CN111332278B - Transverse stable control method and system for distributed driving electric vehicle - Google Patents

Transverse stable control method and system for distributed driving electric vehicle Download PDF

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CN111332278B
CN111332278B CN202010216520.1A CN202010216520A CN111332278B CN 111332278 B CN111332278 B CN 111332278B CN 202010216520 A CN202010216520 A CN 202010216520A CN 111332278 B CN111332278 B CN 111332278B
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vehicle
yaw
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yaw rate
moment
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CN111332278A (en
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赵泽
顾亮
秦也辰
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Beijing Institute of Technology BIT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/02—Control of vehicle driving stability
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle

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Abstract

The invention discloses a method and a system for controlling the lateral stability of a distributed driving electric vehicle. The method comprises the steps of establishing a vehicle yaw rate prediction model according to vehicle yaw rate historical data, vehicle direct yaw moment historical data and noise data, wherein the model does not depend on vehicle parameters and environment parameters which are changeable and cannot be accurately obtained in advance, determining parameter vectors in the model by adopting a recursive least square method according to current vehicle yaw rate data based on the model, predicting the vehicle yaw rate according to the parameter vectors and the current vehicle yaw rate data to obtain a vehicle yaw rate predicted value, and enabling the vehicle model to have adaptivity by estimating and updating the parameter vectors in real time due to the fact that model parameters are time-varying, so that the vehicle yaw rate prediction model can be suitable for all working conditions of vehicle running; the model has certain robustness on the measurement error of the sensor, has certain filtering effect on noise in the measured value of the yaw velocity of the vehicle, and can improve the yaw stability of the vehicle.

Description

Transverse stable control method and system for distributed driving electric vehicle
Technical Field
The invention relates to the technical field of stability control of electric vehicles, in particular to a method and a system for controlling lateral stability of a distributed driving electric vehicle.
Background
Vehicle models adopted by the existing vehicle stability controller are mainly divided into a vehicle dynamics model and a vehicle kinematics model. When the driver is in the linear control interval, the vehicle parameters and the road surface adhesion coefficient are accurate, and the tire parameters are in the nominal values, the vehicle dynamic model has higher accuracy. However, the working conditions in the driving process of the vehicle are complex, the vehicle cannot be located in a linear control interval of a driver at all times, even is often located in a dangerous limit working condition, a dynamic model with limited dimensionality cannot accurately describe the real dynamic characteristics of the vehicle, and the model mismatch is serious; meanwhile, the tire parameters greatly deviate from the nominal values, making the model error serious. In addition, many vehicle parameters cannot be accurately known in advance, and the road adhesion coefficients under different driving conditions have large difference, so that the model accuracy is greatly influenced. Therefore, the above factors have a large influence on the accuracy of the vehicle dynamics model. For a vehicle kinematic model, on one hand, the robustness to sensor measurement errors and sensor drift is poor; on the other hand, observability is easily lost under steady-state conditions. In summary, both the vehicle dynamics model and the vehicle kinematics model have limitations and cannot be applied to all working conditions of vehicle driving.
Disclosure of Invention
The invention aims to provide a method and a system for controlling the lateral stability of a distributed driving electric vehicle, which do not depend on variable vehicle parameters and environment parameters which cannot be accurately obtained in advance, have self-adaptability, are suitable for all working conditions of vehicle running and can effectively improve the yaw stability of the vehicle.
In order to achieve the purpose, the invention provides the following scheme:
a vehicle lateral stability control method comprising:
obtaining vehicle motion parameters and vehicle intrinsic parameters;
determining a vehicle yaw velocity expected value according to the vehicle motion parameters and the vehicle intrinsic parameters;
obtaining historical data of vehicle yaw velocity, historical data of vehicle direct yaw moment and noise data;
establishing a vehicle yaw rate prediction model according to the vehicle yaw rate historical data, the vehicle direct yaw moment historical data and the noise data;
acquiring current data of the yaw velocity of the vehicle;
determining a parameter vector in the model by adopting a recursive least square method according to the current data of the vehicle yaw angular velocity based on the model; the parameter vector comprises a direct yaw moment parameter and a vehicle yaw rate parameter;
based on the model, predicting the vehicle yaw rate according to the parameter vector and the current data of the vehicle yaw rate to obtain a predicted value of the vehicle yaw rate;
and performing vehicle lateral stability control according to the vehicle yaw rate predicted value and the vehicle yaw rate expected value.
Optionally, the determining a vehicle yaw rate expected value according to the vehicle motion parameter and the vehicle intrinsic parameter specifically includes:
determining a vehicle yaw rate desired value according to the following formula:
Figure BDA0002424672030000021
Figure BDA0002424672030000022
|ωmax|＝μg/vx
in the formula, ωdIndicating desired value of yaw rate, v, of vehiclexRepresenting the vehicle longitudinal speed, delta representing the vehicle front wheel angle, a representing the vehicle centre of mass to front axle length, b representing the vehicle centre of mass to rear axle length, L representing the vehicle centre of mass to front axle length plus centre of mass to rear axle length, L ═ a + b, K representing the vehicle stability factor, m representing the vehicle mass, K representing the vehicle mass, and K representing the vehicle mass1Represents the cornering stiffness, k, of the front tyre2Represents the cornering stiffness of the rear tire, sgn (δ) represents a sign function, ωmaxThe maximum value of the yaw rate is shown, and μ represents the road surface adhesion coefficient.
Optionally, the establishing a vehicle yaw rate prediction model according to the vehicle yaw rate historical data, the vehicle direct yaw moment historical data, and the noise data specifically includes:
a vehicle yaw rate prediction model is established according to the following formula:
Figure BDA0002424672030000023
Figure BDA0002424672030000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002424672030000032
represents the predicted yaw rate at the n +1 th time, ynRepresenting the measured value of yaw rate at time n, yn-1Representing the measured value of yaw rate, theta, at time n-1ωRepresenting a parameter vector, θω＝[a1 a2 b1]T，a1Representing a first direct yaw moment parameter, a2Representing a second direct yaw moment parameter, b1Representing a vehicle yaw rate parameter, xn-jActual value, u, representing yaw rate at time n-jnRepresenting the direct yaw moment, u, at time n-1n-1Representing the direct yaw moment at time n-1, enRepresents white noise at the nth time, wnRepresenting the measurement noise at time n, yn-jRepresenting the measured value of yaw rate, w, at time n-jn-jRepresenting the noise measured at time n-j,
Figure BDA0002424672030000033
the regression vector is represented by a vector of coefficients,
Figure BDA0002424672030000034
vωwhich is representative of the noisy data, is,
Figure BDA0002424672030000035
optionally, the performing lateral stability control of the vehicle according to the predicted vehicle yaw rate value and the desired vehicle yaw rate value specifically includes:
optimizing a direct yaw moment according to the vehicle yaw velocity predicted value and the vehicle yaw velocity expected value to obtain an optimized direct yaw moment;
averaging the optimized direct yaw moment to obtain the hub motor torque of each wheel;
and performing vehicle transverse stable control according to the torque of the hub motor.
Optionally, the optimizing a direct yaw moment according to the predicted vehicle yaw rate value and the desired vehicle yaw rate value to obtain an optimized direct yaw moment specifically includes:
the objective function is determined according to the following formula:
Figure BDA0002424672030000036
wherein J represents an objective function, NpDenotes the prediction step size, YiRepresents the i-th prediction step yaw-rate prediction vector, Re represents the vehicle yaw-rate desired-value vector,
Figure BDA0002424672030000037
q denotes a first weight matrix, R denotes a second weight matrix, U denotes a direct yaw moment vector,
Figure BDA0002424672030000041
Figure BDA0002424672030000042
denotes the NthcIndividual control step size direct yaw moment, NcRepresenting a control step size, eta representing a relaxation factor, phi representing a third weight matrix;
the constraints are determined according to the following formula:
Figure BDA0002424672030000043
ΔM≤Mmax
y(n+Nc|n)＝ωd
in the formula (I), the compound is shown in the specification,
Figure BDA0002424672030000044
indicates that the predicted value of the yaw rate of the vehicle at the n +1 th moment is obtained from the known yaw rates of the vehicle at the n moments, phi (n | n) indicates that the regression vector at the n th moment is obtained from the known yaw rates of the vehicle at the n moments,
Figure BDA0002424672030000045
representing a parameter vector for the n-th moment obtained from the known yaw rates of the vehicle at n moments, Δ M representing the direct yaw moment, MmaxRepresenting the maximum value of the direct yaw moment, y (N + N)cN) indicates that the N + N-th vehicle yaw rate is obtained from the known vehicle yaw rates at N timescPredicting the vehicle yaw angular speed at the moment;
and determining a direct yaw moment vector according to the objective function and the constraint condition, and taking a first element in the direct yaw moment vector as the optimized direct yaw moment.
The present invention also provides a vehicle lateral stability control system, comprising:
the first acquisition module is used for acquiring vehicle motion parameters and vehicle intrinsic parameters;
the vehicle yaw rate expected value determining module is used for determining a vehicle yaw rate expected value according to the vehicle motion parameters and the vehicle intrinsic parameters;
the second acquisition module is used for acquiring historical data of the vehicle yaw velocity, historical data of the vehicle direct yaw moment and noise data;
the vehicle yaw rate prediction model establishing module is used for establishing a vehicle yaw rate prediction model according to the vehicle yaw rate historical data, the vehicle direct yaw moment historical data and the noise data;
the third acquisition module is used for acquiring the current data of the yaw rate of the vehicle;
the parameter vector determining module is used for determining parameter vectors in the model by adopting a recursive least square method according to the current data of the vehicle yaw angular velocity based on the model; the parameter vector comprises a direct yaw moment parameter and a vehicle yaw rate parameter;
the vehicle yaw velocity predicted value determining module is used for predicting the vehicle yaw velocity according to the parameter vector and the current data of the vehicle yaw velocity based on the model to obtain a vehicle yaw velocity predicted value;
and the control module is used for carrying out vehicle lateral stability control according to the predicted vehicle yaw rate value and the expected vehicle yaw rate value.
Optionally, the module for determining the desired yaw rate of the vehicle specifically includes:
a vehicle yaw-rate desired-value determining unit for determining a vehicle yaw-rate desired value according to the following formula:
Figure BDA0002424672030000051
Figure BDA0002424672030000052
|ωmax|＝μg/vx
in the formula, ωdIndicating desired value of yaw rate, v, of vehiclexRepresenting the vehicle longitudinal speed, delta representing the vehicle front wheel angle, a representing the vehicle centre of mass to front axle length, b representing the vehicle centre of mass to rear axle length, L representing the vehicle centre of mass to front axle length plus centre of mass to rear axle length, L ═ a + b, K representing the vehicle stability factor, m representing the vehicle mass, K representing the vehicle mass, and K representing the vehicle mass1Represents the cornering stiffness, k, of the front tyre2Represents the cornering stiffness of the rear tire, sgn (δ) represents a sign function, ωmaxThe maximum value of the yaw rate is shown, and μ represents the road surface adhesion coefficient.
Optionally, the vehicle yaw rate prediction model establishing module specifically includes:
a vehicle yaw-rate prediction model creation unit for creating a vehicle yaw-rate prediction model according to the following formula:
Figure BDA0002424672030000053
Figure BDA0002424672030000054
in the formula (I), the compound is shown in the specification,
Figure BDA0002424672030000055
represents the predicted yaw rate at the n +1 th time, ynRepresenting the measured value of yaw rate at time n, yn-1Representing the measured value of yaw rate, theta, at time n-1ωRepresenting a parameter vector, θω＝[a1 a2 b1]T，a1Representing a first direct yaw moment parameter, a2Representing a second direct yaw moment parameter, b1Representing a vehicle yaw rate parameter, xn-jActual value, u, representing yaw rate at time n-jnRepresenting the direct yaw moment, u, at time n-1n-1Representing the direct yaw moment at time n-1, enRepresents white noise at the nth time, wnRepresenting the measurement noise at time n, yn-jRepresenting the measured value of yaw rate, w, at time n-jn-jRepresenting the noise measured at time n-j,
Figure BDA0002424672030000061
the regression vector is represented by a vector of coefficients,
Figure BDA0002424672030000062
vωwhich is representative of the noisy data, is,
Figure BDA0002424672030000063
optionally, the control module specifically includes:
the optimization unit is used for optimizing a direct yaw moment according to the vehicle yaw velocity predicted value and the vehicle yaw velocity expected value to obtain the optimized direct yaw moment;
the motor torque determining unit is used for averaging the optimized direct yaw moment to obtain the hub motor torque of each wheel;
and the control unit is used for performing vehicle transverse stable control according to the torque of the hub motor.
Optionally, the optimization unit specifically includes:
an objective function determining subunit, configured to determine an objective function according to the following formula:
Figure BDA0002424672030000064
wherein J represents an objective function, NpDenotes the prediction step size, YiRepresents the i-th prediction step yaw-rate prediction vector, Re represents the vehicle yaw-rate desired-value vector,
Figure BDA0002424672030000065
q denotes a first weight matrix, R denotes a second weight matrix, U denotes a direct yaw moment vector,
Figure BDA0002424672030000066
Figure BDA0002424672030000067
denotes the NthcIndividual control step size direct yaw moment, NcRepresenting a control step size, eta representing a relaxation factor, phi representing a third weight matrix;
a constraint determining subunit for determining a constraint according to the following formula:
Figure BDA0002424672030000068
ΔM≤Mmax
y(n+Nc|n)＝ωd
in the formula (I), the compound is shown in the specification,
Figure BDA0002424672030000069
indicates that the predicted value of the yaw rate of the vehicle at the n +1 th time is obtained from the known yaw rates of the vehicle at the n times, and phi (n | n) indicates that the predicted value of the yaw rate of the vehicle at the n th time is obtained from the known yaw rates of the vehicle at the n timesThe regression vector of (a) is calculated,
Figure BDA00024246720300000610
representing a parameter vector for the n-th moment obtained from the known yaw rates of the vehicle at n moments, Δ M representing the direct yaw moment, MmaxRepresenting the maximum value of the direct yaw moment, y (N + N)cN) indicates that the N + N-th vehicle yaw rate is obtained from the known vehicle yaw rates at N timescPredicting the vehicle yaw angular speed at the moment;
and the optimization subunit is used for determining a direct yaw moment vector according to the objective function and the constraint condition, and taking a first element in the direct yaw moment vector as the optimized direct yaw moment.
Compared with the prior art, the invention has the beneficial effects that:
the invention has proposed a vehicle lateral stability control method and system, set up the prediction model of vehicle yaw velocity according to vehicle yaw velocity historical data, vehicle direct yaw moment historical data and noise data, the model does not rely on changeable and can't know accurately in advance vehicle parameter and environmental parameter, and on the basis of the model, adopt the parameter vector in the method determination model of the recursive least square according to vehicle yaw velocity current data, carry on the prediction of vehicle yaw velocity according to parameter vector and vehicle yaw velocity current data, get the predicted value of vehicle yaw velocity, because the model parameter is time-varying, through carrying on real-time estimation and renewal to the parameter vector, make the vehicle model have adaptivity, can be suitable for all working conditions that the vehicle runs; in addition, the model has certain robustness on the measurement error of the sensor, has certain filtering effect on noise in the yaw velocity measurement value of the vehicle, and can effectively improve the yaw stability of the vehicle.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram of a control structure according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for controlling lateral stability of a distributed drive electric vehicle according to an embodiment of the present invention;
fig. 3 is a structural diagram of a lateral stability control system of a distributed drive electric vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for controlling the lateral stability of a distributed driving electric vehicle, which do not depend on variable vehicle parameters and environment parameters which cannot be accurately obtained in advance, have self-adaptability, are suitable for all working conditions of vehicle running and can effectively improve the yaw stability of the vehicle.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Examples
Fig. 1 is a schematic diagram of a control structure in an embodiment of the present invention, and as shown in fig. 1, the control structure of the present invention includes four parts: the device comprises a signal input module, an upper layer controller module, a lower layer controller module and an actuator module.
The signal input module is used for inputting signals to an upper layer controller: a yaw rate reference signal.
The upper layer controller module comprises three parts: the system comprises a vehicle prediction model part, an optimization solver part and a parameter estimator part. The module has the function of solving a direct yaw moment for improving the yaw stability of the vehicle.
And thirdly, the lower layer controller module is used for averagely distributing the direct yaw moment generated by the upper layer controller to the actuating mechanism.
The actuator module is an actuating mechanism and comprises: 1. the hub motor is used for executing the torque distributed by the lower layer controller; 2. and the vehicle body is used for feeding back sensor parameters to form closed-loop control.
Fig. 2 is a flowchart of a lateral stability control method for a distributed-drive electric vehicle according to an embodiment of the present invention, and as shown in fig. 2, the lateral stability control method for a distributed-drive electric vehicle includes:
step 101: and acquiring vehicle motion parameters and vehicle intrinsic parameters.
Step 102: and determining the expected value of the vehicle yaw rate according to the vehicle motion parameters and the vehicle intrinsic parameters.
Step 102, specifically comprising:
determining a vehicle yaw rate desired value according to the following formula:
Figure BDA0002424672030000081
Figure BDA0002424672030000082
|ωmax|＝μg/vx
in the formula, ωdIndicating desired value of yaw rate, v, of vehiclexRepresenting the vehicle longitudinal speed, delta representing the vehicle front wheel angle, a representing the vehicle centre of mass to front axle length, b representing the vehicle centre of mass to rear axle length, L representing the vehicle centre of mass to front axle length plus centre of mass to rear axle length, L ═ a + b, K representing the vehicle stability factor, m representing the vehicle mass, K representing the vehicle mass, and K representing the vehicle mass1Represents the cornering stiffness, k, of the front tyre2Represents the cornering stiffness of the rear tire, sgn (δ) represents a sign function, ωmaxRepresents the maximum value of yaw rate, and μ represents the road surface adhesion systemThe number, g, represents the gravitational acceleration.
Step 103: vehicle yaw rate history data, vehicle direct yaw moment history data, and noise data are obtained.
Step 104: and establishing a vehicle yaw rate prediction model according to the vehicle yaw rate historical data, the vehicle direct yaw moment historical data and the noise data.
Step 104, specifically comprising:
a vehicle yaw rate prediction model is established according to the following formula:
Figure BDA0002424672030000091
Figure BDA0002424672030000092
in the formula (I), the compound is shown in the specification,
Figure BDA0002424672030000093
represents the predicted yaw rate at the n +1 th time, ynRepresenting the measured value of yaw rate at time n, yn-1Representing the measured value of yaw rate, theta, at time n-1ωRepresenting a parameter vector, θω＝[a1 a2 b1]T，ajRepresenting the jth direct yaw moment parameter, a1Representing a first direct yaw moment parameter, a2Representing a second direct yaw moment parameter, b1Representing a vehicle yaw rate parameter, xn-jActual value, u, representing yaw rate at time n-jnRepresenting the direct yaw moment, u, at time n-1n-1Representing the direct yaw moment at time n-1, enRepresents white noise at the nth time, wnRepresenting the measurement noise at time n, yn-jRepresenting the measured value of yaw rate, w, at time n-jn-jRepresenting the noise measured at time n-j,
Figure BDA0002424672030000094
the regression vector is represented by a vector of coefficients,
Figure BDA0002424672030000095
vωwhich is representative of the noisy data, is,
Figure BDA0002424672030000096
in particular, the method comprises the following steps of,
the invention adopts a black box model to establish a vehicle yaw velocity prediction model which is used for representing the relationship between the vehicle yaw velocity (output) and the direct yaw moment (input). Black box models are generally divided into two categories: autoregressive model (AR) and autoregressive model with external input (ARX). Because of the constant driver inputs in the vehicle system, the present invention uses the ARX model to build the vehicle model. The ARX model can be expressed as:
A(q)zn＝B(q)un+en (1)
wherein the content of the first and second substances,
Figure BDA0002424672030000101
q denotes the time shift operator, znIndicates the state at the nth time, unRepresents an input, enIs white noise. a is1,…ana,b1,…bnbAre model parameters. Equation (1) can be rewritten as:
Figure BDA0002424672030000102
equation (2) characterizes that the state at each moment is compared with the previous naState of the moment and nbThe inputs are related. Thus, the equation can be passed through a known state zn-naTo zn-1Advance one-step prediction of state znExpressed as:
Figure BDA0002424672030000103
Figure BDA0002424672030000104
Figure BDA0002424672030000105
wherein the content of the first and second substances,
Figure BDA0002424672030000106
representation to true state znThe predicted value of (2).
Figure BDA0002424672030000107
And theta represents a regression vector and a parameter vector respectively, and the vector dimensions of the regression vector and the parameter vector are both na+nb. The vector dimension represents a fixed-length moving window function, and the number of data in the window is a fixed value na+nb. As time goes forward, when a new data comes into the window at each time instant, it means that an old data is removed from the window. If the vector dimension is large, it means that there is more data in the window, which has the advantage of higher signal-to-noise ratio. However, the large amount of data reduces the calculation efficiency of the controller, and affects the real-time performance of the control. In consideration of the problems of prediction accuracy and calculation efficiency, the method selects a model with a parameter vector dimension of 3 to model the yaw angular velocity of the vehicle:
Figure BDA0002424672030000111
wherein xnThe true value of the yaw rate is represented. The yaw-rate signal measured by the sensor can be expressed as: y isn＝xn+wn. Wherein wnRepresenting the measured noise, usually white noise, the ARX model from which the yaw rate can be derived is:
Figure BDA0002424672030000112
wherein the regression vector is
Figure BDA0002424672030000113
The parameter vector is thetaω＝[a1 a2 b1]TNoise is
Figure BDA0002424672030000114
Step 105: current data of the yaw rate of the vehicle is acquired.
Step 106: determining a parameter vector theta in the model by adopting a recursive least square method according to current data of the vehicle yaw angular velocity based on the modelω(ii) a The parameter vector includes a direct yaw moment parameter and a vehicle yaw rate parameter.
The core idea of the least square method is to minimize the estimation error, and the calculation formula is as follows:
Figure BDA0002424672030000115
ψNrepresenting a regression vector of dimension N, YNThe nth prediction step yaw rate prediction vector is shown.
As a solution method with strong feasibility of the least square method, the solution steps of the recursive least square method are as follows:
Figure BDA0002424672030000116
Figure BDA0002424672030000121
Figure BDA0002424672030000122
where λ is 0.99, which represents a forgetting factor, p (t) represents a covariance matrix, and l (t) represents a gain vector.So far, the model parameter vector can be accurately estimated
Figure BDA0002424672030000123
Step 107: and based on the model, predicting the vehicle yaw rate according to the parameter vector and the current data of the vehicle yaw rate to obtain a predicted value of the vehicle yaw rate.
Step 108: and performing the lateral stability control of the vehicle according to the predicted value of the vehicle yaw rate and the expected value of the vehicle yaw rate.
Step 108, specifically comprising:
and optimizing the direct yaw moment according to the vehicle yaw velocity predicted value and the vehicle yaw velocity expected value to obtain the optimized direct yaw moment.
The objective function is determined according to the following formula:
Figure BDA0002424672030000124
wherein J represents an objective function, NpDenotes the prediction step size, YiRepresents the i-th prediction step yaw-rate prediction vector, Re represents the vehicle yaw-rate desired-value vector,
Figure BDA0002424672030000125
q denotes a first weight matrix, R denotes a second weight matrix, U denotes a direct yaw moment vector,
Figure BDA0002424672030000126
Figure BDA0002424672030000127
denotes the NthcIndividual control step size direct yaw moment, NcDenotes the control step size, η denotes the relaxation factor and phi denotes the third weight matrix.
The constraints are determined according to the following formula:
Figure BDA0002424672030000128
ΔM≤Mmax
y(n+Nc|n)＝ωd
in the formula (I), the compound is shown in the specification,
Figure BDA0002424672030000129
indicates that the predicted value of the yaw rate of the vehicle at the n +1 th moment is obtained from the known yaw rates of the vehicle at the n moments, phi (n | n) indicates that the regression vector at the n th moment is obtained from the known yaw rates of the vehicle at the n moments,
Figure BDA00024246720300001210
representing a parameter vector for the n-th moment obtained from the known yaw rates of the vehicle at n moments, Δ M representing the direct yaw moment, MmaxRepresenting the maximum value of the direct yaw moment, y (N + N)cN) indicates that the N + N-th vehicle yaw rate is obtained from the known vehicle yaw rates at N timescPredicting the vehicle yaw angular speed at the moment;
determining a direct yaw moment vector U according to the objective function and the constraint condition, and enabling a first element delta M in the direct yaw moment vector1As an optimized direct yaw moment.
Averaging the optimized direct yaw moment to obtain the hub motor torque of each wheel;
and performing vehicle transverse stable control according to the torque of the hub motor.
In particular, the method comprises the following steps of,
the optimization solver in fig. 1 is an adaptive model predictive controller, and the function of the optimization solver is to optimally solve the direct yaw moment, so as to improve the yaw stability of the vehicle. The design of the controller is based on the vehicle model of equation 5 and is further rewritten as:
Figure BDA0002424672030000131
Figure BDA0002424672030000132
here, the
Figure BDA0002424672030000133
The characterization obtains an estimate of the state at time n +1 from the known n states. In the same way, the method for preparing the composite material,
Figure BDA0002424672030000134
and
Figure BDA0002424672030000135
respectively representing the real value and the estimated value of the nth moment obtained by the known n states. y (n +1| n) represents a measurement equation.
Let equation (8) be at the prediction step size Np10 and control step NcThe expansion is performed within 3, and the vector of equation (9) can be obtained.
Figure BDA0002424672030000136
Y＝[y(n+1|n)y(n+2|n)…y(n+Nc|n)]T
A model predictive controller designed based on this equation can be expressed as:
Figure BDA0002424672030000137
Figure BDA0002424672030000138
ΔM≤Mmax (12)
y(n+Nc|n)＝ωd (13)
where equation (10) characterizes the goal of the controller: causing the yaw rate obtained by the model prediction to track the yaw-rate reference value while causing the control to be performedThe smaller the value of the manipulated variable, the better, and by adding a relaxation factor, the feasibility of the solution is improved. Equations (11) to (13) are constraints in which equation (11) represents that the yaw rate satisfies the prediction model as shown in equation (8). Equation (12) represents the maximum value of the direct yaw moment, constrained by the peak torque of the in-wheel motor. Equation (13) represents the terminal constraints, which can guarantee the stability of the controller closed-loop system. The first direct yaw moment value delta M in the calculated control variable vector U1And is transmitted to the lower layer controller as an output of the upper layer controller.
The lower controller module is used for averagely distributing the direct yaw moment generated by the upper controller to the actuators, namely calculating motor torques T1, T2, T3 and T4 and distributing the motor torques to hub motors of 4 wheels.
The actuator module is an actuating mechanism and comprises: 1. the hub motors of 4 wheels are used for executing the torque distributed by the lower layer controller; 2. and the vehicle body is used for feeding back the parameters of the yaw velocity sensor to the upper layer controller to form closed-loop control.
Fig. 3 is a structural diagram of a lateral stability control system of a distributed drive electric vehicle according to an embodiment of the present invention. As shown in fig. 3, a lateral stability control system for a distributed drive electric vehicle includes:
the first obtaining module 301 is used for obtaining vehicle motion parameters and vehicle intrinsic parameters.
And a vehicle yaw rate expected value determining module 302 for determining a vehicle yaw rate expected value according to the vehicle motion parameters and the vehicle intrinsic parameters.
The vehicle yaw rate desired value determination module 302 specifically includes:
a vehicle yaw-rate desired-value determining unit for determining a vehicle yaw-rate desired value according to the following formula:
Figure BDA0002424672030000141
Figure BDA0002424672030000142
|ωmax|＝μg/vx
in the formula, ωdIndicating desired value of yaw rate, v, of vehiclexRepresenting the vehicle longitudinal speed, delta representing the vehicle front wheel angle, a representing the vehicle centre of mass to front axle length, b representing the vehicle centre of mass to rear axle length, L representing the vehicle centre of mass to front axle length plus centre of mass to rear axle length, L ═ a + b, K representing the vehicle stability factor, m representing the vehicle mass, K representing the vehicle mass, and K representing the vehicle mass1Represents the cornering stiffness, k, of the front tyre2Represents the cornering stiffness of the rear tire, sgn (δ) represents a sign function, ωmaxThe maximum value of the yaw rate is shown, and μ represents the road surface adhesion coefficient.
And a second obtaining module 303, configured to obtain vehicle yaw rate history data, vehicle direct yaw moment history data, and noise data.
A vehicle yaw-rate prediction model building module 304 for building a vehicle yaw-rate prediction model based on vehicle yaw-rate historical data, vehicle direct yaw-moment historical data, and noise data.
The vehicle yaw rate prediction model building module 304 specifically includes:
a vehicle yaw-rate prediction model creation unit for creating a vehicle yaw-rate prediction model according to the following formula:
Figure BDA0002424672030000151
Figure BDA0002424672030000152
in the formula (I), the compound is shown in the specification,
Figure BDA0002424672030000153
represents the predicted yaw rate at the n +1 th time, ynRepresenting the measured value of yaw rate at time n, yn-1Representing the measured value of yaw rate, theta, at time n-1ωRepresenting a parameter vector, θω＝[a1 a2 b1]T，a1Representing a first direct yaw moment parameter, a2Representing a second direct yaw moment parameter, b1Representing a vehicle yaw rate parameter, xn-jActual value, u, representing yaw rate at time n-jnRepresenting the direct yaw moment, u, at time n-1n-1Representing the direct yaw moment at time n-1, enRepresents white noise at the nth time, wnRepresenting the measurement noise at time n, yn-jRepresenting the measured value of yaw rate, w, at time n-jn-jRepresenting the noise measured at time n-j,
Figure BDA0002424672030000154
the regression vector is represented by a vector of coefficients,
Figure BDA0002424672030000155
vωwhich is representative of the noisy data, is,
Figure BDA0002424672030000156
a third obtaining module 305 for obtaining the current data of the vehicle yaw rate.
A parameter vector determination module 306, configured to determine a parameter vector in the model by using a recursive least square method according to current data of the vehicle yaw angular velocity based on the model; the parameter vector includes a direct yaw moment parameter and a vehicle yaw rate parameter.
And the vehicle yaw rate predicted value determining module 307 is used for predicting the vehicle yaw rate according to the parameter vector and the current data of the vehicle yaw rate based on the model to obtain a vehicle yaw rate predicted value.
And the control module 308 is used for carrying out vehicle lateral stability control according to the predicted value of the vehicle yaw rate and the expected value of the vehicle yaw rate.
The control module 308 specifically includes:
and the optimization unit is used for optimizing the direct yaw moment according to the vehicle yaw velocity predicted value and the vehicle yaw velocity expected value to obtain the optimized direct yaw moment.
The optimization unit specifically comprises:
an objective function determining subunit, configured to determine an objective function according to the following formula:
Figure BDA0002424672030000161
wherein J represents an objective function, NpDenotes the prediction step size, YiRepresents the i-th prediction step yaw-rate prediction vector, Re represents the vehicle yaw-rate desired-value vector,
Figure BDA0002424672030000162
q denotes a first weight matrix, R denotes a second weight matrix, U denotes a direct yaw moment vector,
Figure BDA0002424672030000163
Figure BDA0002424672030000164
denotes the NthcIndividual control step size direct yaw moment, NcDenotes the control step size, η denotes the relaxation factor and phi denotes the third weight matrix.
A constraint determining subunit for determining a constraint according to the following formula:
Figure BDA0002424672030000165
ΔM≤Mmax
y(n+Nc|n)＝ωd
in the formula (I), the compound is shown in the specification,
Figure BDA0002424672030000166
indicating that the predicted value of the yaw rate of the vehicle at the n +1 th time is obtained from the known yaw rates of the vehicle at the n timesPhi (n | n) denotes a regression vector at the n-th time obtained from the known yaw rates of the vehicle at the n times,
Figure BDA0002424672030000167
representing a parameter vector for the n-th moment obtained from the known yaw rates of the vehicle at n moments, Δ M representing the direct yaw moment, MmaxRepresenting the maximum value of the direct yaw moment, y (N + N)cN) indicates that the nn + N is obtained by knowing the yaw rate of the vehicle at N timescPredicting the vehicle yaw angular speed at the moment;
an optimization subunit, configured to determine a direct yaw moment vector U according to the objective function and constraint conditions, and to assign a first element Δ M in the direct yaw moment vector U1As an optimized direct yaw moment.
And the motor torque determining unit is used for averaging the optimized direct yaw moment to obtain the hub motor torque of each wheel.
And the control unit is used for performing vehicle transverse stable control according to the torque of the in-wheel motor.
For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The vehicle yaw angular velocity model based on the ARX model provided by the invention does not depend on variable vehicle parameters and environment parameters which cannot be accurately obtained in advance, and only depends on input and output data within a certain step length in the past. The model parameters in the model are time-varying, and the model parameters can be estimated in real time through a parameter estimation method, so that the model is updated in real time, and therefore, the model has self-adaptability and can be suitable for all working conditions of vehicle running. In addition, the model has certain robustness to sensor measurement errors and certain filtering effect on noise in sensor measurement signals. Based on the model, the method and the system for controlling the lateral stability of the distributed driving electric vehicle can improve the yaw stability of the vehicle.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In summary, this summary should not be construed to limit the present invention.

Claims (10)

1. A vehicle lateral stability control method characterized by comprising:
obtaining vehicle motion parameters and vehicle intrinsic parameters;
determining a vehicle yaw velocity expected value according to the vehicle motion parameters and the vehicle intrinsic parameters;
obtaining historical data of vehicle yaw velocity, historical data of vehicle direct yaw moment and noise data;
establishing a vehicle yaw rate prediction model according to the vehicle yaw rate historical data, the vehicle direct yaw moment historical data and the noise data;
acquiring current data of the yaw velocity of the vehicle;
determining a parameter vector in the model by adopting a recursive least square method according to the current data of the vehicle yaw angular velocity based on the model; the parameter vector comprises a direct yaw moment parameter and a vehicle yaw rate parameter;
based on the model, predicting the vehicle yaw rate according to the parameter vector and the current data of the vehicle yaw rate to obtain a predicted value of the vehicle yaw rate;
and performing vehicle lateral stability control according to the vehicle yaw rate predicted value and the vehicle yaw rate expected value.
2. The vehicle lateral stability control method according to claim 1, wherein the determining a vehicle yaw rate desired value from the vehicle motion parameter and the vehicle intrinsic parameter specifically comprises:
determining a vehicle yaw rate desired value according to the following formula:
Figure FDA0002933785560000011
Figure FDA0002933785560000012
|ωmax|＝μg/vx
in the formula, ωdIndicating desired value of yaw rate, v, of vehiclexRepresenting the vehicle longitudinal speed, delta representing the vehicle front wheel angle, a representing the vehicle centre of mass to front axle length, b representing the vehicle centre of mass to rear axle length, L representing the vehicle centre of mass to front axle length plus centre of mass to rear axle length, L ═ a + b, K representing the vehicle stability factor, m representing the vehicle mass, K representing the vehicle mass, and K representing the vehicle mass1Represents the cornering stiffness, k, of the front tyre2Represents the cornering stiffness of the rear tire, sgn (δ) represents a sign function, ωmaxThe maximum value of the yaw rate is shown, and μ represents the road surface adhesion coefficient.
3. The vehicle lateral stability control method according to claim 2, wherein the building of a vehicle yaw rate prediction model from the vehicle yaw rate history data, the vehicle direct yaw moment history data, and the noise data specifically includes:
a vehicle yaw rate prediction model is established according to the following formula:
Figure FDA0002933785560000013
Figure FDA0002933785560000021
in the formula (I), the compound is shown in the specification,
Figure FDA0002933785560000022
represents the predicted yaw rate at the n +1 th time, ynRepresenting the measured value of yaw rate at time n, yn-1Representing the measured value of yaw rate, theta, at time n-1ωRepresenting a parameter vector, θω＝[a1 a2 b1]T，a1Representing a first direct yaw moment parameter, a2Representing a second direct yaw moment parameter, b1Representing a vehicle yaw rate parameter, xn-jActual value, u, representing yaw rate at time n-jnRepresenting the direct yaw moment, u, at time nn-1Representing the direct yaw moment at time n-1, enRepresents white noise at the nth time, wnRepresenting the measurement noise at time n, yn-jRepresenting the measured value of yaw rate, w, at time n-jn-jRepresenting the noise measured at time n-j,
Figure FDA0002933785560000023
the regression vector is represented by a vector of coefficients,
Figure FDA0002933785560000024
vωwhich is representative of the noisy data, is,
Figure FDA0002933785560000025
4. the vehicle lateral stability control method according to claim 3, characterized in that the performing the vehicle lateral stability control based on the predicted vehicle yaw rate value and the desired vehicle yaw rate value specifically includes:
optimizing a direct yaw moment according to the vehicle yaw velocity predicted value and the vehicle yaw velocity expected value to obtain an optimized direct yaw moment;
averaging the optimized direct yaw moment to obtain the hub motor torque of each wheel;
and performing vehicle transverse stable control according to the torque of the hub motor.
5. The vehicle lateral stability control method according to claim 4, wherein the optimizing a direct yaw moment according to the vehicle yaw rate predicted value and the vehicle yaw rate desired value to obtain an optimized direct yaw moment specifically comprises:
the objective function is determined according to the following formula:
Figure FDA0002933785560000026
wherein J represents an objective function, NpDenotes the prediction step size, YiRepresents the i-th prediction step yaw-rate prediction vector, Re represents the vehicle yaw-rate desired-value vector,
Figure FDA0002933785560000027
q denotes a first weight matrix, R denotes a second weight matrix, U denotes a direct yaw moment vector,
Figure FDA0002933785560000031
Figure FDA0002933785560000032
denotes the NthcIndividual control step size direct yaw moment, NcRepresenting a control step size, eta representing a relaxation factor, phi representing a third weight matrix;
the constraints are determined according to the following formula:
Figure FDA0002933785560000033
ΔM≤Mmax
y(n+Nc|n)＝ωd
in the formula (I), the compound is shown in the specification,
Figure FDA0002933785560000034
indicates that the predicted value of the yaw rate of the vehicle at the n +1 th moment is obtained from the known yaw rates of the vehicle at the n moments, phi (n | n) indicates that the regression vector at the n th moment is obtained from the known yaw rates of the vehicle at the n moments,
Figure FDA0002933785560000035
representing a parameter vector for the n-th moment obtained from the known yaw rates of the vehicle at n moments, Δ M representing the direct yaw moment, MmaxRepresenting the maximum value of the direct yaw moment, y (N + N)cN) indicates that the N + N-th vehicle yaw rate is obtained from the known vehicle yaw rates at N timescPredicting the vehicle yaw angular speed at the moment;
and determining a direct yaw moment vector according to the objective function and the constraint condition, and taking a first element in the direct yaw moment vector as the optimized direct yaw moment.
6. A vehicle lateral stability control system, comprising:
the first acquisition module is used for acquiring vehicle motion parameters and vehicle intrinsic parameters;
the vehicle yaw rate expected value determining module is used for determining a vehicle yaw rate expected value according to the vehicle motion parameters and the vehicle intrinsic parameters;
the second acquisition module is used for acquiring historical data of the vehicle yaw velocity, historical data of the vehicle direct yaw moment and noise data;
the vehicle yaw rate prediction model establishing module is used for establishing a vehicle yaw rate prediction model according to the vehicle yaw rate historical data, the vehicle direct yaw moment historical data and the noise data;
the third acquisition module is used for acquiring the current data of the yaw rate of the vehicle;
the parameter vector determining module is used for determining parameter vectors in the model by adopting a recursive least square method according to the current data of the vehicle yaw angular velocity based on the model; the parameter vector comprises a direct yaw moment parameter and a vehicle yaw rate parameter;
the vehicle yaw velocity predicted value determining module is used for predicting the vehicle yaw velocity according to the parameter vector and the current data of the vehicle yaw velocity based on the model to obtain a vehicle yaw velocity predicted value;
and the control module is used for carrying out vehicle lateral stability control according to the predicted vehicle yaw rate value and the expected vehicle yaw rate value.
7. The vehicle lateral stability control system of claim 6, wherein the vehicle yaw-rate desired-value determination module specifically comprises:
a vehicle yaw-rate desired-value determining unit for determining a vehicle yaw-rate desired value according to the following formula:
Figure FDA0002933785560000041
Figure FDA0002933785560000042
|ωmax|＝μg/vx
in the formula, ωdIndicating desired value of yaw rate, v, of vehiclexRepresenting the vehicle longitudinal speed, delta representing the vehicle front wheel angle, a representing the vehicle centre of mass to front axle length, b representing the vehicle centre of mass to rear axle length, L representing the vehicle centre of mass to front axle length plus centre of mass to rear axle length, L ═ a + b, K representing the vehicle stability factor, m representing the vehicle mass, K representing the vehicle mass, and K representing the vehicle mass1Represents the cornering stiffness, k, of the front tyre2Represents the cornering stiffness of the rear tire, sgn (δ) represents a sign function, ωmaxThe maximum value of the yaw rate is shown, and μ represents the road surface adhesion coefficient.
8. The vehicle lateral stability control system of claim 7, wherein the vehicle yaw-rate prediction model building module specifically comprises:
a vehicle yaw-rate prediction model creation unit for creating a vehicle yaw-rate prediction model according to the following formula:
Figure FDA0002933785560000043
Figure FDA0002933785560000044
in the formula (I), the compound is shown in the specification,
Figure FDA0002933785560000045
represents the predicted yaw rate at the n +1 th time, ynRepresenting the measured value of yaw rate at time n, yn-1Representing the measured value of yaw rate, theta, at time n-1ωRepresenting a parameter vector, θω＝[a1 a2 b1]T，a1Representing a first direct yaw moment parameter, a2Representing a second direct yaw moment parameter, b1Representing a vehicle yaw rate parameter, xn-jActual value, u, representing yaw rate at time n-jnRepresenting the direct yaw moment, u, at time nn-1Representing the direct yaw moment at time n-1, enRepresents white noise at the nth time, wnRepresenting the measurement noise at time n, yn-jRepresenting the measured value of yaw rate, w, at time n-jn-jRepresenting the noise measured at time n-j,
Figure FDA0002933785560000051
the regression vector is represented by a vector of coefficients,
Figure FDA0002933785560000052
vωrepresenting noiseThe data of the data is transmitted to the data receiver,
Figure FDA0002933785560000053
9. the vehicle lateral stability control system of claim 8, wherein the control module specifically comprises:
the optimization unit is used for optimizing a direct yaw moment according to the vehicle yaw velocity predicted value and the vehicle yaw velocity expected value to obtain the optimized direct yaw moment;
the motor torque determining unit is used for averaging the optimized direct yaw moment to obtain the hub motor torque of each wheel;
and the control unit is used for performing vehicle transverse stable control according to the torque of the hub motor.
10. The vehicle lateral stability control system of claim 9, wherein the optimization unit specifically comprises:
an objective function determining subunit, configured to determine an objective function according to the following formula:
Figure FDA0002933785560000054
wherein J represents an objective function, NpDenotes the prediction step size, YiRepresents the i-th prediction step yaw-rate prediction vector, Re represents the vehicle yaw-rate desired-value vector,
Figure FDA0002933785560000055
q denotes a first weight matrix, R denotes a second weight matrix, U denotes a direct yaw moment vector,
Figure FDA0002933785560000056
Figure FDA0002933785560000057
denotes the NthcIndividual control step size direct yaw moment, NcRepresenting a control step size, eta representing a relaxation factor, phi representing a third weight matrix;
a constraint determining subunit for determining a constraint according to the following formula:
Figure FDA0002933785560000058
ΔM≤Mmax
y(n+Nc|n)＝ωd
in the formula (I), the compound is shown in the specification,
Figure FDA0002933785560000059
indicates that the predicted value of the yaw rate of the vehicle at the n +1 th moment is obtained from the known yaw rates of the vehicle at the n moments, phi (n | n) indicates that the regression vector at the n th moment is obtained from the known yaw rates of the vehicle at the n moments,
Figure FDA00029337855600000510
representing a parameter vector for the n-th moment obtained from the known yaw rates of the vehicle at n moments, Δ M representing the direct yaw moment, MmaxRepresenting the maximum value of the direct yaw moment, y (N + N)cN) indicates that the N + N-th vehicle yaw rate is obtained from the known vehicle yaw rates at N timescPredicting the vehicle yaw angular speed at the moment;
and the optimization subunit is used for determining a direct yaw moment vector according to the objective function and the constraint condition, and taking a first element in the direct yaw moment vector as the optimized direct yaw moment.
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