CN112270039A - Distributed asynchronous fusion-based nonlinear state estimation method for drive-by-wire chassis vehicle - Google Patents

Distributed asynchronous fusion-based nonlinear state estimation method for drive-by-wire chassis vehicle Download PDF

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CN112270039A
CN112270039A CN202011116288.0A CN202011116288A CN112270039A CN 112270039 A CN112270039 A CN 112270039A CN 202011116288 A CN202011116288 A CN 202011116288A CN 112270039 A CN112270039 A CN 112270039A
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罗建
赵万忠
栾众楷
秦亚娟
郑双权
王崴崴
刘津强
张玉梅
董雪锋
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a distributed asynchronous fusion-based nonlinear state estimation method for a drive-by-wire chassis vehicle, which comprises the following steps: 1) establishing a four-degree-of-freedom motion differential equation of the vehicle including the longitudinal, lateral, transverse and side-rolling motion of the mass center; 2) establishing a vehicle nonlinear state equation and an observation equation according to the vehicle four-degree-of-freedom motion differential equation; 3) and iterating the state parameters in the nonlinear state equation of the vehicle to a nonlinear state time-lag volume Kalman fusion filter to obtain a nonlinear state fusion estimation value of the vehicle, wherein the nonlinear state fusion estimation value is used for real-time fusion estimation of the control key variables of the drive-by-wire chassis system. The invention effectively solves the problem of state time lag when the sensors observe and describe the running state of the vehicle due to different sampling frequencies of different vehicle-mounted sensors.

Description

Distributed asynchronous fusion-based nonlinear state estimation method for drive-by-wire chassis vehicle
Technical Field
The invention belongs to the field of intelligent driving environment perception, and particularly relates to a nonlinear state estimation method of a drive-by-wire chassis vehicle based on distributed asynchronous fusion.
Background
In recent years, with the deep application of information technology in the field of automobiles, the intelligent driving technology is further developed and perfected, and the wire control of an automobile chassis becomes a major trend of the development of modern automobiles. The control key of the drive-by-wire chassis system technology is to accurately acquire key state variables such as yaw rate, longitudinal and transverse speeds, vehicle body roll angle and the like which represent the running state of the vehicle. The state variables are main control variables in the vehicle drive-by-wire chassis control system and are also important basis for identifying the vehicle running state in real time and establishing the coordination control rule of the drive-by-wire chassis subsystem. However, due to the complexity of the vehicle dynamics control process and the influence of various aspects such as the test level and the test cost of the vehicle-mounted sensor, many key state variables cannot be directly, accurately or cheaply measured.
The existing method for estimating the running state of the automobile (such as the authorized publication number CN106250591B) mainly comprises the steps of establishing motion differential equations with nonlinear characteristics of automobile mass center motion, yaw motion, roll motion and the like, and then carrying out indirect estimation on vehicle state parameters by using extended Kalman filtering. However, in practical application, due to different sampling frequencies of different vehicle-mounted sensors, a state time lag situation occurs when the sensors observe and describe the vehicle running state, and great difficulty is brought to accurate description of statistical characteristics of system noise and observation noise, so that if a conventional filter for establishing a noise model in advance is adopted, phenomena such as inaccurate state estimation, even divergence and the like occur, and accurate control of a line control chassis control center on a vehicle chassis subsystem cannot be realized.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide a drive-by-wire chassis vehicle nonlinear state estimation method based on distributed asynchronous fusion, and the invention introduces a volume Kalman filtering theory and a multi-sensor information asynchronous fusion technology into vehicle nonlinear state estimation by taking a vehicle nonlinear dynamic model as a basis, designs a nonlinear state time-lag volume Kalman fusion filter, effectively solves the problem of state time lag when a sensor observes and describes the vehicle running state due to different sampling frequencies of different vehicle-mounted sensors, realizes real-time fusion estimation for actively controlling key variables of a drive-by-wire chassis system, and provides more accurate signals for the active safety control of a vehicle.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention discloses a distributed asynchronous fusion-based nonlinear state estimation method for a drive-by-wire chassis vehicle, which comprises the following steps of:
1) establishing a four-degree-of-freedom motion differential equation of the vehicle including the longitudinal, lateral, transverse and side-rolling motion of the mass center;
2) establishing a vehicle nonlinear state equation and an observation equation according to the vehicle four-degree-of-freedom motion differential equation;
3) and iterating the state parameters in the nonlinear state equation of the vehicle to a nonlinear state time-lag volume Kalman fusion filter to obtain a nonlinear state fusion estimation value of the vehicle, wherein the nonlinear state fusion estimation value is used for real-time fusion estimation of the control key variables of the drive-by-wire chassis system.
Further, the differential equation of motion in step 1) is:
Figure BDA0002730307040000021
in the formula, m is the mass of the whole vehicle, u is the longitudinal speed, v is the lateral speed, and phi and r are the lateral inclination speed and the yaw angular speed respectively; h is0Is the centroid to roll axis distance; h isrIs the roll center height; h isrf、hrrRespectively the distance from the front and rear shafts to the side-tipping shaft; epsilon is an included angle between the roll axis and the longitudinal axis; delta is a front wheel corner; i isb,xFor moment of inertia about the X axis, Ib,zFor moment of inertia about the Z axis, Ib,xzIs the product of inertia about axis X, Z; t is tfIs the front track, trIs the rear wheel track; k is a radical offFront tire cornering stiffness, krRear tire cornering stiffness; a is the distance from the center of mass to the front axle, and b is the distance from the center of mass to the rear axle; c. CfFor front suspension roll damping, crDamping the side inclination angle of the rear suspension; fy1For lateral forces of the left front wheel, Fy2For right front wheel side force, Fy3For lateral forces of the left rear wheel, Fy4Is the right rear wheel lateral force; fx1For left front wheel longitudinal force, Fx2Is the longitudinal force of the right front wheel, Fx3For left rear wheel longitudinal forces, Fx4The right rear wheel longitudinal force.
Further, the nonlinear state equation and the observation equation of the vehicle in the step 2) are as follows:
Figure BDA0002730307040000022
in the formula,
Figure BDA0002730307040000023
is the state estimate at time k +1, xkFor active control of state variables of the drive-by-wire chassis, f (-) represents the system dynamics function, process noise wkIs mean zero and covariance matrix QkWhite gaussian noise of (1); z is a radical ofk,iIs at tk,iMeasured values obtained at the moment, NkThe observed values are obtained by m sensors with different sampling frequencies at time intervals tk-1,tk]Internal acquisition, N acquired in a sequence of sample timeskIndividual observed value
Figure BDA0002730307040000024
Is asynchronous, satisfying the relationship:
Figure BDA0002730307040000025
hk,irepresenting a measurement function, xk,iIs shown at tk,iState quantity of time, measurement noise etak,iIs mean value of zero and covariance matrix of Rk,iWhite gaussian noise.
Further, the state prediction equation of the nonlinear state time-lag volume kalman fusion filter in step 3) is as follows:
Figure BDA0002730307040000031
the prediction covariance matrix is:
Figure BDA0002730307040000032
Figure BDA0002730307040000033
in the formula, Xk-1、
Figure BDA0002730307040000034
The definition is as follows:
Xk-1＝xk-1-j
Figure BDA0002730307040000035
Figure BDA0002730307040000036
wherein j, l belongs to [0,1, … d-1] and j is not equal to l, s, t belongs to [0,1, …, d-1], d is the volume point number.
Further, the state update equation of the middle nonlinear state time-lag volume kalman fusion filter is as follows:
Figure BDA0002730307040000037
updating the covariance matrix as:
Figure BDA0002730307040000038
the gain update equation is:
Figure BDA0002730307040000039
wherein,
Figure BDA00027303070400000310
Figure BDA00027303070400000311
Figure BDA00027303070400000312
further, the nonlinear state time-lag volume Kalman fusion filter is at tkDistributed fused estimation of time of day
Figure BDA0002730307040000041
And its error covariance matrix PkThe calculation formula of (a) is as follows:
Figure BDA0002730307040000042
Pk＝[(Pk|k-1)-1+Xk+Yk(Pk|k-1-Lk)-1Yk T]-1
in the formula,
Figure BDA0002730307040000044
Figure BDA0002730307040000045
Figure BDA0002730307040000046
Figure BDA0002730307040000047
Figure BDA0002730307040000048
Figure BDA0002730307040000051
Figure BDA0002730307040000052
Figure BDA0002730307040000053
Ek,i＝I-Q(tk,tk,i)(Pk|k-1)-1,i＝1,…,Nk
and is
Figure BDA0002730307040000054
In the formula, phi (t)k,tk,i+1) Represents from tkTo tk,i+1System state transition matrix of time phiT(tk,tk,i+1) A transpose matrix representing the state transition matrix; mk,iIs tk,iInformation matrix of time of day, InRepresenting an n-order identity matrix.
Further, the key variables in step 3) include: yaw rate, longitudinal and transverse rates, and vehicle body roll angle.
The invention has the beneficial effects that:
according to the invention, based on a vehicle nonlinear dynamics model, a volume Kalman filtering theory and a multi-sensor information asynchronous fusion technology are introduced into vehicle nonlinear state estimation, and a nonlinear state time-lag volume Kalman fusion filter is designed, so that the problem of state time lag caused by different sampling frequencies of different vehicle-mounted sensors when the sensors observe and describe the vehicle running state is effectively solved, the real-time fusion estimation for the active control key variable of the drive-by-wire chassis system is realized, and a more accurate signal is provided for the active safety control of the vehicle.
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FIG. 1 is a schematic diagram of the process of the present invention.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
Referring to fig. 1, the method for estimating the nonlinear state of the drive-by-wire chassis vehicle based on distributed asynchronous fusion of the invention comprises the following steps:
in order to more comprehensively reflect the nonlinear state of the vehicle, key state variables which are necessary and difficult to directly acquire in the active control of the linear control chassis system are fused and estimated in real time, and differential equations comprising the longitudinal, transverse, rolling and yawing motions of the vehicle are derived based on a nonlinear four-degree-of-freedom automobile model as follows:
force balance equation in X direction:
Figure BDA0002730307040000061
wherein u is a longitudinal velocity and v is a lateral velocity; phi and r are respectively the lateral inclination angular speed and the yaw angular speed; h is0Is the centroid to roll axis distance; epsilon is an included angle between the roll axis and the longitudinal axis; delta is a front wheel corner; fy1For lateral forces of the left front wheel, Fy2Is the right front wheel lateral force; fx1For left front wheel longitudinal force, Fx2Is the longitudinal force of the right front wheel, Fx3For left rear wheel longitudinal forces, Fx4The right rear wheel longitudinal force.
Force balance equation in the Y direction:
Figure BDA0002730307040000062
in the formula, Fy3For lateral forces of the left rear wheel, Fy4Is the right rear wheel lateral force.
Moment equation around the Z axis:
Figure BDA0002730307040000063
in the formula Ib,xFor moment of inertia about the X axis, Ib,zFor moment of inertia about the Z axis, Ib,xzIs the product of inertia about axis X, Z; a is the distance from the center of mass to the front axle, and b is the distance from the center of mass to the rear axle; t is tfIs the front track, trIs the rear track width.
Moment equation around the X-axis:
Figure BDA0002730307040000064
in the formula, hrIs the roll center height; h isrf、hrrRespectively the distance from the front and rear shafts to the side-tipping shaft; k is a radical offFront tire cornering stiffness, krRear tire cornering stiffness; c. CfFor front suspension roll damping, crDamping the rear suspension roll angle.
Establishing a state equation and a measurement equation according to an estimation object, linearizing a nonlinear model and assigning an initial value for recursive estimation, mainly comprising a prediction process and a correction process, wherein the specific process is as follows:
step 1): establishing a vehicle nonlinear state equation and an observation equation
Figure BDA0002730307040000065
In the formula,
Figure BDA0002730307040000066
is the state estimate at time k +1, xkFor active control of state variables of the drive-by-wire chassis, f (-) represents the system dynamics function, process noise wkIs mean zero and covariance matrix QkWhite gaussian noise of (1); z is a radical ofk,iIs at tk,iMeasured values obtained at the moment, NkThe observed values are obtained by m sensors with different sampling frequencies at time intervals tk-1,tk]Internal acquisition, N acquired in a sequence of sample timeskIndividual observed value
Figure BDA0002730307040000071
Is asynchronous, satisfying the relationship:
Figure BDA0002730307040000072
hk,irepresenting a measurement function, xk,iIs shown at tk,iState quantity of time, measurement noise etak,iIs mean value of zero and covariance matrix of Rk,iWhite gaussian noise.
Step 2): and (3) calculating a predicted volume point vector by the following specific calculation method:
pair-covariance matrix Pk-1|k-1Cholesky decomposition was performed:
Figure BDA0002730307040000073
volume point calculation:
Figure BDA0002730307040000074
in the formula, xibThe b-th column vector representing the set { ξ } of volumetric points, the set { ξ } having a total of 2n column vectors, is defined as follows:
Figure BDA0002730307040000075
volume point propagation:
Figure BDA0002730307040000076
in the formula,
Figure BDA0002730307040000077
representing the predicted volume point vector.
Step 3): the state and covariance matrix one-step prediction equation is:
Figure BDA0002730307040000078
Figure BDA0002730307040000079
step 4): calculating the vector of the measured volume point, wherein the specific calculation method comprises the following steps:
pair-covariance matrix Pk|k-1Cholesky decomposition was performed:
Figure BDA0002730307040000081
volume point calculation:
Figure BDA0002730307040000082
volume point propagation: zb,k|k-1＝h(Xb,k|k-1),b＝1,…,2n
Step 5): metrology prediction
Figure BDA0002730307040000083
One-step covariance matrix
Figure BDA0002730307040000084
One-step inter-cooperation variance matrix
Figure BDA0002730307040000085
The equation is:
Figure BDA0002730307040000086
Figure BDA0002730307040000087
Figure BDA0002730307040000088
step 6): updating a gain matrix:
Figure BDA0002730307040000089
step 7): covariance matrix update:
Figure BDA00027303070400000810
step 8): updating and estimating state variables:
Figure BDA00027303070400000811
step 9): information fusion process; local information state reconstruction for each sensor
Figure BDA00027303070400000812
And a correlation information matrix Mk,iAre all obtained asynchronously, at tkDistributed fused estimation of time of day
Figure BDA00027303070400000813
And its error covariance matrix PkThe calculation formula of (a) is as follows:
Figure BDA0002730307040000091
Pk＝[(Pk|k-1)-1+Xk+Yk(Pk|k-1-Lk)-1Yk T]-1
wherein,
Figure BDA0002730307040000093
Figure BDA0002730307040000094
Figure BDA0002730307040000095
Figure BDA0002730307040000096
Figure BDA0002730307040000097
Figure BDA0002730307040000098
Figure BDA0002730307040000101
and is
Figure BDA0002730307040000102
The specific process of the formula is as follows:
Figure BDA0002730307040000103
Figure BDA0002730307040000104
Ek,1＝I-Q(tk,tk,i)(Pk|k-1)-1,i＝1,…,Nk
information matrix Mk,iAnd information state reconstruction
Figure BDA0002730307040000105
The method is obtained through local sensor nodes, and the specific calculation method is as follows:
Figure BDA0002730307040000106
Figure BDA0002730307040000107
in the formula,
Figure BDA0002730307040000108
for an augmented measurement matrix of the sensor, a measurement function h is performedk,iFrom time tk,iTo time tkThe calculation formula of (2) is as follows:
Figure BDA0002730307040000109
in the formula,
Figure BDA0002730307040000111
denotes xk,iAnd time tk,iTo tkState of (1)
Figure BDA0002730307040000112
The covariance between;
Figure BDA0002730307040000113
represents a state xk,iAnd measure zk,iCross covariance matrix between; the calculation method is as follows:
Figure BDA0002730307040000114
Figure BDA0002730307040000115
volume point calculation:
Figure BDA0002730307040000116
in the formula, Sk,i-By a covariance matrix Pk,i-Cholesky decomposition gave:
Figure BDA0002730307040000117
volume point propagation:
Figure BDA0002730307040000118
Figure BDA0002730307040000119
Figure BDA00027303070400001110
in the formula,
Figure BDA00027303070400001111
indicating the time t fromk',i'To tk,iPropagation state of
Figure BDA00027303070400001112
The predicted observation vector of (a) is,
Figure BDA00027303070400001113
indicating the time t fromk,iTo tkThe state of (1).
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.

Claims (7)

1. A drive-by-wire chassis vehicle nonlinear state estimation method based on distributed asynchronous fusion is characterized by comprising the following steps:
1) establishing a four-degree-of-freedom motion differential equation of the vehicle including the longitudinal, lateral, transverse and side-rolling motion of the mass center;
2) establishing a vehicle nonlinear state equation and an observation equation according to the vehicle four-degree-of-freedom motion differential equation;
3) and iterating the state parameters in the nonlinear state equation of the vehicle to a nonlinear state time-lag volume Kalman fusion filter to obtain a nonlinear state fusion estimation value of the vehicle, wherein the nonlinear state fusion estimation value is used for real-time fusion estimation of the control key variables of the drive-by-wire chassis system.
2. The nonlinear state estimation method for the drive-by-wire chassis vehicle based on the distributed asynchronous fusion according to claim 1, characterized in that the kinematic differential equation in the step 1) is as follows:
Figure FDA0002730307030000011
in the formula, m is the mass of the whole vehicle, u is the longitudinal speed, v is the lateral speed, and phi and r are the lateral inclination speed and the yaw angular speed respectively; h is0Is the centroid to roll axis distance; h isrIs the roll center height; h isrf、hrrRespectively the distance from the front and rear shafts to the side-tipping shaft; epsilon is an included angle between the roll axis and the longitudinal axis; delta is a front wheel corner; i isb,xFor moment of inertia about the X axis, Ib,zFor moment of inertia about the Z axis, Ib,xzIs the product of inertia about axis X, Z; t is tfIs the front track, trIs the rear wheel track; k is a radical offFront tire cornering stiffness, krRear tire cornering stiffness; a is the distance from the center of mass to the front axle, and b is the distance from the center of mass to the rear axle; c. CfFor front suspension roll damping, crDamping the side inclination angle of the rear suspension; fy1For lateral forces of the left front wheel, Fy2For right front wheel side force, Fy3For lateral forces of the left rear wheel, Fy4Is the right rear wheel lateral force; fx1For left front wheel longitudinal force, Fx2Is the longitudinal force of the right front wheel, Fx3For left rear wheel longitudinal forces, Fx4The right rear wheel longitudinal force.
3. The distributed asynchronous fusion based nonlinear state estimation method for the drive-by-wire chassis vehicle according to claim 1, characterized in that the nonlinear state equation and the observation equation in the step 2) are as follows:
Figure FDA0002730307030000012
in the formula,
Figure FDA0002730307030000021
is the state estimate at time k +1, xkFor active control of state variables of the drive-by-wire chassis, f (-) represents the system dynamics function, process noise wkIs mean zero and covariance matrix QkWhite gaussian noise of (1); z is a radical ofk,iIs at tk,iMeasured values obtained at the moment, NkThe observed values are obtained by m sensors with different sampling frequencies at time intervals tk-1,tk]Internal acquisition, N acquired in a sequence of sample timeskIndividual observed value
Figure FDA0002730307030000022
Is asynchronous, satisfying the relationship:
Figure FDA0002730307030000023
hk,irepresenting a measurement function, xk,iIs shown at tk,iState quantity of time, measurement noise etak,iIs mean value of zero and covariance matrix of Rk,iWhite gaussian noise.
4. The nonlinear state estimation method for the drive-by-wire chassis vehicle based on the distributed asynchronous fusion of the claim 1, characterized in that the state prediction equation of the nonlinear state time-lag volume Kalman fusion filter in the step 3) is as follows:
Figure FDA0002730307030000024
the prediction covariance matrix is:
Figure FDA0002730307030000025
Figure FDA0002730307030000026
in the formula, Xk-1、
Figure FDA0002730307030000027
The definition is as follows:
Xk-1＝xk-1-j
Figure FDA0002730307030000028
Figure FDA0002730307030000029
wherein j, l belongs to [0,1, … d-1] and j is not equal to l, s, t belongs to [0,1, …, d-1], d is the volume point number.
5. The distributed asynchronous fusion-based nonlinear state estimation method for the drive-by-wire chassis vehicle according to claim 1, characterized in that the state update equation of the middle nonlinear state lag volume Kalman fusion filter is as follows:
Figure FDA0002730307030000031
updating the covariance matrix as:
Figure FDA0002730307030000032
the gain update equation is:
Figure FDA0002730307030000033
wherein,
Figure FDA0002730307030000034
Figure FDA0002730307030000035
Figure FDA0002730307030000036
6. the method for estimating nonlinear state of chassis-by-wire vehicle based on distributed asynchronous fusion according to claim 1, characterized in that the nonlinear state lag cubature Kalman fusion filter is at tkDistributed fused estimation of time of day
Figure FDA0002730307030000037
And its error covariance matrix PkThe calculation formula of (a) is as follows:
Figure FDA0002730307030000038
Figure FDA0002730307030000039
in the formula,
Figure FDA00027303070300000310
Figure FDA0002730307030000041
Figure FDA0002730307030000042
Figure FDA0002730307030000043
Figure FDA0002730307030000044
Figure FDA0002730307030000045
Figure FDA0002730307030000046
Figure FDA0002730307030000047
Ek,i＝I-Q(tk,tk,i)(Pk|k-1)-1,i＝1,…,Nk
and is
Figure FDA0002730307030000048
In the formula, phi (t)k,tk,i+1) Represents from tkTo tk,i+1System state transition matrix of time phiT(tk,tk,i+1) A transpose matrix representing the state transition matrix; mk,iIs tk,iInformation matrix of time of day, InRepresenting an n-order identity matrix.
7. The distributed asynchronous fusion based nonlinear state estimation method for the drive-by-wire chassis vehicle according to claim 1, wherein the key variables in the step 3) comprise: yaw rate, longitudinal and transverse rates, and vehicle body roll angle.
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CN113253615A (en) * 2021-06-22 2021-08-13 季华实验室 Motion state observation method and system based on distributed electric chassis
CN114475624A (en) * 2021-07-20 2022-05-13 浙江万安科技股份有限公司 Fusion estimation method for lateral state of drive-by-wire chassis vehicle considering uncertainty time lag
CN114520777A (en) * 2021-12-27 2022-05-20 上海仙途智能科技有限公司 Time lag identification method and device, computer readable storage medium and terminal

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