CN107901914A - A kind of vehicle centroid side drift angle and coefficient of road adhesion Combined estimator system - Google Patents

A kind of vehicle centroid side drift angle and coefficient of road adhesion Combined estimator system Download PDF

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CN107901914A
CN107901914A CN201710881528.8A CN201710881528A CN107901914A CN 107901914 A CN107901914 A CN 107901914A CN 201710881528 A CN201710881528 A CN 201710881528A CN 107901914 A CN107901914 A CN 107901914A
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moment
represent
coefficient
drift angle
matrix
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CN107901914B (en
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熊璐
林雪峰
夏新
刘伟
余卓平
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Tongji 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
    • B60W40/02—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 ambient conditions
    • B60W40/06—Road conditions
    • B60W40/064—Degree of grip
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight
    • B60W2040/1315—Location of the centre of gravity
    • 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
    • B60W2520/12—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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The present invention relates to a kind of vehicle centroid side drift angle and coefficient of road adhesion Combined estimator system, the system to include：Informaiton fusion module：The module is taken aim at a little apart from right-lane line distance and side acceleration in advance for obtaining aligning torque at stub, video camera；Extended Kalman filter estimator：The estimator connects informaiton fusion module and estimates to obtain coefficient of road adhesion and front-wheel side drift angle；Side slip angle converting unit：The unit connects Extended Kalman filter estimator and is converted to side slip angle according to front-wheel side drift angle.Compared with prior art, estimated result of the present invention is accurate, and can overcome influence of noise, can be widely applied to various intelligent automobiles.

Description

A kind of vehicle centroid side drift angle and coefficient of road adhesion Combined estimator system
Technical field
The present invention relates to Vehicle dynamic parameters estimation, more particularly, to a kind of vehicle centroid side drift angle and road surface attachment system Number Combined estimator system.
Background technology
Side slip angle and coefficient of road adhesion are the key messages of vehicle dynamics stability control, and intelligent automobile The key stato variable of motion tracking control.Side slip angle and coefficient of road adhesion are difficult to directly measure, and barycenter side Drift angle and coefficient of road adhesion are associated together, it is difficult to are separately estimated that its estimation is subject to, it is necessary to be carried out at the same time estimation The influence of the uncertain factor such as Tire nonlinearity and auto model, is the amount that vehicle is most difficult to be estimated, therefore, how to barycenter Side drift angle and coefficient of road adhesion carry out the research weight difficult point that Combined estimator is all automobile dynamics control all the time.
The method of estimation of side slip angle and coefficient of road adhesion mainly has both at home and abroad at present：1st, separately estimated, Estimate to assume coefficient of road adhesion when side slip angle it is known that having assumed side slip angle when estimating coefficient of road adhesion Know, then both are placed under a system by force and is run.Its problem is it cannot be guaranteed that two estimators can convergence at the same time Actual value.2nd, traditionally it is all based on inertance element and gyroscope estimates side slip angle and coefficient of road adhesion, believes Breath amount is lacked.3rd, part research adds front-wheel aligning torque and side slip angle and coefficient of road adhesion is estimated, but before Wheel aligning torque is coupled with lateral force, it is impossible to is directly utilized.
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of vehicle centroid lateral deviation Angle and coefficient of road adhesion Combined estimator system.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of vehicle centroid side drift angle and coefficient of road adhesion Combined estimator system, the system include：
Informaiton fusion module：The module is taken aim at a little apart from right lane in advance for obtaining aligning torque at stub, video camera Linear distance and side acceleration；
Extended Kalman filter estimator：The estimator connects informaiton fusion module and estimates to obtain road surface attachment system Number and front-wheel side drift angle；
Side slip angle converting unit：The unit connects Extended Kalman filter estimator and is changed according to front-wheel side drift angle Obtain side slip angle.
The informaiton fusion module is including being used to estimate the aligning torque estimator of aligning torque at stub, being used for Obtain video camera and take aim at a little inertance element apart from the video camera of right-lane line distance and for obtaining side acceleration in advance.
The aligning torque observer is specially：
Wherein, MkRepresent aligning torque at stub, δwRepresent steering wheel angle, is(δw) represent steering wheel angle to stub Locate rotary driving ratio, im(δw) represent assist motor corner rotary driving ratio, M at stubsRepresent steering wheel torque, MmRepresent Assist motor torque, A and B are constant.
The Extended Kalman filter estimator includes observing matrix prediction module, state matrix prediction module, state Transfer matrix acquisition module, observing matrix acquisition module, covariance matrix prediction module, Kalman filtering gain acquisition module, State matrix update module and covariance update module, the observing matrix prediction module, state matrix prediction module, state Transfer matrix acquisition module and observing matrix acquisition module are connected to informaiton fusion module, observing matrix prediction Module and state matrix prediction module are all connected with state matrix update module, state-transition matrix acquisition module connection association Variance matrix prediction module, the observing matrix acquisition module connect Kalman filtering gain acquisition module and covariance respectively Update module, the covariance matrix prediction module connect Kalman filtering gain acquisition module and covariance renewal mould respectively Block, Kalman filtering gain acquisition module difference connection status matrix update module and covariance update module, it is described State update module and covariance update module feed back to observing matrix prediction module, state matrix prediction module, state Transfer matrix acquisition module and observing matrix acquisition module.
The anticipation function of the state matrix prediction module is：
Wherein, two adjacent sampling instants of k and k-1 expressions, and X (k | k-1) represent to be predicted according to k-1 moment state variable The predicted state variable at k moment out, μ (k | k-1) represent the road at the k moment predicted according to the state variable at k-1 moment Face attachment coefficient, yrIt is right that the k moment video camera that (k | k-1) represents to be predicted according to the state variable at k-1 moment takes aim at a distance in advance Lane line distance, αf(k | k-1) represents the front-wheel side drift angle at the k moment predicted according to the state variable at k-1 moment, μ (k-1) Represent the coefficient of road adhesion at k-1 moment, yr(k-1) represent that k-1 moment video camera is taken aim at a little apart from right-lane line distance, Δ T in advance Represent systematic sampling time interval, vxRepresent longitudinal speed,Represent vehicle course angle, αf(k-1) front-wheel at k-1 moment is represented Side drift angle, r represent yaw rate, lpRepresent that video camera is taken aim at a little away from vehicle centroid distance, l in advancefRepresent front axle to vehicle matter Heart distance, δ represent front wheel angle, fa(μ(k-1),αf(k-1)) coefficient of road adhesion by the k-1 moment and k-1 moment are represented The side acceleration that front wheel side drift angle is calculated；
The anticipation function of the observing matrix prediction module is：
Wherein, Y (k | k-1) represents the observed quantity at the k moment come out according to the predicted state variable prediction at k-1 moment, Mk (k | k-1) represents the aligning torque at the stub at the k moment come out according to the predicted state variable prediction at k-1 moment, yr(k|k- 1) represent that the k moment video camera gone out according to the predicted state variable prediction at k-1 moment is taken aim at a little apart from right-lane line distance, a in advancey(k | k-1) represent side acceleration out, M according to the predicted state variable prediction at k-1 momentp(μ(k|k-1),αf(k|k-1)) Represent the wheel self-aligning torque calculated by the coefficient of road adhesion at the k moment predicted and the front wheel side drift angle at k moment, lm Represent that tire machinery drags square, Fy(μ(k|k-1),αf(k | k-1)) represent the coefficient of road adhesion at k moment by predicting and k moment The wheel lateral force that calculates of front wheel side drift angle, vxRepresent longitudinal speed,Represent vehicle course angle, r represents Vehicular yaw Angular speed, lpRepresent that video camera is taken aim at a little away from vehicle centroid distance, l in advancefFront axle is represented to vehicle centroid distance, rotation before δ is represented Angle, fa(μ(k|k-1),αf(k | k-1)) represent the coefficient of road adhesion at k moment and the front wheel side declinator at k moment by predicting The lateral rotating speed calculated, μ (k | k-1) represent the road surface attachment system at the k moment predicted according to the state variable at k-1 moment Number, αf(k | k-1) represents the front-wheel side drift angle at the k moment predicted according to the state variable at k-1 moment；
The state-transition matrix that the state-transition matrix acquisition module obtains is：
Wherein, φ (k) represents the state-transition matrix at k moment, Δ T systematic sampling time intervals, vxRepresent longitudinal speed,Represent vehicle course angle,Represent that lateral rotating speed road pavement attachment coefficient is asked after local derviation pre- Value at the coefficient of road adhesion at the k moment of survey and the front-wheel side drift angle at k moment,Represent Lateral rotating speed is sought front-wheel side drift angle after local derviation in the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction The value at place, μ (k | k-1) represent the coefficient of road adhesion at the k moment predicted according to the state variable at k-1 moment, αf(k|k- 1) the front-wheel side drift angle at k moment predicted according to the state variable at k-1 moment is represented；
The observing matrix that the observing matrix acquisition module obtains is：
Wherein, H (k) represents the observing matrix at k moment,Represent tyre moment pair Coefficient of road adhesion seeks the value at the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction after local derviation,Represent side force of tire road pavement attachment coefficient ask after local derviation prediction the k moment road surface Value at attachment coefficient and the front-wheel side drift angle at k moment,Represent side acceleration road pavement Attachment coefficient seeks the value at the coefficient of road adhesion of prediction and the front-wheel side drift angle at k moment at k moment after local derviation,Represent tyre moment front-wheel side drift angle is asked after local derviation prediction the k moment road surface Value at attachment coefficient and the front-wheel side drift angle at k moment,Represent side force of tire to front-wheel Side drift angle seeks the value at the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction after local derviation,Represent that it is attached on the road surface at the k moment of prediction after local derviation to seek front-wheel side drift angle for side acceleration The value at coefficient and the front-wheel side drift angle at k moment, μ (k | k-1) represents the k predicted according to the state variable at k-1 moment The coefficient of road adhesion at moment, αf(k | k-1) represents the front-wheel lateral deviation at the k moment predicted according to the state variable at k-1 moment Angle, lmRepresent that tire machinery drags square, vxRepresent longitudinal speed,Represent vehicle course angle.
The anticipation function of the covariance matrix prediction module is：
P (k | k-1)=φ (k) P (k-1) φT(k)+Q,
Wherein, P (k | k-1) represent according to the covariance matrix at k-1 moment predict come the k moment covariance matrix, φ (k) represents the state-transition matrix at k moment, and P (k-1) represents the covariance renewal at k-1 moment, and Q represents state equation mistake The variance of journey noise.
The Kalman filtering gain that the Kalman filtering gain acquisition module obtains is：
K (k)=P (k | k-1) HT(k)(H(k)P(k|k-1)HT(k)+R),
Wherein, the Kalman filtering Gain filter gain at K (k) expression k moment, and P (k | k-1) represent according to the k-1 moment Covariance matrix predicts the covariance matrix at the k moment come, and H (k) represents the observing matrix at k moment, and R represents that observational equation is made an uproar The variance of sound.
The renewal function of the state matrix update module is：
X (k)=X (k | k-1)+K (k) (Y (k)-Y (k | k-1)),
X (k) represents the renewal state variable at k moment, X (k | k-1) represent according to k-1 moment state variables predict come The predicted state variable at k moment, K (k) represent the Kalman filtering Gain filter gain at k moment, and Y (k) represents the k that measurement obtains The observed quantity at moment, Y (k)=[Mk(k),yr(k),ay(k)]T, Mk(k) the aligning torque measured value at the stub at k moment is represented, yr(k) represent that k moment video camera is taken aim at a little apart from right-lane line distance measure, a in advancey(k) represent that the side acceleration at k moment is surveyed Value, Y (k | k-1) represent the observed quantity at the k moment come out according to the predicted state variable prediction at k-1 moment.
The renewal function of the covariance update module is：
P (k)=(I-K (k) H (k)) P (k | k-1),
Wherein, P (k) represents the renewal covariance matrix at k moment, and I represents the unit matrix of three ranks, and K (k) represents the k moment Kalman filtering Gain filter gain, H (k) represents the observing matrix at k moment, and P (k | k-1) represents the association according to the k-1 moment Variance matrix predicts the covariance matrix at the k moment come.
The transfer function of side slip angle converting unit is：
Wherein, β (k) represents the estimate of k moment side slip angles, αf(k | k-1) represent to be become according to the state at k-1 moment Measure the front-wheel side drift angle at the k moment predicted, lfRepresent front axle to the distance of barycenter, vxRepresent longitudinal speed, r represents yaw angle Speed, δ represent front wheel angle.
Compared with prior art, the invention has the advantages that：
(1) present invention utilizes the aligning torque at stub, camera to right car by what informaiton fusion module obtained To side slip angle and coefficient of road adhesion Combined estimator, algorithm can ensure two estimations for diatom distance and side acceleration Amount can tend to actual value；
(2) present invention uses expanded Kalman filtration algorithm, and algorithm can overcome influence of noise, can be used in real vehicle fortune OK.
Brief description of the drawings
Fig. 1 is vehicle centroid side drift angle of the present invention and the structure diagram of coefficient of road adhesion Combined estimator system.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
As shown in Figure 1, a kind of vehicle centroid side drift angle and coefficient of road adhesion Combined estimator system, the system include：
Informaiton fusion module：The module is taken aim at a little apart from right lane in advance for obtaining aligning torque at stub, video camera Linear distance and side acceleration；
Extended Kalman filter estimator：The estimator connects informaiton fusion module and estimates to obtain road surface attachment system Number and front-wheel side drift angle；
Side slip angle converting unit：The unit connects Extended Kalman filter estimator and is changed according to front-wheel side drift angle Obtain side slip angle.
Informaiton fusion module is including being used to estimate the aligning torque estimator of aligning torque at stub, being taken the photograph for obtaining Camera takes aim at a little inertance element apart from the video camera of right-lane line distance and for obtaining side acceleration in advance.
Aligning torque observer is specially：
Wherein, MkRepresent aligning torque at stub, δwRepresent steering wheel angle, is(δw) represent steering wheel angle to stub Locate rotary driving ratio, im(δw) represent assist motor corner rotary driving ratio, M at stubsRepresent steering wheel torque, MmRepresent Assist motor torque, A and B are constant.
Extended Kalman filter estimator includes observing matrix prediction module, state matrix prediction module, state transfer square Battle array acquisition module, observing matrix acquisition module, covariance matrix prediction module, Kalman filtering gain acquisition module, state square Battle array update module and covariance update module, observing matrix prediction module, state matrix prediction module, state-transition matrix obtain Module and observing matrix acquisition module are connected to informaiton fusion module, observing matrix prediction module and state matrix prediction Module is all connected with state matrix update module, and state-transition matrix acquisition module connection covariance matrix prediction module, observes square Battle array acquisition module connects Kalman filtering gain acquisition module and covariance update module, covariance matrix prediction module point respectively Not Lian Jie Kalman filtering gain acquisition module and covariance update module, Kalman filtering gain acquisition module connects shape respectively It is pre- that state matrix update module and covariance update module, state update module and covariance update module feed back to observing matrix Survey module, state matrix prediction module, state-transition matrix acquisition module and observing matrix acquisition module.
The anticipation function of state matrix prediction module is：
Wherein, two adjacent sampling instants of k and k-1 expressions, and X (k | k-1) represent to be predicted according to k-1 moment state variable The predicted state variable at k moment out, μ (k | k-1) represent the road at the k moment predicted according to the state variable at k-1 moment Face attachment coefficient, yrIt is right that the k moment video camera that (k | k-1) represents to be predicted according to the state variable at k-1 moment takes aim at a distance in advance Lane line distance, αf(k | k-1) represents the front-wheel side drift angle at the k moment predicted according to the state variable at k-1 moment, μ (k-1) Represent the coefficient of road adhesion at k-1 moment, yr(k-1) represent that k-1 moment video camera is taken aim at a little apart from right-lane line distance, Δ T in advance Represent systematic sampling time interval, vxRepresent longitudinal speed,Represent vehicle course angle, αf(k-1) front-wheel at k-1 moment is represented Side drift angle, r represent yaw rate, lpRepresent that video camera is taken aim at a little away from vehicle centroid distance, l in advancefRepresent front axle to vehicle matter Heart distance, δ represent front wheel angle, fa(μ(k-1),αf(k-1)) coefficient of road adhesion by the k-1 moment and k-1 moment are represented The side acceleration that front wheel side drift angle is calculated；
The anticipation function of observing matrix prediction module is：
Wherein, Y (k | k-1) represents the observed quantity at the k moment come out according to the predicted state variable prediction at k-1 moment, Mk (k | k-1) represents the aligning torque at the stub at the k moment come out according to the predicted state variable prediction at k-1 moment, yr(k|k- 1) represent that the k moment video camera gone out according to the predicted state variable prediction at k-1 moment is taken aim at a little apart from right-lane line distance, a in advancey(k | k-1) represent side acceleration out, M according to the predicted state variable prediction at k-1 momentp(μ(k|k-1),αf(k|k-1)) Represent the wheel self-aligning torque calculated by the coefficient of road adhesion at the k moment predicted and the front wheel side drift angle at k moment, lm Represent that tire machinery drags square, Fy(μ(k|k-1),αf(k | k-1)) represent the coefficient of road adhesion at k moment by predicting and k moment The wheel lateral force that calculates of front wheel side drift angle, vxRepresent longitudinal speed,Represent vehicle course angle, r represents Vehicular yaw Angular speed, lpRepresent that video camera is taken aim at a little away from vehicle centroid distance, l in advancefFront axle is represented to vehicle centroid distance, rotation before δ is represented Angle, fa(μ(k|k-1),αf(k | k-1)) represent the coefficient of road adhesion at k moment and the front wheel side declinator at k moment by predicting The lateral rotating speed calculated, μ (k | k-1) represent the road surface attachment system at the k moment predicted according to the state variable at k-1 moment Number, αf(k | k-1) represents the front-wheel side drift angle at the k moment predicted according to the state variable at k-1 moment；
State-transition matrix acquisition module obtain state-transition matrix be：
Wherein, φ (k) represents the state-transition matrix at k moment, Δ T systematic sampling time intervals, vxRepresent longitudinal speed,Represent vehicle course angle,Represent that lateral rotating speed road pavement attachment coefficient is asked after local derviation pre- Value at the coefficient of road adhesion at the k moment of survey and the front-wheel side drift angle at k moment,Represent Lateral rotating speed is sought front-wheel side drift angle after local derviation in the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction The value at place, μ (k | k-1) represent the coefficient of road adhesion at the k moment predicted according to the state variable at k-1 moment, αf(k|k- 1) the front-wheel side drift angle at k moment predicted according to the state variable at k-1 moment is represented；
Observing matrix acquisition module obtain observing matrix be：
Wherein, H (k) represents the observing matrix at k moment,Represent tyre moment pair Coefficient of road adhesion seeks the value at the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction after local derviation,Represent side force of tire road pavement attachment coefficient ask after local derviation prediction the k moment road surface Value at attachment coefficient and the front-wheel side drift angle at k moment,Represent side acceleration road pavement Attachment coefficient seeks the value at the coefficient of road adhesion of prediction and the front-wheel side drift angle at k moment at k moment after local derviation,Represent tyre moment front-wheel side drift angle is asked after local derviation prediction the k moment road surface Value at attachment coefficient and the front-wheel side drift angle at k moment,Represent side force of tire to front-wheel Side drift angle seeks the value at the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction after local derviation,Represent that it is attached on the road surface at the k moment of prediction after local derviation to seek front-wheel side drift angle for side acceleration The value at coefficient and the front-wheel side drift angle at k moment, μ (k | k-1) represents the k predicted according to the state variable at k-1 moment The coefficient of road adhesion at moment, αf(k | k-1) represents the front-wheel lateral deviation at the k moment predicted according to the state variable at k-1 moment Angle, lmRepresent that tire machinery drags square, vxRepresent longitudinal speed,Represent vehicle course angle.
The anticipation function of covariance matrix prediction module is：
P (k | k-1)=φ (k) P (k-1) φT(k)+Q,
Wherein, P (k | k-1) represent according to the covariance matrix at k-1 moment predict come the k moment covariance matrix, φ (k) represents the state-transition matrix at k moment, and P (k-1) represents the covariance renewal at k-1 moment, and Q represents state equation mistake The variance of journey noise.
Kalman filtering gain acquisition module obtain Kalman filtering gain be：
K (k)=P (k | k-1) HT(k)(H(k)P(k|k-1)HT(k)+R),
Wherein, the Kalman filtering Gain filter gain at K (k) expression k moment, and P (k | k-1) represent according to the k-1 moment Covariance matrix predicts the covariance matrix at the k moment come, and H (k) represents the observing matrix at k moment, and R represents that observational equation is made an uproar The variance of sound.
The renewal function of state matrix update module is：
X (k)=X (k | k-1)+K (k) (Y (k)-Y (k | k-1)),
X (k) represents the renewal state variable at k moment, X (k | k-1) represent according to k-1 moment state variables predict come The predicted state variable at k moment, K (k) represent the Kalman filtering Gain filter gain at k moment, and Y (k) represents the k that measurement obtains The observed quantity at moment, Y (k)=[Mk(k),yr(k),ay(k)]T, Mk(k) the aligning torque measured value at the stub at k moment is represented, yr(k) represent that k moment video camera is taken aim at a little apart from right-lane line distance measure, a in advancey(k) represent that the side acceleration at k moment is surveyed Value, Y (k | k-1) represent the observed quantity at the k moment come out according to the predicted state variable prediction at k-1 moment.
The renewal function of covariance update module is：
P (k)=(I-K (k) H (k)) P (k | k-1),
Wherein, P (k) represents the renewal covariance matrix at k moment, and I represents the unit matrix of three ranks, and K (k) represents the k moment Kalman filtering Gain filter gain, H (k) represents the observing matrix at k moment, and P (k | k-1) represents the association according to the k-1 moment Variance matrix predicts the covariance matrix at the k moment come.
The transfer function of side slip angle converting unit is：
Wherein, β (k) represents the estimate of k moment side slip angles, αf(k | k-1) represent to be become according to the state at k-1 moment Measure the front-wheel side drift angle at the k moment predicted, lfRepresent front axle to the distance of barycenter, vxRepresent longitudinal speed, r represents yaw angle Speed, δ represent front wheel angle.
Carve at the beginning, it is necessary to initialized to X (0), Y (0) and P (0), in real time the aligning torque at measurement stub, Video camera is taken aim at a little apart from right-lane line distance and side acceleration, while the aligning torque at stub, video camera is taken aim at a little in advance in advance It is predicted apart from right-lane line distance and side acceleration, and is modified according to instantaneous value, and then state variable is carried out Observation obtains the observation of sampling instant coefficient of road adhesion and front-wheel side drift angle, will finally by side slip angle converting unit Front-wheel side drift angle is converted to side slip angle, so as to complete the Combined estimator of coefficient of road adhesion and side slip angle, entirely Estimation procedure can ensure that two estimators can tend to actual value.

Claims (10)

1. a kind of vehicle centroid side drift angle and coefficient of road adhesion Combined estimator system, it is characterised in that the system includes：
Informaiton fusion module：The module be used for obtain aligning torque at stub, video camera take aim in advance a little apart from right-lane line away from From and side acceleration；
Extended Kalman filter estimator：The estimator connect informaiton fusion module and estimate obtain coefficient of road adhesion and Front-wheel side drift angle；
Side slip angle converting unit：The unit connects Extended Kalman filter estimator and is converted to according to front-wheel side drift angle Side slip angle.
2. a kind of vehicle centroid side drift angle according to claim 1 and coefficient of road adhesion Combined estimator system, its feature It is, the informaiton fusion module includes being used to estimate the aligning torque estimator of aligning torque at stub, for obtaining Video camera is taken to take aim at a little inertance element apart from the video camera of right-lane line distance and for obtaining side acceleration in advance.
3. a kind of vehicle centroid side drift angle according to claim 2 and coefficient of road adhesion Combined estimator system, its feature It is, the aligning torque observer is specially：
<mrow> <msub> <mi>M</mi> <mi>k</mi> </msub> <mo>=</mo> <mi>A</mi> <msub> <mover> <mi>&amp;delta;</mi> <mo>&amp;CenterDot;</mo> </mover> <mi>w</mi> </msub> <mo>+</mo> <mi>B</mi> <msub> <mover> <mi>&amp;delta;</mi> <mo>&amp;CenterDot;&amp;CenterDot;</mo> </mover> <mi>w</mi> </msub> <mo>+</mo> <msub> <mi>i</mi> <mi>s</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>&amp;delta;</mi> <mi>w</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>M</mi> <mi>s</mi> </msub> <mo>+</mo> <msub> <mi>i</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>&amp;delta;</mi> <mi>w</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>M</mi> <mi>m</mi> </msub> <mo>,</mo> </mrow>
Wherein, MkRepresent aligning torque at stub, δwRepresent steering wheel angle, is(δw) represent steering wheel angle corner at stub Gearratio, im(δw) represent assist motor corner rotary driving ratio, M at stubsRepresent steering wheel torque, MmRepresent power-assisted electricity Machine power square, A and B are constant.
4. a kind of vehicle centroid side drift angle according to claim 1 and coefficient of road adhesion Combined estimator system, its feature It is, the Extended Kalman filter estimator includes observing matrix prediction module, state matrix prediction module, state transfer Matrix acquisition module, observing matrix acquisition module, covariance matrix prediction module, Kalman filtering gain acquisition module, state Matrix update module and covariance update module, the observing matrix prediction module, state matrix prediction module, state transfer Matrix acquisition module and observing matrix acquisition module are connected to informaiton fusion module, the observing matrix prediction module State matrix update module, state-transition matrix acquisition module connection covariance are all connected with state matrix prediction module Matrix prediction module, the observing matrix acquisition module connect Kalman filtering gain acquisition module and covariance renewal respectively Module, the covariance matrix prediction module connect Kalman filtering gain acquisition module and covariance update module respectively, The Kalman filtering gain acquisition module difference connection status matrix update module and covariance update module, the shape State update module and covariance update module feed back to observing matrix prediction module, state matrix prediction module, state transfer Matrix acquisition module and observing matrix acquisition module.
5. a kind of vehicle centroid side drift angle according to claim 4 and coefficient of road adhesion Combined estimator system, its feature It is, the anticipation function of the state matrix prediction module is：
Wherein, k and k-1 represent two adjacent sampling instants, X (k | k-1) represent according to k-1 moment state variables predict come The k moment predicted state variable, μ (k | k-1) represents that the road surface at the k moment predicted according to the state variable at k-1 moment is attached Coefficient, yrThe k moment video camera that (k | k-1) represents to be predicted according to the state variable at k-1 moment is taken aim at a little apart from right lane in advance Linear distance, αf(k | k-1) represents the front-wheel side drift angle at the k moment predicted according to the state variable at k-1 moment, and μ (k-1) is represented The coefficient of road adhesion at k-1 moment, yr(k-1) represent that k-1 moment video camera is taken aim in advance a little to represent apart from right-lane line distance, Δ T Systematic sampling time interval, vxRepresent longitudinal speed,Represent vehicle course angle, αf(k-1) the front-wheel lateral deviation at k-1 moment is represented Angle, r represent yaw rate, lpRepresent that video camera is taken aim at a little away from vehicle centroid distance, l in advancefRepresent front axle to vehicle centroid away from From δ represents front wheel angle, fa(μ(k-1),αf(k-1)) coefficient of road adhesion and the front-wheel at k-1 moment by the k-1 moment are represented The side acceleration that side drift angle calculates；
The anticipation function of the observing matrix prediction module is：
Wherein, Y (k | k-1) represents the observed quantity at the k moment come out according to the predicted state variable prediction at k-1 moment, Mk(k|k- 1) aligning torque at the stub at the k moment according to the predicted state variable prediction at k-1 moment out, y are representedr(k | k-1) table Show that the k moment video camera gone out according to the predicted state variable prediction at k-1 moment is taken aim at a little apart from right-lane line distance, a in advancey(k|k- 1) side acceleration come out according to the predicted state variable prediction at k-1 moment, M are representedp(μ(k|k-1),αf(k | k-1)) represent The wheel self-aligning torque calculated by the coefficient of road adhesion at the k moment predicted and the front wheel side drift angle at k moment, lmRepresent Tire machinery drags square, Fy(μ(k|k-1),αf(k | k-1)) represent the coefficient of road adhesion at k moment by predicting and before the k moment The wheel lateral force that wheel side drift angle calculates, vxRepresent longitudinal speed,Represent vehicle course angle, r represents Vehicular yaw angle speed Degree, lpRepresent that video camera is taken aim at a little away from vehicle centroid distance, l in advancefRepresent that front axle represents front wheel angle, f to vehicle centroid distance, δa (μ(k|k-1),αf(k | k-1)) represent to be calculated by the coefficient of road adhesion at the k moment predicted and the front wheel side drift angle at k moment The lateral rotating speed come, μ (k | k-1) represent the coefficient of road adhesion at the k moment predicted according to the state variable at k-1 moment, αf(k | k-1) represents the front-wheel side drift angle at the k moment predicted according to the state variable at k-1 moment；
The state-transition matrix that the state-transition matrix acquisition module obtains is：
Wherein, φ (k) represents the state-transition matrix at k moment, Δ T systematic sampling time intervals, vxRepresent longitudinal speed,Represent Vehicle course angle,Represent that lateral rotating speed road pavement attachment coefficient is asked after local derviation in the k of prediction Value at the coefficient of road adhesion at moment and the front-wheel side drift angle at k moment,Represent lateral angle Speed seeks front-wheel side drift angle taking at the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction after local derviation Value, μ (k | k-1) represent the coefficient of road adhesion at the k moment predicted according to the state variable at k-1 moment, αf(k | k-1) represent The front-wheel side drift angle at the k moment predicted according to the state variable at k-1 moment；
The observing matrix that the observing matrix acquisition module obtains is：
Wherein, H (k) represents the observing matrix at k moment,Represent tyre moment road pavement Attachment coefficient seeks the value at the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction after local derviation,Represent side force of tire road pavement attachment coefficient ask after local derviation prediction the k moment road surface Value at attachment coefficient and the front-wheel side drift angle at k moment,Represent side acceleration road pavement Attachment coefficient seeks the value at the coefficient of road adhesion of prediction and the front-wheel side drift angle at k moment at k moment after local derviation,Represent tyre moment front-wheel side drift angle is asked after local derviation prediction the k moment road surface Value at attachment coefficient and the front-wheel side drift angle at k moment,Represent side force of tire to front-wheel Side drift angle seeks the value at the coefficient of road adhesion at k moment and the front-wheel side drift angle at k moment of prediction after local derviation,Representing, side acceleration is adhered to after local derviation is sought front-wheel side drift angle on the road surface at the k moment of prediction Value at coefficient and the front-wheel side drift angle at k moment, when μ (k | k-1) represents the k predicted according to the state variable at k-1 moment The coefficient of road adhesion at quarter, αf(k | k-1) represents the front-wheel side drift angle at the k moment predicted according to the state variable at k-1 moment, lmRepresent that tire machinery drags square, vxRepresent longitudinal speed,Represent vehicle course angle.
6. a kind of vehicle centroid side drift angle according to claim 4 and coefficient of road adhesion Combined estimator system, its feature It is, the anticipation function of the covariance matrix prediction module is：
P (k | k-1)=φ (k) P (k-1) φT(k)+Q,
Wherein, P (k | k-1) represent according to the covariance matrix at k-1 moment predict come the k moment covariance matrix, φ (k) Represent the state-transition matrix at k moment, P (k-1) represents the covariance renewal at k-1 moment, and Q represents state equation process noise Variance.
7. a kind of vehicle centroid side drift angle according to claim 4 and coefficient of road adhesion Combined estimator system, its feature It is, the Kalman filtering gain that the Kalman filtering gain acquisition module obtains is：
K (k)=P (k | k-1) HT(k)(H(k)P(k|k-1)HT(k)+R),
Wherein, K (k) represents the Kalman filtering Gain filter gain at k moment, and P (k | k-1) represents the association side according to the k-1 moment The covariance matrix at the k moment that poor Matrix prediction comes out, H (k) represent the observing matrix at k moment, and R represents observational equation noise Variance.
8. a kind of vehicle centroid side drift angle according to claim 4 and coefficient of road adhesion Combined estimator system, its feature It is, the renewal function of the state matrix update module is：
X (k)=X (k | k-1)+K (k) (Y (k)-Y (k | k-1)),
X (k) represents the renewal state variable at k moment, X (k | k-1) represent according to k-1 moment state variables predict come k when The predicted state variable at quarter, K (k) represents the Kalman filtering Gain filter gain at k moment, when Y (k) represents the k that measurement obtains The observed quantity at quarter, Y (k)=[Mk(k),yr(k),ay(k)]T, Mk(k) the aligning torque measured value at the stub at k moment, y are representedr (k) represent that k moment video camera is taken aim at a little apart from right-lane line distance measure, a in advancey(k) the side acceleration measurement at k moment is represented Value, Y (k | k-1) represent the observed quantity at the k moment come out according to the predicted state variable prediction at k-1 moment.
9. a kind of vehicle centroid side drift angle according to claim 4 and coefficient of road adhesion Combined estimator system, its feature It is, the renewal function of the covariance update module is：
P (k)=(I-K (k) H (k)) P (k | k-1),
Wherein, P (k) represents the renewal covariance matrix at k moment, and I represents the unit matrix of three ranks, and K (k) represents the card at k moment Kalman Filtering Gain filter gain, H (k) represent the observing matrix at k moment, and P (k | k-1) represents the covariance according to the k-1 moment The covariance matrix at the k moment that Matrix prediction comes out.
10. a kind of vehicle centroid side drift angle according to claim 1 and coefficient of road adhesion Combined estimator system, its feature It is, the transfer function of side slip angle converting unit is：
<mrow> <mi>&amp;beta;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>&amp;alpha;</mi> <mi>f</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>|</mo> <mi>k</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>-</mo> <mfrac> <msub> <mi>l</mi> <mi>f</mi> </msub> <msub> <mi>v</mi> <mi>x</mi> </msub> </mfrac> <mi>r</mi> <mo>+</mo> <mi>&amp;delta;</mi> <mo>,</mo> </mrow>
Wherein, β (k) represents the estimate of k moment side slip angles, αf(k | k-1) represents pre- according to the state variable at k-1 moment The front-wheel side drift angle at the k moment measured, lfRepresent front axle to the distance of barycenter, vxRepresenting longitudinal speed, r represents yaw velocity, δ represents front wheel angle.
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