CN111959514B - Automobile mass center slip angle observation method based on fuzzy dynamics system - Google Patents

Automobile mass center slip angle observation method based on fuzzy dynamics system Download PDF

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CN111959514B
CN111959514B CN202010812335.9A CN202010812335A CN111959514B CN 111959514 B CN111959514 B CN 111959514B CN 202010812335 A CN202010812335 A CN 202010812335A CN 111959514 B CN111959514 B CN 111959514B
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黄晋
胡展溢
杨泽宇
江昆
杨殿阁
钟志华
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Abstract

The application discloses an automobile mass center slip angle observation method based on a fuzzy dynamic system, which comprises the following steps: step 1, constructing a vehicle dynamics model according to collected vehicle running information, introducing uncertainty parameters, and generating a centroid slip angle observation equation, wherein the values of the uncertainty parameters are described by a first fuzzy set; step 2, calculating a transient performance function and a steady-state performance function of the centroid side deflection angle observation equation, and calculating optimal adjustable parameters of the centroid side deflection angle observation equation through a dynamic game algorithm, wherein the optimal adjustable parameters comprise a first adjustable parameter and a second adjustable parameter; and 3, calculating a centroid slip angle observation value corresponding to the vehicle running information according to the optimal adjustable parameter and the centroid slip angle observation equation. Through the technical scheme in the application, the error of the existing observation method based on the dynamic model in modeling is reduced, the accuracy of calculating the side slip angle of the mass center of the automobile is improved, and the integral performance of the observer is improved.

Description

Automobile mass center slip angle observation method based on fuzzy dynamics system
Technical Field
The application relates to the technical field of vehicle safety, in particular to an automobile mass center slip angle observation method based on a fuzzy dynamic system.
Background
At present, various active safety systems are widely applied to the field of vehicles, including an electronic vehicle body stabilizing system, an anti-lock brake system and the like, the working processes of the systems can not leave information such as vehicle parameters, vehicle states and the like, and the mass center slip angle of an automobile is important information.
In the prior art, the mass center slip angle of the automobile is difficult to directly measure, the measurement cost is too high, and industrial application is difficult to realize, so that the mass center slip angle of the automobile is generally observed by adopting measurable parameters. The conventional methods and the problems involved are as follows:
1. the method is based on an estimation method of a vehicle kinematic model, the method obtains the automobile mass center slip angle by measuring the vehicle transverse acceleration and performing integration, but because of the existence of an integration link, the method is easy to generate larger accumulated errors, thereby influencing the accuracy of the observation result of the automobile mass center slip angle;
2. the method is based on an observation method of a vehicle dynamic model, the method observes the mass center slip angle of the automobile by measuring the yaw velocity of the vehicle, but the observation result of the mass center slip angle of the automobile is greatly influenced by model parameters, and the parameter fluctuation can directly influence the accuracy of the observation result.
Therefore, the accuracy of the observation result of the mass center slip angle of the automobile cannot meet the requirement of an active safety system of the automobile.
Disclosure of Invention
The purpose of this application lies in: the method reduces the error of the existing observation method based on the dynamic model in modeling, improves the accuracy of calculating the side slip angle of the mass center of the automobile, and is beneficial to improving the overall performance of the observer.
The technical scheme of the application is as follows: the method for observing the automobile mass center slip angle based on the fuzzy dynamic system comprises the following steps: step 1, constructing a vehicle dynamics model according to collected vehicle running information, introducing uncertainty parameters, and generating a centroid slip angle observation equation, wherein the values of the uncertainty parameters are described by a first fuzzy set; step 2, calculating a transient performance function and a steady-state performance function of the centroid side deflection angle observation equation, and calculating optimal adjustable parameters of the centroid side deflection angle observation equation through a dynamic game algorithm, wherein the optimal adjustable parameters comprise a first adjustable parameter and a second adjustable parameter; and 3, calculating a centroid slip angle observation value corresponding to the vehicle running information according to the optimal adjustable parameter and the centroid slip angle observation equation.
In any one of the above technical solutions, further, the vehicle driving information includes: vehicle yaw rate, front wheel angle, vehicle longitudinal speed.
In any one of the above technical solutions, further, a calculation formula of the centroid slip angle observation equation is:
Figure BDA0002631412890000021
Figure BDA0002631412890000022
Figure BDA0002631412890000023
Figure BDA0002631412890000024
Figure BDA0002631412890000025
C＝[0 1]
y(t)＝C x(t)
wherein, the matrix L and the matrix G satisfy the following relation:
P(A+LC)+(A+LC)TP＝-Q
BTP＝GC
Figure BDA0002631412890000031
wherein t is the collection time of the vehicle running information,
Figure BDA0002631412890000032
in order to be a state observation value,
Figure BDA0002631412890000033
as a result of the lateral vehicle speed observation,
Figure BDA0002631412890000034
observed yaw rate, x (t) actual state, y (t) measurable system output, vy(t) is the first vehicle lateral velocity corresponding to the acquisition time t,
Figure BDA0002631412890000035
for acquiring the yaw rate, C, of the vehicle corresponding to the time tfFor front wheel cornering stiffness, CrFor rear wheel cornering stiffness, /)fIs the distance from the center of mass of the car to the front axle,/rIs the distance from the center of mass of the automobile to the rear axle, m is the mass of the automobile, vx(t) is the longitudinal speed of the vehicle corresponding to the acquisition time t, IzIs yaw moment of inertia, u (t) is a front wheel corner corresponding to the acquisition time t,
Figure BDA0002631412890000036
for uncertainty boundaries, P is the matrix to be solved, Q is a given positive definite matrix, which is the identity matrix I2×2，η、τ1、δ1、δ2For the given constant number of the light-emitting elements,
Figure BDA0002631412890000037
is the first tunable parameter, and epsilon is the second tunable parameter.
In any one of the above technical solutions, further, in the step 2, specifically including:
step 21, according to the system performance function, calculating the boundary of the system performance function at any moment by solving a boundary differential inequality equation, dividing the first part of the boundary into a transient performance function, and dividing the second part of the boundary into a steady-state performance function, wherein the calculation formula of the boundary differential inequality equation is as follows:
Figure BDA0002631412890000038
where V is the system performance function, κ and
Figure BDA0002631412890000039
is a preset constant and is used as a reference,
Figure BDA00026314128900000310
is an intermediate parameter;
step 22, solving a partial derivative of a first adjustable parameter according to a first cost function through a dynamic game algorithm, substituting a first minimum value point corresponding to the partial derivative of the first cost function into a second cost function, and solving the partial derivative of the second adjustable parameter, wherein the first cost function comprises a transient performance function, and the second cost function comprises a steady-state performance function;
and step 23, taking the second minimum point corresponding to the second cost function partial derivative as the first optimal solution of the second adjustable parameter, calculating the second optimal solution of the first adjustable parameter corresponding to the first optimal solution according to the first cost function, and recording the first optimal solution and the second optimal solution as the optimal adjustable parameters.
In any of the above technical solutions, further, the calculation formula of the first cost function is:
Figure BDA0002631412890000041
wherein epsilon is a second adjustable parameter,
Figure BDA0002631412890000042
as a first adjustable parameter, D [. X [ ]]For mapping operations of fuzzy numbers and real numbers, ηtranAs a function of transient performance, t0For the moment at which the observer starts to observe,
Figure BDA0002631412890000043
is a first cost function, wherein the strategy set of the second adjustable parameter epsilon is a value range [2, + ∞ ].
In any of the above technical solutions, further, the calculation formula of the second cost function is:
Figure BDA0002631412890000044
wherein epsilon is a second adjustable parameter,
Figure BDA0002631412890000045
as a first adjustable parameter, D [. X [ ]]For mapping operations of fuzzy numbers and real numbers, ηsteaIn order to be a function of the steady-state performance,
Figure BDA0002631412890000046
is a second cost function, wherein the first adjustable parameter
Figure BDA0002631412890000047
The set of strategies of (2) is a value range (0, + ∞).
The beneficial effect of this application is:
1. according to the method, on the basis of an ideal dynamic model, the influence generated by tire parameter change and vehicle speed change is supplemented, an actual dynamic system with an uncertainty parameter part is obtained, the uncertainty parameter part is represented by a fuzzy set, and then the vehicle fuzzy dynamic system based on the fuzzy set theory is obtained, the method reduces the error of the existing dynamic model observation method in modeling, the constructed model is more consistent with the actual vehicle model, and the accuracy of calculation of the mass center slip angle of the automobile is improved;
2. the observer not only estimates a nominal value of the automobile mass center slip angle through a nominal part and is used as a first fuzzy set of a model, but also estimates a floating value of the automobile mass center slip angle through an uncertainty parameter part and superposes the nominal value and the floating value, so that an actual value of the automobile mass center slip angle is estimated, and an observation result of the automobile mass center slip angle is more accurate and reliable;
3. according to the method based on the dynamic game, transient performance and steady-state performance of the observer are respectively used as cost functions, and an adjustable parameter combination enabling the centroid observation effect to be optimal is found through solving the Starkelberg strategy of the game problem, so that the overall performance of the observer is improved.
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The advantages of the above and/or additional aspects of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow diagram of a method for observing the slip angle of the center of mass of an automobile based on a fuzzy dynamic system according to one embodiment of the present application;
FIG. 2 is a schematic diagram of a linear two-degree-of-freedom monorail vehicle model structure in accordance with an embodiment of the present application;
FIG. 3 is a block diagram schematic of a structure of an observer with parameter optimization according to one embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present application can be more clearly understood, the present application will be described in further detail with reference to the accompanying drawings and detailed description. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited by the specific embodiments disclosed below.
As shown in fig. 1, the present embodiment provides a method for observing a centroid slip angle of an automobile based on a fuzzy dynamic system, where the method includes:
step 1, according to the collected vehicle running information, a vehicle dynamics model is built, uncertainty parameters are introduced, a centroid slip angle observation equation is generated, wherein the value of the uncertainty parameters is described by a first fuzzy set, and the vehicle running information comprises: yaw rate of vehicle
Figure BDA0002631412890000051
Front wheel steering angle delta, vehicle longitudinal speed vx。
As shown in fig. 2, the present embodiment introduces a linear two-degree-of-freedom monorail vehicle model:
Figure BDA0002631412890000052
Figure BDA0002631412890000053
and:
Fyf＝-Cfαf
Fyr＝-Crαr
Figure BDA0002631412890000061
Figure BDA0002631412890000062
wherein m is the mass of the vehicle,
Figure BDA0002631412890000063
for yaw rate, v, of the vehiclexIs the longitudinal speed, v, of the vehicleyAt a first vehicle transverse speed, CfFor front wheel cornering stiffness, CrFor rear wheel cornering stiffness, /)fIs the distance from the center of mass of the car to the front axle,/rIs the distance from the center of mass of the car to the rear axle, IzIs the yaw moment of inertia, δ is the front wheel angle.
Therefore, the present embodiment is based on the vehicle yaw rate
Figure BDA0002631412890000064
Front wheel steering angle delta, vehicle longitudinal speed vxThe vehicle dynamics model constructed by equal parameters is as follows:
Figure BDA0002631412890000065
Figure BDA0002631412890000066
u＝δ
wherein m is the mass of the vehicle,
Figure BDA0002631412890000067
for yaw rate, v, of the vehiclexIs the longitudinal speed, v, of the vehicleyAt a first vehicle transverse speed, CfFor front wheel cornering stiffness, CrFor rear wheel cornering stiffness, /)fIs the distance from the center of mass of the car to the front axle,/rIs the distance from the center of mass of the car to the rear axle, IzIs the yaw moment of inertia, δ is the front wheel angle.
Can be abbreviated as:
Figure BDA0002631412890000068
the yaw rate of the vehicle
Figure BDA0002631412890000069
The rotation angle delta of the front wheel can be measured in real time through a gyroscope and a potentiometer. Thus yielding a measurable system output y:
y＝Cx
wherein, C ═ 01.
Considering the parameter variation fluctuation in the dynamic model, the method specifically includes: front wheel cornering stiffness CfRear wheel side cornering stiffness CrAnd the longitudinal speed v of the vehiclexAccordingly, the matrix corresponding to the uncertainty parameter can be expressed as:
Figure BDA0002631412890000071
Figure BDA0002631412890000072
Figure BDA0002631412890000073
in which Δ represents the uncertainty, e1For a first vehicle transverse velocity vy(t) first equivalent Effect, e2Yaw rate for vehicle
Figure BDA0002631412890000077
A second equivalent effect of.
Introducing the matrix corresponding to the uncertainty parameter into a vehicle dynamics model to obtain an actual vehicle dynamics model:
Figure BDA0002631412890000074
F＝ΔAx+ΔBu+ΔV
considering that uncertainty parameters in engineering systems are generally bounded, a first fuzzy set is used for describing the uncertainty parameters, and the first fuzzy set comprises:
S1＝{(ΔCf,μ1(ΔCf))|ΔCf∈Ω1}
S2＝{(ΔCr,μ2(ΔCr))|ΔCr∈Ω2}
S3＝{(e1,μ3(e1))|e1∈Ω3}
S4＝{(e2,μ4(e2))|e2∈Ω4}
where i is 1,2,3,4, the number of the first fuzzy set, μiMembership function, Ω, corresponding to the ith first fuzzy setiIs correspondingly boundedThe value set is determined by a designer, and generally-30% to + 30% of a nominal value is taken as the value set.
As shown in fig. 3, an observer corresponding to the actual vehicle dynamics model is constructed, and a calculation formula of a centroid slip angle observation equation generated by calculation is as follows:
Figure BDA0002631412890000075
Figure BDA0002631412890000076
Figure BDA0002631412890000081
Figure BDA0002631412890000082
Figure BDA0002631412890000083
C＝[0 1]
y(t)＝C x(t)
wherein the content of the first and second substances,
Figure BDA0002631412890000084
is the nominal value of the observer,
Figure BDA0002631412890000085
for the floating values of the observer, the matrix L and the matrix G satisfy the following relationship:
P(A+LC)+(A+LC)TP＝-Q
BTP＝GC
Figure BDA0002631412890000086
wherein t is the collection time of the vehicle running information,
Figure BDA0002631412890000087
in order to be a state observation value,
Figure BDA0002631412890000088
as a result of the lateral vehicle speed observation,
Figure BDA0002631412890000089
observed yaw rate, x (t) actual state, y (t) measurable system output, vy(t) is the first vehicle lateral velocity corresponding to the acquisition time t,
Figure BDA00026314128900000810
is a first vehicle lateral velocity observation,
Figure BDA00026314128900000811
in order to acquire the yaw rate of the vehicle corresponding to the time t,
Figure BDA00026314128900000812
observed value of vehicle yaw rate, x (t) actual value of state, y (t) measurable system output, CfFor front wheel cornering stiffness, CrFor rear wheel cornering stiffness, /)fIs the distance from the center of mass of the car to the front axle,/rIs the distance from the center of mass of the automobile to the rear axle, m is the mass of the automobile, vx(t) is the longitudinal speed of the vehicle corresponding to the acquisition time t, IzIs yaw moment of inertia, u (t) is a front wheel corner corresponding to the acquisition time t,
Figure BDA00026314128900000813
for uncertainty boundaries, P is the matrix to be solved, Q is a given positive definite matrix, which is the identity matrix I2×2，η、τ1、δ1、δ2For the given constant number of the light-emitting elements,
Figure BDA00026314128900000814
is as followsOne tunable parameter, e is the second tunable parameter.
γ (y (t), t) is a time-varying coefficient in the observer, which is used to suppress the influence of uncertainty.
It should be noted that the present embodiment utilizes uncertainty boundaries
Figure BDA0002631412890000091
Describing the boundaries of the individual uncertainty quantities in the uncertainty equation F, i.e. the uncertainty boundaries
Figure BDA0002631412890000092
Satisfies the following conditions:
Figure BDA0002631412890000093
in the formula, τ0、τ1Given a constant.
Since the uncertainty equation F is bounded, such uncertainty boundaries
Figure BDA0002631412890000094
And is positively present.
Step 2, calculating a transient performance function and a steady-state performance function of the centroid side deflection angle observation equation, and calculating optimal adjustable parameters of the centroid side deflection angle observation equation through a dynamic game algorithm, wherein the optimal adjustable parameters comprise a first adjustable parameter and a second adjustable parameter;
further, step 2 specifically includes:
step 21, calculating the boundary of the system performance function at any moment by solving a boundary differential inequality equation according to the system performance function, dividing a first part of the boundary into a transient performance function, and dividing a second part of the boundary into a steady-state performance function;
specifically, the calculation formula for setting the system performance function is as follows:
Figure BDA0002631412890000095
in the formula, P is a matrix to be solved. Obtaining the boundary of the system performance function V at any moment by solving a boundary differential inequality equation:
Figure BDA0002631412890000096
wherein V is the system performance function, k is a predetermined constant,
Figure BDA0002631412890000097
is the first adjustable parameter of the optical system,
Figure BDA00026314128900000910
is an intermediate parameter, and the expression is:
Figure BDA0002631412890000098
wherein, tau0、δ1、δ2Is a constant, ρ0As a fuzzy set S1-S4Sum of respective upper bounds, p1As a fuzzy set S1-S4The sum of the respective lower bounds;
obtaining:
Figure BDA0002631412890000099
in the formula, V (t) represents the system performance at the moment t; xi is an intermediate variable expressed as
Figure BDA0002631412890000101
Wherein, k is a preset constant, t0For the moment at which the observer starts to observe,
Figure BDA0002631412890000102
is t0The system performance at time can be represented by t0And calculating the system state at the moment.
Thus, the calculation formula defining the transient performance function is:
Figure BDA0002631412890000103
the steady state performance function is calculated as:
ηstea＝κΞ
step 22, solving a partial derivative of a first adjustable parameter according to a first cost function through a dynamic game algorithm, substituting a first minimum value point corresponding to the partial derivative of the first cost function into a second cost function, and solving the partial derivative of the second adjustable parameter, wherein the first cost function comprises a transient performance function, and the second cost function comprises a steady-state performance function;
step 23, using the second minimum point corresponding to the second cost function partial derivative as the first optimal solution of the second adjustable parameter, calculating the second optimal solution of the first adjustable parameter corresponding to the first optimal solution according to the first cost function, and applying the first optimal solution
Figure BDA0002631412890000104
And a second optimal solution e*Recording as the optimal adjustable parameter.
Specifically, the first cost function may be first achieved by using the second adjustable parameter as a player of the prior action and the first adjustable parameter as a player of the subsequent action
Figure BDA0002631412890000105
Players acting backwards
Figure BDA0002631412890000106
Calculating the partial derivative, finding the minimum value point, i.e. finding
Figure BDA0002631412890000107
Such that:
Figure BDA0002631412890000108
then substituted into a second cost function
Figure BDA0002631412890000109
To obtain
Figure BDA00026314128900001010
Finding the value of the function at the minimum value by solving the partial derivative belonging to the player belonging to the action in advance, namely the optimal solution belonging to the function belonging to the group*To thereby obtain the optimum
Figure BDA00026314128900001011
Therefore, through the dynamic game algorithm, the optimal adjustable parameters can be obtained
Figure BDA00026314128900001012
Further, the calculation formula of the first cost function is as follows:
Figure BDA00026314128900001013
wherein epsilon is a second adjustable parameter,
Figure BDA00026314128900001014
as a first adjustable parameter, D [. X [ ]]The specific calculation formula for the mapping operation of the fuzzy number and the real number is as follows:
Figure BDA0002631412890000111
wherein f (zeta) is an objective function, phi is a value set of a parameter zeta, and muΦAnd (zeta) is a membership function.
ηtranAs a function of transient performance, t0For the moment at which the observer starts to observe,
Figure BDA0002631412890000112
is a first cost function, wherein the strategy set of the second adjustable parameter epsilon is a value range [2, + ∞ ].
Further, the calculation formula of the second cost function is:
Figure BDA0002631412890000113
wherein epsilon is a second adjustable parameter,
Figure BDA0002631412890000114
as a first adjustable parameter, D [. X [ ]]For mapping operations of fuzzy numbers and real numbers, ηsteaIn order to be a function of the steady-state performance,
Figure BDA0002631412890000115
is a second cost function, wherein the first adjustable parameter
Figure BDA0002631412890000116
The set of strategies of (2) is a value range (0, + ∞).
And 3, calculating a centroid slip angle observation value corresponding to the vehicle running information according to the optimal adjustable parameter and the centroid slip angle observation equation.
In particular, the optimum adjustable parameters
Figure BDA0002631412890000117
The centroid slip angle observation equation is brought in, so that the current first vehicle transverse speed is observed in real time
Figure BDA0002631412890000118
Since the longitudinal vehicle speed v is knownxTherefore, the current automobile mass center slip angle observed value can be obtained through calculation through the mass center slip angle observed value calculation formula
Figure BDA0002631412890000119
Wherein, the observed value of the centroid slip angle
Figure BDA00026314128900001110
Calculating the formula:
Figure BDA00026314128900001111
namely the centroid slip angle observed value corresponding to the actual vehicle dynamics model.
Further, in order to further improve the accuracy of the calculated value of the centroid slip angle, the present embodiment further introduces a kinetic equation, calculates the corresponding centroid slip angle, and obtains a centroid slip angle fusion value by combining with a weighting algorithm, so as to ensure the accuracy and reliability of the centroid slip angle calculation. Thus, the method further comprises:
and 4, calculating an observed value of the transverse speed of the second vehicle according to the kinetic equation and the collected vehicle running information, and calculating a centroid sideslip angle observed value under the kinetic equation according to the observed value of the transverse speed of the second vehicle and the collected longitudinal speed of the vehicle.
Specifically, setting the kinetic equation includes:
Figure BDA0002631412890000121
Figure BDA0002631412890000122
therefore, the calculation formula of the corresponding two-degree-of-freedom vehicle kinematic model is as follows:
Figure BDA0002631412890000123
ykin＝Ckinxkin
xkin＝[vx vy′]T
Figure BDA0002631412890000124
Figure BDA0002631412890000125
Ckin＝[1 0]
u＝δ
in the formula, vxAs is the longitudinal speed of the vehicle,
Figure BDA0002631412890000126
for the second vehicle lateral speed,
Figure BDA0002631412890000127
the vehicle yaw rate, δ is the front wheel steering angle.
The calculation formula of the state observer corresponding to the system can be obtained through a mathematical method and is as follows:
Figure BDA0002631412890000128
wherein, the matrix LkinThe following conditions are satisfied:
Pkin(Akin+LkinCkin)+(Akin+LkinCkin)TPkin＝-Qkin
in the formula, matrix PkinAnd matrix QkinA symmetric matrix is defined for a given positive.
Vehicle longitudinal speed v in combination with vehicle travel informationxThen the observed value of the lateral speed of the second vehicle can be calculated
Figure BDA0002631412890000129
By adopting the calculation formula which is the same as the centroid slip angle observed value, the centroid slip angle observed value under the kinetic equation can be calculated
Figure BDA00026314128900001210
Figure BDA00026314128900001211
Step 5, adopting a weighting algorithm, and observing values according to the centroid slip angles corresponding to the actual vehicle dynamics model
Figure BDA00026314128900001212
Observed value of centroid slip angle under sum of kinetic equation
Figure BDA00026314128900001213
And calculating a centroid slip angle fusion value, wherein the weighting coefficient is determined by a filter constant and a Laplace operator.
In particular, the centroid slip angle fusion value
Figure BDA00026314128900001214
The calculation formula of (2) is as follows:
Figure BDA00026314128900001215
in the formula, τ is a filter constant, and s is a laplacian operator.
In order to verify the accuracy of the centroid slip angle observation method in the embodiment, three different centroid slip angle values are set, centroid slip angle observation is performed, and the observation results are shown in table 1.
TABLE 1
Figure BDA0002631412890000131
Observed value of centroid slip angle under traditional kinetic equation
Figure BDA0002631412890000132
In contrast, in the embodiment, the centroid slip angle view corresponding to the actual vehicle dynamics model after the fuzzy set is introducedMeasured value
Figure BDA0002631412890000133
And centroid slip angle fusion value
Figure BDA0002631412890000134
The error rate of the method is obviously reduced and is superior to the centroid slip angle observation method under the traditional kinetic equation.
The technical scheme of the application is explained in detail in the above with reference to the accompanying drawings, and the application provides an automobile mass center slip angle observation method based on a fuzzy dynamic system, which comprises the following steps: step 1, constructing a vehicle dynamics model according to collected vehicle running information, introducing uncertainty parameters, and generating a centroid slip angle observation equation, wherein the values of the uncertainty parameters are described by a first fuzzy set; step 2, calculating a transient performance function and a steady-state performance function of the centroid side deflection angle observation equation, and calculating optimal adjustable parameters of the centroid side deflection angle observation equation through a dynamic game algorithm, wherein the optimal adjustable parameters comprise a first adjustable parameter and a second adjustable parameter; and 3, calculating a centroid slip angle observation value corresponding to the vehicle running information according to the optimal adjustable parameter and the centroid slip angle observation equation. Through the technical scheme in the application, the error of the existing observation method based on the dynamic model in modeling is reduced, the accuracy of calculating the side slip angle of the mass center of the automobile is improved, and the integral performance of the observer is improved.
The steps in the present application may be sequentially adjusted, combined, and subtracted according to actual requirements.
The units in the device can be merged, divided and deleted according to actual requirements.
Although the present application has been disclosed in detail with reference to the accompanying drawings, it is to be understood that such description is merely illustrative and not restrictive of the application of the present application. The scope of the present application is defined by the appended claims and may include various modifications, adaptations, and equivalents of the invention without departing from the scope and spirit of the application.

Claims (6)

1. An automobile mass center slip angle observation method based on a fuzzy dynamic system is characterized by comprising the following steps:
step 1, constructing a vehicle dynamics model according to collected vehicle running information, introducing uncertainty parameters, and generating a centroid slip angle observation equation, wherein the values of the uncertainty parameters are described by a first fuzzy set;
step 2, calculating a transient performance function and a steady-state performance function of the centroid side deflection angle observation equation, and calculating optimal adjustable parameters of the centroid side deflection angle observation equation through a dynamic game algorithm, wherein the optimal adjustable parameters comprise a first adjustable parameter and a second adjustable parameter;
and 3, calculating a centroid slip angle observation value corresponding to the vehicle running information according to the optimal adjustable parameter and the centroid slip angle observation equation.
2. The method of observing the centroid slip angle of an automobile based on a fuzzy dynamic system as claimed in claim 1, wherein said vehicle driving information comprises: vehicle yaw rate, front wheel angle, vehicle longitudinal speed.
3. The method for observing the automobile centroid slip angle based on the fuzzy dynamic system as claimed in claim 2, wherein the equation for observing the centroid slip angle is as follows:
Figure FDA0003158880440000011
Figure FDA0003158880440000012
Figure FDA0003158880440000013
Figure FDA0003158880440000014
Figure FDA0003158880440000015
C＝[0 1]
y(t)＝C x(t)
wherein, the matrix L and the matrix G satisfy the following relation:
P(A+LC)+(A+LC)TP＝-Q
BTP＝GC
Figure FDA0003158880440000021
wherein t is the collection time of the vehicle running information,
Figure FDA0003158880440000022
in order to be a state observation value,
Figure FDA0003158880440000023
as a result of the lateral vehicle speed observation,
Figure FDA0003158880440000024
observed yaw rate, x (t) actual state, y (t) measurable system output, vy(t) is the first vehicle lateral velocity corresponding to the acquisition time t,
Figure FDA0003158880440000025
for acquiring the yaw rate, C, of the vehicle corresponding to the time tfFor front wheel cornering stiffness, CrFor rear wheel cornering stiffness, /)fIs the distance from the center of mass of the car to the front axle,/rIs the distance from the center of mass of the automobile to the rear axle, m is the mass of the automobile, vx(t) is the vehicle corresponding to the acquisition time tLongitudinal speed, IzIs yaw moment of inertia, u (t) is a front wheel corner corresponding to the acquisition time t,
Figure FDA0003158880440000026
for uncertainty boundaries, P is the matrix to be solved, Q is a given positive definite matrix, which is the identity matrix I2×2，η、τ1、δ1、δ2For the given constant number of the light-emitting elements,
Figure FDA0003158880440000027
and e is the first adjustable parameter, and is the second adjustable parameter.
4. The method for observing the automobile centroid slip angle based on the fuzzy dynamic system as claimed in claim 3, wherein in the step 2, the method specifically comprises:
step 21, according to the system performance function, calculating the boundary of the system performance function at any time by solving a boundary differential inequality equation, dividing the first part of the boundary into the transient performance function, and dividing the second part of the boundary into the steady-state performance function, wherein the calculation formula of the boundary differential inequality equation is as follows:
Figure FDA0003158880440000028
wherein V is the system performance function, k is a predetermined constant,
Figure FDA0003158880440000029
for the purpose of said first adjustable parameter,
Figure FDA00031588804400000210
is an intermediate parameter;
step 22, obtaining a partial derivative of the first adjustable parameter according to a first cost function through the dynamic game algorithm, substituting a first minimum value point corresponding to the partial derivative of the first cost function into a second cost function, and obtaining the partial derivative of the second adjustable parameter, wherein the first cost function comprises the transient performance function, and the second cost function comprises the steady-state performance function;
and step 23, taking a second minimum point corresponding to a second cost function partial derivative as a first optimal solution of the second adjustable parameter, calculating a second optimal solution of the first adjustable parameter corresponding to the first optimal solution according to the first cost function, and recording the first optimal solution and the second optimal solution as the optimal adjustable parameters.
5. The method for observing the mass center and the slip angle of an automobile based on a fuzzy dynamic system as claimed in claim 4, wherein the first cost function is calculated by the following formula:
Figure FDA0003158880440000031
wherein e is the second adjustable parameter,
Figure FDA0003158880440000032
for said first adjustable parameter, D [. X [ ]]For mapping operations of fuzzy numbers and real numbers, ηtranAs a function of the transient performance, t0For the moment at which the observer starts to observe,
Figure FDA0003158880440000033
and the first cost function is the strategy set of the second adjustable parameter epsilon, wherein the strategy set of the second adjustable parameter epsilon is a value range [2, + ∞ ].
6. The method for observing the mass center and the slip angle of an automobile based on a fuzzy dynamic system as claimed in claim 4, wherein the second cost function is calculated by the formula:
Figure FDA0003158880440000034
wherein e is the second adjustable parameter,
Figure FDA0003158880440000035
for said first adjustable parameter, D [. X [ ]]For mapping operations of fuzzy numbers and real numbers, ηsteaFor the purpose of the steady-state performance function,
Figure FDA0003158880440000036
is a second cost function, wherein the first adjustable parameter
Figure FDA0003158880440000037
The set of strategies of (2) is a value range (0, + ∞).
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