CN109849898A - Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC - Google Patents
Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC Download PDFInfo
- Publication number
- CN109849898A CN109849898A CN201811615282.0A CN201811615282A CN109849898A CN 109849898 A CN109849898 A CN 109849898A CN 201811615282 A CN201811615282 A CN 201811615282A CN 109849898 A CN109849898 A CN 109849898A
- Authority
- CN
- China
- Prior art keywords
- formula
- moment
- vehicle
- yaw
- wheel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000005457 optimization Methods 0.000 title claims abstract description 28
- 230000002068 genetic effect Effects 0.000 title claims abstract description 26
- 238000005096 rolling process Methods 0.000 claims abstract description 5
- 238000001514 detection method Methods 0.000 claims abstract description 4
- 238000005070 sampling Methods 0.000 claims description 12
- 230000001052 transient effect Effects 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 7
- 230000007812 deficiency Effects 0.000 claims description 7
- 238000011897 real-time detection Methods 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 6
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012512 characterization method Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 230000007935 neutral effect Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000009941 weaving Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
Classifications
-
- 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
Landscapes
- Arrangement And Driving Of Transmission Devices (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
本发明公开了一种基于遗传算法混合优化GPC的车辆横摆稳定性控制方法，建立车辆二自由度线性模型作为预测模型，利用预测模型计算获得理想横摆角速度和理想质心侧偏角；利用传感器进行检测获得各实时数据，针对各实时数据利用遗传算法混合优化GPC的方法计算获得最优附加横摆力矩；采用左右侧车轮驱动力规则分配方法，将最优附加横摆力矩分配为四轮独立驱动轮毂电机电动汽车的四个车轮的驱动力，并一一对应作用于各个车轮。本发明提出的算法与普通广义预测算法相比，在GPC的滚动优化过程中引入遗传算法进行混合优化的方法使得该算法具有较强的全局搜索能力与全局收敛性，对所求的附加横摆力矩进行混合优化，大大地提高了最优解精度。
The invention discloses a vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC. A vehicle two-degree-of-freedom linear model is established as a prediction model, and the prediction model is used to calculate and obtain an ideal yaw angular velocity and an ideal center of mass sideslip angle; Perform detection to obtain various real-time data, and use the genetic algorithm hybrid optimization GPC method to calculate and obtain the optimal additional yaw moment for each real-time data; adopt the rule distribution method of the driving force of the left and right wheels, and distribute the optimal additional yaw moment to four independent wheels. The driving force that drives the four wheels of the in-wheel motor electric vehicle acts on each wheel in a one-to-one correspondence. Compared with the general generalized prediction algorithm, the algorithm proposed by the present invention introduces the genetic algorithm to perform hybrid optimization in the rolling optimization process of GPC, so that the algorithm has a strong global search ability and global convergence, and has a strong global search ability and global convergence. The hybrid optimization of torque greatly improves the accuracy of the optimal solution.
Description
Technical field
The present invention relates to safety assistant driving and field of intelligent control more particularly to a kind of four motorized wheels hub motors
Electric car stability control method.
Background technique
The control stability of automobile be influence automobile high-speed safety traffic an important performance, when automobile encounter it is extraneous because
When the interference (such as lateral wind) of element, separation road traveling, the urgent avoidance of high speed, vehicle can deviate ideal trailer reversing
Characteristic, driver can lose the control to vehicle when serious, be in breakneck circumstances, and Yaw stability controls (YSC) system
System can be corrected to automobile in ideal handling characteristic, from unstable region control to stability region.Yaw stability control
System processed, very with the active safety feature of development potentiality, has obtained being widely recognized as society as a.
Currently, each scientific research institution has conducted extensive research Yaw stability control, and common method is optimum control,
Such as LQR and LQG, but these control methods require to be accurately controlled model, and automobile is a complicated nonlinear system,
And engineering is generally required in model simplification, thus model accuracy it is difficult to ensure that；In addition, vehicle is under steam,
Parameter, environment all have very big uncertainty, so that the optimum control obtained by ideal model cannot keep most in practice
It is excellent, even cause the degradation of quality sometimes.
Summary of the invention
The present invention is provided a kind of based on genetic algorithm hybrid optimization to avoid above-mentioned deficiency of the prior art
The vehicle yaw stability control method of GPC carries out hybrid optimization to required additional yaw moment, improves optimal solution precision.
The present invention adopts the following technical scheme that in order to solve the technical problem
It is by following step the present invention is based on the characteristics of vehicle yaw stability control method of genetic algorithm hybrid optimization GPC
It is rapid to carry out:
Step 1: establishing vehicle two degrees of freedom linear model as prediction model, managed using prediction model calculating
Think yaw velocity and ideal side slip angle；
Step 2: carrying out detection using sensor and obtain each real time data, examined in real time including the use of yaw-rate sensor
It surveys and obtains the practical yaw velocity of vehicle, obtain vehicle actual travel speed using vehicle speed sensor real-time detection, utilize steering
Angle transducer real-time detection measures vehicle actual front wheel corner, utilizes genetic algorithm hybrid optimization for each real time data
The method of GPC, which calculates, obtains optimal additional yaw moment；
Step 3: using left and right sides wheel driving force rule distribution method, the optimal additional yaw moment is assigned as four
The driving force of four wheels of independent drive hub motor powered automobile is taken turns, and corresponds and acts on each wheel.
The present invention is based on the characteristics of vehicle yaw stability control method of genetic algorithm hybrid optimization GPC to lie also in:
In the step 1, calculates obtain ideal yaw velocity and ideal side slip angle according to the following procedure:
Step 1.1: two freedom degrees of lateral movement and weaving based on automobile, what foundation was characterized by formula (1)
Vehicle two degrees of freedom linear model:
β is side slip angle,For side slip angle speed, r is yaw velocity,For sideway angular acceleration；
l1For the distance of mass center to front axle, l2For the distance of mass center to rear axle；k1For front-wheel cornering stiffness, k2For rear wheel-side
Inclined rigidity；
IzFor rotary inertia, m is complete vehicle quality, σfFor front wheel angle, vdFor speed, M is additional yaw moment；
Enable front wheel angle σf=0, the biography of the vehicle two degrees of freedom linear model characterized by formula (2) is obtained according to formula (1)
Delivery function F (s):
R (s) is the Laplace transform of yaw velocity r,
M (s) is the Laplace transform of additional yaw moment M, and s indicates the complex variable in transmission function F (s)；
Using bilinear transformation, by the discrete discrete transfer function F (z) to be characterized by formula (3) of transmission function F (s):
R (z) is the z-transform of yaw velocity r,
M (z) is the z-transform of additional yaw moment M, and z indicates the complex variable in discrete transfer function F (z)；
T0For the sampling period；
n0=T0 2(a11a22+a12a21)-2T0(a11+a22)+4；n1=2T0 2(a11a22-a12a21)-8；
n2=T0 2(a11a22-a12a21)+2T0(a11+a22)+4；
Step 1.2: by ideal side slip angle βdIt is set as 0, i.e. βd=0；Using automobile dynamics theoretical calculation obtain by
The vehicle ideal yaw velocity r at the k+j moment that formula (4) is characterizedd(k+j):
K is stability factor,
vdIt (k+j) is the speed at k+j moment；σfIt (k+j) is the vehicle front wheel angle at k+j moment；
J=1,2 ... n, n are predetermined period, indicate the k+1 moment with k+1, indicate the subsequent time of k+1, with k+2 with k+j
Indicate the subsequent time of k+j-1；G is acceleration of gravity, and l is vehicle wheelbase, and u is coefficient of road adhesion.
The present invention is based on the characteristics of vehicle yaw stability control method of genetic algorithm hybrid optimization GPC to lie also in: institute
Stating step 2 is to obtain optimal additional yaw moment according to the following procedure:
Step 2.1: the discrete transfer function F (z) characterized by formula (3) is converted into formula (5):
R(z)(n0+n1z-1+n2z-2)=M (z) (m0+m1z-1+m2z-2) (5)
Step 2.2: according to GPC control algolithm, Diophantine equation is introduced in formula (5), obtains the k+ characterized by formula (6)
The yaw velocity predicted value r at j momente(k+j):
re(k+j)=Fj(z-1)r(k)+Gj(z-1)ΔM(k+j-1) (6)
R (k) is by the detected k moment yaw velocity actual value of yaw-rate sensor；
Fj(z-1) and Gj(z-1) it is multinomial in Diophantine equation；
Δ M (k+j-1) is the increment of the additional yaw moment actual value at k+j-1 moment and k+j-2 moment；
According to GPC principle, given prediction period n and control period N, n and N are integer, show that yaw velocity is predicted
The vector form of expression such as formula (7) of value:
R=G Δ M+f (7)
In formula (7):
R=[re(k+1),re(k+2),…,re(k+n)]T；
Δ M=[Δ M (k), Δ M (k+1) ..., Δ M (k+N-1)]T；
F=H Δ M (k)+Fr (k)=[f (k+1), f (k+2) ..., f (k+n)]T；
H1(z-1),H2(z-1)...Hn(z-1) be Diophantine equation in multinomial,
g0,g1,...gn-1For multinomial Gj(z-1) in multinomial coefficient；
The optimal cost characteristic index function J (k) at step 2.3:k moment is characterized by formula (8):
In formula (8), λ is control weighting constant,
Convolution (7) and formula (8) obtain the characterization vector form such as formula of additional yaw moment increment using rolling optimizing method
(9):
Δ M=(GTG+λI)-1GT(RD-f) (9)
In formula: RD=[rd(k+1),rd(k+2),…,rd(k+n)]T (10)
I is unit vector；rd(k+1),rd(k+2),...,rdIt (k+n) is to calculate predetermined period n obtained using formula (4)
Each of sampling instant ideal yaw velocity；
Step 2.4: in each of predetermined period n sampling instant, being acquired respectively using sensor and obtain front wheel angle
σfWith speed vd, the ideal yaw velocity r for obtaining each of predetermined period n sampling instant is calculated using formula (4)d(k+
1),rd(k+2),...,rd(k+n), and the R characterized by formula (10) is obtainedD；It recycles Diophantine equation to calculate and obtains Gj(z-1)、H1(z-1),H2(z-1)...Hn(z-1) and Fj(z-1) value；The k for obtaining and being characterized by formula (11) is finally calculated using formula (9)
The value of the additional yaw moment M (k) at moment:
M (k)=M (k-1)+gT(RD-f) (11)
In formula (11), gTIndicate (GTG+λI)-1GTThe first row vector；
Step 2.5: the value of additional yaw moment M (k) is optimized by genetic algorithm to get optimal additional cross is arrived
Put moment values Mm(k)。
The present invention is based on the characteristics of vehicle yaw stability control method of genetic algorithm hybrid optimization GPC to lie also in: institute
Step 3 is stated to specifically include:
Vehicular four wheels driving moment is characterized with four-wheel drive power relational expression by formula (12):
F1And T1Respectively left front wheel drive force and driving moment, F2And T2Respectively off-front wheel driving force and driving moment,
F3And T2Respectively left back wheel drive force and driving moment, F4And T4Respectively off hind wheel driving force and driving moment；
B is radius of wheel；
Vehicular four wheels driving moment and given target drives torque TobjRelational expression characterized by formula (13):
Tobj=T1+T2+T3+T4 (13)
By the optimal additional yaw moment value Mm(k) it distributes according to the following rules:
Work as Mm(k)=0 when, vehicle is in neutral steer state, distributes four-wheel drive power such as formula by the principle of mean allocation
(14):
F1=F2=F3=F4 (14)
Work as Mm(k) > 0 when, vehicle is in left steering deficiency or right turn transient state, and convolution (12)-formula (14) obtains
The four-wheel drive torque redistributed characterized by formula (15):
Work as Mm(k) < 0 when, vehicle is in right turn deficiency or left steering transient state, and convolution (12)-formula (14) obtains
The four-wheel drive torque redistributed characterized by formula (16):
By the optimal additional yaw moment value Mm(k) the four of four motorized wheels hub motor electric car are assigned as
The driving force of a wheel, one-to-one correspondence act on each wheel.
Compared with the prior art, the invention has the advantages that:
1, the method that the present invention introduces genetic algorithm progress hybrid optimization during the rolling optimization of GPC makes the algorithm
With stronger ability of searching optimum and global convergence, hybrid optimization is carried out to required additional yaw moment, is greatly mentioned
High optimal solution precision.
2, the genetic algorithm hybrid optimization GPC in the present invention combines multi-step prediction with adaptively, therefore is more suitable for not
The process objects such as determination, time-varying, time lag.
Detailed description of the invention
Fig. 1 is the vehicle two degrees of freedom linear model in the method for the present invention；
Fig. 2 is the control block diagram of the genetic algorithm hybrid optimization GPC in the method for the present invention；
Fig. 3 is the control flow chart of the genetic algorithm hybrid optimization GPC in the method for the present invention；
Specific embodiment
Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC in the present embodiment is as follows
It carries out:
Step 1: establishing vehicle two degrees of freedom linear model as shown in Figure 1 as prediction model, utilize prediction model meter
It calculates and obtains ideal yaw velocity and ideal side slip angle.
It is calculated according to the following procedure in specific implementation and obtains ideal yaw velocity and ideal side slip angle:
Step 1.1: two freedom degrees of lateral movement and weaving based on automobile, what foundation was characterized by formula (1)
Vehicle two degrees of freedom linear model:
β is side slip angle,For side slip angle speed, r is yaw velocity,For sideway angular acceleration；
l1For the distance of mass center to front axle, l2For the distance of mass center to rear axle；k1For front-wheel cornering stiffness, k2For rear wheel-side
Inclined rigidity；
IzFor rotary inertia, m is complete vehicle quality, σfFor front wheel angle, vdFor speed, M is additional yaw moment；
Enable front wheel angle σf=0, the biography of the vehicle two degrees of freedom linear model characterized by formula (2) is obtained according to formula (1)
Delivery function F (s):
R (s) is the Laplace transform of yaw velocity r,
M (s) is the Laplace transform of additional yaw moment M, and s indicates the complex variable in transmission function F (s)；
Using bilinear transformation, by the discrete discrete transfer function F (z) to be characterized by formula (3) of transmission function F (s):
R (z) is the z-transform of yaw velocity r,
M (z) is the z-transform of additional yaw moment M, and z indicates the complex variable in discrete transfer function F (z)；
T0For the sampling period；
n0=T0 2(a11a22+a12a21)-2T0(a11+a22)+4；n1=2T0 2(a11a22-a12a21)-8；
n2=T0 2(a11a22-a12a21)+2T0(a11+a22)+4。
Step 1.2: to guarantee system control performance, by ideal side slip angle βdIt is set as 0, i.e. βd=0；It simultaneously will be steady
The response of state yaw velocity is as ideal yaw velocity.The limitation for considering road surface attachment condition, is modified it, utilizes vapour
Vehicle dynamics theoretical calculation obtains the vehicle ideal yaw velocity r at the k+j moment characterized by formula (4)d(k+j):
K is stability factor,
vdIt (k+j) is the speed at k+j moment；σfIt (k+j) is the vehicle front wheel angle at k+j moment；
J=1,2 ... n, n are predetermined period, indicate the k+1 moment with k+1, indicate the subsequent time of k+1, with k+2 with k+j
Indicate the subsequent time of k+j-1；G is acceleration of gravity, and l is vehicle wheelbase, and u is coefficient of road adhesion.
Step 2: carrying out detection using sensor and obtain each real time data, examined in real time including the use of yaw-rate sensor
It surveys and obtains the practical yaw velocity of vehicle, obtain vehicle actual travel speed using vehicle speed sensor real-time detection, utilize steering
Angle transducer real-time detection measures vehicle actual front wheel corner；For each real time data, control block diagram as shown in Figure 2 is utilized
The method of genetic algorithm hybrid optimization GPC, which calculates, obtains optimal additional yaw moment.
Optimal additional yaw moment is obtained in specific implementation according to the following procedure:
Step 2.1: the discrete transfer function F (z) characterized by formula (3) is converted into formula (5):
R(z)(n0+n1z-1+n2z-2)=M (z) (m0+m1z-1+m2z-2) (5)
Step 2.2: according to GPC control algolithm, Diophantine equation is introduced in formula (5), obtains the k+ characterized by formula (6)
The yaw velocity predicted value r at j momente(k+j):
re(k+j)=Fj(z-1)r(k)+Gj(z-1)ΔM(k+j-1) (6)
R (k) is by the detected k moment yaw velocity actual value of yaw-rate sensor；
Fj(z-1) and Gj(z-1) it is multinomial in Diophantine equation；
Δ M (k+j-1) is the increment of the additional yaw moment actual value at k+j-1 moment and k+j-2 moment；
Diophantine equation such as formula (6-1) and (6-2) in the present embodiment
Ej(z-1)(n0+n1z-1+n2z-2)Δ+z-jFj(z-1)=1 (6-1)
Ej(z-1)(m0+m1z-1+m2z-2)=Gj(z-1)+z-jHj(z-1) (6-2)
In formula:
Ej(z-1)=1+e1z-1+...+ej-1z-(j-1), Ej(z-1) it is multinomial, e1...ejFor Ej(z-1) system of polynomials
Number；
Fj(z-1)=f0 j+f1 jz-1+f2 jz-2, Fj(z-1) it is multinomial, f0, f1, f2For Fj(z-1) multinomial coefficient；
Gj(z-1)=g0+g1z-1+...+gj-1z-(j-1), Gj(z-1) it is multinomial, g0...gj-1For Gj(z-1) system of polynomials
Number；
Hj(z-1)=h0 j+h1 jz-1, Hj(z-1) it is multinomial, h0, h1For Hj(z-1) multinomial coefficient；
Δ=1-z-1For difference operator；
According to GPC principle, given prediction period n and control period N, n and N are integer, show that yaw velocity is predicted
The vector form of expression such as formula (7) of value:
R=G Δ M+f (7)
In formula (7):
R=[re(k+1),re(k+2),…,re(k+n)]T；
Δ M=[Δ M (k), Δ M (k+1) ..., Δ M (k+N-1)]T；
F=H Δ M (k)+Fr (k)=[f (k+1), f (k+2) ..., f (k+n)]T；
H1(z-1),H2(z-1)...Hn(z-1) be Diophantine equation in multinomial,
g0,g1,...gn-1For multinomial Gj(z-1) in multinomial coefficient；
Step 2.3: since ideal side slip angle is 0, therefore only considering following for yaw velocity, passed by yaw velocity
Sensor measures the actual value of yaw velocity, and the optimality criterion at k moment takes the actual value containing yaw velocity to expectation
It is worth the quadratic model object function of error and additional yaw moment weighted term, the optimal cost characteristic index function J (k) at k moment is by formula
(8) it is characterized:
In formula (8), λ is control weighting constant,
Convolution (7) and formula (8) obtain the characterization vector form such as formula of additional yaw moment increment using rolling optimizing method
(9):
Δ M=(GTG+λI)-1GT(RD-f) (9)
In formula: RD=[rd(k+1),rd(k+2),…,rd(k+n)]T (10)
I is unit vector；rd(k+1),rd(k+2),...,rdIt (k+n) is to calculate predetermined period n obtained using formula (4)
Each of sampling instant ideal yaw velocity；
Step 2.4: in each of predetermined period n sampling instant, being acquired respectively using sensor and obtain front wheel angle
σfWith speed vd, the ideal yaw velocity r for obtaining each of predetermined period n sampling instant is calculated using formula (4)d(k+
1),rd(k+2),...,rd(k+n), and the R characterized by formula (10) is obtainedD；It recycles Diophantine equation to calculate and obtains Gj(z-1)、H1(z-1),H2(z-1)...Hn(z-1) and Fj(z-1) value；The k for obtaining and being characterized by formula (11) is finally calculated using formula (9)
The value of the additional yaw moment M (k) at moment:
M (k)=M (k-1)+gT(RD-f) (11)
G in formulaTIndicate (GTG+λI)-1GTThe first row vector；
Step 2.5: the value of additional yaw moment M (k) being optimized by genetic algorithm, including initialization of population, choosing
The operation selected, intersect and made a variation obtains optimal additional yaw moment value Mm(k)；The control stream of genetic algorithm hybrid optimization GPC
Journey is as shown in Figure 3.
Step 3: using left and right sides wheel driving force rule distribution method, it is only that optimal additional yaw moment is assigned as four-wheel
The driving force of four wheels of vertical drive hub motor powered automobile, and correspond and act on each wheel.
It specifically includes:
Vehicular four wheels driving moment is characterized with four-wheel drive power relational expression by formula (12):
F1And T1Respectively left front wheel drive force and driving moment, F2And T2Respectively off-front wheel driving force and driving moment,
F3And T2Respectively left back wheel drive force and driving moment, F4And T4Respectively off hind wheel driving force and driving moment；
B is radius of wheel；
Vehicular four wheels driving moment and given target drives torque TobjRelational expression characterized by formula (13):
Tobj=T1+T2+T3+T4 (13)
By the optimal additional yaw moment value Mm(k) it distributes according to the following rules:
It is set in the direction of advance of vehicle, is positive to the left, that is, correspond to positive additional yaw moment Mm(k), then it is to the right
It is negative, corresponding negative additional yaw moment Mm(k)；Set direction disk corner is positive to the left, is negative to the right.Vehicular four wheels driving moment by
Four-wheel drive motor output torque directly provides.
Work as Mm(k)=0 when, vehicle is in neutral steer state, distributes four-wheel drive power such as formula by the principle of mean allocation
(14):
F1=F2=F3=F4 (14)
Work as Mm(k) > 0 when, vehicle is in that left steering is insufficient or right turn transient state, it may be assumed that steering wheel angle to the left when, vehicle
Be in left steering deficiency state；Or steering wheel angle to the right when, vehicle is in right turn transient state, should suitably reduce at this time
Left side wheel driving moment increases right side wheels driving moment, i.e. left side wheel reduces 1/4 additional yaw moment, right side wheels
Increase by 1/4 additional yaw moment, convolution (12)-formula (14) obtains the four-wheel drive power redistributed characterized by formula (15)
Square:
Work as Mm(k) < 0 when, vehicle is in that right turn is insufficient or left steering transient state, it may be assumed that steering wheel angle to the left when, vehicle
Be in left steering transient state；Or steering wheel angle to the right when, vehicle is in right turn deficiency state, should suitably increase at this time
Left side wheel driving moment reduces right side wheels driving moment, i.e. left side wheel increases by 1/4 additional yaw moment, right side wheels
Reduce by 1/4 additional yaw moment, convolution (12)-formula (14) obtains the four-wheel drive power redistributed characterized by formula (16)
Square:
By optimal additional yaw moment value Mm(k) four vehicles of four motorized wheels hub motor electric car are assigned as
The driving force of wheel, one-to-one correspondence act on each wheel.
Claims (4)
1. a kind of vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC, it is characterized in that as follows
It carries out:
Step 1: establishing vehicle two degrees of freedom linear model as prediction model, calculated using the prediction model and obtain ideal cross
Pivot angle speed and ideal side slip angle；
Step 2: carrying out detection using sensor and obtain each real time data, obtained including the use of yaw-rate sensor real-time detection
The practical yaw velocity of vehicle is obtained, vehicle actual travel speed is obtained using vehicle speed sensor real-time detection, is passed using steering angle
Sensor real-time detection measures vehicle actual front wheel corner, utilizes genetic algorithm hybrid optimization GPC's for each real time data
Method, which calculates, obtains optimal additional yaw moment；
Step 3: using left and right sides wheel driving force rule distribution method, it is only that the optimal additional yaw moment is assigned as four-wheel
The driving force of four wheels of vertical drive hub motor powered automobile, and correspond and act on each wheel.
2. the vehicle yaw stability control method according to claim 1 based on genetic algorithm hybrid optimization GPC, special
Sign is:
In the step 1, calculates obtain ideal yaw velocity and ideal side slip angle according to the following procedure:
Step 1.1: two freedom degrees of lateral movement and weaving based on automobile establish the vehicle characterized by formula (1)
Two degrees of freedom linear model:
β is side slip angle,For side slip angle speed, r is yaw velocity,For sideway angular acceleration；
l1For the distance of mass center to front axle, l2For the distance of mass center to rear axle；k1For front-wheel cornering stiffness, k2It is rigid for rear-wheel lateral deviation
Degree；
IzFor rotary inertia, m is complete vehicle quality, σfFor front wheel angle, vdFor speed, M is additional yaw moment；
Enable front wheel angle σf=0, the transmission function of the vehicle two degrees of freedom linear model characterized by formula (2) is obtained according to formula (1)
F (s):
R (s) is the Laplace transform of yaw velocity r,
M (s) is the Laplace transform of additional yaw moment M, and s indicates the complex variable in transmission function F (s)；
Using bilinear transformation, by the discrete discrete transfer function F (z) to be characterized by formula (3) of transmission function F (s):
R (z) is the z-transform of yaw velocity r,
M (z) is the z-transform of additional yaw moment M, and z indicates the complex variable in discrete transfer function F (z)；
T0For the sampling period；
n0=T0 2(a11a22+a12a21)-2T0(a11+a22)+4；n1=2T0 2(a11a22-a12a21)-8；
n2=T0 2(a11a22-a12a21)+2T0(a11+a22)+4；
Step 1.2: by ideal side slip angle βdIt is set as 0, i.e. βd=0；It is obtained using automobile dynamics theoretical calculation by formula
(4) the vehicle ideal yaw velocity r at the k+j moment characterizedd(k+j):
K is stability factor,
vdIt (k+j) is the speed at k+j moment；σfIt (k+j) is the vehicle front wheel angle at k+j moment；
J=1,2 ... n, n are predetermined period, indicate the k+1 moment with k+1, the subsequent time of k+1 is indicated with k+2, indicate k with k+j
The subsequent time of+j-1；G is acceleration of gravity, and l is vehicle wheelbase, and u is coefficient of road adhesion.
3. the vehicle yaw stability control method according to claim 2 based on genetic algorithm hybrid optimization GPC, special
Sign is: the step 2 is to obtain optimal additional yaw moment according to the following procedure:
Step 2.1: the discrete transfer function F (z) characterized by formula (3) is converted into formula (5):
R(z)(n0+n1z-1+n2z-2)=M (z) (m0+m1z-1+m2z-2) (5)
Step 2.2: according to GPC control algolithm, Diophantine equation is introduced in formula (5), when obtaining the k+j characterized by formula (6)
The yaw velocity predicted value r at quartere(k+j):
re(k+j)=Fj(z-1)r(k)+Gj(z-1)ΔM(k+j-1) (6)
R (k) is by the detected k moment yaw velocity actual value of yaw-rate sensor；
Fj(z-1) and Gj(z-1) it is multinomial in Diophantine equation；
Δ M (k+j-1) is the increment of the additional yaw moment actual value at k+j-1 moment and k+j-2 moment；
According to GPC principle, given prediction period n and control period N, n and N are integer, obtain yaw velocity predicted value
The vector form of expression such as formula (7):
R=G Δ M+f (7)
In formula (7):
R=[re(k+1),re(k+2),…,re(k+n)]T；
Δ M=[Δ M (k), Δ M (k+1) ..., Δ M (k+N-1)]T；
F=H Δ M (k)+Fr (k)=[f (k+1), f (k+2) ..., f (k+n)]T；
H1(z-1),H2(z-1)...Hn(z-1) be Diophantine equation in multinomial,
g0,g1,...gn-1For multinomial Gj(z-1) in multinomial coefficient；
The optimal cost characteristic index function J (k) at step 2.3:k moment is characterized by formula (8):
In formula (8), λ is control weighting constant,
Convolution (7) and formula (8) obtain the characterization vector form such as formula (9) of additional yaw moment increment using rolling optimizing method:
Δ M=(GTG+λI)-1GT(RD-f) (9)
In formula: RD=[rd(k+1),rd(k+2),…,rd(k+n)]T (10)
I is unit vector；rd(k+1),rd(k+2),...,rdIt (k+n) is calculated in predetermined period n obtained using formula (4)
The ideal yaw velocity of each sampling instant；
Step 2.4: in each of predetermined period n sampling instant, being acquired respectively using sensor and obtain front wheel angle σfAnd vehicle
Fast vd, the ideal yaw velocity r for obtaining each of predetermined period n sampling instant is calculated using formula (4)d(k+1),rd(k+
2),...,rd(k+n), and the R characterized by formula (10) is obtainedD；It recycles Diophantine equation to calculate and obtains Gj(z-1)、H1(z-1),
H2(z-1)...Hn(z-1) and Fj(z-1) value；It is finally calculated using formula (9) and obtains the additional of the k moment characterized by formula (11)
The value of yaw moment M (k):
M (k)=M (k-1)+gT(RD-f) (11)
In formula (11), gTIndicate (GTG+λI)-1GTThe first row vector；
Step 2.5: the value of additional yaw moment M (k) is optimized by genetic algorithm to get optimal additional sideway power is arrived
Square value Mm(k)。
4. the vehicle yaw stability control method according to claim 3 based on genetic algorithm hybrid optimization GPC, special
Sign is: the step 3 specifically includes:
Vehicular four wheels driving moment is characterized with four-wheel drive power relational expression by formula (12):
F1And T1Respectively left front wheel drive force and driving moment, F2And T2Respectively off-front wheel driving force and driving moment,
F3And T2Respectively left back wheel drive force and driving moment, F4And T4Respectively off hind wheel driving force and driving moment；
B is radius of wheel；
Vehicular four wheels driving moment and given target drives torque TobjRelational expression characterized by formula (13):
Tobj=T1+T2+T3+T4 (13)
By the optimal additional yaw moment value Mm(k) it distributes according to the following rules:
Work as Mm(k)=0 when, vehicle is in neutral steer state, distributes four-wheel drive power such as formula (14) by the principle of mean allocation:
F1=F2=F3=F4 (14)
Work as Mm(k) > 0 when, vehicle is in left steering deficiency or right turn transient state, and convolution (12)-formula (14) is obtained by formula
(15) the four-wheel drive torque redistributed characterized:
Work as Mm(k) < 0 when, vehicle is in right turn deficiency or left steering transient state, and convolution (12)-formula (14) is obtained by formula
(16) the four-wheel drive torque redistributed characterized:
By the optimal additional yaw moment value Mm(k) four vehicles of four motorized wheels hub motor electric car are assigned as
The driving force of wheel, one-to-one correspondence act on each wheel.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811615282.0A CN109849898B (en) | 2018-12-27 | 2018-12-27 | Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811615282.0A CN109849898B (en) | 2018-12-27 | 2018-12-27 | Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109849898A true CN109849898A (en) | 2019-06-07 |
| CN109849898B CN109849898B (en) | 2020-06-26 |
Family
ID=66892696
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811615282.0A Active CN109849898B (en) | 2018-12-27 | 2018-12-27 | Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109849898B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110371106A (en) * | 2019-07-04 | 2019-10-25 | 辽宁工业大学 | A kind of steering stability method based on four motorized wheels electric car |
| CN111055837A (en) * | 2019-11-28 | 2020-04-24 | 广东工业大学 | Heuristic algorithm-based vehicle lateral stability control method |
| CN112659921A (en) * | 2020-12-14 | 2021-04-16 | 北京航空航天大学 | Design method of independent drive electric vehicle optimization controller based on genetic algorithm |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5735584A (en) * | 1994-11-25 | 1998-04-07 | Itt Automotive Europe Gmbh | Process for driving stability control with control via pressure gradients |
| JP2006021589A (en) * | 2004-07-07 | 2006-01-26 | Toyota Motor Corp | Vehicle steering control device |
| CN102616223A (en) * | 2011-01-28 | 2012-08-01 | 比亚迪股份有限公司 | Automobile stability control method and automobile stability control system |
| CN104787039A (en) * | 2015-04-13 | 2015-07-22 | 电子科技大学 | Car body stable control method of four-wheel independent drive electric car |
| CN105253141A (en) * | 2015-09-09 | 2016-01-20 | 北京理工大学 | Wheel longitudinal force regulation-based vehicle handling stability control method |
| CN105946858A (en) * | 2016-06-08 | 2016-09-21 | 吉林大学 | Method for optimizing parameters of four-driving electric car state observer based on genetic algorithm |
| CN107042841A (en) * | 2016-12-14 | 2017-08-15 | 合肥工业大学 | A kind of differential power-assisted steering stability control method of In-wheel motor driving electric automobile |
| CN107054453A (en) * | 2017-04-28 | 2017-08-18 | 南京航空航天大学 | A kind of motor turning stabilitrak and its control method |
-
2018
- 2018-12-27 CN CN201811615282.0A patent/CN109849898B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5735584A (en) * | 1994-11-25 | 1998-04-07 | Itt Automotive Europe Gmbh | Process for driving stability control with control via pressure gradients |
| JP2006021589A (en) * | 2004-07-07 | 2006-01-26 | Toyota Motor Corp | Vehicle steering control device |
| CN102616223A (en) * | 2011-01-28 | 2012-08-01 | 比亚迪股份有限公司 | Automobile stability control method and automobile stability control system |
| CN104787039A (en) * | 2015-04-13 | 2015-07-22 | 电子科技大学 | Car body stable control method of four-wheel independent drive electric car |
| CN105253141A (en) * | 2015-09-09 | 2016-01-20 | 北京理工大学 | Wheel longitudinal force regulation-based vehicle handling stability control method |
| CN105946858A (en) * | 2016-06-08 | 2016-09-21 | 吉林大学 | Method for optimizing parameters of four-driving electric car state observer based on genetic algorithm |
| CN107042841A (en) * | 2016-12-14 | 2017-08-15 | 合肥工业大学 | A kind of differential power-assisted steering stability control method of In-wheel motor driving electric automobile |
| CN107054453A (en) * | 2017-04-28 | 2017-08-18 | 南京航空航天大学 | A kind of motor turning stabilitrak and its control method |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110371106A (en) * | 2019-07-04 | 2019-10-25 | 辽宁工业大学 | A kind of steering stability method based on four motorized wheels electric car |
| CN111055837A (en) * | 2019-11-28 | 2020-04-24 | 广东工业大学 | Heuristic algorithm-based vehicle lateral stability control method |
| CN112659921A (en) * | 2020-12-14 | 2021-04-16 | 北京航空航天大学 | Design method of independent drive electric vehicle optimization controller based on genetic algorithm |
| CN112659921B (en) * | 2020-12-14 | 2022-11-22 | 北京航空航天大学 | A Genetic Algorithm Based Optimal Controller Design Method for Independently Driven Electric Vehicles |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109849898B (en) | 2020-06-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11364895B2 (en) | Yaw motion control method for four-wheel distributed vehicle | |
| CN109522666B (en) | Distributed electric automobile stability control method | |
| Pérez et al. | Cascade architecture for lateral control in autonomous vehicles | |
| CN111391822B (en) | A collaborative control method for vehicle lateral and longitudinal stability under extreme working conditions | |
| CN103057436B (en) | Yawing moment control method of individual driven electromobile based on multi-agent | |
| CN102303602B (en) | Coordination method and control device for ride comfort and handling stability of passenger car | |
| Xu et al. | Model predictive control for lane keeping system in autonomous vehicle | |
| CN111796522B (en) | Vehicle state estimation method | |
| CN109849898A (en) | Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC | |
| CN113650621B (en) | State Parameter Estimation Method for Distributed Drive Electric Vehicles for Complex Operating Conditions | |
| CN114148403B (en) | Multi-working-condition stability control method for wire-controlled steering system | |
| CN112829766A (en) | An Adaptive Path Tracking Method Based on Distributed Drive Electric Vehicles | |
| CN113119753B (en) | A Distributed Electric Drive Vehicle Differential Steering Adaptive Control Method | |
| CN117485324A (en) | Four-wheel distributed driving electric vehicle tire force vector control method | |
| Li et al. | Adaptive sliding mode control of lateral stability of four wheel hub electric vehicles | |
| CN114637292A (en) | A Robust Control Method and System for Vehicle Trajectory Tracking Considering Obstacle Avoidance | |
| CN112346337A (en) | Vehicle stability control method based on active rear wheel steering under extreme conditions | |
| CN115848162B (en) | Control method for six-wheel independent electrically-driven unmanned vehicle differential steering | |
| CN117622125A (en) | Path tracking control method based on differential steering | |
| CN113044047B (en) | A kind of AFS/DYC integrated control method based on PID-STSM | |
| CN113602278B (en) | Distributed model predictive path tracking control method for four-wheel independent drive electric vehicles | |
| CN114194035B (en) | Balanced arm type six-wheel independent drive intelligent chassis torque distribution method | |
| CN114148411B (en) | A drift control method for a wheeled unmanned platform | |
| Tang et al. | Tracking control for autonomous four-wheel independently driven vehicle based on deep reinforcement learning | |
| CN112835292B (en) | Transverse control method of automatic driving electric vehicle on rugged road |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |