automobile path trace and direct yaw moment cooperative control method as described in claim 1, special
It levies and is in step 2, the intelligent electric automobile transversal sectional multi-model of the foundation based on velocity partition, with intelligent electric vapour
Vehicle travelling state is optimal and controls the minimum control target of input quantity, designs intelligent electric automobile path trace and direct sideway power
Square upper layer multi-model predicts Collaborative Control module method particularly includes:
(1) size based on automobile longitudinal speed constructs intelligent electric automobile transversal sectional time-varying multi-model collection；
(2) constant when keeping being converted into being segmented by intelligent electric automobile transversal sectional time-varying continuous time multi-model collection using zeroth order
Discrete time multi-model collection；
(3) to avoid multi-model switching and causing chattering phenomenon, the velocity ambiguity factor is introduced by piecewise linearity time-varying discrete time
Multi-model collection is normalized；
(4) performance indicator of intelligent electric automobile path trace and direct yaw moment upper layer multi-model prediction Collaborative Control is designed
Function and constraint condition establish the calculation formula of multi-model prediction optimization problem, solve the optimal intelligence electricity of performance indicator of sening as an envoy to
Electrical automobile front wheel angle and additional yaw moment input quantity.
4. a kind of intelligent electric automobile path trace and direct yaw moment cooperative control method as described in claim 1, special
Sign is that in step 3 the design intelligent electric automobile lower layer controls distributor, is asked in real time according to practical additional yaw moment
Release out the optimal longitudinal tire force of wheel method particularly includes:
(1) with control distribution deviation and control input consumption energy minimum target, the optimization for solving each wheel tyre power is established
Performance index function and constraint condition；
(2) longitudinal force of tire for being assigned to each intelligent electric automobile wheel is calculated using Newton method in real time.
5. the intelligent electric automobile path that intelligent electric automobile path trace and direct yaw moment cooperative control method use with
Track and direct yaw moment cooperative control system, it is characterised in that the system comprises data obtaining modules, lateral deviation angular estimation mould
Block, LTV-MPC top level control module, lower layer control distribution module；Initially set up the lateral dynamics state side of intelligent electric motor car
Journey is then based on vision system, GPS and INS acquisition information and establishes intelligent electric automobile path following system model, designs LTV-
MPC top level control module Real-time solution goes out expected front wheel angle and additional yaw moment, and expected additional yaw moment is passed through
Lower layer's control distributor is assigned to the longitudinal force of tire of each tire, to realize that intelligent electric automobile driving status is optimal.
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| CN110275434A (en) * | 2019-05-20 | 2019-09-24 | 江苏大学 | A kind of independent suspension structure control method for vehicle triggering unbiased MPC algorithm based on condition |
| CN110654386A (en) * | 2019-10-10 | 2020-01-07 | 厦门大学 | Cooperative cruise longitudinal and transverse comprehensive control method for multiple intelligent electric vehicles under curve |
| CN111959500A (en) * | 2020-08-07 | 2020-11-20 | 长春工业大学 | Automobile path tracking performance improving method based on tire force distribution |
| CN113954833A (en) * | 2020-07-06 | 2022-01-21 | 湖南工业大学 | All-electric drive distributed unmanned vehicle path tracking and stability coordination control method |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110275434A (en) * | 2019-05-20 | 2019-09-24 | 江苏大学 | A kind of independent suspension structure control method for vehicle triggering unbiased MPC algorithm based on condition |
| CN110275434B (en) * | 2019-05-20 | 2022-10-25 | 江苏大学 | Independent suspension structure vehicle control method based on condition-triggered unbiased MPC algorithm |
| CN110654386A (en) * | 2019-10-10 | 2020-01-07 | 厦门大学 | Cooperative cruise longitudinal and transverse comprehensive control method for multiple intelligent electric vehicles under curve |
| CN113954833A (en) * | 2020-07-06 | 2022-01-21 | 湖南工业大学 | All-electric drive distributed unmanned vehicle path tracking and stability coordination control method |
| CN113954833B (en) * | 2020-07-06 | 2023-05-30 | 湖南工业大学 | Full-electric-drive distributed unmanned vehicle path tracking and stability coordination control method |
| CN111959500A (en) * | 2020-08-07 | 2020-11-20 | 长春工业大学 | Automobile path tracking performance improving method based on tire force distribution |
| CN111959500B (en) * | 2020-08-07 | 2022-11-11 | 长春工业大学 | Automobile path tracking performance improving method based on tire force distribution |
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