CN107972667A - The man-machine harmony control method and its control system of a kind of deviation auxiliary system - Google Patents
The man-machine harmony control method and its control system of a kind of deviation auxiliary system Download PDFInfo
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Abstract
The invention discloses the man-machine harmony control method and its control system of a kind of deviation auxiliary system.Man-machine harmony control method comprises the following steps：After deviation auxiliary system starts, according to lateral direction of car deviation y and destination path f (t), the desired orientation disk rotational angle theta needed for Vehicular turn is drawn*；According to θ*Draw expectation assist torqueDesign the operation torque T of driver's realitydWith y as dual input, weight coefficient σ as the man-machine harmony controller singly exported；By σ andProduct is done dynamically to adjust the actual assist torque T of the deviation auxiliary systemaSize.The man-machine control method for coordinating of the present invention dynamically adjusts the assist torque of deviation auxiliary system by exporting auxiliary weight, realize that the coordination of driver and auxiliary system controls, can be while being effectively prevented from vehicle and deviating from track, reduce interfering with each other between driver and auxiliary system, man-machine conflict is avoided, there is preferable man-machine harmony performance.
Description
Technical field
The present invention relates to a kind of man-machine harmony control method in the auxiliary driving technology field of intelligent automobile and its man-machine
Coordinated control system, more particularly to a kind of man-machine harmony control method of deviation auxiliary system and its man-machine harmony control system
System.
Background technology
Deviation auxiliary system (Lane departure assistance system, LDAS) is intelligent automobile auxiliary
The important component of driving technology, can aid in driver to control vehicle by way of active applies and intervenes, thus, such as
The control what is coordinated between driver and auxiliary system has become the hot spot that domestic and international intelligent automobile auxiliary drives area research
Problem.
Realizing the approach of deviation auxiliary control mainly has two kinds：Course changing control and differential braking control.Course changing control
Direct torque and corner control can be divided into.Direct torque applies an extra steering force based on steering to steering mechanism,
To realize auxiliary control；Corner control then needs to control wheel to go to desired angle by steering to realize auxiliary control
System.Differential braking control is that desired brake pressure distribution to both sides wheel is carried out differential braking so that Vehicular yaw responds
Tracking desired value simultaneously realizes deviation auxiliary control.
When carrying out deviation auxiliary using electric power steering, vehicle can realize deviation under various working
Auxiliary, has stronger adaptability.However, carrying out deviation using course changing control, auxiliary can there are driver and auxiliary system
Between interfere with each other problem, if coordinate it is inconsistent if can cause man-machine conflict, this be possible to aggravate pilot control burden,
Influence automobile transverse direction security.Thus, effectively coordinate driver and auxiliary system carries out the control of deviation auxiliary to be lifted
Man-machine harmony performance is of great significance.
The content of the invention
Based on technical problem existing for background technology, the present invention proposes a kind of man-machine harmony of deviation auxiliary system
Control method and its man-machine harmony control system.
The present invention solution be：A kind of man-machine harmony control method of deviation auxiliary system, it includes following
Step：
After the deviation auxiliary system starts, according to lateral direction of car deviation y and destination path f (t), show that vehicle turns
To required desired orientation disk rotational angle theta*；
According to desired orientation disk rotational angle theta*Draw expectation assist torque
Design the man-machine harmony controller of dual input list output, operation torque TdWith lateral direction of car deviation y as man-machine association
Two inputs of controller are adjusted, the output of man-machine harmony controller is weight coefficient σ；
By weight coefficient σ and it is expected assist torqueProduct is done dynamically to adjust the reality of the deviation auxiliary system
Border assist torque TaSize.
Further as such scheme is improved, using judgement algorithm of across the road time as deviation, based on across road
The vehicle of time, which deviates, judges vehicle movement model prediction vehicle driving trace of the algorithm by foundation, is connect so as to calculate wheel
Contact the minimum time needed for the edge of track.
Further as such scheme is improved, and according to lateral direction of car deviation y, actual steering wheel rotational angle theta, passes through driver
Model calculates desired orientation disk rotational angle theta*。
Preferably, by actual steering wheel rotational angle theta and desired orientation disk rotational angle theta*Make the difference, and the PID for passing through BP neural network
Controller draws the expectation assist torque needed for Vehicular turn
Further as such scheme is improved, and the man-machine harmony controller includes fuzzy based on five etale topology structures
Nerve network controller, five etale topology structures of the fuzzy neural network controller are：Input layer, blurring layer, reasoning layer,
Normalize layer and output layer；To operate torque TdIt is dual input with lateral direction of car deviation y, weight coefficient σ exports to be single.
Preferably, the principle that the fuzzy neural network controller meets includes：
(1) as | Td| ＞ Td max, at this time vehicle be in a state of emergency, actual assist torque TaWeight coefficient σ it is minimum, drive
The person of sailing fully takes up vehicle traveling sovereignty, wherein,It is expressed as judging threshold value two set by driver's mode of operation most
Big value；
(2) as | Td| ＜ Td 0, at this time driver do not operate steering wheel, the deviation auxiliary system occupies vehicle row
Sovereignty are sailed, weight coefficient σ increases with the increase of lateral direction of car deviation y, wherein,Represent the minimum of set threshold value two
Value；
(3) T is worked asd 0≤|Td|≤Td maxAnd | y | ＜ ymin, at this time vehicle be in lane center, without departing from the danger for going out track
Danger, so to reduce actual assist torque TaWeight coefficient σ, give driver's vehicle as much as possible traveling sovereignty, wherein,
yminExpression thinks that vehicle is still within the threshold value three set by lane center；
(4) T is worked asd 0≤|Td|≤Td maxAnd | y | >=ymin, the three kinds of situation discussion of this time-division：If operate torque TdIt is auxiliary with reality
Help torque TaDirection is needed to actual assist torque T at this time on the contrary, explanation driver's maloperationaWeight coefficient σ is heightened to correct
Vehicle driving trace；If operate torque TdWith actual assist torque TaDirection is identical, and it is correct to illustrate that driver turns to.
Preferably, if the operation torque T of inputdDomain be [- 8,8], fuzzy subset for NB, NM, NS, Z, PS, PM,
PB }, represent respectively negative big, it is negative small in bearing, zero, just small, center is honest }；The domain of the lateral direction of car deviation y of input is set to
[- 0.6,0.6], fuzzy subset are also { NB, NM, NS, Z, PS, PM, PB }, represent respectively negative big, it is negative small in bearing, zero, it is just small,
Center, honest；The domain of the weight coefficient σ of output is [0,1], and fuzzy subset is { Z, S, M, L, VL }, represent respectively zero,
It is small, in, greatly, very greatly }；Make input vector X=[x1,x2]T(x1=Td,x2=y), the output y of kth layer(k), (k=1,2,3,
4,5) represent, each layer function is：First layer：Input layer, the second layer：It is blurred layer, third layer：Reasoning layer, the 4th layer：Normalization
Layer, layer 5：Output layer.
More preferably, first layer：Input layer, each neuron node of input layer correspond to a continuous variable xi, this
Input data is directly transmitted to the second node layer by the node of layer, thus, outputRepresent as follows：；
The second layer：Layer is blurred, by the continuous variable x of inputiValue, according to being subordinate on the three of definition fuzzy subsets
Spend function and carry out Fuzzy processing, each node on behalf of the layer a linguistic variable value, total node number 14, first layer i-th
It is a to export corresponding j-th stage degree of membershipCalculation formula is expressed as：
In formula：cij,σijCenter and the width of membership function are represented respectively；
Third layer：Reasoning layer, each neuron node represents a corresponding fuzzy rule, by matching the second node layer
Obtained degree of membership, calculates the relevance grade of every fuzzy rule, total node number n, wherein n=49, then third layer saves for m-th
PointOutput be：
In formula,For first layer, the 1st exports corresponding j-th stage degree of membership,Corresponded to for the 2nd output of first layer
J-th stage degree of membership；
4th layer：Normalize layer, overall normalization carried out to network structure and is calculated, total node number n, the 4th layer m-th
NodeOutput be：
Layer 5：Output layer, by the variable sharpening after blurring, carries out Anti-fuzzy calculating, network output y(5)Equal to
4 layers of each node export the product summation of corresponding weight：
In formula：wmRepresent the 4th layer of m-th of node and output nodeBetween connection weight.
The present invention also provides a kind of man-machine harmony control system of deviation auxiliary system, it includes：
Desired orientation disk rotational angle theta*With expectation assist torqueAcquisition module, after the deviation auxiliary system starts,
According to lateral direction of car deviation y and destination path f (t), the desired orientation disk rotational angle theta needed for Vehicular turn is drawn*Aided in expectation
Torque
Man-machine harmony control is according to acquisition module, the operation torque T of acquisition driver's realityd, torque T will be operateddAnd vehicle
The foundation that lateral deviation y is controlled as man-machine harmony；
Man-machine harmony controller design module, the man-machine harmony controller of design dual input list output, operation torque TdWith
Two inputs of the lateral direction of car deviation y as man-machine harmony controller, the output of man-machine harmony controller is weight coefficient σ；With
And
Actual assist torque TaOptimization module, by weight coefficient σ and it is expected assist torqueProduct is done dynamically to adjust
The actual assist torque T of the deviation auxiliary systemaSize.
The man-machine harmony control method of the deviation auxiliary system of the present invention, it is theoretical based on Fuzzy Neural-network Control,
For the man-machine harmony problem in deviation supporting process between driver and auxiliary system, devise and consider driver's torque
With the man-machine harmony controller of lateral direction of car deviation.To dynamically adjust track inclined by exporting auxiliary weight for man-machine harmony controller
From the assist torque of auxiliary system, realize that the coordination of driver and auxiliary system controls.The present invention can be effectively prevented from car
While deviating from track, reduce interfering with each other between driver and auxiliary system, avoid man-machine conflict, there is preferable people
Machine coordinates performance.
Brief description of the drawings
Fig. 1 is the flow chart of the man-machine harmony control method of the deviation auxiliary system of the present invention.
Fig. 2 is the structure diagram for using the man-machine harmony control system of man-machine harmony control method in Fig. 1.
Fig. 3 is the Single-point preview model schematic that pilot model uses in Fig. 2.
Fig. 4 is the control structure figure of PID controller in Fig. 2.
Fig. 5 is the fuzzy neural network topological structure schematic diagram of tuning controller in Fig. 2.
Fig. 6 is the actual assist torque T of the deviation auxiliary system of the present inventionaOptimization method flow chart.
Fig. 7 is the hardware-in-the-loop test FB(flow block) of man-machine harmony control system in Fig. 2.
Fig. 8 is the operation torque T of the i.e. driver of driver's input torque of man-machine harmony control system in Fig. 2dExperiment
Result curve figure.
Fig. 9 is the result of the test curve map of the weight coefficient σ of man-machine harmony control system in Fig. 2.
Figure 10 is the actual assist torque T of man-machine harmony control system in Fig. 2aResult of the test curve map.
Figure 11 is the result of the test curve map of the lateral direction of car deviation y of man-machine harmony control system in Fig. 2.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, it is right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Traditional deviation auxiliary system, judges that vehicle will deviate from track and driver does not operate steering wheel working as
When, it will enable, once driver intervenes, auxiliary system will be stopped.System passes through electric booster turning mechanism, that is, EPS
(Electric Power steering system) carries out deviation auxiliary.The motor of EPS is such as driven to apply to steering column
Torque changes vehicle front corner δf, vehicle front corner δfChange cause the adjustment of vehicle-state and position, be embodied in vehicle
During traveling vehicle on road surface relative to lane center lateral direction of car deviation y adjustment.
The man-machine harmony control method of the deviation auxiliary system of the present invention is used for when vehicle will deviate from track,
Collaboration driver completes to turn to jointly.The system can effectively coordinate driver and deviation auxiliary system, carry out in due course
Deviation auxiliary is controlled to lift man-machine harmony performance.Thus, the present invention can deviate from track being effectively prevented from vehicle
While, reduce interfering with each other between driver and deviation auxiliary system, avoid man-machine conflict, there is preferable man-machine association
Tonality energy.
Embodiment 1
Please refer to Fig.1 and Fig. 2, the man-machine harmony control method of deviation auxiliary system of the invention include following step
Suddenly.
Step S11, obtain vehicle travel process in yaw velocity ω, speed v and vehicle on road surface relative to
The lateral direction of car deviation y of lane center, and using yaw velocity ω, speed v and lateral direction of car deviation y as deviation
Basis for estimation.
Step S12, will predict that wheel touches minimum time needed for the edge of track as across the road time, will across the road time
Contrasted with the threshold value one of setting, the threshold value that the setting is less than in judges that vehicle will deviate from for the moment
Track.
In the present embodiment, using judgement algorithm of across the road time as deviation.By calculated and
The threshold value one of setting is contrasted, and then judges whether vehicle will deviate from track.
Deviateed based on the vehicle across the road time and judge vehicle movement model prediction vehicle driving trace of the algorithm by foundation,
Minimum time Ji Kua road times needed for the edge of track are touched so as to calculate wheel.Calculate the specific table of across road time TLC
It is up to formula：
In formula, dlaneRepresent lane width, dbRepresent wheelspan, θ is vehicle course angle (i.e. actual steering wheel corner), can be by
Yaw velocity ω integrates to obtain, and L represents wheelbase, and the yaw velocity ω, speed v, vehicle that ω, v, y are all from step S11 are horizontal
To deviation y.
Step S13, decides whether to start deviation auxiliary system according to judging result.
When judging that vehicle will deviate from track, start the deviation auxiliary system.If in step S12, meter
Calculated is less than the threshold value one of setting, illustrates that vehicle will deviate from track, then it is auxiliary to start deviation by step S13
Auxiliary system.If calculated is more than or equal to the threshold value one of setting, illustrate that vehicle will not will deviate from track, then
Deviation auxiliary system is not started.
Step S14, according to lateral direction of car deviation y and actual steering wheel rotational angle theta, the desired orientation needed for Vehicular turn is drawn
Disk rotational angle theta*With expectation assist torque
In the present embodiment, according to the state parameter such as lateral direction of car deviation y and actual steering wheel rotational angle theta, driver is passed through
Model and the pid algorithm of neutral net draw the desired orientation disk rotational angle theta needed for Vehicular turn respectively*With expectation assist torqueFirst pass through pilot model and calculate desired orientation disk rotational angle theta*, by actual steering wheel rotational angle theta and desired orientation disk rotational angle theta*
Make the difference, and the expectation assist torque needed for Vehicular turn is drawn by the PID controller of BP neural network
Pilot model is Single-point preview model as shown in Figure 3：F (t) is vehicle target track, and y (t) is current for vehicle
The lateral coordinate in position, T are to take aim at the time in advance.
Assuming that preview distance is d, the relation taken aim in advance between time T and preview distance d is：
According to the side acceleration of the side velocity of vehicle, that is, speed v and vehicle, t+T moment vehicle locations can be predicted
Lateral coordinate y (t+T), selects a preferable steering angle to cause vehicle to produce side acceleration at this timeIn t+T moment cars
The lateral coordinate y (t+T) of position is equal with the lateral coordinate f (t+T) of target trajectory, then can obtain：
F (t+T)=y (t+T)
Two formula of simultaneous can obtain optimal side acceleration
According to vehicle kinematics relation, actual side acceleration can be obtainedWith the pass between actual steering wheel rotational angle theta
System：
In formula, R is motor turning radius, iswRepresent steering system ratio.
Finally show that the optimal steering wheel angle needed for tracking target trajectory it is expected steering wheel angle θ*：
The PID controller of BP neural network is as shown in figure 4, i.e. Neural Network PID Control structure is mainly controlled by classical PID
Device processed and neutral net two parts are formed.Classical PID controller：Directly to controlled device carry out closed-loop control, the three of controller
A parameter is on-line tuning.Neutral net：The output state of its output layer neuron corresponds to three adjustable ginsengs of PID controller
Number, self study and adjustment weighting coefficient by neutral net so that the output of neutral net corresponds to certain optimal control law
Under pid control parameter.
Neutral net uses three layers of feedforward network of 3-5-3 structures.The number of input layer is 3, is respectively yaw
Angular speed desired value, actual value and deviation；Hidden layer neuron number is 5；Output layer neuron number is 3, i.e. PID control is joined
Number.
Make input vector X=[x1(n),x2(n),x3(n)]T, x1(n),x2(n),x3(n) ω is represented respectively*(n),ω(n)
And its deviation e (n)；The output y of kth layer(k)(n), (k=1,2,3) is represented；The activation primitive of hidden layer neuron takes positive and negative
Symmetrical Sigmoid functions：
Output layer exports
Since these three parameters cannot be negative, so the activation primitive of output layer is
Therefore, the control law of BP neural network PID controller is
Defining performance index function is
As shown in figure 5, amendment is iterated to network weights coefficient using BP learning algorithms, i.e., by ε (n) to weighting coefficient
Negative gradient direction search adjustment, and add a momentum term for making search Fast Convergent global minimal
In formula, η is learning rate；α is factor of momentum；wliFor hidden layer and the weighting coefficient of output layer.
Step S15, obtains the operation torque T of driver's realityd, torque T will be operateddWith lateral direction of car deviation y as man-machine
Coordinate the foundation of control.
Step S16, the man-machine harmony controller of dual input list output, operation torque T are designeddMake with lateral direction of car deviation y
For two inputs of man-machine tuning controller, the output of man-machine harmony controller is weight coefficient σ.That is, according to operation torque Td
With the man-machine harmony controller of lateral direction of car deviation y design dual input list outputs.
The man-machine harmony controller includes the fuzzy neural network controller based on five etale topology structures, the fuzzy god
The five etale topology structures through network controller are：Input layer, blurring layer, reasoning layer, normalization layer and output layer；Turned with operation
Square TdIt is dual input with lateral direction of car deviation y, weight coefficient σ exports to be single.Therefore based on the fuzznet of five etale topology structures
The man-machine harmony controller of network Theoretical Design dual input list output.
The man-machine harmony controller is based on Fuzzy Neural Network Theory and takes into full account that driver operates torque TdAnd car
Lateral deviation y and design.
For man-machine harmony fuzzy neural network controller design need meet principle specifically includes.
(1) when driver's torque | Td| ＞ Td max, at this time vehicle be in a state of emergency, actual assist torque TaWeight system
Number is minimum, and driver fully takes up the sovereignty of vehicle traveling.
(2) as | Td| ＜ Td 0, at this time driver do not operate steering wheel, the deviation auxiliary system occupies vehicle row
Sovereignty are sailed, weight coefficient σ increases with the increase of lateral lateral direction of car deviation y.Wherein,It is expressed as judging driver
The maximum and minimum value of threshold value two set by mode of operation.
(3) T is worked asd 0≤|Td|≤Td maxAnd | y | ＜ ymin, at this time vehicle be in lane center, without departing from the danger for going out track
Danger, so to reduce actual assist torque TaWeight coefficient σ, give driver's vehicle as much as possible traveling sovereignty.Wherein,
yminExpression thinks that vehicle is still within the threshold value three set by lane center.
(4) T is worked asd 0≤|Td|≤Td maxAnd | y | >=ymin, the three kinds of situation discussion of this time-division：Turn if driver's torque operates
Square TdWith actual assist torque TaDirection is needed to actual assist torque T at this time on the contrary, explanation driver's maloperationaLarger power
Factor sigma is weighed to correct vehicle driving trace；If operate torque TdWith actual assist torque TaDirection is identical, illustrates that driver turns to
Correctly.Driver's torque is bigger, actual assist torque TaWeight coefficient σ with regard to smaller, to reduce auxiliary system to driver's
Intervene；If lateral deviation y is larger, actual assist torque TaWeight coefficient σ it is also larger, vice versa.
The fuzzy neural network of designed man-machine harmony controller uses dual input/mono- five etale topology structures exported,
That is input layer, blurring layer, reasoning layer, normalization layer and output layer.To operate torque TdIt is input with lateral direction of car deviation y,
Weight coefficient σ is output.
If the operation torque T of inputdDomain be [- 8,8], fuzzy subset is { NB, NM, NS, Z, PS, PM, PB }, respectively
Represent negative big, it is negative small in bearing, zero, just small, center is honest }；The domain of vehicle lateral deviation y is set to [- 0.6,0.6], obscures
Subset is also { NB, NM, NS, Z, PS, PM, PB }, represent respectively negative big, it is negative small in bearing, zero, just small, center is honest }；Output
The domain of weight coefficient σ be [0,1], fuzzy subset is { Z, S, M, L, VL }, represent respectively zero, it is small, in, greatly, very greatly.Order
Input vector X=[x1,x2]T(x1=Td,x2=y), the output y of kth layer(k), (k=1,2,3,4,5) is represented, each layer function
It is as follows：
First layer：Input layer.Each neuron node of input layer corresponds to a continuous variable xi, the node of this layer is straight
Connect and input data is transmitted to the second node layer, thus, outputRepresent as follows：
The second layer：It is blurred layer.By the continuous variable x of inputiValue according to the degree of membership letter on the fuzzy subset of definition
Number carries out Fuzzy processing, and each node on behalf of the layer a linguistic variable value, total node number 14.1st layer of i-th of output
Corresponding j-th stage degree of membershipCalculation formula is represented by：
In formula：cij,σijCenter and the width of membership function are represented respectively.
Third layer：Reasoning layer.Each neuron node represents a corresponding fuzzy rule, is obtained by matching the 2nd layer
Degree of membership, calculate the relevance grade of every rule.Total node number is n (n=49), then m-th of nodeOutput be：
In formula,For first layer, the 1st exports corresponding j-th stage degree of membership,Corresponded to for the 2nd output of first layer
J-th stage degree of membership.It is exactly briefly the output of the second layer when i is respectively 1 and 2.
4th layer：Normalize layer.Carry out overall normalization to network structure to calculate, total node number n, the 4th layer m-th
NodeOutput be：
Layer 5：Output layer.By the variable sharpening after blurring, Anti-fuzzy calculating is carried out.Network exports y(5)Equal to
4 layers of each node export the product summation of corresponding weight.
In formula：wmRepresent the 4th layer of m-th of node and output nodeBetween connection weight.
Step S17, by weight coefficient σ and it is expected assist torqueProduct is done dynamically to adjust the deviation auxiliary
The actual assist torque T of systemaSize.
Man-machine harmony controller is according to operation torque TdA weight coefficient σ is produced in real time with the value of lateral direction of car deviation y,
And actual assist torque T is dynamically adjusted by this weight coefficient σaSize, coordinate driver while security is ensured
Control between auxiliary system；
Designed man-machine harmony controller is according to the operation torque T of driverdProduced in real time with the value of lateral direction of car deviation y
A raw dynamic weight coefficient σ, and the expectation assist torque needed for by this weight coefficient σ and Vehicular turnDo product with
The actual assist torque T of adjustment in real timeaSize, not only can guarantee that vehicle without departing from going out track but also realize driver and auxiliary system
Between coordination control.
The actual assist torque T obtained by above-mentioned stepsaWith the operation torque T of driverdCollective effect is in steering system
System, if driver's torque operates torque TdWith actual assist torque TaDirection needs at this time on the contrary, explanation driver's maloperation
To actual assist torque TaLarger weight coefficient σ is to correct vehicle driving trace.Can be by EPS system individually into runway
Deviate auxiliary, such as change vehicle front corner δf, vehicle front corner δfChange cause the adjustment of vehicle-state, it is final to change
Lateral direction of car deviation y.
If operate torque TdWith actual assist torque TaDirection is identical, and it is correct to illustrate that driver turns to.Without passing through EPS machines
Structure carries out deviation auxiliary.Operate torque TdIt is bigger, actual assist torque TaWeight coefficient σ with regard to smaller, with reduce auxiliary system
The intervention united to driver, at this time, the assist torque Collaborative Control Vehicular turn of the operation of driver and auxiliary system offer.If
Lateral direction of car deviation y is larger, actual assist torque TaWeight coefficient σ it is also larger, vice versa.
In other embodiments, the man-machine harmony control method of deviation auxiliary system of the invention, it may include following
Simplify step：
It will predict that wheel touches minimum time needed for the edge of track and is used as across the road time, by across the road time and set
Threshold value one is contrasted, and the threshold value that the setting is less than in across the road time starts the deviation auxiliary system for the moment；
According to lateral direction of car deviation y and destination path f (t), the desired orientation disk rotational angle theta needed for Vehicular turn is drawn*；
According to desired orientation disk rotational angle theta*Draw expectation assist torque
Design the operation torque T of driver's realitydWith lateral direction of car deviation y as dual input, weight coefficient σ as single defeated
The man-machine harmony controller gone out；
By weight coefficient σ and it is expected assist torqueProduct is done dynamically to adjust the reality of the deviation auxiliary system
Border assist torque TaSize.
The method that present embodiment is proposed aims to provide a kind of man-machine harmony control method of deviation auxiliary system,
This method is directed to the man-machine harmony problem between the driver in deviation supporting process and deviation auxiliary system, application
Fuzzy Neural-network Control is theoretical, and design considers the operation torque T of driverdMan-machine harmony with lateral direction of car deviation y controls
Device, the actual assist torque T of deviation auxiliary system is dynamically adjusted by exporting auxiliary weight coefficient σa, realize driver
Coordination with auxiliary system controls.The present invention can while being effectively prevented from vehicle and deviating from track, reduce driver and
Interfering with each other between auxiliary system, avoids man-machine conflict, there is preferable man-machine harmony performance, can further genralrlization.
Embodiment 2
Referring to Fig. 2, Fig. 2 shows the man-machine harmony control using the man-machine harmony control method of embodiment 1
The structure diagram of system.The man-machine harmony control system of the present invention includes EPS mechanisms, actual assist torque TaOptimization system
System.
EPS mechanisms include deviation basis for estimation acquisition module, deviation judgment module, deviate supplementary controlled system
Starting module.
The deviation basis for estimation acquisition module obtain yaw velocity ω, speed v in vehicle travel process with
And vehicle on road surface relative to the lateral direction of car deviation y of lane center, it is and yaw velocity ω, speed v and vehicle is horizontal
The basis for estimation of deviation is carried out as the deviation judgment module to deviation y.
The deviation judgment module will predict wheel touch minimum time needed for the edge of track as it is across road when
Between, the threshold value one of Bing Jiangkua roads time and setting is contrasted, and the threshold value that the setting is less than in is sentenced for the moment
Disconnected vehicle will deviate from track.
The deviation supplementary controlled system starting module is determined according to the judging result of the deviation judgment module
No startup deviation auxiliary system.
Actual assist torque TaOptimization system include desired orientation disk rotational angle theta*With expectation assist torqueAcquisition module,
Man-machine harmony control is according to acquisition module, man-machine harmony controller design module, actual assist torque TaOptimization module.
Desired orientation disk rotational angle theta*With expectation assist torqueAcquisition module, according to lateral direction of car deviation y and destination path f
(t), the desired orientation disk rotational angle theta needed for Vehicular turn is drawn*With expectation assist torque
Man-machine harmony control obtains the operation torque T of driver's reality according to acquisition moduled, torque T will be operateddAnd vehicle
The foundation that lateral deviation y is controlled as man-machine harmony.
The man-machine harmony controller of man-machine harmony controller design module design dual input list output, operation torque TdAnd car
Two inputs of lateral deviation y as man-machine harmony controller, the output of man-machine harmony controller is weight coefficient σ.
Actual assist torque TaOptimization module is by weight coefficient σ and it is expected assist torqueProduct is done dynamically to adjust institute
State the actual assist torque T of deviation auxiliary systemaSize.
The details of man-machine harmony control system described in the man-machine harmony control method of embodiment 1, is no longer tired out herein
State.
Embodiment 3
Fig. 2, Fig. 6 are referred to, the present embodiment 3 illustrates the actual assist torque T of the deviation auxiliary system of the present inventiona
Optimization method, the optimization method comprises the following steps.
Step S21, lateral direction of car deviation y and destination path f (t) in vehicle travel process, draws Vehicular turn
Required desired orientation disk rotational angle theta*。
According to lateral direction of car deviation y and destination path f (t), desired orientation disk rotational angle theta is calculated by pilot model*,
Desired orientation disk rotational angle theta*Computational methods as described by the step S14 in embodiment 1, be not repeated introduction herein.
Step S22, according to actual steering wheel rotational angle theta and desired orientation disk rotational angle theta*, draw the expectation needed for Vehicular turn
Assist torque
By actual steering wheel rotational angle theta and desired orientation disk rotational angle theta*Make the difference, and obtained by the PID controller of BP neural network
Go out the expectation assist torque needed for Vehicular turnIt is expected assist torqueComputational methods such as embodiment 1 in step S14
It is described, introduction is not repeated herein.
Step S23, the man-machine harmony controller of design dual input list output, the operation torque T in vehicle travel processdWith
Two inputs of the lateral direction of car deviation y as man-machine harmony controller, the output of man-machine harmony controller is weight coefficient σ.
The computational methods of weight coefficient σ are not repeated introduction herein as described by the step S16 in embodiment 1.
Step S24, by weight coefficient σ and it is expected assist torqueDo product and carry out deviation auxiliary described in dynamic optimization
The actual assist torque T of systemaSize.
If driver's torque operates torque TdWith actual assist torque TaDirection is on the contrary, illustrate driver's maloperation, at this time
Need to actual assist torque TaLarger weight coefficient σ is to correct vehicle driving trace.Can individually it be carried out by EPS system
Deviation aids in, and such as changes vehicle front corner δf, vehicle front corner δfChange cause the adjustment of bus or train route model, finally
Change lateral direction of car deviation y.
If operate torque TdWith actual assist torque TaDirection is identical, and it is correct to illustrate that driver turns to.Without passing through EPS machines
Structure carries out deviation auxiliary.Operate torque TdIt is bigger, actual assist torque TaWeight coefficient σ with regard to smaller, with reduce auxiliary system
The intervention united to driver, at this time, the operation of driver and the deviation auxiliary of EPS mechanisms can be carried out synchronously.If vehicle
Lateral deviation y is larger, actual assist torque TaWeight coefficient σ it is also larger, vice versa.
Embodiment 4
Referring to Fig. 2, Fig. 2 also illustrates the actual assist torque T using embodiment 3aOptimization method reality
Assist torque TaOptimization system structure diagram.The actual assist torque T of the present inventionaOptimization system include desired orientation
Disk rotational angle theta*Acquisition module, it is expected assist torqueAcquisition module, man-machine harmony controller design module, actual assist torque Ta
Optimization module.
Desired orientation disk rotational angle theta*Lateral direction of car deviation y and destination path f of the acquisition module in vehicle travel process
(t), the desired orientation disk rotational angle theta needed for Vehicular turn is drawn*。
It is expected assist torqueAcquisition module is according to actual steering wheel rotational angle theta and desired orientation disk rotational angle theta*, draw vehicle
Expectation assist torque needed for turning to
The man-machine harmony controller of man-machine harmony controller design module design dual input list output, in vehicle travel process
Operation torque TdWith two inputs of the lateral direction of car deviation y as man-machine harmony controller, the output of man-machine harmony controller
For weight coefficient σ.
Actual assist torque TaOptimization module is by weight coefficient σ and it is expected assist torqueDo product and come dynamic optimization institute
State the actual assist torque T of deviation auxiliary systemaSize.
Actual assist torque TaOptimization system details in the actual assist torque T of embodiment 3aOptimization method in
Description, is not repeated herein.
Embodiment 5
To verify the validity and feasibility of man-machine harmony control method in embodiment 1, below in conjunction with specifically to man-machine association
Control method is verified.
Using the simulated environment based on CarSim auto models, joint LabVIEW carries out hardware-in-the-loop test research.Experiment
Platform and experiment block diagram are as shown in Figure 7.The testing stand that the present invention is built mainly by host computer, slave computer, interface system and turns
To a few part compositions of system.CarSim Full Vehicle Dynamics model and virtual road are established according to vehicle parameter in host computer, joined
CarSim/LabVIEW is closed, writes LabVIEW deviations auxiliary control program；Slave computer be NI PXI systems, real time execution
The program that host computer is established；Interface system is that the signals such as the torque for collecting sensor are transmitted to PXI systems, while will control
Signal output (such as controls the EPS motor controller of assist torque and watching for generation steering response to the controller of executing agency
Take motor).
Selecting forthright, have a lot of social connections 3.75m, and constant speed is 80km/h to emulate road, applies turning for 10Nm in 1s-1.5s
Square makes automotive run-off-road center, chooses two kinds of representative driver's modes of operation and carries out man-machine harmony control strategy
Verification experimental verification, i.e., in automotive run-off-road, driver reacts, and carries out maloperation and correct operation.
Fig. 8-Figure 11 is man-machine coordination control strategy result of the test, and wherein Fig. 8 is driver's input torque, that is, driver's
Operate torque TdResult of the test curve map, Fig. 9 be weight coefficient σ result of the test curve map, Figure 10 is actual assist torque Ta
Result of the test curve map, Figure 11 be lateral direction of car deviation y result of the test curve map.
When driver's steering is correct, the output weight coefficient σ of man-machine harmony controller is decreased obviously, actual assist torque
TaAlso it is relatively small, thus given driver more with sovereign right, reduce interference of the auxiliary system to driver.When driver misses
When operating steering wheel, output weight is maintained at higher value, and pilot controller, that is, EPS mechanisms export larger actual assist torque Ta
To make up the operation torque T that driver applies mistaked.From fig. 10 it can be seen that no matter what driver carries out when vehicle deviates
Kind operation, LDAS, that is, deviation auxiliary system can still ensure that vehicle does not deflect away from track.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement made within refreshing and principle etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of man-machine harmony control method of deviation auxiliary system, it is characterised in that it comprises the following steps：
After the deviation auxiliary system starts, according to lateral direction of car deviation y and destination path f (t), Vehicular turn institute is drawn
The desired orientation disk rotational angle theta needed*；
According to desired orientation disk rotational angle theta*Draw expectation assist torque；
Design the man-machine harmony controller of dual input list output, operation torque TdControlled with lateral direction of car deviation y as man-machine harmony
Two inputs of device, the output of man-machine harmony controller is weight coefficient σ；
By weight coefficient σ and it is expected assist torqueIt is auxiliary dynamically to adjust the reality of the deviation auxiliary system to do product
Help torque TaSize.
2. the man-machine harmony control method of deviation auxiliary system as claimed in claim 1, it is characterised in that by pre- measuring car
Take turns the minimum time touched needed for the edge of track and be used as across the road time, the threshold value one of across road time and setting are contrasted,
The threshold value for being less than the setting in across the road time starts the deviation auxiliary system for the moment.
3. the man-machine harmony control method of deviation auxiliary system as claimed in claim 2, it is characterised in that using across road
Judgement algorithm of the time as deviation, is deviateed based on the vehicle across the road time and judges vehicle movement mould of the algorithm by foundation
Type predicts vehicle driving trace, so that calculating wheel touches minimum time needed for the edge of track.
4. the man-machine harmony control method of deviation auxiliary system as claimed in claim 1, it is characterised in that according to vehicle
Lateral deviation y, destination path f (t), desired orientation disk rotational angle theta is calculated by pilot model*。
5. the man-machine harmony control method of deviation auxiliary system as claimed in claim 4, it is characterised in that by reality side
To disk rotational angle theta and desired orientation disk rotational angle theta*Make the difference, and drawn by the PID controller of BP neural network needed for Vehicular turn
It is expected assist torque。
6. the man-machine harmony control method of deviation auxiliary system as claimed in claim 1, it is characterised in that described man-machine
Tuning controller includes the fuzzy neural network controller based on five etale topology structures, and the five of the fuzzy neural network controller
Etale topology structure is：Input layer, blurring layer, reasoning layer, normalization layer and output layer；To operate torque TdIt is inclined with lateral direction of car
Poor y is dual input, and weight coefficient σ exports to be single.
7. the man-machine harmony control method of deviation auxiliary system as claimed in claim 6, it is characterised in that described fuzzy
The principle that nerve network controller meets includes：
(1) as | Td| ＞ Td max, at this time vehicle be in a state of emergency, actual assist torque TaWeight coefficient σ it is minimum, driver
Vehicle traveling sovereignty are fully taken up, wherein,The maximum for the threshold value two for being expressed as judging set by driver's mode of operation；
(2) as | Td| ＜ Td 0, at this time driver do not operate steering wheel, the deviation auxiliary system occupies vehicle traveling master
Power, weight coefficient σ increase with the increase of lateral direction of car deviation y, wherein,Represent the minimum value of set threshold value two；
(3) T is worked asd 0≤|Td|≤Td maxAnd | y | ＜ ymin, at this time vehicle be in lane center, without departing from the danger for going out track, institute
To reduce actual assist torque TaWeight coefficient σ, give driver's vehicle as much as possible traveling sovereignty, wherein, yminRepresent
Think that vehicle is still within the threshold value three set by lane center；
(4) T is worked asd 0≤|Td|≤Td maxAnd | y | >=ymin, the three kinds of situation discussion of this time-division：If operate torque TdTurn with actual auxiliary
Square TaDirection is needed to actual assist torque T at this time on the contrary, explanation driver's maloperationaWeight coefficient σ is heightened to correct vehicle
Driving trace；If operate torque TdWith actual assist torque TaDirection is identical, and it is correct to illustrate that driver turns to.
8. the man-machine harmony control method of deviation auxiliary system as claimed in claim 6, it is characterised in that set input
Operate torque TdDomain be [- 8,8], fuzzy subset is { NB, NM, NS, Z, PS, PM, PB }, NB, NM, NS, Z, PS, PM, PB
It is operation torque TdFuzzy Linguistic Variable after blurring, represent respectively negative big, it is negative small in bearing, zero, just small, center, just
Greatly }；The domain of the lateral direction of car deviation y of input is set to [- 0.6,0.6], fuzzy subset also for NB, NM, NS, Z, PS, PM,
PB }, represent respectively negative big, it is negative small in bearing, zero, just small, center is honest }；The domain of the weight coefficient σ of output is [0,1],
Fuzzy subset is { Z, S, M, L, VL }, represent respectively zero, it is small, in, greatly, very greatly }；Make input vector X=[x1,x2]T(x1=Td,
x2=y), the output y of kth layer(k), (k=1,2,3,4,5) is represented, each layer function is：First layer：Input layer, the second layer：Mould
It is gelatinized layer, third layer：Reasoning layer, the 4th layer：Normalize layer, layer 5：Output layer.
9. the man-machine harmony control method of deviation auxiliary system as claimed in claim 8, it is characterised in that first layer：
Input layer, each neuron node of input layer correspond to a continuous variable xi, the node of this layer directly passes input data
To the second node layer, thus, outputRepresent as follows：
<mrow>
<msubsup>
<mi>y</mi>
<mi>i</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
The second layer：Layer is blurred, by the continuous variable x of inputiValue, according to the degree of membership letter on the three of definition fuzzy subsets
Number carries out Fuzzy processings, and each node on behalf of the layer a linguistic variable value, total node number 14, and i-th of first layer is defeated
Go out corresponding j-th stage degree of membershipCalculation formula is expressed as：
<mrow>
<msubsup>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mo>-</mo>
<mfrac>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>c</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>)</mo>
</mrow>
</mrow>
In formula：cij,σijCenter and the width of membership function are represented respectively；
Third layer：Reasoning layer, each neuron node represent a corresponding fuzzy rule, are obtained by matching the second node layer
Degree of membership, calculate the relevance grade of every fuzzy rule, total node number n, wherein n=49, then m-th of node of third layerOutput be：
<mrow>
<msubsup>
<mi>y</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>y</mi>
<mrow>
<mn>1</mn>
<mi>j</mi>
</mrow>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</msubsup>
<msubsup>
<mi>y</mi>
<mrow>
<mn>2</mn>
<mi>j</mi>
</mrow>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mo>,</mo>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
</mrow>
In formula,For first layer, the 1st exports corresponding j-th stage degree of membership,For the corresponding jth of the 2nd output of first layer
Level degree of membership；
4th layer：Layer is normalized, overall normalization is carried out to network structure and is calculated, total node number n, the 4th layer of m-th of nodeOutput be：
<mrow>
<msubsup>
<mi>y</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<mfrac>
<msubsup>
<mi>y</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</msubsup>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msubsup>
<mi>y</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</msubsup>
</mrow>
</mfrac>
</mrow>
Layer 5：Output layer, by the variable sharpening after blurring, carries out Anti-fuzzy calculating, network output y(5)Equal to the 4th layer
Each node exports the product summation of corresponding weight：
<mrow>
<msup>
<mi>y</mi>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</msup>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>w</mi>
<mi>m</mi>
</msub>
<msubsup>
<mi>y</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</msubsup>
</mrow>
In formula：wmRepresent the 4th layer of m-th of node and output nodeBetween connection weight.
10. a kind of man-machine harmony control system of deviation auxiliary system, it is characterised in that it includes：
Desired orientation disk rotational angle theta*With expectation assist torque Ta *Acquisition module, after the deviation auxiliary system starts, according to
Lateral direction of car deviation y and destination path f (t), draw the desired orientation disk rotational angle theta needed for Vehicular turn*With expectation assist torque；
Man-machine harmony control is according to acquisition module, the operation torque T of acquisition driver's realityd, torque T will be operateddAnd lateral direction of car
The foundation that deviation y is controlled as man-machine harmony；
Man-machine harmony controller design module, the man-machine harmony controller of design dual input list output, operation torque TdIt is horizontal with vehicle
Two inputs to deviation y as man-machine harmony controller, the output of man-machine harmony controller is weight coefficient σ；And
Actual assist torque TaOptimization module, by weight coefficient σ and it is expected assist torqueProduct is done dynamically to adjust the car
Deviate the actual assist torque T of auxiliary system in roadaSize.
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| CN201810031566.9A CN107972667B (en) | 2018-01-12 | 2018-01-12 | A kind of man-machine harmony control method of deviation auxiliary system |
| CN201910298019.1A CN109969181B (en) | 2018-01-12 | 2018-01-12 | Lane departure auxiliary system and lane departure auxiliary method thereof |
| CN201910298010.0A CN109969180B (en) | 2018-01-12 | 2018-01-12 | Man-machine coordination control system of lane departure auxiliary system |
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| CN201910298019.1A Active CN109969181B (en) | 2018-01-12 | 2018-01-12 | Lane departure auxiliary system and lane departure auxiliary method thereof |
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Also Published As
| Publication number | Publication date |
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| CN109969181B (en) | 2020-06-05 |
| CN109969181A (en) | 2019-07-05 |
| CN109969180B (en) | 2020-05-22 |
| CN107972667B (en) | 2019-07-02 |
| CN109969180A (en) | 2019-07-05 |
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