CN112270039A - Nonlinear state estimation method for drive-by-wire chassis vehicles based on distributed asynchronous fusion - Google Patents

Nonlinear state estimation method for drive-by-wire chassis vehicles based on distributed asynchronous fusion 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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Abstract

本发明公开了一种基于分布式异步融合的线控底盘车辆非线性状态估计方法,步骤如下:1)建立包含质心纵向、侧向、横摆及侧倾运动的车辆四自由度运动微分方程;2)根据所述车辆四自由度运动微分方程建立车辆非线性状态方程和观测方程;3)将所述车辆非线性状态方程中的状态参数迭代至非线性状态时滞容积卡尔曼融合滤波器,得到车辆非线性状态融合估计值,用于线控底盘系统控制关键变量的实时融合估计。本发明有效解决了由于不同车载传感器的采样频率不同所导致传感器对车辆行驶状态进行观测描述时出现的状态时滞问题。

Figure 202011116288

The invention discloses a nonlinear state estimation method of a wire-controlled chassis vehicle based on distributed asynchronous fusion. The steps are as follows: 1) establishing a four-degree-of-freedom motion differential equation of the vehicle including the longitudinal, lateral, yaw and roll motions of the center of mass; 2) establishing a vehicle nonlinear state equation and an observation equation according to the vehicle four-degree-of-freedom motion differential equation; 3) iterating the state parameters in the vehicle nonlinear state equation to a nonlinear state time-delay volume Kalman fusion filter, The non-linear state fusion estimation value of the vehicle is obtained, which is used for the real-time fusion estimation of the key control variables of the drive-by-wire chassis system. The present invention effectively solves the state time delay problem that occurs when the sensor observes and describes the driving state of the vehicle due to the different sampling frequencies of different on-board sensors.

Figure 202011116288

Description

基于分布式异步融合的线控底盘车辆非线性状态估计方法Nonlinear state estimation method for drive-by-wire chassis vehicles based on distributed asynchronous fusion

技术领域technical field

本发明属于智能驾驶环境感知领域,具体涉及一种基于分布式异步融合的线控底盘车辆非线性状态估计方法。The invention belongs to the field of intelligent driving environment perception, and in particular relates to a nonlinear state estimation method of a wire-controlled chassis vehicle based on distributed asynchronous fusion.

背景技术Background technique

近年来,随着信息技术在汽车领域的深入应用,智能驾驶技术得到进一步的发展与完善,汽车底盘线控化成为现代汽车发展的一大主流趋势。线控底盘系统技术的控制关键在于精确地获取表征车辆自身运行状态的横摆角速度、纵横向速度、车身侧倾角等关键状态变量。这些状态变量是车辆线控底盘控制系统中的主要控制变量,也是实时辨识车辆行驶状态及制定线控底盘子系统协调控制规则的重要依据。但由于汽车动力学控制过程的复杂性及车载传感器的测试水平和测试成本等多方面的影响,很多关键状态变量无法直接、准确或低成本的测量。In recent years, with the in-depth application of information technology in the field of automobiles, intelligent driving technology has been further developed and improved, and the control of automobile chassis by wire has become a major trend in the development of modern automobiles. The key to the control of the drive-by-wire chassis system technology is to accurately obtain key state variables such as yaw rate, longitudinal and lateral speed, and body roll angle that characterize the running state of the vehicle itself. These state variables are the main control variables in the vehicle-by-wire chassis control system, and are also an important basis for real-time identification of the vehicle's driving state and formulation of coordinated control rules for the drive-by-wire chassis subsystem. However, many key state variables cannot be measured directly, accurately or at low cost due to the complexity of the vehicle dynamics control process and the testing level and testing cost of on-board sensors.

现有的汽车行驶状态估计方法(如授权公布号CN106250591B)主要是先建立汽车质心运动、横摆运动及侧倾运动等具有非线性特征的运动微分方程,再用扩展Kalman滤波进行间接的车辆状态参数估计。但在实际应用中,由于不同车载传感器的采样频率不同,会导致传感器对车辆行驶状态进行观测描述时出现的状态时滞的情况,给系统噪声和观测噪声统计特性的准确描述带来了很大的困难,因此,如果采用预先建立噪声模型的常规滤波器,将会出现状态估计不准,甚至发散等现象,导致无法实现线控底盘控制中心对车辆底盘子系统的精准控制。Existing vehicle driving state estimation methods (such as authorized publication number CN106250591B) mainly establish motion differential equations with nonlinear characteristics such as vehicle mass center motion, yaw motion and roll motion, and then use extended Kalman filtering to indirectly calculate the vehicle state. Parameter Estimation. However, in practical applications, due to the different sampling frequencies of different on-board sensors, the state time lag occurs when the sensor observes and describes the driving state of the vehicle, which brings great influence to the accurate description of system noise and statistical characteristics of observation noise Therefore, if a conventional filter with a pre-established noise model is used, there will be inaccurate state estimation and even divergence, which makes it impossible to realize the precise control of the vehicle chassis subsystem by the wire-controlled chassis control center.

发明内容SUMMARY OF THE INVENTION

针对于上述现有技术的不足,本发明的目的在于提供一种基于分布式异步融合的线控底盘车辆非线性状态估计方法,本发明通过以车辆非线性动力学模型为基础,将容积卡尔曼滤波理论及多传感器信息异步融合技术引入到车辆非线性状态估计中,设计了非线性状态时滞容积卡尔曼融合滤波器,有效解决了由于不同车载传感器的采样频率不同所导致传感器对车辆行驶状态进行观测描述时出现的状态时滞问题,实现了用于线控底盘系统主动控制关键变量的实时融合估计,为车辆的主动安全控制提供了更为精确的信号。In view of the above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a nonlinear state estimation method for a chassis-by-wire vehicle based on distributed asynchronous fusion. The filtering theory and multi-sensor information asynchronous fusion technology are introduced into the nonlinear state estimation of the vehicle, and the nonlinear state time-delay volume Kalman fusion filter is designed, which effectively solves the problem of the sensor's impact on the vehicle's driving state caused by the different sampling frequencies of different on-board sensors. The state time-delay problem that occurs in the observation and description realizes the real-time fusion estimation of the key variables for active control of the drive-by-wire chassis system, and provides a more accurate signal for the active safety control of the vehicle.

为达到上述目的,本发明采用的技术方案如下:For achieving the above object, the technical scheme adopted in the present invention is as follows:

本发明的一种基于分布式异步融合的线控底盘车辆非线性状态估计方法,步骤如下:A method for estimating the nonlinear state of a chassis-by-wire vehicle based on distributed asynchronous fusion of the present invention, the steps are as follows:

1)建立包含质心纵向、侧向、横摆及侧倾运动的车辆四自由度运动微分方程;1) Establish a differential equation of four-degree-of-freedom motion of the vehicle including the longitudinal, lateral, yaw and roll motions of the center of mass;

2)根据所述车辆四自由度运动微分方程建立车辆非线性状态方程和观测方程;2) establishing a nonlinear state equation and an observation equation of the vehicle according to the four-degree-of-freedom motion differential equation of the vehicle;

3)将所述车辆非线性状态方程中的状态参数迭代至非线性状态时滞容积卡尔曼融合滤波器,得到车辆非线性状态融合估计值,用于线控底盘系统控制关键变量的实时融合估计。3) Iterating the state parameters in the nonlinear state equation of the vehicle to the nonlinear state time-delay volume Kalman fusion filter to obtain the vehicle nonlinear state fusion estimation value, which is used for real-time fusion estimation of the key variables of the drive-by-wire chassis system control .

进一步地,所述步骤1)中的运动微分方程为:Further, the differential equation of motion in the step 1) is:

Figure BDA0002730307040000021
Figure BDA0002730307040000021

式中,m为整车质量,u为纵向速度,v为侧向速度,φ、r分别为侧倾、横摆角速度;h0为质心到侧倾轴距离;hr为侧倾中心高度;hrf、hrr分别为前后轴到侧倾轴距离;ε为侧倾轴与纵轴之间的夹角;δ为前轮转角;Ib,x为绕X轴转动惯量,Ib,z为绕Z轴转动惯量,Ib,xz为绕X、Z轴的惯量积;tf为前轮距,tr为后轮距;kf前轮胎侧偏刚度,kr为后轮胎侧偏刚度;a为质心至前轴距离,b为质心至后轴距离;cf为前悬架侧倾角阻尼,cr为后悬架侧倾角阻尼;Fy1为左前轮侧向力,Fy2为右前轮侧向力,Fy3为左后轮侧向力,Fy4为右后轮侧向力;Fx1为左前轮纵向力,Fx2为右前轮纵向力,Fx3为左后轮纵向力,Fx4右后轮纵向力。where m is the vehicle mass, u is the longitudinal speed, v is the lateral speed, φ and r are the roll and yaw angular velocities, respectively; h 0 is the distance from the center of mass to the roll axis; h r is the height of the roll center; h rf and h rr are the distance from the front and rear axles to the roll axis respectively; ε is the angle between the roll axis and the longitudinal axis; δ is the front wheel rotation angle; I b,x is the moment of inertia around the X axis, I b,z is the moment of inertia around the Z axis, I b,xz is the inertia product around the X and Z axes; t f is the front track, t r is the rear track; k f is the cornering stiffness of the front tire, and k r is the rear tire cornering stiffness; a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle; c f is the roll angle damping of the front suspension, cr is the roll angle damping of the rear suspension; F y1 is the left front wheel lateral force, F y2 is the lateral force of the right front wheel, F y3 is the lateral force of the left rear wheel, F y4 is the lateral force of the right rear wheel; F x1 is the longitudinal force of the left front wheel, F x2 is the longitudinal force of the right front wheel, and F x3 is the left Rear wheel longitudinal force, F x4 right rear wheel longitudinal force.

进一步地,所述步骤2)中的车辆非线性状态方程和观测方程为:Further, the nonlinear state equation and observation equation of the vehicle in the step 2) are:

Figure BDA0002730307040000022
Figure BDA0002730307040000022

式中,

Figure BDA0002730307040000023
为k+1时刻的状态估计值,xk为线控底盘主动控制状态变量,f(·)表示系统动力学函数,过程噪声wk是均值为零、协方差矩阵为Qk的高斯白噪声;zk,i是在tk,i时刻获得的测量值,Nk个观测值是由具有不同采样频率的m个传感器在时间间隔[tk-1,tk]内获得,在采样时间序列内获得的Nk个观测值
Figure BDA0002730307040000024
是异步的,满足关系:
Figure BDA0002730307040000025
hk,i表示测量函数,xk,i表示在tk,i时刻的状态量,测量噪声ηk,i是均值为零、协方差矩阵为Rk,i的高斯白噪声。In the formula,
Figure BDA0002730307040000023
is the state estimate value at time k+1, x k is the active control state variable of the drive-by-wire chassis, f( ) represents the system dynamics function, and the process noise w k is Gaussian white noise with zero mean and covariance matrix Q k ; z k,i is the measurement obtained at time t k,i , N k observations are obtained by m sensors with different sampling frequencies in the time interval [t k-1 ,t k ], at the sampling time N k observations obtained within the sequence
Figure BDA0002730307040000024
is asynchronous and satisfies the relation:
Figure BDA0002730307040000025
h k,i represents the measurement function, x k,i represents the state quantity at time t k,i , and the measurement noise η k,i is Gaussian white noise with zero mean and covariance matrix R k,i .

进一步地,所述步骤3)中非线性状态时滞容积卡尔曼融合滤波器的状态预测方程为:Further, the state prediction equation of the nonlinear state time-delay volume Kalman fusion filter in the step 3) is:

Figure BDA0002730307040000031
Figure BDA0002730307040000031

预测协方差阵为:The prediction covariance matrix is:

Figure BDA0002730307040000032
Figure BDA0002730307040000032

Figure BDA0002730307040000033
Figure BDA0002730307040000033

式中,Xk-1

Figure BDA0002730307040000034
定义如下:In the formula, X k-1 ,
Figure BDA0002730307040000034
Defined as follows:

Xk-1=xk-1-j X k-1 =x k-1-j

Figure BDA0002730307040000035
Figure BDA0002730307040000035

Figure BDA0002730307040000036
Figure BDA0002730307040000036

式中,j,l∈[0,1,…d-1]且j≠l,s,t∈[0,1,…,d-1],d为容积点数量。In the formula, j,l∈[0,1,…d-1] and j≠l, s,t∈[0,1,…,d-1], d is the number of volume points.

进一步地,所述中非线性状态时滞容积卡尔曼融合滤波器的状态更新方程为:Further, the state update equation of the medium nonlinear state time-delay volume Kalman fusion filter is:

Figure BDA0002730307040000037
Figure BDA0002730307040000037

更新协方差阵为:The updated covariance matrix is:

Figure BDA0002730307040000038
Figure BDA0002730307040000038

增益更新方程为:The gain update equation is:

Figure BDA0002730307040000039
Figure BDA0002730307040000039

其中,in,

Figure BDA00027303070400000310
Figure BDA00027303070400000310

Figure BDA00027303070400000311
Figure BDA00027303070400000311

Figure BDA00027303070400000312
Figure BDA00027303070400000312

进一步地,所述非线性状态时滞容积卡尔曼融合滤波器在tk时刻的分布式融合估计

Figure BDA0002730307040000041
及其误差协方差矩阵Pk的计算公式如下:Further, the distributed fusion estimation of the nonlinear state time-delay volume Kalman fusion filter at time tk is
Figure BDA0002730307040000041
The calculation formula of and its error covariance matrix P k is as follows:

Figure BDA0002730307040000042
Figure BDA0002730307040000042

Pk=[(Pk|k-1)-1+Xk+Yk(Pk|k-1-Lk)-1Yk T]-1 P k =[(P k|k-1 ) -1 +X k +Y k (P k|k-1 -L k ) -1 Y k T ] -1

式中,In the formula,

Figure BDA0002730307040000044
Figure BDA0002730307040000044

Figure BDA0002730307040000045
Figure BDA0002730307040000045

Figure BDA0002730307040000046
Figure BDA0002730307040000046

Figure BDA0002730307040000047
Figure BDA0002730307040000047

Figure BDA0002730307040000048
Figure BDA0002730307040000048

Figure BDA0002730307040000051
Figure BDA0002730307040000051

Figure BDA0002730307040000052
Figure BDA0002730307040000052

Figure BDA0002730307040000053
Figure BDA0002730307040000053

Ek,i=I-Q(tk,tk,i)(Pk|k-1)-1,i=1,…,Nk E k,i =IQ(t k ,t k,i )(P k|k-1 ) -1 ,i=1,...,N k

and

Figure BDA0002730307040000054
Figure BDA0002730307040000054

式中,Φ(tk,tk,i+1)表示从tk到tk,i+1时刻的系统状态转移矩阵,ΦT(tk,tk,i+1)表示该状态转移矩阵的转置矩阵;Mk,i为tk,i时刻的信息矩阵,In表示n阶单位矩阵。In the formula, Φ(t k , t k, i+1 ) represents the system state transition matrix from t k to t k, i+1 , and Φ T (t k , t k, i+1 ) represents the state transition The transpose matrix of the matrix; M k,i is the information matrix at time t k,i , and I n represents the n-order identity matrix.

进一步地,所述步骤3)中的关键变量包括:横摆角速度、纵横向速度、车身侧倾角。Further, the key variables in the step 3) include: yaw angular velocity, longitudinal and lateral velocity, and body roll angle.

本发明的有益效果:Beneficial effects of the present invention:

本发明通过以车辆非线性动力学模型为基础,将容积卡尔曼滤波理论及多传感器信息异步融合技术引入到车辆非线性状态估计中,设计了非线性状态时滞容积卡尔曼融合滤波器,有效解决了由于不同车载传感器的采样频率不同所导致传感器对车辆行驶状态进行观测描述时出现的状态时滞问题,实现了用于线控底盘系统主动控制关键变量的实时融合估计,为车辆的主动安全控制提供了更为精确的信号。Based on the nonlinear dynamic model of the vehicle, the present invention introduces the volume Kalman filter theory and multi-sensor information asynchronous fusion technology into the nonlinear state estimation of the vehicle, and designs a nonlinear state time-delay volume Kalman fusion filter, which is effective It solves the state time lag problem that occurs when the sensor observes and describes the driving state of the vehicle due to the different sampling frequencies of different on-board sensors, and realizes the real-time fusion estimation of key variables for active control of the drive-by-wire chassis system, which is the active safety of the vehicle. Control provides a more precise signal.

附图说明Description of drawings

图1为本发明方法的示意图。Figure 1 is a schematic diagram of the method of the present invention.

具体实施方式Detailed ways

为了便于本领域技术人员的理解,下面结合实施例与附图对本发明作进一步的说明,实施方式提及的内容并非对本发明的限定。In order to facilitate the understanding of those skilled in the art, the present invention will be further described below with reference to the embodiments and the accompanying drawings, and the contents mentioned in the embodiments are not intended to limit the present invention.

参照图1所示,本发明的一种基于分布式异步融合的线控底盘车辆非线性状态估计方法,步骤如下:Referring to FIG. 1 , a method for estimating nonlinear state of a controlled-by-wire chassis vehicle based on distributed asynchronous fusion of the present invention, the steps are as follows:

为了较全面地反映车辆非线性状态,实时融合估计出线控底盘系统主动控制中必需而又难以直接获取的关键状态变量,基于非线性四自由度汽车模型,推导包含车辆纵向、横向、侧倾及横摆运动的微分方程如下:In order to reflect the nonlinear state of the vehicle more comprehensively, the key state variables necessary for the active control of the drive-by-wire chassis system but difficult to obtain directly are estimated by real-time fusion. The differential equation for the yaw motion is as follows:

X方向的受力平衡方程:The force balance equation in the X direction:

Figure BDA0002730307040000061
Figure BDA0002730307040000061

式中,u为纵向速度,v为侧向速度;φ、r分别为侧倾、横摆角速度;h0为质心到侧倾轴距离;ε为侧倾轴与纵轴之间的夹角;δ为前轮转角;Fy1为左前轮侧向力,Fy2为右前轮侧向力;Fx1为左前轮纵向力,Fx2为右前轮纵向力,Fx3为左后轮纵向力,Fx4右后轮纵向力。where u is the longitudinal velocity, v is the lateral velocity; φ and r are the roll and yaw angular velocities, respectively; h 0 is the distance from the center of mass to the roll axis; ε is the angle between the roll axis and the longitudinal axis; δ is the front wheel rotation angle; F y1 is the lateral force of the left front wheel, F y2 is the lateral force of the right front wheel; F x1 is the longitudinal force of the left front wheel, F x2 is the longitudinal force of the right front wheel, and F x3 is the left rear wheel Longitudinal force, F x4 right rear wheel longitudinal force.

Y方向的受力平衡方程:The force balance equation in the Y direction:

Figure BDA0002730307040000062
Figure BDA0002730307040000062

式中,Fy3为左后轮侧向力,Fy4为右后轮侧向力。In the formula, F y3 is the lateral force of the left rear wheel, and F y4 is the lateral force of the right rear wheel.

绕Z轴的力矩方程:Moment equation about the Z axis:

Figure BDA0002730307040000063
Figure BDA0002730307040000063

式中,Ib,x为绕X轴转动惯量,Ib,z为绕Z轴转动惯量,Ib,xz为绕X、Z轴的惯量积;a为质心至前轴距离,b为质心至后轴距离;tf为前轮距,tr为后轮距。In the formula, I b,x is the moment of inertia around the X axis, I b,z is the moment of inertia around the Z axis, I b,xz is the inertia product around the X and Z axes; a is the distance from the center of mass to the front axis, and b is the center of mass Distance to the rear axle; t f is the front track, t r is the rear track.

绕X轴的力矩方程:Moment equation about the X axis:

Figure BDA0002730307040000064
Figure BDA0002730307040000064

式中,hr为侧倾中心高度;hrf、hrr分别为前后轴到侧倾轴距离;kf前轮胎侧偏刚度,kr为后轮胎侧偏刚度;cf为前悬架侧倾角阻尼,cr为后悬架侧倾角阻尼。In the formula, h r is the height of the roll center; h rf and hr are the distance from the front and rear axles to the roll axle respectively; k f is the cornering stiffness of the front tire, k r is the cornering stiffness of the rear tire ; c f is the front suspension side Inclination damping, cr is the rear suspension roll damping.

根据估计对象建立状态方程与量测方程,对非线性模型进行线性化并赋初值进行递推估计,主要包括预测过程与校正过程,其具体过程如下:According to the estimated object, the state equation and the measurement equation are established, the nonlinear model is linearized and the initial value is assigned to perform recursive estimation, which mainly includes the prediction process and the correction process. The specific process is as follows:

步骤1):建立车辆非线性状态方程与观测方程Step 1): Establish vehicle nonlinear state equation and observation equation

Figure BDA0002730307040000065
Figure BDA0002730307040000065

式中,

Figure BDA0002730307040000066
为k+1时刻的状态估计值,xk为线控底盘主动控制状态变量,f(·)表示系统动力学函数,过程噪声wk是均值为零、协方差矩阵为Qk的高斯白噪声;zk,i是在tk,i时刻获得的测量值,Nk个观测值是由具有不同采样频率的m个传感器在时间间隔[tk-1,tk]内获得,在采样时间序列内获得的Nk个观测值
Figure BDA0002730307040000071
是异步的,满足关系:
Figure BDA0002730307040000072
hk,i表示测量函数,xk,i表示在tk,i时刻的状态量,测量噪声ηk,i是均值为零、协方差矩阵为Rk,i的高斯白噪声。In the formula,
Figure BDA0002730307040000066
is the state estimate value at time k+1, x k is the active control state variable of the drive-by-wire chassis, f( ) represents the system dynamics function, and the process noise w k is Gaussian white noise with zero mean and covariance matrix Q k ; z k,i is the measurement obtained at time t k,i , N k observations are obtained by m sensors with different sampling frequencies in the time interval [t k-1 ,t k ], at the sampling time N k observations obtained within the sequence
Figure BDA0002730307040000071
is asynchronous and satisfies the relation:
Figure BDA0002730307040000072
h k,i represents the measurement function, x k,i represents the state quantity at time t k,i , and the measurement noise η k,i is Gaussian white noise with zero mean and covariance matrix R k,i .

步骤2):计算预测容积点向量,具体计算方法如下:Step 2): Calculate the predicted volume point vector, and the specific calculation method is as follows:

对协方差阵Pk-1|k-1进行Cholesky分解:Cholesky decomposition of the covariance matrix P k-1|k-1 :

Figure BDA0002730307040000073
Figure BDA0002730307040000073

容积点计算:Volume point calculation:

Figure BDA0002730307040000074
Figure BDA0002730307040000074

式中,ξb表示容积点集合{ξ}的第b列向量,集合{ξ}一共有2n个列向量,定义如下:In the formula, ξ b represents the b-th column vector of the volume point set {ξ}, and the set {ξ} has a total of 2n column vectors, which are defined as follows:

Figure BDA0002730307040000075
Figure BDA0002730307040000075

容积点传播:Volume point spread:

Figure BDA0002730307040000076
Figure BDA0002730307040000076

式中,

Figure BDA0002730307040000077
表示预测容积点向量。In the formula,
Figure BDA0002730307040000077
represents a vector of predicted volume points.

步骤3):状态和协方差阵一步预测方程为:Step 3): The state and covariance matrix one-step prediction equation is:

Figure BDA0002730307040000078
Figure BDA0002730307040000078

Figure BDA0002730307040000079
Figure BDA0002730307040000079

步骤4):计算量测容积点向量,具体计算方法如下:Step 4): Calculate the measurement volume point vector, and the specific calculation method is as follows:

对协方差阵Pk|k-1进行Cholesky分解:

Figure BDA0002730307040000081
Cholesky decomposition of the covariance matrix P k|k-1 :
Figure BDA0002730307040000081

容积点计算:

Figure BDA0002730307040000082
Volume point calculation:
Figure BDA0002730307040000082

容积点传播:Zb,k|k-1=h(Xb,k|k-1),b=1,…,2nVolume point propagation: Z b,k|k-1 =h( Xb,k|k-1 ),b=1,...,2n

步骤5):量测预测

Figure BDA0002730307040000083
一步协方差阵
Figure BDA0002730307040000084
一步互协方差阵
Figure BDA0002730307040000085
方程为:Step 5): Measurement and prediction
Figure BDA0002730307040000083
one-step covariance matrix
Figure BDA0002730307040000084
one-step cross-covariance matrix
Figure BDA0002730307040000085
The equation is:

Figure BDA0002730307040000086
Figure BDA0002730307040000086

Figure BDA0002730307040000087
Figure BDA0002730307040000087

Figure BDA0002730307040000088
Figure BDA0002730307040000088

步骤6):增益矩阵更新:Step 6): Gain matrix update:

Figure BDA0002730307040000089
Figure BDA0002730307040000089

步骤7):协方差阵更新:Step 7): Covariance matrix update:

Figure BDA00027303070400000810
Figure BDA00027303070400000810

步骤8):状态变量更新估计:Step 8): State variable update estimation:

Figure BDA00027303070400000811
Figure BDA00027303070400000811

步骤9):信息融合过程;每个传感器的局部信息状态重构

Figure BDA00027303070400000812
和相关信息矩阵Mk,i均是异步获得的,在tk时刻的分布式融合估计
Figure BDA00027303070400000813
及其误差协方差矩阵Pk的计算公式如下:Step 9): Information fusion process; local information state reconstruction of each sensor
Figure BDA00027303070400000812
and the related information matrix M k,i are obtained asynchronously, and the distributed fusion estimation at time t k
Figure BDA00027303070400000813
The calculation formula of and its error covariance matrix P k is as follows:

Figure BDA0002730307040000091
Figure BDA0002730307040000091

Pk=[(Pk|k-1)-1+Xk+Yk(Pk|k-1-Lk)-1Yk T]-1 P k =[(P k|k-1 ) -1 +X k +Y k (P k|k-1 -L k ) -1 Y k T ] -1

其中,in,

Figure BDA0002730307040000093
Figure BDA0002730307040000093

Figure BDA0002730307040000094
Figure BDA0002730307040000094

Figure BDA0002730307040000095
Figure BDA0002730307040000095

Figure BDA0002730307040000096
Figure BDA0002730307040000096

Figure BDA0002730307040000097
Figure BDA0002730307040000097

Figure BDA0002730307040000098
Figure BDA0002730307040000098

Figure BDA0002730307040000101
Figure BDA0002730307040000101

and

Figure BDA0002730307040000102
Figure BDA0002730307040000102

上式具体过程为:The specific process of the above formula is:

Figure BDA0002730307040000103
Figure BDA0002730307040000103

Figure BDA0002730307040000104
Figure BDA0002730307040000104

Ek,1=I-Q(tk,tk,i)(Pk|k-1)-1,i=1,…,Nk E k,1 =IQ(t k ,t k,i )(P k|k-1 ) -1 ,i=1,...,N k

信息矩阵Mk,i和信息状态重构

Figure BDA0002730307040000105
通过局部传感器节点获得,具体计算方法如下:Information Matrix M k,i and Information State Reconstruction
Figure BDA0002730307040000105
Obtained through local sensor nodes, the specific calculation method is as follows:

Figure BDA0002730307040000106
Figure BDA0002730307040000106

Figure BDA0002730307040000107
Figure BDA0002730307040000107

式中,

Figure BDA0002730307040000108
为传感器的增广测量矩阵,完成测量函数hk,i从时刻tk,i到时刻tk的过渡,其计算公式如下:In the formula,
Figure BDA0002730307040000108
is the augmented measurement matrix of the sensor, which completes the transition of the measurement function h k,i from time t k,i to time t k , and its calculation formula is as follows:

Figure BDA0002730307040000109
Figure BDA0002730307040000109

式中,

Figure BDA0002730307040000111
表示xk,i和时刻tk,i到tk的状态
Figure BDA0002730307040000112
之间的协方差;
Figure BDA0002730307040000113
表示状态xk,i和测量zk,i间的互协方差矩阵;计算方式如下:In the formula,
Figure BDA0002730307040000111
Represents the state of x k,i and time tk ,i to tk
Figure BDA0002730307040000112
covariance between;
Figure BDA0002730307040000113
represents the cross-covariance matrix between states x k,i and measurements z k,i ; it is calculated as follows:

Figure BDA0002730307040000114
Figure BDA0002730307040000114

Figure BDA0002730307040000115
Figure BDA0002730307040000115

容积点计算:Volume point calculation:

Figure BDA0002730307040000116
Figure BDA0002730307040000116

式中,Sk,i-由协方差矩阵Pk,i-进行Cholesky分解得到:In the formula, S k,i- is obtained by Cholesky decomposition of the covariance matrix P k,i- :

Figure BDA0002730307040000117
Figure BDA0002730307040000117

容积点传播:Volume point spread:

Figure BDA0002730307040000118
Figure BDA0002730307040000118

Figure BDA0002730307040000119
Figure BDA0002730307040000119

Figure BDA00027303070400001110
Figure BDA00027303070400001110

式中,

Figure BDA00027303070400001111
表示从时刻tk',i'到tk,i的传播状态
Figure BDA00027303070400001112
的预测观测向量,
Figure BDA00027303070400001113
表示从时刻tk,i到tk的状态。In the formula,
Figure BDA00027303070400001111
Represents the propagation state from time t k',i' to t k,i
Figure BDA00027303070400001112
The predicted observation vector of ,
Figure BDA00027303070400001113
represents the state from time t k,i to t k .

本发明具体应用途径很多,以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以作出若干改进,这些改进也应视为本发明的保护范围。There are many specific application ways of the present invention, and the above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, several improvements can be made. These Improvements should also be considered as the protection scope of the present invention.

Claims (7)

1.一种基于分布式异步融合的线控底盘车辆非线性状态估计方法,其特征在于,步骤如下:1. a method for estimating the nonlinear state of a chassis-by-wire vehicle based on distributed asynchronous fusion, is characterized in that, step is as follows: 1)建立包含质心纵向、侧向、横摆及侧倾运动的车辆四自由度运动微分方程;1) Establish a differential equation of four-degree-of-freedom motion of the vehicle including the longitudinal, lateral, yaw and roll motions of the center of mass; 2)根据所述车辆四自由度运动微分方程建立车辆非线性状态方程和观测方程;2) establishing a nonlinear state equation and an observation equation of the vehicle according to the four-degree-of-freedom motion differential equation of the vehicle; 3)将所述车辆非线性状态方程中的状态参数迭代至非线性状态时滞容积卡尔曼融合滤波器,得到车辆非线性状态融合估计值,用于线控底盘系统控制关键变量的实时融合估计。3) Iterating the state parameters in the nonlinear state equation of the vehicle to the nonlinear state time-delay volume Kalman fusion filter to obtain the vehicle nonlinear state fusion estimation value, which is used for real-time fusion estimation of the key variables of the drive-by-wire chassis system control . 2.根据权利要求1所述的基于分布式异步融合的线控底盘车辆非线性状态估计方法,其特征在于,所述步骤1)中的运动微分方程为:2. the non-linear state estimation method of the chassis-by-wire vehicle based on distributed asynchronous fusion according to claim 1, is characterized in that, the differential equation of motion in described step 1) is:
Figure FDA0002730307030000011
Figure FDA0002730307030000011
式中,m为整车质量,u为纵向速度,v为侧向速度,φ、r分别为侧倾、横摆角速度;h0为质心到侧倾轴距离;hr为侧倾中心高度;hrf、hrr分别为前后轴到侧倾轴距离;ε为侧倾轴与纵轴之间的夹角;δ为前轮转角;Ib,x为绕X轴转动惯量,Ib,z为绕Z轴转动惯量,Ib,xz为绕X、Z轴的惯量积;tf为前轮距,tr为后轮距;kf前轮胎侧偏刚度,kr为后轮胎侧偏刚度;a为质心至前轴距离,b为质心至后轴距离;cf为前悬架侧倾角阻尼,cr为后悬架侧倾角阻尼;Fy1为左前轮侧向力,Fy2为右前轮侧向力,Fy3为左后轮侧向力,Fy4为右后轮侧向力;Fx1为左前轮纵向力,Fx2为右前轮纵向力,Fx3为左后轮纵向力,Fx4右后轮纵向力。where m is the vehicle mass, u is the longitudinal speed, v is the lateral speed, φ and r are the roll and yaw angular velocities, respectively; h 0 is the distance from the center of mass to the roll axis; h r is the height of the roll center; h rf and h rr are the distance from the front and rear axles to the roll axis respectively; ε is the angle between the roll axis and the longitudinal axis; δ is the front wheel rotation angle; I b,x is the moment of inertia around the X axis, I b,z is the moment of inertia around the Z axis, I b,xz is the inertia product around the X and Z axes; t f is the front track, t r is the rear track; k f is the cornering stiffness of the front tire, and k r is the rear tire cornering stiffness; a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle; c f is the roll angle damping of the front suspension, cr is the roll angle damping of the rear suspension; F y1 is the left front wheel lateral force, F y2 is the lateral force of the right front wheel, F y3 is the lateral force of the left rear wheel, F y4 is the lateral force of the right rear wheel; F x1 is the longitudinal force of the left front wheel, F x2 is the longitudinal force of the right front wheel, and F x3 is the left Rear wheel longitudinal force, F x4 right rear wheel longitudinal force.
3.根据权利要求1所述的基于分布式异步融合的线控底盘车辆非线性状态估计方法,其特征在于,所述步骤2)中的车辆非线性状态方程和观测方程为:3. the non-linear state estimation method of the chassis-by-wire vehicle based on distributed asynchronous fusion according to claim 1, is characterized in that, the non-linear state equation and observation equation of vehicle in described step 2) are:
Figure FDA0002730307030000012
Figure FDA0002730307030000012
式中,
Figure FDA0002730307030000021
为k+1时刻的状态估计值,xk为线控底盘主动控制状态变量,f(·)表示系统动力学函数,过程噪声wk是均值为零、协方差矩阵为Qk的高斯白噪声;zk,i是在tk,i时刻获得的测量值,Nk个观测值是由具有不同采样频率的m个传感器在时间间隔[tk-1,tk]内获得,在采样时间序列内获得的Nk个观测值
Figure FDA0002730307030000022
是异步的,满足关系:
Figure FDA0002730307030000023
hk,i表示测量函数,xk,i表示在tk,i时刻的状态量,测量噪声ηk,i是均值为零、协方差矩阵为Rk,i的高斯白噪声。
In the formula,
Figure FDA0002730307030000021
is the state estimate value at time k+1, x k is the active control state variable of the drive-by-wire chassis, f( ) represents the system dynamics function, and the process noise w k is Gaussian white noise with zero mean and covariance matrix Q k ; z k,i is the measurement obtained at time t k,i , N k observations are obtained by m sensors with different sampling frequencies in the time interval [t k-1 ,t k ], at the sampling time N k observations obtained within the sequence
Figure FDA0002730307030000022
is asynchronous and satisfies the relation:
Figure FDA0002730307030000023
h k,i represents the measurement function, x k,i represents the state quantity at time t k,i , and the measurement noise η k,i is Gaussian white noise with zero mean and covariance matrix R k,i .
4.根据权利要求1所述的基于分布式异步融合的线控底盘车辆非线性状态估计方法,其特征在于,所述步骤3)中非线性状态时滞容积卡尔曼融合滤波器的状态预测方程为:4. the non-linear state estimation method of the chassis-by-wire vehicle based on distributed asynchronous fusion according to claim 1, is characterized in that, the state prediction equation of nonlinear state time-delay volume Kalman fusion filter in described step 3) for:
Figure FDA0002730307030000024
Figure FDA0002730307030000024
预测协方差阵为:The prediction covariance matrix is:
Figure FDA0002730307030000025
Figure FDA0002730307030000025
Figure FDA0002730307030000026
Figure FDA0002730307030000026
式中,Xk-1
Figure FDA0002730307030000027
定义如下:
In the formula, X k-1 ,
Figure FDA0002730307030000027
Defined as follows:
Xk-1=xk-1-j X k-1 =x k-1-j
Figure FDA0002730307030000028
Figure FDA0002730307030000028
Figure FDA0002730307030000029
Figure FDA0002730307030000029
式中,j,l∈[0,1,…d-1]且j≠l,s,t∈[0,1,…,d-1],d为容积点数量。In the formula, j,l∈[0,1,…d-1] and j≠l, s,t∈[0,1,…,d-1], d is the number of volume points.
5.根据权利要求1所述的基于分布式异步融合的线控底盘车辆非线性状态估计方法,其特征在于,所述中非线性状态时滞容积卡尔曼融合滤波器的状态更新方程为:5. the non-linear state estimation method of the chassis-by-wire vehicle based on distributed asynchronous fusion according to claim 1, is characterized in that, the state update equation of described medium nonlinear state time-delay volume Kalman fusion filter is:
Figure FDA0002730307030000031
Figure FDA0002730307030000031
更新协方差阵为:The updated covariance matrix is:
Figure FDA0002730307030000032
Figure FDA0002730307030000032
增益更新方程为:The gain update equation is:
Figure FDA0002730307030000033
Figure FDA0002730307030000033
其中,in,
Figure FDA0002730307030000034
Figure FDA0002730307030000034
Figure FDA0002730307030000035
Figure FDA0002730307030000035
Figure FDA0002730307030000036
Figure FDA0002730307030000036
6.根据权利要求1所述的基于分布式异步融合的线控底盘车辆非线性状态估计方法,其特征在于,所述非线性状态时滞容积卡尔曼融合滤波器在tk时刻的分布式融合估计
Figure FDA0002730307030000037
及其误差协方差矩阵Pk的计算公式如下:
6 . The nonlinear state estimation method for a controlled chassis vehicle based on distributed asynchronous fusion according to claim 1 , wherein the distributed fusion of the nonlinear state time-delay volume Kalman fusion filter at time t k estimate
Figure FDA0002730307030000037
The calculation formula of and its error covariance matrix P k is as follows:
Figure FDA0002730307030000038
Figure FDA0002730307030000038
Figure FDA0002730307030000039
Figure FDA0002730307030000039
式中,In the formula,
Figure FDA00027303070300000310
Figure FDA00027303070300000310
Figure FDA0002730307030000041
Figure FDA0002730307030000041
Figure FDA0002730307030000042
Figure FDA0002730307030000042
Figure FDA0002730307030000043
Figure FDA0002730307030000043
Figure FDA0002730307030000044
Figure FDA0002730307030000044
Figure FDA0002730307030000045
Figure FDA0002730307030000045
Figure FDA0002730307030000046
Figure FDA0002730307030000046
Figure FDA0002730307030000047
Figure FDA0002730307030000047
Ek,i=I-Q(tk,tk,i)(Pk|k-1)-1,i=1,…,Nk E k,i =IQ(t k ,t k,i )(P k|k-1 ) -1 ,i=1,...,N k and
Figure FDA0002730307030000048
Figure FDA0002730307030000048
式中,Φ(tk,tk,i+1)表示从tk到tk,i+1时刻的系统状态转移矩阵,ΦT(tk,tk,i+1)表示该状态转移矩阵的转置矩阵;Mk,i为tk,i时刻的信息矩阵,In表示n阶单位矩阵。In the formula, Φ(t k , t k, i+1 ) represents the system state transition matrix from t k to t k, i+1 , and Φ T (t k , t k, i+1 ) represents the state transition The transpose matrix of the matrix; M k,i is the information matrix at time t k,i , and I n represents the n-order identity matrix.
7.根据权利要求1所述的基于分布式异步融合的线控底盘车辆非线性状态估计方法,其特征在于,所述步骤3)中的关键变量包括:横摆角速度、纵横向速度、车身侧倾角。7. The method for estimating the nonlinear state of a chassis-by-wire vehicle based on distributed asynchronous fusion according to claim 1, wherein the key variables in the step 3) include: yaw angular velocity, longitudinal and lateral velocity, body side inclination.
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