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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- state
- vehicle
- fusion
- equation
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
- G06F17/13—Differential equations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Geometry (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Operations Research (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
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。
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.
Description
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:
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:
式中,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个观测值是异步的,满足关系: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, 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 is asynchronous and satisfies the relation: 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:
预测协方差阵为:The prediction covariance matrix is:
式中,Xk-1、定义如下:In the formula, X k-1 , Defined as follows:
Xk-1=xk-1-j X k-1 =x k-1-j
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:
更新协方差阵为:The updated covariance matrix is:
增益更新方程为:The gain update equation is:
其中,in,
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 The calculation formula of and its error covariance matrix P k is as follows:
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,
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
且and
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:
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:
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:
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:
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
式中,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个观测值是异步的,满足关系: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, 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 is asynchronous and satisfies the relation: 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 :
容积点计算:Volume point calculation:
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:
容积点传播:Volume point spread:
式中,表示预测容积点向量。In the formula, 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:
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: Cholesky decomposition of the covariance matrix P k|k-1 :
容积点计算: Volume point calculation:
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):量测预测一步协方差阵一步互协方差阵方程为:Step 5): Measurement and prediction one-step covariance matrix one-step cross-covariance matrix The equation is:
步骤6):增益矩阵更新:Step 6): Gain matrix update:
步骤7):协方差阵更新:Step 7): Covariance matrix update:
步骤8):状态变量更新估计:Step 8): State variable update estimation:
步骤9):信息融合过程;每个传感器的局部信息状态重构和相关信息矩阵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 and the related information matrix M k,i are obtained asynchronously, and the distributed fusion estimation at time t k The calculation formula of and its error covariance matrix P k is as follows:
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,
且and
上式具体过程为:The specific process of the above formula is:
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 Obtained through local sensor nodes, the specific calculation method is as follows:
式中,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, 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:
式中,表示xk,i和时刻tk,i到tk的状态之间的协方差;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, Represents the state of x k,i and time tk ,i to tk covariance between; represents the cross-covariance matrix between states x k,i and measurements z k,i ; it is calculated as follows:
容积点计算:Volume point calculation:
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- :
容积点传播:Volume point spread:
式中,表示从时刻tk',i'到tk,i的传播状态的预测观测向量,表示从时刻tk,i到tk的状态。In the formula, Represents the propagation state from time t k',i' to t k,i The predicted observation vector of , 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)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011116288.0A CN112270039A (en) | 2020-10-19 | 2020-10-19 | Nonlinear state estimation method for drive-by-wire chassis vehicles based on distributed asynchronous fusion |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011116288.0A CN112270039A (en) | 2020-10-19 | 2020-10-19 | Nonlinear state estimation method for drive-by-wire chassis vehicles based on distributed asynchronous fusion |
Publications (1)
Publication Number | Publication Date |
---|---|
CN112270039A true CN112270039A (en) | 2021-01-26 |
Family
ID=74338341
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202011116288.0A Pending CN112270039A (en) | 2020-10-19 | 2020-10-19 | Nonlinear state estimation method for drive-by-wire chassis vehicles based on distributed asynchronous fusion |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN112270039A (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104182991A (en) * | 2014-08-15 | 2014-12-03 | 辽宁工业大学 | Vehicle running state estimation method and vehicle running state estimation device |
CN107015944A (en) * | 2017-03-28 | 2017-08-04 | 南京理工大学 | A kind of mixing square root volume kalman filter method for target following |
CN108241773A (en) * | 2017-12-21 | 2018-07-03 | 江苏大学 | An Improved Vehicle Driving State Estimation Method |
CN111152795A (en) * | 2020-01-08 | 2020-05-15 | 东南大学 | Model and parameter dynamic adjustment based adaptive vehicle state prediction system and prediction method |
-
2020
- 2020-10-19 CN CN202011116288.0A patent/CN112270039A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104182991A (en) * | 2014-08-15 | 2014-12-03 | 辽宁工业大学 | Vehicle running state estimation method and vehicle running state estimation device |
CN107015944A (en) * | 2017-03-28 | 2017-08-04 | 南京理工大学 | A kind of mixing square root volume kalman filter method for target following |
CN108241773A (en) * | 2017-12-21 | 2018-07-03 | 江苏大学 | An Improved Vehicle Driving State Estimation Method |
CN111152795A (en) * | 2020-01-08 | 2020-05-15 | 东南大学 | Model and parameter dynamic adjustment based adaptive vehicle state prediction system and prediction method |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
CN114520777B (en) * | 2021-12-27 | 2023-12-26 | 上海仙途智能科技有限公司 | Time lag identification method and device, computer readable storage medium and terminal |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108594652B (en) | An Iterative Vehicle State Fusion Estimation Method Based on Observer Information | |
CN109606378B (en) | A Vehicle Driving State Estimation Method for Non-Gaussian Noise Environment | |
CN106250591B (en) | A Vehicle Driving State Estimation Method Considering Roll Effect | |
CN103434511B (en) | The combined estimation method of a kind of speed of a motor vehicle and road-adhesion coefficient | |
CN110532590B (en) | Vehicle state estimation method based on self-adaptive volume particle filtering | |
Lian et al. | Cornering stiffness and sideslip angle estimation based on simplified lateral dynamic models for four-in-wheel-motor-driven electric vehicles with lateral tire force information | |
Doumiati et al. | A method to estimate the lateral tire force and the sideslip angle of a vehicle: Experimental validation | |
CN115406446B (en) | State estimation method for multi-axle special vehicle based on neural network and unscented Kalman filter | |
CN112270039A (en) | Nonlinear state estimation method for drive-by-wire chassis vehicles based on distributed asynchronous fusion | |
WO2007018765A2 (en) | Online estimation of vehicle side-slip under linear operating region | |
CN110497915B (en) | A Vehicle Driving State Estimation Method Based on Weighted Fusion Algorithm | |
Pi et al. | Design and evaluation of sideslip angle observer for vehicle stability control | |
CN113104040B (en) | Tire-road surface adhesion coefficient acquisition method considering observation information time domain attenuation | |
Singh et al. | Integrated state and parameter estimation for vehicle dynamics control | |
Doumiati et al. | Unscented Kalman filter for real-time vehicle lateral tire forces and sideslip angle estimation | |
Wang et al. | Estimation of vehicle state using robust cubature Kalman filter | |
Tong | An approach for vehicle state estimation using extended Kalman filter | |
CN114043986B (en) | A multi-model fusion estimation method for tire-road adhesion coefficient considering mass mismatch | |
Sebsadji et al. | Vehicle roll and road bank angles estimation | |
CN111703429B (en) | A method for estimating the longitudinal speed of an in-wheel motor-driven vehicle | |
CN112287289A (en) | Vehicle nonlinear state fusion estimation method for cloud control intelligent chassis | |
CN114139360B (en) | Vehicle roll state estimation method based on double-extended Kalman filtering | |
CN113978476B (en) | A method for estimating the lateral force of a car tire by wire considering the loss of sensor data | |
Doumiati et al. | Embedded estimation of the tire/road forces and validation in a laboratory vehicle | |
Doumiati et al. | Virtual sensors, application to vehicle tire-road normal forces for road safety |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WD01 | Invention patent application deemed withdrawn after publication | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210126 |