CN109017446A - Expect path vehicular longitudinal velocity tracing control method and device - Google Patents
Expect path vehicular longitudinal velocity tracing control method and device Download PDFInfo
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- 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
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- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/32—Control or regulation of multiple-unit electrically-propelled vehicles
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Abstract
Expect path vehicular longitudinal velocity tracing control method and device, belong to automatic driving vehicle control field, in order to solve the problems, such as to expect path vehicular longitudinal velocity Tracing Control, calculated by Torque distribution controller and distributed the driving moment of total driving moment to each hub motor, the output torque of hub motor acts on wheel, longitudinal velocity is controlled to track desired trajectory, wherein, with tire utilization rate as majorized function, according to pseudoinverse technique design moment allocation algorithm to total Torque distribution, effect is to guarantee that tire is in stability range without super limit of adhesion, the demand precision that it is distributed is higher, so that the tracking of longitudinal velocity is more acurrate.
Description
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, also
The NVH quality that conventional fuel oil vehicle can be brought to be difficult to reach.It is a kind of unique drive of electric car that four hub motors, which independently drive,
Dynamic form, since dynamical system is directly integrated in wheel, it is possible to which independent accurate control is carried out to each wheel drive torque and revolving speed
System, this structure are that the realization of advanced control algorithm is laid a good foundation.Unmanned technology is the advanced stage of Vehicular intelligent, is
Realize traffic accident " zero death " key technology, and track following is the basic demand for realizing intelligent vehicle autonomous driving.
Trajectory Tracking Control is the key technology and automatic driving vehicle 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 control, transverse movement
Control, vertically and horizontally motion control.Longitudinal movement control refer to holding enable car speed rapidly, maintain target vehicle speed in high precision
In range.Transverse movement control is then control vehicle yaw motion and divertical motion, it is therefore an objective to make vehicle under different operating conditions
Not only it had been able to maintain lateral stability but also can smoothly track desired trajectory, so that vehicle realization lane is made to keep or independently overtake other vehicles,
The functions such as avoidance.Overwhelming majority automatic driving vehicle track following algorithm only carries out letter to longitudinal movement and transverse movement at present
Single decoupling, and assume that speed is certain value, but vehicle is the system of a nonlinearity and close coupling, if not considering to indulge
Correlation between transverse direction, then then it cannot be guaranteed that control precision and intact stability.Especially vehicle high-speed working condition with
And when low attached operating condition traveling, it is easier to that unstability situation occurs.On the other hand, presently, there are control algolithm mostly what is involved is
Kinematics control is not taken into account lateral stability of cars and longitudinal movement control, if not considering Dynamic Constraints
Vehicle be will increase in the insecurity of high speed and low attached road surface operating condition downward driving, reduce control precision.Therefore, design FWID nobody
When driving electric vehicle Trajectory Tracking Control strategy, need to fully consider the calculation for vertically and horizontally moving correlation and riding stability
Method is particularly important.
Summary of the invention
In order to solve the problems, such as to expect path vehicular longitudinal velocity Tracing Control, the following technical solutions are proposed by the present invention: one
Kind expect path vehicular longitudinal velocity tracing control method, by the error of vehicle ideal longitudinal velocity and practical longitudinal velocity and
Error rate is inputted as controller, and controller exports electronic throttle aperture, and searches the electronic throttle worked out in advance
The corresponding hub motor torque Map figure of aperture, to export total driving moment of vehicle；It is calculated and is divided by Torque distribution controller
With total driving moment to the driving moment of each hub motor, the output torque of hub motor acts on wheel, to longitudinal speed
Degree control is to track desired trajectory, wherein with tire utilization rate as majorized function, according to pseudoinverse technique design moment allocation algorithm
To total Torque distribution.
A kind of expectation path vehicular longitudinal velocity Tracing Control device, the control device are stored with a plurality of instruction, institute
Instruction is stated to load and execute suitable for processor:
The error and error rate of vehicle ideal longitudinal velocity and practical longitudinal velocity are inputted as controller；
Controller exports electronic throttle aperture, and searches the corresponding hub motor of electronic throttle aperture worked out in advance
Torque Map figure, to export total driving moment of vehicle；
Calculated by Torque distribution controller and distributed the driving moment of total driving moment to each hub motor；Wheel hub electricity
The output torque of machine acts on wheel, controls longitudinal velocity to track desired trajectory, wherein with tire utilization rate as optimization
Function, according to pseudoinverse technique design moment allocation algorithm to total Torque distribution.
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, distributes torque for each wheel and provides accurately total torque.
The present invention, as majorized function, is devised Torque distribution algorithm to total Torque distribution based on pseudoinverse technique, protected with tire utilization rate
Card tire is in stability range without super limit of adhesion, and the demand precision of distribution is higher, so that the tracking of longitudinal velocity is more quasi-
Really.
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 longitudinal velocity error e and the subordinating degree function of error rate ec: (a) degree of membership of longitudinal velocity error e
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, will also meet the requirement of high speed and low attached operating condition riding stability.
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 four
Wheel is independent to drive unmanned electric vehicle track following algorithm.In view of grinding for previous automatic driving vehicle track following algorithm
Content is studied carefully it is not intended that vehicle stabilization control and longitudinal speed control, and are not suitable for four motorized wheels electric vehicle.This
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, and upper layer establishes the rolling time horizon of front-wheel active steering
Optimization algorithm, when design optimization function, using tracking accuracy as most basic target；Secondly to improve riding comfort,
Control quantity constraint be joined into optimization problem.To allow yaw velocity to characterize intact stability, matter is added in Optimization Solution
The constraint of heart side drift angle.Middle layer controller is to track desired yaw velocity for control target, when algorithm designs, with equivalent synovial membrane control
It is made as basis and devises equivalent control term using Three Degree Of Freedom auto model；And discontinuous symbol is replaced with hyperbolic tangent function
Function design switching robust control item, effectively reduces chattering phenomenon.Lower layer's controller be consider velocity variations to track with
The influence of track precision improves the stability and robustness of longitudinal speed control, using velocity error and its change rate as Fuzzy Control
The input of device processed ensure that the trace performance of longitudinal speed by fuzzy reasoning on-line tuning PI controller parameter.With tire benefit
With 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.Modeling when make as
Lower hypothesis: assuming that vehicle is travelled in flat road surface, the catenary motion and Suspension movement of vehicle are not considered, and assume that vehicle is rigid
Property；Front and back and the left and right load transfer of vehicle are not considered；The longitudinal and lateral coupling relationship for not considering tire force, only considers pure lateral deviation
Tire characteristics；Ignore vertically and horizontally aerodynamics simultaneously.Two degrees of freedom vehicle dynamic model is established on the basis of assumed above,
As shown in Figure 1.
Two degrees of freedom vehicle dynamic model according to Fig. 1 improves system to reduce the influence of strong coupling constant
The longitudinal dynamics of vehicle are ignored in flexibility, and the transverse movement and weaving of consideration automobile can derive two degrees of freedom
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, IzIt is vehicle body around Z
Rotary inertia, the l of axisfDistance, l for mass center to front axlerDistance, F for mass center to rear axlexfFor front-wheel longitudinal force, FxrIt is rear
Take turns 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 momentfIt (k) is control amount u (k), it is defeated for selecting the lateral position y (k) at k moment
Above-mentioned kinetic model is write as the form of discrete-state space epuation by output are as follows:
In formula:TsFor the sampling period, τ is integration variable, A be sytem matrix,
B is 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 fixed outside control time domain
Value, i.e. u (k+M-1)=u (k+M)=...=u (k+P-1) determines the prediction at the k moment according to lateral direction of car kinetic model
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 weight of time domain M step is controlled for the k moment
Coefficient.
It is limited by vehicle steering structure, front wheel angle is no more than ultimate angle, simultaneously, it is contemplated that mechanical structure is rung
Speed and riding comfort are answered, needs to limit the increment of control amount, therefore, constraint condition is set 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；uminFor vehicle front wheel angle
Limit on the left position.
Yaw velocity can directly reflect intact stability, be control side slip angle β within smaller range, about
The constraint to side slip angle is added in beam 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 following excellent
Change 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 direct for the QP problem with inequality constraints
It 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 process.
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, the dynamics of vehicle modeling for needing to establish can accurately describe vehicle power as far as possible
System can be reduced calculation amount again.Thus, it is assumed that vehicle is travelled in flat road surface, does not consider the catenary motion and suspension of vehicle
Movement, and assume that vehicle is rigid；The longitudinal and lateral coupling relationship for not considering tire force only considers pure lateral deviation tire characteristics；No
Front and back and the left and right load transfer for considering vehicle, ignore vertically and horizontally aerodynamics, establish a consideration vehicle longitudinally, 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 following
Precision plays great influence.Velocity perturbation can bring the unstability to desired trajectory tracking therefore to have in normal driving process
Necessity controls longitudinal velocity.
The error and error rate of ideal longitudinal velocity and practical longitudinal velocity are inputted as controller, obscure PI
Controller exports electronic throttle aperture, then by searching the electronic throttle aperture and hub motor torque worked out in advance
Total driving moment of Map figure output vehicle.Total driving moment calculates each hub motor by Torque distribution controller
The output torque of driving moment, hub motor acts on wheel, and that realizes vehicle stablizes traveling and the control to longitudinal velocity
System, 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 subsets on domain [- 1,1]
[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],
It is obscured at it and defines 3 fuzzy subsets [negative (being replaced with N), zero (being replaced with Z), positive (being replaced with P)] on domain [- 1,1].
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 system
Number kp；(e N), Δ k when overshootpFor N, i.e. reduction proportionality coefficient kp；When e is Z, point three kinds of situation discussion: when ec is
When 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 more next
It is 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.It is opposite, when there is over control
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 near Z
When, 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, in real time
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=[Fx1Fx2Fx3Fx4]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 mention
High intact stability regard the sum of tire utilization rate of each wheel as research object, it is desirable that the sum of tire utilization rate is as far as possible
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, and it is steady to ensure that vehicle tracks desired longitudinal velocity
It is qualitative.Lower layer's controller of the invention solves the Torque distribution controller established using pseudoinverse technique, and algorithm simply has
Effect, solves that the time is short, real-time is good.
2. dynamics of vehicle is constrained and upper controller is added by the present invention, the peace of model accuracy and vehicle driving can be improved
Quan Xing.The considerations of upper controller is by state change to vehicle and reference locus future time instance, improves track following
Precision.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 letter is replaced using hyperbolic tangent function
Number 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 invention protection scope it
It is interior.
Claims (2)
1. a kind of expectation path vehicular longitudinal velocity tracing control method, 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；By power
Square dispensing controller calculates and distributes the driving moment of total driving moment to each hub motor, the output torque of hub motor
Wheel is acted on, longitudinal velocity is controlled to track desired trajectory, wherein with tire utilization rate as majorized function, according to puppet
Inverse method design moment allocation algorithm is to total Torque distribution.
2. a kind of expectation path vehicular longitudinal velocity Tracing Control device, it is characterised in that: the control device is stored with more
Item instruction, described instruction are suitable for processor and load and execute:
The error and error rate of vehicle ideal longitudinal velocity and practical longitudinal velocity are inputted as controller；
Controller exports electronic throttle aperture, and searches the corresponding hub motor torque of electronic throttle aperture worked out in advance
Map figure, to export total driving moment of vehicle；
Calculated by Torque distribution controller and distributed the driving moment of total driving moment to each hub motor；
The output torque of hub motor acts on wheel, controls longitudinal velocity to track desired trajectory, wherein utilized with tire
Rate is as majorized function, according to pseudoinverse technique design moment allocation algorithm to total Torque distribution.
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