CN115195758A - Longitudinal slip rate estimation method for articulated vehicle - Google Patents

Longitudinal slip rate estimation method for articulated vehicle Download PDF

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CN115195758A
CN115195758A CN202210549704.9A CN202210549704A CN115195758A CN 115195758 A CN115195758 A CN 115195758A CN 202210549704 A CN202210549704 A CN 202210549704A CN 115195758 A CN115195758 A CN 115195758A
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vehicle
wheel
state
articulated
mass
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杨昌霖
祝青园
程家琪
祝赫森
曾军
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Xiamen University
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Xiamen University
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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
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/12—Lateral speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/12—Lateral speed
    • B60W2520/125—Lateral acceleration
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/28—Wheel speed

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  • Physics & Mathematics (AREA)
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Abstract

The invention discloses a longitudinal slip ratio estimation method for an articulated vehicle, aiming at providing a more effective implementation scheme of vehicle state estimation for a vehicle with an articulated steering structure, comprising the following steps: initializing estimator data at the start of estimation; then, according to the dynamic characteristics of the articulated wheeled vehicle, a two-degree-of-freedom vehicle model is built; estimating the vehicle speed state information by adopting an Unscented Kalman Filter algorithm; further based on the kinematics of the articulated wheeled vehicle, estimating a longitudinal slip rate of the articulated wheeled vehicle with the aid of the estimated speed state information and the wheel speed information. According to the invention, by designing a two-degree-of-freedom dynamic model for the articulated wheeled engineering vehicle and designing an unscented Kalman estimation method based on the two-degree-of-freedom dynamic model, the problem of estimation algorithm distortion possibly caused by strong nonlinearity of the articulated wheeled engineering vehicle model is solved, and accurate estimation of the four-wheel longitudinal slip rate state of the articulated wheeled engineering vehicle is realized.

Description

Longitudinal slip rate estimation method for articulated vehicle
Technical Field
The invention relates to the field of automatic driving vehicle state estimation, in particular to a longitudinal slip ratio estimation method for an articulated wheeled vehicle.
Background
The articulated wheel type engineering vehicle is mainly divided into a front vehicle body and a rear vehicle body, the middle of the articulated wheel type engineering vehicle is connected by a rigid articulated body, the front vehicle body and the rear vehicle body are driven by a hydraulic cylinder to perform articulated steering, and the articulated wheel type engineering vehicle is widely applied to non-structural scenes such as construction sites, mines and the like. The accurate acquisition of the vehicle state is essential to the intellectualization, automation and vehicle safety of the articulated wheeled vehicle, and various applications of path tracking control, safety state detection and the like of the articulated vehicle depend on state parameters such as yaw angular velocity, articulation angular velocity, front and rear vehicle body speeds, slip rate and the like to different degrees.
And wherein, longitudinal slip rate has the effect of putting a great deal of weight in articulated vehicle dynamics identification and safety control aspect: on one hand, the longitudinal slip rate is highly related to tire force modeling and is an indispensable parameter based on dynamic modeling, on the other hand, the longitudinal slip rate corresponds to the road adhesion coefficient, and the tire can work in an adhesion characteristic stable area by controlling the slip rate, so that the vehicle safety is guaranteed.
However, the longitudinal slip ratio of the vehicle itself cannot be directly obtained by measurement on the premise of meeting the practicability, so that indirect estimation by means of a state estimation technology is required. At present, the research of a vehicle state estimation method based on model prediction is still mainly and intensively applied to common vehicles, a dynamic model of an articulated vehicle has high nonlinearity, the involved dynamic parameters have extremely strong coupling, and a dynamic model capable of accurately describing the dynamic characteristics of the articulated wheel vehicle is temporarily lacked, so that the estimation of the longitudinal slip ratio of an object by means of the constructed dynamic model of the vehicle has certain challenges.
Therefore, the current state acquisition of the articulated wheeled vehicle still has the defects, and the longitudinal slip rate of the vehicle cannot be accurately estimated.
Disclosure of Invention
The invention mainly aims to overcome the defects in the estimation problem of the longitudinal state of the articulated wheeled vehicle and provides an estimation method for the longitudinal slip rate of the articulated wheeled vehicle.
The invention adopts the following technical scheme:
the invention discloses a longitudinal slip rate estimation method for an articulated vehicle, which is characterized in that the dynamic characteristics of the articulated vehicle are completely considered from links such as a vertical load, a tire force, a dynamic model and the like, the problem of estimation algorithm distortion possibly caused by strong nonlinearity of the articulated vehicle model is solved by means of an Unscented Kalman method, and the estimation method comprises the following steps:
1) Estimator data initialization
2) According to the dynamic characteristics of the articulated wheeled vehicle, a two-degree-of-freedom vehicle model is built;
3) And estimating the vehicle speed state information by adopting an Unscented Kalman Filter algorithm.
4) Estimating a longitudinal slip rate of the articulated wheeled vehicle by means of the estimated speed state information and the wheel speed information based on kinematics of the articulated wheeled vehicle.
The vehicle state initialization formula is expressed as:
[x,y]'＝[N,N]
where x denotes the vehicle total center of mass longitudinal speed, y denotes the vehicle total center of mass lateral speed, and N denotes a very small constant not equal to 0.
The state co-mode initialization formula is expressed as:
Figure BDA0003654279910000021
wherein P is 0 Denotes the initial state covariance, E]Representing calculation of an expected value, x 0 Refers to the state vector matrix true value at the initial instant,
Figure BDA0003654279910000022
state vector matrix x referred to as initial time instant 0 Estimate of (a), x 0 Is represented as follows:
x 0 ＝[x,y]'
the two-degree-of-freedom dynamic model comprises the following concrete steps:
Figure BDA0003654279910000023
Figure BDA0003654279910000024
wherein v is x Representing the longitudinal speed of the mass centre, v, of the vehicle y The transverse speed of the mass center of the whole vehicle is shown,
Figure BDA0003654279910000025
the longitudinal acceleration of the mass center of the whole vehicle is shown,
Figure BDA0003654279910000026
and represents the transverse acceleration of the mass center of the whole vehicle. F xii And F yii And ii represents the front-rear (f/r) and left-right (l/r) of the corresponding wheel. T is f And T r Respectively refer to the wheel track of the front wheel and the wheel track of the rear wheel of the vehicle, gamma represents the yaw rate, and delta represents the hinged rotation angle. l fb 、l rb Respectively indicating the distances from the mass center of the front vehicle body and the mass center of the rear vehicle body to the articulated shaft, wherein m is the mass of the whole vehicle and m is the mass of the whole vehicle fb 、m rb Respectively representing the front body mass and the rear body mass.
F xii And F yii Expressed as:
Figure BDA0003654279910000031
Figure BDA0003654279910000032
Figure BDA0003654279910000033
Figure BDA0003654279910000034
where σ represents the longitudinal slip ratio and α represents the sideDeflection angle, λ t Is an operation process parameter, C σ 、C α Respectively representing the longitudinal and transverse stiffness of the tyre, mu representing the coefficient of ground friction, eta being an operational process parameter, F z Indicating the vertical load of the tire.
The vertical load F is planted by a two-degree-of-freedom dynamic model z Specifically, the following are shown:
Figure BDA0003654279910000035
Figure BDA0003654279910000036
Figure BDA0003654279910000037
Figure BDA0003654279910000038
wherein L is fr 、L fl 、L r Respectively, the longitudinal distances W from the front right wheel, the front left wheel and the rear wheel of the vehicle to the mass center of the vehicle fr 、W fl Then the transverse distance between two front wheels of the vehicle and the hinge point of the vehicle is represented, g represents the gravity acceleration, F f Denotes air resistance, F zd Indicating hinge point vertical load, T r Indicates the rear wheel track, m f And m r Respectively representing front and rear body masses, a yf And a yr Respectively representing the lateral acceleration of the front and rear bodies, d being the distance of the centre of mass of the vehicle from the ground
According to the complete dynamic model, the state equation and the measurement equation needed by the algorithm are determined as follows:
the equation of state is expressed as:
Figure BDA0003654279910000039
the measurement equation is expressed as:
y(t)＝h(x(t))＝[a x ,a y ]+v(t)
estimating the speed state of the vehicle by adopting an Unscented Kalman filtering algorithm according to a state equation and a measurement equation, which comprises the following steps:
the step of projecting the state quantity to the sigma point set is as follows:
Figure BDA0003654279910000041
wherein k denotes k time, χ x (k-1) refers to the set of state quantity sigma sampling points at time (k-1),
Figure BDA0003654279910000042
denotes the state quantity estimate at time (k-1), L x Represents the dimension of the state quantity, and P (k-1) represents the state covariance at time (k-1).
The weight value required by the sigma point weighting is as follows:
Figure BDA0003654279910000043
Figure BDA0003654279910000044
Figure BDA0003654279910000045
wherein W (m) 、W (c) The weights required for subsequent weighting, n representing the state vector dimension, and a representing a [10 ] value -4 ,1]The real number in the range, λ, is calculated as follows
λ＝α 2 (L x +3-L x )-L x
The time update formula is:
Figure BDA0003654279910000046
Figure BDA0003654279910000047
Figure BDA0003654279910000048
wherein, k refers to the time of k,
Figure BDA0003654279910000049
refers to a set of state quantity sigma sampling points, u refers to an input quantity,
Figure BDA00036542799100000410
refers to a priori prediction of the sigma point set,
Figure BDA00036542799100000411
a priori prediction of the state quantity, W (c) Denotes the weight used for weighting the sigma point set, and the subscript i denotes the ith column of the matrix, P xx Refers to a prior estimated covariance matrix.
The observation update formula is:
Figure BDA00036542799100000412
Figure BDA00036542799100000413
Figure BDA0003654279910000051
Figure BDA0003654279910000052
wherein Q is a process noise covariance matrix; r is a measurement noise covariance matrix,
Figure BDA0003654279910000053
a priori prediction of an observed value sigma point set, h [ 2 ]]Refers to the equation of the observation,
Figure BDA0003654279910000054
for a priori prediction of observed values, P yy Covariance matrix, P, of observations xy Refers to a covariance matrix of the state quantities and the observed quantities.
The Kalman gain calculation and updating formula is as follows:
K(k)＝P xy (k|k-1)P yy (k|k-1) -1
Figure BDA0003654279910000055
P(k|k)＝P(k|k-1)-K(k)P yy (k|k-1)K(k) T
wherein K is the Kalman gain,
Figure BDA00036542799100000511
for the posterior state estimator at time k, P (k | k) is the posterior state covariance matrix.
And estimating the slip angle and the slip rate of four wheels of the articulated wheeled vehicle by utilizing the vehicle speed information estimated by the UKF algorithm and the wheel speed information obtained by measurement.
The longitudinal slip ratio is formulated as:
Figure BDA0003654279910000056
the formula for the slip angle is:
Figure BDA0003654279910000057
in the formula r e Representing the wheel radius, ω ij Indicating vehicleWheel speed, v xij Representing the longitudinal speed, v, of each wheel center yij The longitudinal speed of each wheel center is shown, wherein i (f/r) represents front and back, and j (l/r) represents left and right. v. of xij 、v yij It is determined by the following equation.
v xij The formula is expressed as follows:
Figure BDA0003654279910000058
Figure BDA0003654279910000059
Figure BDA00036542799100000510
Figure BDA0003654279910000061
v yij the formula is expressed as follows:
Figure BDA0003654279910000062
Figure BDA0003654279910000063
v yrl ＝v y -r(l ra -l rb )
v yrr ＝v y -r(l ra -l rb )
in the formula L fl Indicating the distance, T, from the center of the front left wheel to the hinge point f Indicating the track width of the front wheel, L fr Indicates the distance from the wheel center of the front right wheel to the hinge point, l ra Indicating the distance from the rear axle of the wheel to the hinge point, l rb Indicating the distance of the rear body center of mass to the hinge point.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
(1) The invention provides a simple and available two-degree-of-freedom dynamic model aiming at the dynamic characteristics of an articulated wheeled vehicle, provides an effective method for acquiring the speed state of the vehicle, and provides a basic model basis for estimating the longitudinal slip rate.
(2) In the method provided by the invention, the uncertain Kalman algorithm is introduced for estimation by combining the acceleration information of the front and rear vehicle bodies, so that the result distortion caused by the nonlinearity of the articulated vehicle dynamics model height is effectively overcome.
(3) According to the method, the vertical load and the tire force are sequentially calculated according to the torque balance relation of the whole vehicle and the action relation of the tire road surface aiming at any corner working condition of the articulated wheeled vehicle, a two-degree-of-freedom dynamic model is introduced, and the vehicle speed information is calculated. And finally, the estimation aiming at the longitudinal slip rate of four wheels under the working condition of any corner is realized, the defects of the existing articulated wheeled vehicle in state acquisition are overcome, and the vehicle state can be estimated more accurately.
Drawings
Fig. 1 is a flow chart of a method provided by an embodiment of the invention.
Fig. 2 is a first schematic diagram of a two-degree-of-freedom vehicle model provided in an embodiment of the present invention.
Fig. 3 is a schematic diagram of the Unscented Kalman filter algorithm solution provided by the embodiment of the present invention.
The invention is described in further detail below with reference to the figures and specific examples.
Detailed Description
The invention is further described below by means of specific embodiments.
As shown in fig. 1, the present invention provides a flow chart of a longitudinal slip ratio estimation method for an articulated vehicle, specifically:
s101: initializing data according to the state of the articulated vehicle;
in order to prevent the occurrence of non-positive definite matrix in the operation, it is necessary to perform initialization setting on some estimator parameters when starting estimation, so that the whole estimation process can be normally operated, and therefore, the parameter initialization is performed in advance, as shown in fig. 3, specifically as follows:
x 0 ＝[x,y]'＝[N,N]
Figure BDA0003654279910000071
wherein P is 0 Denotes the initial state covariance, E [ ]]Representing calculation of an expected value, x 0 Refers to the state vector matrix true value at the initial time,
Figure BDA0003654279910000072
state vector matrix x referred to as initial time instant 0 An estimate of (d). x denotes the vehicle total centroid longitudinal speed, y denotes the vehicle total centroid lateral speed, and N denotes a very small constant not equal to 0.
S102: according to the dynamic characteristics of the articulated wheeled vehicle, a two-degree-of-freedom vehicle model is built;
on this basis, in order to accurately describe the overall vehicle dynamics performance of the articulated wheeled vehicle, a two-degree-of-freedom dynamics model of the articulated wheeled vehicle is established, as shown in fig. 2, specifically as follows:
Figure BDA0003654279910000073
Figure BDA0003654279910000074
wherein v is x Representing the longitudinal speed of the mass centre, v, of the vehicle y The transverse speed of the mass center of the whole vehicle is shown,
Figure BDA0003654279910000075
the longitudinal acceleration of the mass center of the whole vehicle is shown,
Figure BDA0003654279910000076
and represents the transverse acceleration of the mass center of the whole vehicle. F xii And F yii And ii represents the front-rear (f/r) and left-right (l/r) of the corresponding wheel. T is f And T r Respectively refer to the wheel track of the front wheel and the wheel track of the rear wheel of the vehicle, gamma represents the yaw rate, and delta represents the hinged rotation angle. l fb 、l rb Respectively indicating the distances from the mass center of the front vehicle body and the mass center of the rear vehicle body to the articulated shaft, wherein m is the mass of the whole vehicle and m is the mass of the whole vehicle fb 、m rb Respectively representing the front body mass and the rear body mass.
In the dynamic model, the interaction of the vehicle with the ground is represented by tire forces. The tire force F of the four wheels is calculated according to the DUGOFF tire model xii With transverse tyre force F yii The concrete expression is as follows:
Figure BDA0003654279910000081
Figure BDA0003654279910000082
Figure BDA0003654279910000083
Figure BDA0003654279910000084
where σ denotes the longitudinal slip ratio, α denotes the slip angle, λ t Is an operation process parameter, C σ 、C α Respectively representing the longitudinal and transverse stiffness of the tyre, mu representing the coefficient of ground friction, eta being an operational process parameter, F z Indicating the vertical load of the tire.
The vertical load F in the model zii Specifically, the following are shown:
Figure BDA0003654279910000085
Figure BDA0003654279910000086
Figure BDA0003654279910000087
Figure BDA0003654279910000088
wherein L is fr 、L fl 、L r Respectively refer to the longitudinal distances W from the front right wheel, the front left wheel and the rear wheel of the vehicle to the mass center of the vehicle fr 、W fl The lateral distance between two front wheels of the vehicle and a hinge point of the vehicle is represented by g, F f denotes air resistance, F zd Indicating hinge point vertical load, T r Indicating the rear wheel track, m f And m r Respectively representing front and rear body masses, a yf And a yr Respectively representing the lateral acceleration of the front and rear bodies, d being the distance of the centre of mass of the vehicle from the ground
According to the complete dynamic model, the state equation and the measurement equation required by the algorithm are determined as follows:
the equation of state is expressed as:
Figure BDA0003654279910000091
the measurement equation is expressed as:
y(t)＝h(x(t))＝[a x ,a y ]+v(t)
wherein, a x Longitudinal acceleration, a, representing the total centre of mass of the vehicle y Representing the lateral acceleration of the total center of mass of the entire vehicle. The articulated vehicle has variable structural characteristics, so the acceleration information of the front vehicle body and the acceleration information of the rear vehicle body are measured and converted correspondingly.
Figure BDA0003654279910000092
Figure BDA0003654279910000093
Wherein a is xf And a xr Respectively representing the longitudinal acceleration, a, of the front and rear bodywork centroids yf And a yr Respectively representing the longitudinal acceleration of the front and rear body centroids.
S103: estimating the vehicle speed state information by adopting an Unscented Kalman Filter algorithm;
on this basis, an Unscented Kalman filter algorithm is adopted to estimate the speed state of the articulated wheeled vehicle, as shown in fig. 3, specifically as follows:
the step of projecting the state quantity to the sigma point set is as follows:
Figure BDA0003654279910000094
wherein k denotes k time, χ x (k-1) refers to the set of state quantity sigma sampling points at time (k-1),
Figure BDA0003654279910000095
refers to the state quantity estimated value, L, at the time (k-1) x The magnitude is equal to the dimension of the state quantity, and P (k-1) represents the state covariance at time (k-1).
The weight value required by the sigma point weighting is as follows:
Figure BDA0003654279910000096
Figure BDA0003654279910000097
Figure BDA0003654279910000098
wherein W (m) 、W (c) The weights required for subsequent weighting, n representing the state vector dimension, α representing a [10 ] value -4 ,1]The real number in the range, λ, is calculated as follows
λ＝α 2 (L x +3-L x )-L x
The time update formula is:
Figure BDA0003654279910000101
Figure BDA0003654279910000102
Figure BDA0003654279910000103
wherein, k refers to the time of k,
Figure BDA0003654279910000104
refers to a set of state quantity sigma sampling points, u refers to an input quantity,
Figure BDA0003654279910000105
refers to a priori prediction of the sigma point set,
Figure BDA0003654279910000106
a priori prediction of the state quantity, W (c) Denotes the weight used for weighting the sigma point set, and the subscript i denotes the ith column of the matrix, P xx Refers to a prior estimated covariance matrix.
The observation update formula is:
Figure BDA0003654279910000107
Figure BDA0003654279910000108
Figure BDA0003654279910000109
Figure BDA00036542799100001010
wherein Q is a process noise covariance matrix; r is a measurement noise covariance matrix,
Figure BDA00036542799100001011
a priori prediction of an observed value sigma point set, h [ 2 ]]Refers to the equation of the observation,
Figure BDA00036542799100001012
for a priori prediction of the observed values, L x For a parameter for calculation, the size is equal to the state variable dimension, P, in the context of the invention yy (k | k-1) denotes the covariance matrix of the observations, P xy (k | k-1) covariance matrices of the finger state quantities and the observed quantities.
The Kalman gain calculation and update formula is as follows:
K(k)＝P xy (k|k-1)P yy (k|k-1) -1
Figure BDA00036542799100001013
P(k|k)＝P(k|k-1)-K(k)P yy (k|k-1)K(k) T
wherein K is the Kalman gain,
Figure BDA00036542799100001014
for the posterior state estimator at time k, P (k | k) is the posterior state covariance matrix.
S104: estimating a longitudinal slip rate of the articulated wheeled vehicle by means of the estimated speed state information and wheel speed information according to kinematics of the articulated wheeled vehicle;
the speed information of the mass center of the whole vehicle can be obtained through the process. On the basis of the wheel speed information, the longitudinal slip rate and the slip angle of the wheel are calculated by introducing the four wheel speed information, and are output as final results and input parameters estimated at the next moment.
The longitudinal slip ratio is formulated as:
Figure BDA0003654279910000111
the formula for the slip angle is:
Figure BDA0003654279910000112
in the formula r e Representing the wheel radius, ω ij Indicating wheel speed, v xij Representing the longitudinal speed, v, of each wheel center yij The longitudinal speed of each wheel center is shown, wherein i (f/r) represents front and back, and j (l/r) represents left and right. v. of xij 、v yij It is determined by the following equation. For the sake of calculation convenience, the longitudinal speed and the transverse speed of the mass center of the rear vehicle body are correspondingly converted, as shown in fig. 2, and are expressed as follows:
Figure BDA0003654279910000113
Figure BDA0003654279910000114
wherein v is xr Is the longitudinal speed of the mass center of the rear body, middle v yr The transverse speed of the mass center of the rear car body is delta, and the angle delta is a hinged corner.
By introducing vehicle kinematic parameters including rear body yaw rate, articulated steering angular velocity, finished vehicle mass center velocity information and the like, vehicle geometric parameters and four-wheel speed information obtained through measurement, longitudinal velocity and transverse velocity of four-wheel centers are respectively calculated according to kinematic relationships, and are specifically determined by the following formula as shown in fig. 2:
longitudinal wheel speed v xij The formula is expressed as follows:
Figure BDA0003654279910000115
Figure BDA0003654279910000116
Figure BDA0003654279910000117
Figure BDA0003654279910000118
transverse wheel speed v yij The formula is expressed as follows:
Figure BDA0003654279910000119
Figure BDA0003654279910000121
v yrl ＝v y -r(l ra -l rb )
v yrr ＝v y -r(l ra -l rb )
in the formula I ra Indicating the distance from the rear axle of the wheel to the hinge point, l rb Showing the distance from the center of mass of the rear body to the hinge point, T f Indicating the track width of the front wheel, L fta 、L rta Indicating the distance from the front and rear wheel axles to the hinge axis.
S105: after the calculation is completed, the estimation result of the longitudinal slip ratio is output as an output, and meanwhile, the four-wheel longitudinal slip ratio and the slip angle are returned to the step S102 again to calculate the tire force of the next estimation period, so as to estimate the vehicle longitudinal slip ratio information of the next period.
The invention provides a simple and available two-degree-of-freedom dynamic model aiming at the dynamic characteristics of an articulated wheeled vehicle, provides an effective method for acquiring the speed state of the vehicle, and provides a basic model basis for estimating the longitudinal slip rate.
In the method provided by the invention, the uncertain Kalman algorithm is introduced for estimation by combining the acceleration information of the front and rear vehicle bodies, so that the result distortion caused by the nonlinearity of the articulated vehicle dynamics model height is effectively overcome.
According to the method, the vertical load and the tire force are sequentially calculated according to the torque balance relation of the whole vehicle and the action relation of the tire road surface aiming at any corner working condition of the articulated wheeled vehicle, a two-degree-of-freedom dynamic model is introduced, and the vehicle speed information is calculated. And finally, the estimation aiming at the longitudinal slip rate of four wheels under the working condition of any corner is realized, the defects of the existing articulated wheeled vehicle in state acquisition are overcome, and the vehicle state can be estimated more accurately.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.

Claims (7)

1. A method of estimating longitudinal slip ratio for an articulated vehicle, comprising the steps of:
initializing data according to the state of the articulated vehicle;
according to the dynamic characteristics of the articulated wheeled vehicle, a two-degree-of-freedom vehicle model is built;
estimating the vehicle speed state information by adopting an Unscented Kalman Filter filtering algorithm;
estimating a longitudinal slip ratio of the articulated wheeled vehicle based on the kinematics of the articulated wheeled vehicle using the estimated speed state information and the wheel speed information.
2. A longitudinal slip ratio estimation method for an articulated vehicle according to claim 1, characterized in that the data are initially set at the start of the estimation, in particular as follows:
the vehicle state initialization formula is expressed as:
[x,y]'＝[N,N]
wherein x denotes the vehicle total center of mass longitudinal speed, y denotes the vehicle total center of mass lateral speed, and N denotes a very small constant not equal to 0;
the state co-mode initialization formula is expressed as:
Figure FDA0003654279900000011
wherein P is 0 Denotes the initial state covariance, E]Representing calculation of an expected value, x 0 Refers to the state vector matrix true value at the initial instant,
Figure FDA0003654279900000012
state vector matrix x denoted as initial time instant 0 Estimate of (a), x 0 Is represented as follows:
x 0 ＝[x,y]'
3. the method for estimating longitudinal slip ratio for an articulated vehicle according to claim 1, wherein a two-degree-of-freedom vehicle model is constructed, and the two-degree-of-freedom vehicle model is specifically as follows:
Figure FDA0003654279900000013
Figure FDA0003654279900000014
wherein v is x Represents the mass center of the whole vehicleVelocity, v y The transverse speed of the mass center of the whole vehicle is shown,
Figure FDA0003654279900000015
the longitudinal acceleration of the mass center of the whole vehicle is shown,
Figure FDA0003654279900000021
representing the transverse acceleration of the mass center of the whole vehicle; f xii And F yii Means longitudinal and lateral tire forces, ii means front-to-back (f/r) and left-to-right (l/r) of the corresponding wheel; t is f And T r Respectively refer to the wheel track of the front wheel and the rear wheel of the vehicle, gamma represents the yaw rate, delta is the hinged corner,
Figure FDA0003654279900000022
for angular velocity of articulation angle, /) fb 、l rb Respectively indicating the distances from the mass center of the front vehicle body and the mass center of the rear vehicle body to the articulated shaft, wherein m is the mass of the whole vehicle and m is the mass of the whole vehicle fb 、m rb Respectively representing the mass of the front vehicle body and the mass of the rear vehicle body;
F xii and F yii Expressed as:
Figure FDA0003654279900000023
Figure FDA0003654279900000024
Figure FDA0003654279900000025
Figure FDA0003654279900000026
where σ denotes the longitudinal slip ratio, α denotes the slip angle, λ t Is an operation process parameter, C σ 、C α Respectively representing the longitudinal and transverse stiffness of the tyre, mu representing the coefficient of ground friction, eta being an operational process parameter, F z Indicating the vertical load of the tire.
4. A method of longitudinal slip ratio estimation for an articulated vehicle according to claim 1, characterized in that the velocity state information of the vehicle is estimated using an Unscented Kalman filter algorithm, as follows:
the step of projecting the state quantity to the sigma point set is as follows:
Figure FDA0003654279900000027
wherein k denotes time k, χ x (k-1) refers to the set of state quantity sigma sampling points at time (k-1),
Figure FDA00036542799000000211
refers to the state quantity estimated value, L, at the time (k-1) x Represents the dimension of the state quantity, and P (k-1) represents the state covariance at time (k-1).
The weight value required by the sigma point weighting is as follows:
Figure FDA0003654279900000028
Figure FDA0003654279900000029
Figure FDA00036542799000000210
wherein W (m) 、W (c) The weights required for subsequent weighting, n representing the state vector dimension, and a representing a [10 ] value -4 ,1]The real number in the range, λ, is calculated as follows
λ＝α 2 (L x +3-L x )-L x
The time update formula is:
Figure FDA0003654279900000031
Figure FDA0003654279900000032
Figure FDA0003654279900000033
wherein, k refers to the time of k,
Figure FDA0003654279900000034
refers to a set of state quantity sigma sampling points, u refers to an input quantity,
Figure FDA0003654279900000035
refers to a priori prediction of the sigma point set,
Figure FDA0003654279900000036
a priori prediction of the state quantity, W (c) Denotes the weight used for weighting the sigma point set, and the subscript i denotes the ith column of the matrix, P xx Means prior estimation covariance matrix;
the observation update formula is:
Figure FDA0003654279900000037
Figure FDA0003654279900000038
Figure FDA0003654279900000039
Figure FDA00036542799000000310
wherein Q is a process noise covariance matrix; r is a measurement noise covariance matrix,
Figure FDA00036542799000000311
a priori prediction of an observed value sigma point set, h [ 2 ]]Refers to the equation of the observation,
Figure FDA00036542799000000312
for a priori prediction of observed values, P yy Covariance matrix, P, of observations xy Refers to a covariance matrix of the state quantities and the observed quantities.
The Kalman gain calculation and updating formula is as follows:
K(k)＝P xy (k|k-1)P yy (k|k-1) -1
Figure FDA00036542799000000313
P(k|k)＝P(k|k-1)-K(k)P yy (k|k-1)K(k) T
wherein K is the Kalman gain,
Figure FDA00036542799000000314
for the posterior state estimator at time k, P (k | k) is the posterior state covariance matrix.
5. The longitudinal slip ratio estimation method for an articulated vehicle according to claim 3, characterized in that the longitudinal slip ratio and the slip angle of the vehicle are estimated by means of the estimated speed state information and wheel speed information according to the kinematics of the articulated wheeled vehicle, as follows:
the longitudinal slip ratio is formulated as:
Figure FDA00036542799000000315
the formula for the slip angle is:
Figure FDA0003654279900000041
in the formula r e Representing the wheel radius, ω ij Indicating wheel speed, r e Representing wheel radius, v xij Representing the longitudinal speed, v, of each wheel center yij Representing the longitudinal speed of each wheel center, wherein i (f/r) represents front and back, and j (l/r) represents left and right; v. of xij 、v yij Is determined by the following formula;
v xij the formula is expressed as follows:
Figure FDA0003654279900000042
Figure FDA0003654279900000043
Figure FDA0003654279900000044
Figure FDA0003654279900000045
v yij the formula is expressed as follows:
Figure FDA0003654279900000046
Figure FDA0003654279900000047
v yrl ＝v y -r(l ra -l rb )
v yrr ＝v y -r(l ra -l rb )
in the formula L fl Indicating the distance, L, from the center of the front left wheel to the hinge point fr Indicates the distance from the wheel center of the front right wheel to the hinge point, l ra Indicating the distance, T, from the rear axle of the wheel to the hinge point f Indicating the track of the front wheel, /) rb Indicating the distance of the rear body center of mass to the hinge point.
6. The longitudinal slip rate estimation method for an articulated vehicle according to claim 3, wherein in the two-degree-of-freedom vehicle model, the vertical loads of four wheels are respectively calculated by vehicle dynamic parameters and the vertical load at the measuring hinge point through a vehicle moment balance relation based on the ground; the vertical load model is as follows:
Figure FDA0003654279900000048
Figure FDA0003654279900000051
Figure FDA0003654279900000052
Figure FDA0003654279900000053
wherein L is fr 、L fl 、L r Respectively refer to the front right wheel and the front left wheel of the vehicleAnd the longitudinal distance, W, of the rear wheel to the center of mass of the vehicle fr 、W fl Then the lateral distance between two front wheels of the vehicle and a hinge point of the vehicle is represented, g represents the gravity acceleration, F f Denotes air resistance, F zd Indicating hinge point vertical load, T r Indicates the rear wheel track, m f And m r Respectively representing front and rear body masses, a yf And a yr Which respectively represent the lateral acceleration of the front and rear bodies, and d denotes the distance from the center of mass of the vehicle to the ground.
7. The method of claim 4, wherein the velocity state information of the vehicle is estimated using an Unscented Kalman filter algorithm, using the following equations of state and measurement:
the equation of state is expressed as:
Figure FDA0003654279900000054
the measurement equation is expressed as:
y(t)＝h(x(t))＝[a x ,a y ]+v(t)
wherein, a x Representing the longitudinal acceleration of the total centre of mass of the vehicle, a y Representing the lateral acceleration of the total center of mass of the entire vehicle.
CN202210549704.9A 2022-05-20 2022-05-20 Longitudinal slip rate estimation method for articulated vehicle Pending CN115195758A (en)

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