CN109017447A - The total driving moment output method of automatic driving vehicle - Google Patents

The total driving moment output method of automatic driving vehicle Download PDF

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CN109017447A
CN109017447A CN201810857862.4A CN201810857862A CN109017447A CN 109017447 A CN109017447 A CN 109017447A CN 201810857862 A CN201810857862 A CN 201810857862A CN 109017447 A CN109017447 A CN 109017447A
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vehicle
controller
error
fuzzy
automatic driving
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CN109017447B (en
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葛平淑
薄纯娟
赵秀春
张江燕
张涛
宋鹏
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Dalian Minzu University
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Dalian Nationalities University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/423—Torque
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The total driving moment output method of automatic driving vehicle, belong to automatic driving vehicle control field, in order to solve the precision problem of the total driving moment output of the automatic driving vehicle sought, main points are that the error and error rate using vehicle ideal longitudinal velocity and practical longitudinal velocity are inputted as controller, controller exports electronic throttle aperture, and search the corresponding hub motor torque Map figure of the electronic throttle aperture worked out in advance, to export total driving moment of vehicle, effect is the trace performance that ensure that longitudinal speed, torque, which is distributed, for each wheel provides accurately total torque.

Description

The total driving moment output method of automatic driving vehicle
Technical field
The invention belongs to automatic driving vehicle control field, especially a kind of unmanned electric vehicle of four motorized wheels Trajectory Tracking Control working method.
Background technique
Motorized and the intelligent developing direction as current auto industry, have become domestic and foreign scholars, scientific research institutions With the research hotspot of enterprise.Electric car can not only reduce consumption of the mankind to non-renewable resources, improve environmental problem, The NVH quality that conventional fuel oil vehicle can also be brought to be difficult to reach.It is a kind of uniqueness of electric car that four hub motors, which independently drive, Drive form, since dynamical system is directly integrated in wheel, it is possible to each wheel drive torque and revolving speed be carried out independent accurate Control, this structure are that the realization of advanced control algorithm is laid a good foundation.Unmanned technology is the advanced rank of Vehicular intelligent Section is to realize traffic accident " zero death " key technology, and track following is to realize wanting substantially for intelligent vehicle autonomous driving It asks.
Trajectory Tracking Control is the key technology and automatic driving car that automatic driving vehicle realizes precise motion control Realize the intelligent and practical most important condition.The motion control of vehicle can be divided into three kinds: longitudinal movement controls, laterally Motion control, vertically and horizontally motion control.Longitudinal movement control refer to holding enable car speed rapidly, maintain mesh in high precision It marks in vehicle speed range.Transverse movement control is then control vehicle yaw motion and divertical motion, it is therefore an objective to make vehicle in difference Not only be able to maintain lateral stability under operating condition but also can smoothly track desired trajectory, thus make vehicle realize lane keep or from It is main overtake other vehicles, the functions such as avoidance.Overwhelming majority automatic driving vehicle track following algorithm is only transported to longitudinal movement and laterally at present It is dynamic simply to be decoupled, and assume that speed is certain value, but vehicle is the system of a nonlinearity and close coupling, such as Fruit does not consider the correlation between vertically and horizontally, then then it cannot be guaranteed that control precision and intact stability.Especially vehicle When high-speed working condition and low attached operating condition travel, it is easier to that unstability situation occurs.On the other hand, presently, there are control calculation What is involved is kinematics controls mostly for method, i.e., do not take into account lateral stability of cars and longitudinal movement control, if not Consider that Dynamic Constraints will increase vehicle in the insecurity of high speed and low attached road surface operating condition downward driving, reduces control precision.Cause This, when the design unmanned electric vehicle Trajectory Tracking Control strategy of FWID, needs to fully consider and vertically and horizontally moves correlation It is particularly important with the algorithm of riding stability.
Summary of the invention
In order to solve the precision problem of the total driving moment output of the automatic driving vehicle sought, the present invention proposes as follows Technical solution: a kind of driving moment output method that automatic driving vehicle is total, by vehicle ideal longitudinal velocity and practical longitudinal speed The error and error rate of degree are inputted as controller, and controller exports electronic throttle aperture, and searches and work out in advance Electronic throttle aperture corresponding hub motor torque Map figure, to export total driving moment of vehicle.
Further, the controller is Fuzzy PI Controller:
The basic domain of longitudinal velocity error e is [- 2,2], obscures at it and defines 3 fuzzy subsets on domain [- 1,1] [negative, zero, just]；The basic domain of longitudinal velocity error rate ec is [- 3,3], obscures at it and defines 3 on domain [- 1,1] A fuzzy subset [negative, zero, just], and provide the subordinating degree function of e, ec；
Controller parameter Δ kpBasic domain be [- 3,3], its obscure on domain [- 1,1] define 3 fuzzy subsets [negative, zero, just]；Controller parameter Δ kiBasic domain be [- 0.1,0.1], its obscure on domain [- 1,1] define 3 Fuzzy subset [negative, zero, just], and provide Δ kp、ΔkiSubordinating degree function；
It wherein bears and is replaced, the Z that uses separately replacement, just replaced with P with N；
Further, controller proportionality coefficient kpSetting principle are as follows: when respond increase when, Δ kpFor P, scaling up system Number kp；When overshoot, Δ kpFor N, reduce proportionality coefficient kp；When e is Z, point three kinds of situation discussion: when ec is N, overshoot It is increasing, Δ kpFor N, when ec is Z, Δ kpIt is P to reduce error, when ec is P, positive error is increasing, Δ kpFor N。
Further, controller proportionality coefficient kiSetting principle are as follows: using integral separation method determine, when e is attached in Z When close, Δ kiFor P, otherwise Δ kiFor N.
The Δ k of foundationp、ΔkiFuzzy reasoning table is obtained to be respectively as follows:
1 Δ k of tablepFuzzy reasoning table
2 Δ k of tableiFuzzy reasoning table
Further, it when e increases, indicates that practical longitudinal velocity and the error of ideal longitudinal velocity increase, needs at this time Scaling up coefficient kp, Δ kpOutput area is 0 to 2, opposite, when there is over control, when e range is -1 to 0, need Reduce proportionality coefficient kp, then Δ kpOutput area is -2 to 0, when error e is near Z, Δ kiFor P, otherwise Δ kiFor N.
Compared with prior art, beneficial effects of the present invention are as follows: fuzzy controller input of the invention is ideal longitudinal Speed and practical longitudinal velocity establish being associated with for electronic throttle aperture and hub motor torque Map figure, obtain vehicle with this Total driving moment, ensure that the trace performance of longitudinal speed, distribute torque for each wheel and provide accurately total power Square.
Detailed description of the invention
Fig. 1 is two degrees of freedom vehicle dynamic model
Fig. 2 is Three Degree Of Freedom vehicle dynamic model
Fig. 3 is Fuzzy self-adjusted PI longitudinal velocity controller
Fig. 4 is the subordinating degree function of longitudinal velocity error e and error rate ec: (a) longitudinal velocity error e is subordinate to Spend function；(b) subordinating degree function of longitudinal velocity error rate ec；
Fig. 5 is longitudinal velocity controller parameter Δ kpWith Δ kiSubordinating degree function: (a) parameter, Δ kpInput and output close System, (b) parameter, Δ kiInput/output relation；
Fig. 6 is the structural schematic block diagram of tracking system.
Specific embodiment
The present invention will be with four motorized wheels electric car (FWID-EV, Four-Wheel-Independent Electric vehicle) it is object, automatic driving vehicle Trajectory Tracking Control strategy is studied, should be met to desired trajectory Accurate tracking, also to meet high speed and low attached operating condition riding stability requirement.
To improve vehicle in the stability and accuracy of the track following on high speed and low attached road surface, the present invention provides one kind The unmanned electric vehicle track following algorithm of four motorized wheels.In view of previous automatic driving vehicle track following algorithm Research contents it is not intended that vehicle stabilization control and the control of longitudinal speed, and be not suitable for four motorized wheels electric vehicle ?.The present invention proposes a kind of for the unmanned electric vehicle layering Trajectory Tracking Control strategy of four motorized wheels.
Track following strategy designed by the present invention is divided into three layers, when upper layer establishes the rolling of front-wheel active steering Domain optimization algorithm, when design optimization function, using tracking accuracy as most basic target；It secondly is raising ride comfort Property, control quantity constraint be joined into optimization problem.To allow yaw velocity to characterize intact stability, add in Optimization Solution Enter side slip angle constraint.Middle layer controller is to track desired yaw velocity for control target, when algorithm designs, with equivalent Equivalent control term is devised using Three Degree Of Freedom auto model based on synovial membrane control；And it replaces not connecting with hyperbolic tangent function Continuous sign function design switching robust control item, effectively reduces chattering phenomenon.Lower layer's controller is to consider velocity variations Influence to tracking accuracy improves the stability and robustness of longitudinal speed control, velocity error and its change rate is made The followability of longitudinal speed ensure that by fuzzy reasoning on-line tuning PI controller parameter for the input of fuzzy controller Energy.With tire utilization rate as majorized function, Torque distribution algorithm is devised based on pseudoinverse technique.
1 upper controller realizes active steering control according to desired trajectory
1.1 establish lateral direction of car kinetic model
Two degrees of freedom linear bicycle model is commonly used to the movement of description lateral direction of car and weaving.It is made in modeling It is following to assume: assuming that vehicle is travelled in flat road surface, not consider the catenary motion and Suspension movement of vehicle, and assume vehicle It is rigid；Front and back and the left and right load transfer of vehicle are not considered；The longitudinal and lateral coupling relationship of tire force is not considered, is only considered Pure lateral deviation tire characteristics；Ignore vertically and horizontally aerodynamics simultaneously.Two degrees of freedom vehicle power is established on the basis of assumed above Model is learned, as shown in Figure 1.
Two degrees of freedom vehicle dynamic model in order to reduce the influence of strong coupling constant improves system to root according to the figure The longitudinal dynamics of vehicle are ignored in flexibility, the transverse movement and weaving of consideration automobile, can derive that two is free Spend lateral direction of car kinetics equation are as follows:
In formula: m is car mass, vxIt is side slip angle for longitudinal velocity, β, γ is yaw velocity, IzFor vehicle body around Rotary inertia, the l of Z axisfDistance, l for mass center to front axlerDistance, F for mass center to rear axlexfFor front-wheel longitudinal force, FxrFor Rear-wheel longitudinal force, FyfFor front-wheel lateral force, FyrFor rear-wheel lateral force.
Front and rear wheel lateral deviation power can be calculated with following formula:
In formula: CfFor front-wheel cornering stiffness, CrFor rear-wheel cornering stiffness, αfFor front-wheel side drift angle, αrFor rear-wheel side drift angle.
According to low-angle it is assumed that front and rear wheel side drift angle by can letter are as follows:
In formula: δfFor front wheel angle.
Therefore, available two degrees of freedom lateral direction of car kinetic model are as follows:
In formula: vyFor lateral velocity,For yaw angle.
Select lateral position y (k), the yaw angle at k momentSide slip angle β (k), yaw velocity γ (k) are Quantity of state is x (k), selects the front wheel angle δ at k momentf(k) be control amount u (k), select the lateral position y (k) at k moment for Above-mentioned kinetic model is write as the form of discrete-state space epuation by output quantity are as follows:
In formula:TsFor the sampling period, τ is integration variable, and A is system square Battle array, B are input matrix, and
1.2 track following active steering controllers of the design based on rolling time horizon optimization algorithm
Rolling time horizon optimization algorithm is made of three parts such as prediction model, rolling optimization and feedback compensations.
Prediction time domain is P, and control time domain is M, and M≤P.Current time k, it is assumed that control amount is outside control time domain Definite value, i.e. u (k+M-1)=u (k+M)=...=u (k+P-1) is determined pre- at the k moment according to lateral direction of car kinetic model Survey model are as follows:
Definition prediction output vector Y (k+1 | k) and control input vector U (k) are as follows:
In formula: y (k+P) is to predict that the lateral position of time domain P step, u (k+M-1) are to control time domain M at the k moment k moment The control amount of step.
Above-mentioned prediction model can simplify are as follows:
Y (k+1)=Sxx(k)+SuU(k) (9)
In formula:
In formula:
Definition expectation lateral position sequence Ydes(k+i) are as follows:
In formula: ydes(k+P) the expectation lateral position of time domain P step is predicted for the k moment.
To enable automatic driving vehicle quickly to track desired trajectory, reasonable front wheel angle is cooked up, following two is selected Control target: first is that reducing the error between vehicle actual path and desired trajectory；Second is that adding in order not to generate excessive transverse direction Speed guarantees vehicle driving ride comfort, it is desirable that control amount is small as far as possible.Therefore, rolling optimization problem is established:
In formula: J is rolling optimization objective function, Γy、ΓuFor weight coefficient.
Weight coefficient may be defined as diagonal matrix:
In formula: ΓyPWeight coefficient, the Γ of time domain P step are predicted for the k momentuMThe power of time domain M step is controlled for the k moment Weight coefficient.
It is limited by vehicle steering structure, front wheel angle is no more than ultimate angle, simultaneously, it is contemplated that mechanical structure Response speed and riding comfort need to limit the increment of control amount, therefore, constraint condition are arranged are as follows:
In formula: Δ u (k+i)=u (k+i+1)-u (k+i) represents the increment of control amount, i=0,1 ..., M-1；u(k+i) The control amount of the i-th step of time domain is controlled for the k moment；umaxFor the limit on the right-right-hand limit position of vehicle front wheel angle；uminTo be rotated before vehicle The limit on the left position at angle.
Yaw velocity can directly reflect intact stability, be control side slip angle β within smaller range, The constraint to side slip angle is added in constraint condition:
βmin≤β(k+i)≤βmax (14)
In formula: β (k+i) is the side slip angle for predicting the i-th step of time domain at the k moment, βminAnd βmaxRespectively side slip angle Minimum value and maximum value.
In conclusion the track following active steering controller based on rolling time horizon optimization algorithm can be converted to it is as follows Optimization problem:
Constraint condition are as follows:
Δumin≤Δu(k+i)≤Δumax
umin≤u(k+i)≤umax
βmin≤β(k+i)≤βmax
Above-mentioned optimization problem can be transformed into quadratic programming problem, can be straight for the QP problem with inequality constraints It connects and is solved with active set solution.Input vector U (k)=[u (k) u (k+1) ... u (k+ is controlled by the k moment that will be solved M-1)]TObtained front wheel angle realizes the control of vehicle active steering, repeats the above process, i.e. completion Trajectory Tracking Control mistake Journey.
2 middle layer controllers, design yaw moment control device track ideal yaw velocity
2.1 establish Three Degree Of Freedom vehicle dynamic model
For the weaving for studying vehicle, it is dynamic that the dynamics of vehicle modeling for needing to establish can accurately describe vehicle as far as possible Mechanical system can be reduced calculation amount again.Thus, it is assumed that vehicle flat road surface travel, do not consider vehicle catenary motion and Suspension movement, and assume that vehicle is rigid；The longitudinal and lateral coupling relationship for not considering tire force only considers that pure lateral deviation tire is special Property；Front and back and the left and right load transfer for not considering vehicle, ignore vertically and horizontally aerodynamics, establish a consideration vehicle longitudinal direction, Laterally, the Three Degree Of Freedom vehicle dynamic model of weaving, as shown in Figure 2.
Force analysis is carried out to it in x-axis, y-axis and around z-axis direction according to Newton's second law, obtains Three Degree Of Freedom vehicle Kinetic model are as follows:
In formula: m is car mass, vxFor longitudinal velocity, vyIt is yaw velocity, δ for lateral velocity, γfFor preceding rotation Angle, IzFor rotary inertia, the l of vehicle body about the z axisfDistance, l for mass center to front axlerDistance, l for mass center to rear axlewBetween wheel Away from, MxFor yaw moment；Fx1、Fx2、Fx3And Fx4Respectively the near front wheel, off-front wheel, left rear wheel and off hind wheel longitudinal force；,Fy1、 Fy2、Fy3And Fy4Respectively the near front wheel, off-front wheel, left rear wheel and off hind wheel lateral force.
2.2 establish yaw moment control device based on equivalent synovial membrane control theory
The expectation yaw velocity of vehicle can be calculated by following formula:
In formula: γdIt is expected yaw velocity, γ0For ideal yaw velocity, γmaxFor yaw velocity maximum value, Sgn () is sign function.
Ideal yaw velocity can be calculated by following formula:
In view of the limitation for the adhesive force that ground can be provided, the maximum value of yaw velocity can have following formula to determine:
In formula: g is acceleration of gravity, μ is coefficient of road adhesion.
Enable error s=γ-γd, takeThen
Equivalent control term design are as follows:
In order to reduce the chattering phenomenon of control process appearance, sign function is replaced using continuous function, using tanh Function design switching robust control item, hyperbolic tangent function are as follows:
In formula: ε > 0, ε value determine the pace of change of function inflection point.
To guaranteeIt sets up, takes switching control item are as follows:
Wherein: D > 0.
Derive the yaw moment control device based on equivalent synovial membrane are as follows:
The driving moment that longitudinal velocity controller obtains is assigned to often by 3 lower layer's controllers, design moment dispensing controller A hub motor
3.1 design longitudinal velocity controller based on Fuzzy self-adjusted PI algorithm
Longitudinal velocity control is not only related to automatic driving vehicle driving safety and riding comfort, and to track with Track precision plays great influence.Velocity perturbation can bring the unstability to desired trajectory tracking in normal driving process, because This, it is necessary to longitudinal velocity is controlled.
The error and error rate of ideal longitudinal velocity and practical longitudinal velocity are inputted as controller, obscured PI controller exports electronic throttle aperture, then by searching the electronic throttle aperture and hub motor power worked out in advance Total driving moment of square Map figure output vehicle.Total driving moment calculates each hub motor by Torque distribution controller Driving moment, the output torque of hub motor acts on wheel, and that realizes vehicle stablizes traveling and to longitudinal velocity Control, wherein with tire utilization rate as majorized function, according to pseudoinverse technique design moment allocation algorithm to total Torque distribution.
It is as shown in Figure 3 based on Fuzzy self-adjusted PI algorithm design longitudinal velocity controller.
The basic domain of longitudinal velocity error e is [- 2,2], obscures at it and defines 3 fuzzy sons on domain [- 1,1] Collection [negative (being replaced with N), zero (being replaced with Z), positive (being replaced with P)]；The basic domain of longitudinal velocity error rate ec be [- 3, 3], obscure that 3 fuzzy subsets are defined on domain [- 1,1] is [negative (being replaced with N), zero (being replaced with Z), positive (with P generation at it For)].E, the subordinating degree function of ec is as shown in Figure 4.
Controller parameter Δ kpBasic domain be [- 3,3], obscured at it and define 3 fuzzy sons on domain [- 1,1] Collection [negative (being replaced with N), zero (being replaced with Z), positive (being replaced with P)]；Controller parameter Δ kiBasic domain be [- 0.1, 0.1], obscure that 3 fuzzy subsets are defined on domain [- 1,1] is [negative (being replaced with N), zero (being replaced with Z), positive (with P generation at it For)].Δkp、ΔkiSubordinating degree function it is as shown in Figure 4.
Controller proportionality coefficient kpSetting principle are as follows: when respond increase when (e P), Δ kpFor P, i.e. scaling up Coefficient kp；(e N), Δ k when overshootpFor N, i.e. reduction proportionality coefficient kp；When e is Z, point three kinds of situation discussion: work as ec When for N, overshoot is increasing, Δ kpFor N, when ec is Z, Δ kpError can be reduced for P, when ec is P, positive error is got over Carry out bigger, Δ kpFor N.
Controller proportionality coefficient kiSetting principle are as follows: using integral separation method determine, i.e., when e is near Z, Δ ki For P, otherwise Δ kiFor N.
The Δ k established based on the above analysisp、ΔkiFuzzy reasoning table is obtained to be respectively as follows:
1 Δ k of tablepFuzzy reasoning table
2 Δ k of tableiFuzzy reasoning table
Fuzzy controller input/output relation indicates that practical longitudinal velocity and ideal are longitudinal as shown in figure 5, when e increases The error of speed increases, and needs scaling up coefficient k at this timep, Δ kpOutput area is 0 to 2.Opposite, it is existing when there is overshoot As when, i.e., when e range is -1 to 0, need to reduce proportionality coefficient kp, then Δ kpOutput area is -2 to 0.When error e is attached in Z When close, Δ kiFor P, otherwise Δ kiFor N.As shown in Figure 5, input/output relation meets the adjusting requirement of PI parameter.
3.2 Torque distribution controller designs
In order to realize the stability control of vehicle, the vehicle for needing to control longitudinal speed, yaw moment control obtains is total Driving moment be reasonably allocated to each hub motor.A large amount of on-line optimization algorithms that previous scholars propose, it is computationally intensive, it is real When property is poor.To solve this problem, a kind of Torque distribution controller is proposed.The wheel longitudinal force of vehicle may be expressed as:
FX=[Fx1 Fx2 Fx3 Fx4]T (25)
In formula: FXFor wheel longitudinal force vector, Fx1、Fx2、Fx3And Fx4Respectively the near front wheel, off-front wheel, left rear wheel and the right side Rear-wheel longitudinal force.
Enable FTFor the left and right wheel longitudinal force vector of vehicle, then
In formula:
Defining the ratio between limit adhesive force provided by practical adhesive force suffered by wheel and road surface is tire utilization rate, in order to Intact stability is improved, regard the sum of tire utilization rate of each wheel as research object, it is desirable that the sum of tire utilization rate to the greatest extent may be used Can it is small, can guarantee that tire is in stability range without super limit of adhesion as far as possible in this way.
In formula: ηiFor tire adhesive rate, the F of i-th of wheelxiLongitudinal force, F for i-th of wheelyiFor i-th wheel Lateral force, FziFor the vertical load of i-th of wheel, i=1, after 2,3,4 respectively represent the near front wheel, off-front wheel, left rear wheel and the right side Wheel.
When studying longitudinal moment distribution, ignore wheel lateral force, the calculating of tire utilization rate can simplify are as follows:
It is right using the sum of tire utilization rate as optimization aim in order to improve vehicle in the safety traffic ability on low attached road surface Total driving moment of vehicle is solved, it may be assumed that
In formula: μ is coefficient of road adhesion, weighting matrix
Establish following optimization problem:
In order to solve the problem, building Hamiltonian is as follows:
In formula: ξ ∈ R4For Lagrange multiplier.
To the F in HamiltonianxLocal derviation is sought with ξ and it is enabled to be equal to zero, then is had:
As available from the above equation:
That is:
Then the wheel longitudinal force of vehicle can be write as:
Relationship between wheel driving force and wheel wheel longitudinal force can be write as:
In formula: r is wheel effective rolling radius, TiFor the driving moment of i-th of wheel, i=1,2,3,4 respectively represent a left side Front-wheel, off-front wheel, left rear wheel and off hind wheel.
Therefore, the driving moment distribution of each wheel can be expressed as:
In formula: Δ T1、ΔT2The respectively total driving moment of left and right side wheel.
When yaw moment control device does not work, Δ T1, Δ T2Total driving moment T should be equal todHalf, i.e.,
When the work of yaw moment control device, yaw moment, the total driving of left and right side wheel are applied to left and right side wheel Torque Δ T1、ΔT2Relationship are as follows:
In formula: MxFor yaw moment, lwTo take turns spacing.
ΔT1、ΔT2It can be calculate by the following formula:
Then it is finally allocated to the driving moment of hub motor are as follows:
The preferred embodiment of aforementioned present invention, has the following beneficial effects:
1. the present invention devises a kind of unmanned electric vehicle of four motorized wheels for considering lateral stability of cars point Layer Trajectory Tracking Control strategy, tracks desired trajectory by upper controller, and middle layer controller utilizes upper controller The front wheel angle cooked up tracks desired yaw velocity, realizes stability of the vehicle in track following.Lower layer Controller is based on fuzzy PI hybrid control and devises vehicular longitudinal velocity controller, ensure that vehicle tracked desired longitudinal velocity Stability.Lower layer's controller of the invention solves the Torque distribution controller established using pseudoinverse technique, and algorithm is simple Effectively, the solution time is short, real-time is good.
2. dynamics of vehicle is constrained and upper controller is added by the present invention, model accuracy and vehicle driving can be improved Safety.The considerations of upper controller is by state change to vehicle and reference locus future time instance, improve track with The precision of track.And designed upper controller has good robustness to speed, road surface attachment condition, reference locus.
3. the present invention is based on the controls of quasi- synovial membrane to establish yaw moment control device, symbol is replaced using hyperbolic tangent function Function effectively reduces the chattering phenomenon of quasi- synovial membrane control.
The preferable specific embodiment of the above, only the invention, but the protection scope of the invention is not It is confined to this, anyone skilled in the art is in the technical scope that the invention discloses, according to the present invention The technical solution of creation and its inventive concept are subject to equivalent substitution or change, should all cover the protection scope in the invention Within.

Claims (6)

1. a kind of driving moment output method that automatic driving vehicle is total, it is characterised in that: by vehicle ideal longitudinal velocity and reality The error and error rate of border longitudinal velocity are inputted as controller, and controller exports electronic throttle aperture, and searches The corresponding hub motor torque Map figure of the electronic throttle aperture worked out in advance, to export total driving moment of vehicle.
2. the total driving moment output method of automatic driving vehicle as described in claim 1, it is characterised in that: the controller It is Fuzzy PI Controller:
The basic domain of longitudinal velocity error e is [- 2,2], its obscure defined on domain [- 1,1] 3 fuzzy subsets [it is negative, Zero, just]；The basic domain of longitudinal velocity error rate ec is [- 3,3], its obscure defined on domain [- 1,1] 3 it is fuzzy Subset [negative, zero, just], and provide the subordinating degree function of e, ec；
Controller parameter Δ kpBasic domain be [- 3,3], its obscure defined on domain [- 1,1] 3 fuzzy subsets [it is negative, Zero, just]；Controller parameter Δ kiBasic domain be [- 0.1,0.1], obscured at it and define 3 fuzzy sons on domain [- 1,1] Collection [negative, zero, just], and provide Δ kp、ΔkiSubordinating degree function；
It wherein bears and is replaced, the Z that uses separately replacement, just replaced with P with N.
3. the total driving moment output method of automatic driving vehicle as claimed in claim 2, it is characterised in that: controller ratio Coefficient kpSetting principle are as follows: when respond increase when, Δ kpFor P, scaling up coefficient kp；When overshoot, Δ kpFor N, reduce ratio Example coefficient kp；When e is Z, point three kinds of situation discussion: when ec is N, overshoot is increasing, Δ kpFor N, when ec is Z, Δ kpIt is P to reduce error, when ec is P, positive error is increasing, Δ kpFor N.
4. the total driving moment output method of automatic driving vehicle as claimed in claim 2, it is characterised in that: controller ratio Coefficient kiSetting principle are as follows: using integral separation method determine, when e is near Z, Δ kiFor P, otherwise Δ kiFor N.
5. the total driving moment output method of automatic driving vehicle as claimed in claim 2, it is characterised in that: the Δ k of foundationp、 ΔkiFuzzy reasoning table is obtained to be respectively as follows:
1 Δ k of tablepFuzzy reasoning table
2 Δ k of tableiFuzzy reasoning table
6. the total driving moment output method of automatic driving vehicle as claimed in claim 5, which is characterized in that when e increases, It indicates that practical longitudinal velocity and the error of ideal longitudinal velocity increase, needs scaling up coefficient k at this timep, Δ kpOutput area is 0 to 2, opposite, when there is over control, when e range is -1 to 0, need to reduce proportionality coefficient kp, then Δ kpExport model Enclosing is -2 to 0, when error e is near Z, Δ kiFor P, otherwise Δ kiFor N.
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