CN109849898B - 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
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Abstract
The invention discloses a vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC, which comprises the steps of establishing a vehicle two-degree-of-freedom linear model as a prediction model, and calculating by using the prediction model to obtain an ideal yaw angular velocity and an ideal centroid slip angle; detecting by using a sensor to obtain each real-time data, and calculating by using a genetic algorithm hybrid optimization GPC (phase change memory) method aiming at each real-time data to obtain an optimal additional yaw moment; the optimal additional yaw moment is distributed into the driving forces of four wheels of the four-wheel independent driving hub motor electric automobile by adopting a method for regularly distributing the driving forces of the left and right wheels, and the driving forces act on the wheels one by one. Compared with the common generalized prediction algorithm, the algorithm provided by the invention has the advantages that the genetic algorithm is introduced to carry out hybrid optimization in the rolling optimization process of GPC, so that the algorithm has stronger global search capability and global convergence, the required additional yaw moment is subjected to hybrid optimization, and the optimal solution precision is greatly improved.
Description
Technical Field
The invention relates to the field of safe auxiliary driving and intelligent control, in particular to a method for controlling the stability of an electric automobile with four-wheel independent driving hub motors.
Background
The operation stability of the automobile is an important performance influencing the high-speed safe driving of the automobile, when the automobile encounters the interference of external factors (such as lateral wind), road surface separation driving, high-speed emergency obstacle avoidance and the like, the automobile deviates from an ideal vehicle operation characteristic, and in severe cases, a driver loses the control on the automobile and is in a dangerous situation, and a Yaw Stability Control (YSC) system can correct the automobile to the ideal operation characteristic and control the automobile from an unstable area to a stable area. The yaw stability control system has gained wide acceptance in society as an active safety device with great potential for development.
Currently, various research institutions have conducted a great deal of research on yaw stability control, and common methods are optimal control, such as LQR and LQG, but these control methods all need accurate control models, while an automobile is a complex nonlinear system, and the models often need to be simplified in engineering use, so that the accuracy of the models is difficult to guarantee; in addition, when the vehicle is running, parameters and environment have great uncertainty, so that the optimal control obtained according to an ideal model cannot be kept optimal in practice, and sometimes, the quality is even seriously reduced.
Disclosure of Invention
The invention provides a vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC for avoiding the defects in the prior art, and the method is used for performing hybrid optimization on the obtained additional yaw moment and improving the optimal solution precision.
The invention adopts the following technical scheme for solving the technical problems:
the invention discloses a vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC, which is characterized by comprising the following steps of:
step 1: establishing a two-degree-of-freedom linear model of the vehicle as a prediction model, and calculating by using the prediction model to obtain an ideal yaw angular velocity and an ideal centroid slip angle;
step 2: the method comprises the steps that a sensor is used for detecting to obtain each piece of real-time data, the real-time data comprise the steps that a yaw velocity sensor is used for detecting in real time to obtain the actual yaw velocity of a vehicle, a vehicle speed sensor is used for detecting in real time to obtain the actual running velocity of the vehicle, a steering angle sensor is used for detecting in real time to obtain the actual front wheel turning angle of the vehicle, and the optimal additional yaw moment is calculated and obtained by aiming at each piece of real-time data through a genetic algorithm hybrid;
and step 3: and distributing the optimal additional yaw moment into the driving forces of four wheels of the four-wheel independent drive hub motor electric automobile by adopting a regular distribution method of the driving forces of the left and right wheels, and acting on each wheel in a one-to-one correspondence manner.
The vehicle yaw stability control method based on the genetic algorithm hybrid optimization GPC is also characterized in that:
in the step 1, the ideal yaw rate and the ideal centroid slip angle are obtained by the following calculation:
step 1.1: establishing a two-degree-of-freedom linear model of the vehicle characterized by equation (1) based on two degrees of freedom of lateral motion and yaw motion of the vehicle:
β is the side slip angle of the mass center,
is the centroid yaw angular velocity, r is the yaw angular velocity,
yaw angular acceleration;
l1is the distance of the center of mass to the front axis,/2Is the distance from the center of mass to the rear axis; k is a radical of1For front wheel cornering stiffness, k2Is rear wheel cornering stiffness;
Izis moment of inertia, m is the mass of the whole vehicle, σfIs the angle of rotation of the front wheel, vdThe vehicle speed, M is the additional yaw moment;
make the front wheel turn angle sigmafObtaining a transfer function F(s) of a two-degree-of-freedom linear model of the vehicle characterized by equation (2) according to equation (1):
r(s) is the Laplace transform of the yaw rate R,
m(s) is the laplace transform of the additional yaw moment M, s representing the complex variable in the transfer function F(s);
discretizing the transfer function F(s) into a discrete transfer function F (z) characterized by equation (3) using a bilinear transformation:
r (z) is a z-transform of the yaw rate R,
m (z) is the z-transform of the additional yaw moment M, z representing the complex variable in the discrete transfer function F (z);
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, the ideal centroid side deflection angle beta is calculateddis set to 0, i.e. βd0; calculating the ideal yaw rate r of the vehicle at the k + j moment represented by the formula (4) by utilizing an automobile dynamics theoryd(k+j)：
K is a factor of stability,
vd(k + j) is the vehicle speed at time k + j; sigmaf(k + j) is the vehicle front wheel steering angle at time k + j;
j is 1,2, … n, n is a prediction period, k +1 represents the time k +1, k +2 represents the next time of k +1, and k + j represents the next time of k + j-1; g is the gravity acceleration, l is the vehicle wheelbase, and u is the road adhesion coefficient.
The vehicle yaw stability control method based on the genetic algorithm hybrid optimization GPC is also characterized in that: the step 2 is to obtain the optimal additional yaw moment according to the following processes:
step 2.1: converting a discrete transfer function F (z) characterized by equation (3) to equation (5):
R(z)(n0+n1z-1+n2z-2)＝M(z)(m0+m1z-1+m2z-2) (5)
step 2.2: according to a GPC control algorithm, introducing a loss map equation into the equation (5) to obtain a predicted value r of the yaw rate at the moment k + j represented by the equation (6)e(k+j)：
re(k+j)＝Fj(z-1)r(k)+Gj(z-1)ΔM(k+j-1) (6)
r (k) is an actual value of yaw rate at the time k detected by the yaw rate sensor;
Fj(z-1) And Gj(z-1) Are all polynomials in the chartlet equation;
delta M (k + j-1) is the increment of the actual value of the additional yaw moment at the moment k + j-1 and the moment k + j-2;
according to the GPC principle, given that a prediction period N and a control period N are integers, the vector expression form of the predicted value of the yaw rate is as shown in formula (7):
R＝GΔM+f (7)
in formula (7):
R＝[re(k+1),re(k+2),…,re(k+n)]T；
ΔM＝[ΔM(k),ΔM(k+1),…,ΔM(k+N-1)]T；
f＝HΔM(k)+Fr(k)＝[f(k+1),f(k+2),…,f(k+n)]T；
H1(z-1),H2(z-1)...Hn(z-1) To de-plot the polynomials in the equation,
g0,g1,...gn-1is a polynomial Gj(z-1) Polynomial coefficient of (5);
step 2.3: the optimized performance indicator function J (k) at time k is characterized by equation (8):
in the formula (8), λ is a control weight constant,
and combining the formula (7) and the formula (8), and obtaining the vector representation form of the additional yaw moment increment by using a rolling optimization method according to the formula (9):
ΔM＝(GTG+λI)-1GT(RD-f) (9)
in the formula: rD＝[rd(k+1),rd(k+2),…,rd(k+n)]T(10)
I is a unit vector; r isd(k+1),rd(k+2),...,rd(k + n) is an ideal yaw rate at each sampling time in the prediction period n obtained by calculation using equation (4);
step 2.4: at each sampling moment in the prediction period n, respectively acquiring and obtaining the front wheel rotation angle sigma by using a sensorfAnd vehicle speed vdThe ideal yaw rate r at each sampling instant in the prediction period n is obtained by calculation using equation (4)d(k+1),rd(k+2),...,rd(k + n) and obtaining R characterized by formula (10)D(ii) a Then G is obtained by calculation by utilizing the equation of the loss of the patternj(z-1)、H1(z-1),H2(z-1)...Hn(z-1) And Fj(z-1) A value of (d); finally, the value of the additional yaw moment M (k) at the time k, which is characterized by equation (11), is obtained by calculation using equation (9):
M(k)＝M(k-1)+gT(RD-f) (11)
in formula (11), gTIs represented by (G)TG+λI)-1GTA first row vector of (a);
step 2.5: optimizing the value of the additional yaw moment M (k) through a genetic algorithm to obtain the optimal additional yaw moment value Mm(k)。
The vehicle yaw stability control method based on the genetic algorithm hybrid optimization GPC is also characterized in that: the step 3 specifically includes:
the vehicle four-wheel drive torque-four-wheel drive force relationship is characterized by equation (12):
F1and T1Left front wheel drive force and drive torque, F2And T2Respectively a right front wheel driving force and a driving torque,
F3and T2Driving force and torque of the left rear wheel, F4And T4Respectively is a right rear wheel driving force and a driving moment;
b is the radius of the wheel;
vehicle four-wheel drive torque and given target drive torque TobjIs characterized by equation (13):
Tobj＝T1+T2+T3+T4(13)
setting the optimal additional yaw moment value Mm(k) The following rules are assigned:
when M ism(k) When the vehicle is in a neutral steering state, the four-wheel driving force is distributed according to the average distribution principle as shown in the formula (14):
F1＝F2＝F3＝F4(14)
when M ism(k)>At 0, the vehicle is in a left understeer or right oversteer condition, and the redistributed four-wheel drive torque characterized by equation (15) is obtained by combining equation (12) -equation (14):
when M ism(k)<At 0, the vehicle is in an understeer or oversteer right state, and the redistributed four-wheel drive torque characterized by equation (16) is obtained by combining equation (12) -equation (14):
setting the optimal additional yaw moment value Mm(k) Four wheels of electric automobile with hub motor driven by four wheels independentlyThe driving forces of (a) are applied to the respective wheels in a one-to-one correspondence.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention introduces the genetic algorithm to carry out the hybrid optimization method in the rolling optimization process of GPC, so that the algorithm has stronger global search capability and global convergence, and carries out the hybrid optimization on the obtained additional yaw moment, thereby greatly improving the accuracy of the optimal solution.
2. The genetic algorithm hybrid optimization GPC of the invention combines multi-step prediction and self-adaptation, so that the method is more suitable for uncertain, time-varying, time-lag and other process objects.
Drawings
FIG. 1 is a two degree of freedom linear model of a vehicle in the method of the present invention;
FIG. 2 is a control block diagram of genetic algorithm hybrid optimized GPC in the method of the present invention;
FIG. 3 is a control flow diagram of genetic algorithm hybrid optimized GPC in the method of the present invention;
Detailed Description
The vehicle yaw stability control method based on the genetic algorithm hybrid optimization GPC in the embodiment is carried out as follows:
step 1: a two-degree-of-freedom linear model of the vehicle shown in figure 1 is established as a prediction model, and an ideal yaw angular velocity and an ideal centroid slip angle are calculated and obtained by using the prediction model.
In the specific implementation, the ideal yaw angular velocity and the ideal centroid slip angle are obtained by calculation according to the following processes:
step 1.1: establishing a two-degree-of-freedom linear model of the vehicle characterized by equation (1) based on two degrees of freedom of lateral motion and yaw motion of the vehicle:
β is the side slip angle of the mass center,
is the side-slip angular velocity of the mass center,r is the yaw rate of the vehicle,
yaw angular acceleration;
l1is the distance of the center of mass to the front axis,/2Is the distance from the center of mass to the rear axis; k is a radical of1For front wheel cornering stiffness, k2Is rear wheel cornering stiffness;
Izis moment of inertia, m is the mass of the whole vehicle, σfIs the angle of rotation of the front wheel, vdThe vehicle speed, M is the additional yaw moment;
make the front wheel turn angle sigmafObtaining a transfer function F(s) of a two-degree-of-freedom linear model of the vehicle characterized by equation (2) according to equation (1):
r(s) is the Laplace transform of the yaw rate R,
m(s) is the laplace transform of the additional yaw moment M, s representing the complex variable in the transfer function F(s);
discretizing the transfer function F(s) into a discrete transfer function F (z) characterized by equation (3) using a bilinear transformation:
r (z) is a z-transform of the yaw rate R,
m (z) is the z-transform of the additional yaw moment M, z representing the complex variable in the discrete transfer function F (z);
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, in order to ensure the control performance of the system, the ideal mass center slip angle beta is setdis set to 0, i.e. βd0; while the steady-state yaw-rate response is taken as the ideal yaw-rate. The ideal yaw rate r of the vehicle at the time k + j represented by the formula (4) is obtained by calculation using the theory of vehicle dynamics by correcting the road adhesion condition in consideration of the restriction thereofd(k+j)：
K is a factor of stability,
vd(k + j) is the vehicle speed at time k + j; sigmaf(k + j) is the vehicle front wheel steering angle at time k + j;
j is 1,2, … n, n is a prediction period, k +1 represents the time k +1, k +2 represents the next time of k +1, and k + j represents the next time of k + j-1; g is the gravity acceleration, l is the vehicle wheelbase, and u is the road adhesion coefficient.
Step 2: the method comprises the steps that a sensor is used for detecting to obtain each real-time data, including the steps that a yaw velocity sensor is used for detecting in real time to obtain the actual yaw velocity of a vehicle, a vehicle speed sensor is used for detecting in real time to obtain the actual running velocity of the vehicle, and a steering angle sensor is used for detecting in real time to obtain the actual front wheel turning angle of the vehicle; for each real-time data, an optimal additional yaw moment is calculated by using a genetic algorithm hybrid optimization GPC method according to a control block diagram shown in FIG. 2.
In the specific implementation, the optimal additional yaw moment is obtained according to the following processes:
step 2.1: converting a discrete transfer function F (z) characterized by equation (3) to equation (5):
R(z)(n0+n1z-1+n2z-2)＝M(z)(m0+m1z-1+m2z-2) (5)
step 2.2: according to a GPC control algorithm, introducing a loss map equation into the equation (5) to obtain a predicted value r of the yaw rate at the moment k + j represented by the equation (6)e(k+j)：
re(k+j)＝Fj(z-1)r(k)+Gj(z-1)ΔM(k+j-1) (6)
r (k) is an actual value of yaw rate at the time k detected by the yaw rate sensor;
Fj(z-1) And Gj(z-1) Are all polynomials in the chartlet equation;
delta M (k + j-1) is the increment of the actual value of the additional yaw moment at the moment k + j-1 and the moment k + j-2;
the expression of the charpy in this embodiment is as shown in formulas (6-1) and (6-2)
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 the formula:
Ej(z-1)＝1+e1z-1+...+ej-1z-(j-1)，Ej(z-1) Is a polynomial of e1...ejIs Ej(z-1) A polynomial coefficient of (d);
Fj(z-1)＝f0 j+f1 jz-1+f2 jz-2，Fj(z-1) Is a polynomial of f0，f1，f2Is Fj(z-1) A polynomial coefficient of (d);
Gj(z-1)＝g0+g1z-1+...+gj-1z-(j-1)，Gj(z-1) Is a polynomial of g0...gj-1Is Gj(z-1) A polynomial coefficient of (d);
Hj(z-1)＝h0 j+h1 jz-1，Hj(z-1) Is a polynomial of h0，h1Is Hj(z-1) A polynomial coefficient of (d);
Δ＝1-z-1is a difference operator;
according to the GPC principle, given that a prediction period N and a control period N are integers, the vector expression form of the predicted value of the yaw rate is as shown in formula (7):
R＝GΔM+f (7)
in formula (7):
R＝[re(k+1),re(k+2),…,re(k+n)]T；
ΔM＝[ΔM(k),ΔM(k+1),…,ΔM(k+N-1)]T；
f＝HΔM(k)+Fr(k)＝[f(k+1),f(k+2),…,f(k+n)]T；
H1(z-1),H2(z-1)...Hn(z-1) To de-plot the polynomials in the equation,
g0,g1,...gn-1is a polynomial Gj(z-1) Polynomial coefficient of (5);
step 2.3: since the ideal centroid slip angle is 0, only the following of the yaw velocity is considered, the actual value of the yaw velocity is measured by the yaw velocity sensor, the optimization performance index at the time k is a quadratic objective function containing the error of the actual value of the yaw velocity to the expected value and an additional yaw moment weighting term, and the optimization performance index function J (k) at the time k is characterized by the equation (8):
in the formula (8), λ is a control weight constant,
and combining the formula (7) and the formula (8), and obtaining the vector representation form of the additional yaw moment increment by using a rolling optimization method according to the formula (9):
ΔM＝(GTG+λI)-1GT(RD-f) (9)
in the formula: rD＝[rd(k+1),rd(k+2),…,rd(k+n)]T(10)
I is a unit vector; r isd(k+1),rd(k+2),...,rd(k + n) is an ideal yaw rate at each sampling time in the prediction period n obtained by calculation using equation (4);
step 2.4: at each sampling moment in the prediction period n, respectively acquiring and obtaining the front wheel rotation angle sigma by using a sensorfAnd vehicle speed vdThe ideal yaw rate r at each sampling instant in the prediction period n is obtained by calculation using equation (4)d(k+1),rd(k+2),...,rd(k + n) and obtaining R characterized by formula (10)D(ii) a Then G is obtained by calculation by utilizing the equation of the loss of the patternj(z-1)、H1(z-1),H2(z-1)...Hn(z-1) And Fj(z-1) A value of (d); finally, the value of the additional yaw moment M (k) at the time k, which is characterized by equation (11), is obtained by calculation using equation (9):
M(k)＝M(k-1)+gT(RD-f) (11)
in the formula gTIs represented by (G)TG+λI)-1GTA first row vector of (a);
step 2.5: optimizing the value of the additional yaw moment M (k) through a genetic algorithm, including population initialization, selection, intersection and mutation operations to obtain the optimal additional yaw moment value Mm(k) (ii) a Genetic algorithm mixtureThe control flow for co-optimized GPC is shown in fig. 3.
And step 3: the optimal additional yaw moment is distributed into the driving forces of four wheels of the four-wheel independent driving hub motor electric automobile by adopting a method for regularly distributing the driving forces of the left and right wheels, and the driving forces act on the wheels one by one.
The method specifically comprises the following steps:
the vehicle four-wheel drive torque-four-wheel drive force relationship is characterized by equation (12):
F1and T1Left front wheel drive force and drive torque, F2And T2Respectively a right front wheel driving force and a driving torque,
F3and T2Driving force and torque of the left rear wheel, F4And T4Respectively is a right rear wheel driving force and a driving moment;
b is the radius of the wheel;
vehicle four-wheel drive torque and given target drive torque TobjIs characterized by equation (13):
Tobj＝T1+T2+T3+T4(13)
setting the optimal additional yaw moment value Mm(k) The following rules are assigned:
set positive to the left in the direction of travel of the vehicle, i.e. corresponding to a positive additional yaw moment Mm(k) Then negative to the right, corresponding to a negative additional yaw moment Mm(k) (ii) a The steering wheel angle is set to be positive to the left and negative to the right. The vehicle four-wheel drive torque is directly provided by the four-wheel drive motor output torque.
When M ism(k) When the vehicle is in a neutral steering state, the four-wheel driving force is distributed according to the average distribution principle as shown in the formula (14):
F1＝F2＝F3＝F4(14)
when M ism(k)>At 0, the vehicle is in a left understeer or right oversteer condition, i.e.: when the steering wheel turns left, the vehicle is in a left understeer state; or the steering wheel angle to the right, the vehicle is in a right-hand oversteer condition, in which the left-hand wheel drive torque is appropriately decreased and the right-hand wheel drive torque is increased, i.e., the left-hand wheel is decreased by 1/4 of the additional yaw moment and the right-hand wheel is increased by 1/4 of the additional yaw moment, and the redistributed four-wheel drive torque represented by equation (15) is obtained by combining equation (12) -equation (14):
when M ism(k)<At 0, the vehicle is in an understeer or oversteer right state, i.e.: when the steering wheel turns to the left, the vehicle is in a left oversteer state; or the steering wheel angle to the right, the vehicle is in an understeer right state, when the left wheel drive torque is appropriately increased and the right wheel drive torque is decreased, i.e., the left wheel is increased by 1/4 of the additional yaw moment and the right wheel is decreased by 1/4 of the additional yaw moment, the redistributed four-wheel drive torque represented by equation (16) is obtained by combining equation (12) -equation (14):
will optimize the additional yaw moment value Mm(k) The driving force of four wheels of the electric automobile with the four-wheel independent driving hub motor is distributed and acts on each wheel in a one-to-one correspondence mode.
Claims (2)
1. A vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC is characterized by comprising the following steps:
step 1: establishing a two-degree-of-freedom linear model of the vehicle as a prediction model, and calculating by using the prediction model according to the following process to obtain an ideal yaw angular velocity and an ideal centroid slip angle;
step 1.1: establishing a two-degree-of-freedom linear model of the vehicle characterized by equation (1) based on two degrees of freedom of lateral motion and yaw motion of the vehicle:
β is the side slip angle of the mass center,
is the centroid yaw angular velocity, r is the yaw angular velocity,
yaw angular acceleration;
l1is the distance of the center of mass to the front axis,/2Is the distance from the center of mass to the rear axis; k is a radical of1For front wheel cornering stiffness, k2Is rear wheel cornering stiffness;
Izis moment of inertia, m is the mass of the whole vehicle, σfIs the angle of rotation of the front wheel, vdThe vehicle speed, M is the additional yaw moment;
make the front wheel turn angle sigmafObtaining a transfer function F(s) of a two-degree-of-freedom linear model of the vehicle characterized by equation (2) according to equation (1):
r(s) is the Laplace transform of the yaw rate R,
m(s) is the laplace transform of the additional yaw moment M, s representing the complex variable in the transfer function F(s);
discretizing the transfer function F(s) into a discrete transfer function F (z) characterized by equation (3) using a bilinear transformation:
r (z) is a z-transform of the yaw rate R,
m (z) is the z-transform of the additional yaw moment M, z representing the complex variable in the discrete transfer function F (z);
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, the ideal centroid side deflection angle beta is calculateddis set to 0, i.e. βd0; calculating the ideal yaw rate r of the vehicle at the k + j moment represented by the formula (4) by utilizing an automobile dynamics theoryd(k+j)：
K is a factor of stability,
vd(k + j) is the vehicle speed at time k + j; sigmaf(k + j) is the vehicle front wheel steering angle at time k + j;
j is 1,2, … n, n is a prediction period, k +1 represents the time k +1, k +2 represents the next time of k +1, and k + j represents the next time of k + j-1; g is the gravity acceleration, l is the vehicle wheelbase, and u is the road surface adhesion coefficient;
step 2: the method comprises the following steps of utilizing a sensor to detect and obtain each real-time data, wherein the real-time data comprises the steps of utilizing a yaw velocity sensor to detect and obtain the actual yaw velocity of a vehicle in real time, utilizing a vehicle speed sensor to detect and obtain the actual running velocity of the vehicle in real time, utilizing a steering angle sensor to detect and obtain the actual front wheel turning angle of the vehicle in real time, and aiming at each real-time data, utilizing a genetic algorithm to optimize a GPC method to calculate and obtain the optimal additional yaw moment according to the following processes:
step 2.1: converting a discrete transfer function F (z) characterized by equation (3) to equation (5):
R(z)(n0+n1z-1+n2z-2)＝M(z)(m0+m1z-1+m2z-2) (5)
step 2.2: according to a GPC control algorithm, introducing a loss map equation into the equation (5) to obtain a predicted value r of the yaw rate at the moment k + j represented by the equation (6)e(k+j)：
re(k+j)＝Fj(z-1)r(k)+Gj(z-1)ΔM(k+j-1) (6)
r (k) is an actual value of yaw rate at the time k detected by the yaw rate sensor;
Fj(z-1) And Gj(z-1) Are all polynomials in the chartlet equation;
delta M (k + j-1) is the increment of the actual value of the additional yaw moment at the moment k + j-1 and the moment k + j-2;
according to the GPC principle, given that a prediction period N and a control period N are integers, the vector expression form of the predicted value of the yaw rate is as shown in formula (7):
R＝GΔM+f (7)
in formula (7):
R＝[re(k+1),re(k+2),…,re(k+n)]T；
ΔM＝[ΔM(k),ΔM(k+1),…,ΔM(k+N-1)]T；
f＝HΔM(k)+Fr(k)＝[f(k+1),f(k+2),…,f(k+n)]T；
H1(z-1),H2(z-1)...Hn(z-1) To de-plot the polynomials in the equation,
g0,g1,...gn-1is a polynomial Gj(z-1) Polynomial coefficient of (5);
step 2.3: the optimized performance indicator function J (k) at time k is characterized by equation (8):
in the formula (8), λ is a control weight constant,
and combining the formula (7) and the formula (8), and obtaining the vector representation form of the additional yaw moment increment by using a rolling optimization method according to the formula (9):
ΔM＝(GTG+λI)-1GT(RD-f) (9)
in the formula: rD＝[rd(k+1),rd(k+2),…,rd(k+n)]T(10)
I is a unit vector; r isd(k+1),rd(k+2),...,rd(k + n) is an ideal yaw rate at each sampling time in the prediction period n obtained by calculation using equation (4);
step 2.4: at each sampling moment in the prediction period n, respectively acquiring and obtaining the front wheel rotation angle sigma by using a sensorfAnd vehicle speed vdThe ideal yaw rate r at each sampling instant in the prediction period n is obtained by calculation using equation (4)d(k+1),rd(k+2),...,rd(k + n) and obtaining R characterized by formula (10)D(ii) a Then G is obtained by calculation by utilizing the equation of the loss of the patternj(z-1)、H1(z-1),H2(z-1)...Hn(z-1) And Fj(z-1) A value of (d); finally, the value of the additional yaw moment M (k) at the time k, which is characterized by equation (11), is obtained by calculation using equation (9):
M(k)＝M(k-1)+gT(RD-f) (11)
in formula (11), gTIs represented by (G)TG+λI)-1GTA first row vector of (a);
step 2.5: optimizing the value of the additional yaw moment M (k) through a genetic algorithm to obtain the optimal additional yaw moment value Mm(k)；
And step 3: and distributing the optimal additional yaw moment into the driving forces of four wheels of the four-wheel independent drive hub motor electric automobile by adopting a regular distribution method of the driving forces of the left and right wheels, and acting on each wheel in a one-to-one correspondence manner.
2. The method for vehicle yaw stability control based on hybrid optimized GPC of genetic algorithms according to claim 1, characterized in that: the step 3 specifically includes:
the vehicle four-wheel drive torque-four-wheel drive force relationship is characterized by equation (12):
F1and T1Left front wheel drive force and drive torque, F2And T2Respectively a right front wheel driving force and a driving torque,
F3and T2Driving force and torque of the left rear wheel, F4And T4Respectively is a right rear wheel driving force and a driving moment;
b is the radius of the wheel;
vehicle four-wheel drive torque and given target drive torque TobjIs characterized by equation (13):
Tobj＝T1+T2+T3+T4(13)
setting the optimal additional yaw moment value Mm(k) The following rules are assigned:
when M ism(k) When the vehicle is in a neutral steering state, the four-wheel driving force is distributed according to the average distribution principle as shown in the formula (14):
F1＝F2＝F3＝F4(14)
when M ism(k)>At 0, the vehicle is in a left understeer or right oversteer condition, and the redistributed four-wheel drive torque characterized by equation (15) is obtained by combining equation (12) -equation (14):
when M ism(k)<At 0, the vehicle is in an understeer or oversteer right state, and the redistributed four-wheel drive torque characterized by equation (16) is obtained by combining equation (12) -equation (14):
setting the optimal additional yaw moment value Mm(k) The driving force of four wheels of the electric automobile with the four-wheel independent driving hub motor is distributed and acts on each wheel in a one-to-one correspondence mode.
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