CN110516311A - A kind of comprehensive compensation construction of strategy method for automobile-used acceleration transducer constant error - Google Patents
A kind of comprehensive compensation construction of strategy method for automobile-used acceleration transducer constant error Download PDFInfo
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Abstract
The invention discloses a kind of comprehensive compensation construction of strategy method for automobile-used acceleration transducer constant error, laterally and the kinetic expression of weaving observation of the realization to vehicle side velocity is initially set up about vehicle；Secondly the quantity of state and measurement that estimator is determined according to the horizontal and vertical kinematic relation of acceleration transducer error drift model and vehicle, utilize Kalman filter estimated acceleration sensor constant error；Finally by the amendment to tire normal pressure model and tire cornering stiffness and to the feedback of vehicle lateral speed observer input information, the compensation for completing acceleration transducer constant error updates.The present invention can realize that the estimation of automobile-used acceleration transducer constant error and iteration are updated with higher frequency, do not lose real-time too much while improving state of motion of vehicle estimation overall precision, have the characteristics that be simple and efficient.
Description
Technical Field
The invention belongs to the field of error estimation and compensation of vehicle sensors, and particularly relates to a comprehensive compensation strategy construction method for a constant error of a vehicle acceleration sensor, aiming at further improving the accuracy and the real-time property of vehicle driving state estimation.
Background
With the development of active safety technology of vehicles, the intelligent sensing theory has become one of the important research hotspots. For the vehicle, the anti-lock system needs to judge the slip degree of the wheels according to the output of the wheel speed sensor, and the electronic stability control program determines the motion mode of the vehicle according to the signal of the yaw rate sensor to realize the direct yaw moment control. However, the output signals of these conventional sensors all contain unavoidable noise, and the output of some other sensors (such as an acceleration sensor for a vehicle) may have a phenomenon that an error drifts with time. These noises and drifts amplify the error of the output signal during the time domain integration of the sensor signal.
Disclosure of Invention
In order to solve the problems in the prior art and reduce the precision loss caused by the error drift of the sensor, the invention provides a comprehensive compensation strategy construction method aiming at the constant error of the vehicle acceleration sensor.
The technical purpose is achieved through the following technical scheme.
A comprehensive compensation strategy construction method for constant errors of an acceleration sensor for a vehicle comprises the steps of constructing a lateral speed observer for data preprocessing of constant error estimation of the acceleration sensor, obtaining an observation result of the lateral speed of the vehicle, achieving online estimation of the constant errors of the acceleration sensor according to the observation result and a fused state equation, achieving correction of tire side deflection rigidity through constructing a tire positive pressure error compensation model, and finally feeding the correction result back to the lateral speed observer to complete compensation and updating of the constant errors of the acceleration sensor.
Further, the state equation adopted for estimating the constant value error fuses a vehicle kinematic model and an acceleration sensor error model.
Further, the state equation is:wherein a isx,mes、ay,mesAs the measurement output of the acceleration sensor, ax,bias、ay,biasIs a constant error of the acceleration sensor, w1、w2、w3、w4Are respectively white Gaussian noise, vxIs the longitudinal speed, v, of the vehicleyIs the lateral velocity of the vehicle and r is the yaw rate of the vehicle.
Still further, the equation of state selection measuresv'xThe longitudinal speed of the driven wheel speed sensor is,is an observation of lateral velocity.
Further, the cornering stiffness CσWith positive pressure F to which four tyres are subjectedzThe relationship between them is: cσ＝a1sin[a2arctan(a3Fz)]Wherein a is1、a2、a3Are parameters to be fitted.
Further, the positive pressure is calculated based on a correction result of the acceleration signal, which is determined by a constant error of the acceleration sensor.
Further, the result of the correction of the acceleration signalSatisfies the following conditions:wherein a isx,mes、ay,mesIs the measurement output of the acceleration sensor,and the optimal estimated value of the constant error of the acceleration sensor is obtained.
Further, the error compensation model of the positive pressure is as follows:where m is the vehicle body mass, i ═ { F, R }, F denotes the front wheels, R denotes the rear wheels; j ═ { L, R }, L denotes the left wheel, R denotes the right wheel; h is the vertical height of the mass center of the automobile from the ground, and l is equal to lf+lrDistance between front and rear axles of the vehicle, bF、bRThe front and rear wheel tracks of the automobile.
The invention has the beneficial effects that:
1. the method disclosed by the invention integrates the vehicle kinematic model while modeling the sensor error, constructs the lateral speed observer, and measures the lateral speed obtained by the lateral speed observer as the quantity of the integrated model, so that the updating speed of the constant error of the acceleration sensor is increased; the method improves the overall accuracy of the estimation of the motion state of the vehicle, does not lose instantaneity excessively, has the characteristics of simplicity and high efficiency, and can well track the error drift of the acceleration sensor.
2. According to the optimal estimated value of the constant error of the acceleration sensor, the compensation and the correction of the constant error of the acceleration sensor for the vehicle can be realized, so that accurate information is provided for the measurement of the motion state of the vehicle and other vehicle control schemes.
Drawings
Fig. 1 is a flow chart of an error compensation strategy.
Detailed Description
The embodiments of the present invention will be further described with reference to the accompanying drawings, but the scope of the invention is not limited thereto.
As shown in fig. 1, a method for constructing a comprehensive compensation strategy for a constant error of an acceleration sensor for a vehicle includes the following steps:
step (1), observing the lateral speed of the vehicle
The vehicle adopts a front wheel driving mode, the center of mass of the vehicle is specified as the origin of a vehicle body coordinate system, the horizontal forward direction is the positive direction of an x axis, the horizontal leftward direction is the positive direction of a y axis, the vertical horizontal plane upwards is the positive direction of a z axis, and all the revolution angles and moments are positive in the anticlockwise direction in the horizontal plane.
First, the kinetic equations of the vehicle with respect to lateral and yaw motion are determined:
wherein v isyR are the lateral velocity, yaw rate, C of the vehicle, respectivelyf、CrYaw stiffness of the front and rear wheels of the vehicle, delta being the angle of rotation of the front wheel of the vehicle, lf、lrRespectively the distance of the vehicle mass center from the front axle and the rear axle, m is the vehicle body mass, IzIs the moment of inertia, v, of the vehicle about the z-axisxIs the longitudinal speed of the vehicle, anReffRepresenting the effective radius, omega, of the tyre3And ω4The wheel speeds of the left rear wheel and the right rear wheel are respectively.
And secondly, designing a Longbeige observer as data preprocessing of constant error estimation of the acceleration sensor to realize observation of the lateral speed of the vehicle.
The real vehicle lateral speed observation model is assumed as follows:
wherein the state quantity X ═ vy r]TThe control quantity U is δ, the quantity measurement Y is r,
vehicle dynamic model for lateral and yaw motionMatrixVehicle dynamics control matrix for lateral and yaw motionVehicle dynamics measurement matrix for lateral and yaw motion C ═ 01]。
The following structure of the Roeberg observer is adopted:
wherein, L ═ k1k2]TA pole configuration for vehicle lateral velocity observation.
In order to make the error between the real value and the estimated value of the lateral velocity observation converge to 0 quickly, the equation (2) and the equation (3) are subtracted to obtain the state equation of the error:
the pole configuration parameter k required by the observation of the lateral speed can be determined by making the characteristic value of the matrix A-LC less than zero1And k2，k1、k2The selection of (1) needs to consider the convergence rate of the state error under the condition that the characteristic value of the matrix A-LC is less than zero.
Step (2), establishing an error model of the acceleration sensor
Wherein, ax,mes、ay,mesAs the measurement output of the acceleration sensor, ax、ayIs the longitudinal and lateral acceleration of the vehicle, ax,bias、ay,biasIs a constant error of the acceleration sensor, w1、w2、w3、w4Respectively gaussian white noise. In this embodiment, approximationConsidering an acceleration sensor error model as a random walk model, namely:
and (3) estimating the constant error of the acceleration sensor by using a Kalman filter
Establishing a kinematic model about the longitudinal direction and the lateral direction of the vehicle, namely:
the error model of the acceleration sensor and the kinematic model of the vehicle are fused (hereinafter referred to as a fusion model), that is, the error model of the sensor is substituted into the kinematic model of the vehicle about the longitudinal direction and the lateral direction, so as to obtain the following state equation:
selecting a state quantity x ═ vx ax,bias vy ay,bias]TControl quantity u ═ ax,mes ay,mes]TMeasurement of quantityWhereinIs observed as lateral velocity and has longitudinal velocity v 'of driven wheel speed sensor'x＝vxThe state equation and the metrology equation can be further expressed as:
wherein model state matrices are fusedFusion moldModel control matrixFusion model measurement matrixWhite gaussian noise
The vehicle acceleration constant error estimation based on the Kalman filtering algorithm comprises the following steps:
1) time updating
And (3) carrying out one-step prediction on the state quantities such as the longitudinal and lateral speeds of the vehicle and the constant deviation of the longitudinal and lateral accelerations of the vehicle:
xk|k-1＝Φk-1xk-1|k-1+Guk-1 (10)
wherein: x is the number ofk|k-1For the fusion model, the state at time k-1 is transferred to the predicted state at time k using the state equationk-1The control quantity at the moment of fusing the model k-1 is obtained;
calculating an error covariance prediction value of the state quantity:
Pk|k-1＝Φk-1Pk-1|k-1Φk-1 T+Q (11)
where Q is the noise covariance of the estimation process, Pk-1|k-1For the optimal covariance, P, at the moment of fusing the model state quantities k-1k|k-1The prediction covariance of the model state quantity at the moment k is fused, and a state transition matrix is provided:
Φk-1＝exp(Fk-1Tt)≈I+Fk-1Tt (12)
wherein, TtFor updating the step size of the Kalman Filter Algorithm, Fk-1And (3) a state matrix of the fusion model at the moment of k-1, wherein I is an identity matrix, and the dimensionality is consistent with the dimensionality of the state quantity x.
2) Measurement update
Determining the kalman gain of the state quantity estimation:
Kk＝Pk|k-1Hk T(HkPk|k-1Hk T+R)-1 (13)
wherein HkA measurement matrix at the moment k is obtained, and R is the covariance of measurement noise;
and updating the state quantities such as the longitudinal and lateral speeds of the vehicle and the constant deviation of the longitudinal and lateral accelerations of the vehicle:
xk|k＝xk|k-1+Kk(yk-Hkxk|k-1) (14)
wherein, ykAnd measuring values of the state at the moment k, wherein the measured values comprise a longitudinal speed and a lateral speed, the longitudinal speed is provided by a wheel speed sensor, and the lateral speed is provided by a Roeberg observer.
Updating the error covariance of the state quantities:
Pk|k＝(I-KkHk)Pk|k-1(I-KkHk)T+KkRKk T (15)
considering that the processing of the noise characteristics by the kalman filter is limited to the gaussian distribution, it is necessary to comprehensively refer to the accuracy of the established model (equation (8)) in which the information constituting the quantity measurement is derived from the longitudinal velocity v 'of the driven wheel speed sensor and the accuracy level of the sensor when selecting the initial value of the noise covariance'xAnd observed value of lateral velocity
The Kalman filtering continuously predicts and corrects the constant error state quantity based on an error model of the sensor and a longitudinal and lateral kinematic model of the vehicle, so that the accurate constant error of the acceleration sensor is finally obtained.
Step (4), determining the correction result of the acceleration signal according to the constant error of the acceleration sensor obtained by estimation
Wherein,and obtaining the optimal estimation value of the constant error of the acceleration sensor by the fusion model estimation.
Step (5), according to the correction result of the acceleration signal, calculating the positive pressure F received by the four tireszij
From equation (17), the compensated tire positive pressure error Fzij,biasThe compensation model is as follows:
wherein g is the acceleration of gravity, i ═ { F, R }, F denotes the front wheels, R denotes the rear wheels; j ═ { L, R }, L denotes the left wheel, R denotes the right wheel; h is the vertical height of the mass center of the automobile from the ground, and l is equal to lf+lrDistance between front and rear axles of the vehicle, bF、bRThe front and rear wheel tracks of the automobile.
Step (6), calculating the cornering stiffness of the front and rear wheels
Since the load of the wheel is time-varying during driving, the cornering stiffness of the corresponding tire is also varied, the cornering stiffness C of the tire beingσAnd a positive pressure FzThe relationship between them is as follows:
Cσ＝a1sin[a2arctan(a3Fz)] (19)
wherein, a1、a2、a3As the parameters to be fitted, a Tester module in CarSim software can be used for data fitting;
binding formula (17) to determineCornering stiffness C of front and rear wheels in a vehicle modelf、Cr：
Wherein, CσFL、CσFR、CσRL、CσRRThe cornering stiffnesses of the front left, front right, rear left and rear right tires of the vehicle, respectively.
And (7) feeding the calculated tire cornering stiffness back to the Roeberg observer in the step (1), wherein the Roeberg observer is a vehicle dynamic model about lateral and yaw motion, the model comprises tire parameters, namely cornering stiffness, the corrected tire cornering stiffness is used as the tire cornering stiffness required by the Roeberg observer by estimating a constant error of acceleration, and the compensation and updating of the constant error of the acceleration sensor are completed, so that the observation of the lateral speed of the vehicle are updated.
And mutual iteration is performed between the lateral speed observation based on the LongBege observer and the constant error estimation of the acceleration sensor based on Kalman filtering to form a complementary closed loop structure, and finally the estimation precision of the motion state of the vehicle is gradually improved.
The invention has been described in detail, and it is within the scope of the invention to adopt the concept and working method of the invention to make simple modifications, or to make improvements and decorations without changing the principle of the main concept of the invention.
Claims (8)
1. A comprehensive compensation strategy construction method for constant errors of an acceleration sensor for a vehicle is characterized in that a lateral speed observer is constructed to be used as data preprocessing of constant error estimation of the acceleration sensor, an observation result of the lateral speed of the vehicle is obtained, online estimation of the constant errors of the acceleration sensor is achieved according to the observation result and a fused state equation, correction of tire lateral deflection rigidity is achieved through construction of a tire positive pressure error compensation model, and finally the correction result is fed back to the lateral speed observer to complete compensation updating of the constant errors of the acceleration sensor.
2. The method for constructing the comprehensive compensation strategy for the constant error of the vehicular acceleration sensor according to the claim 1, characterized in that the estimation of the constant error adopts a state equation which fuses a vehicle kinematic model and an acceleration sensor error model.
3. The method for constructing the comprehensive compensation strategy for the constant error of the vehicular acceleration sensor according to claim 2, wherein the state equation is as follows:wherein a isx,mes、ay,mesAs the measurement output of the acceleration sensor, ax,bias、ay,biasIs a constant error of the acceleration sensor, w1、w2、w3、w4Are respectively white Gaussian noise, vxIs the longitudinal speed, v, of the vehicleyIs the lateral velocity of the vehicle and r is the yaw rate of the vehicle.
4. The method as claimed in claim 3, wherein the equation of state selects quantity measurement to construct the comprehensive compensation strategy for the constant error of the vehicular acceleration sensorv'xThe longitudinal speed of the driven wheel speed sensor is,is an observation of lateral velocity.
5. The method for constructing the comprehensive compensation strategy for the constant error of the vehicular acceleration sensor according to claim 1, characterized in that the cornering stiffness C isσWith positive pressure F to which four tyres are subjectedzThe relationship between them is: cσ＝a1 sin[a2arctan(a3Fz)]Wherein a is1、a2、a3Are parameters to be fitted.
6. The method for constructing the comprehensive compensation strategy for the constant error of the acceleration sensor for the vehicle as claimed in claim 5, wherein the positive pressure is calculated according to the correction result of the acceleration signal, and the correction result of the acceleration signal is determined by the constant error of the acceleration sensor.
7. The method for constructing the comprehensive compensation strategy for the constant error of the vehicular acceleration sensor according to claim 6, characterized in that the correction result of the acceleration signalSatisfies the following conditions:wherein a isx,mes、ay,mesIs the measurement output of the acceleration sensor,and the optimal estimated value of the constant error of the acceleration sensor is obtained.
8. The method for constructing the comprehensive compensation strategy for the constant error of the vehicular acceleration sensor according to claim 7, wherein the error compensation model of the positive pressure is as follows:where m is the vehicle body mass, i ═ { F, R }, F denotes the front wheels, R denotes the rear wheels; j ═ { L, R }, L denotes the left wheel, R denotes the right wheel; h is the vertical height of the mass center of the automobile from the ground, and l is equal to lf+lrDistance between front and rear axles of the vehicle, bF、bRThe front and rear wheel tracks of the automobile.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112182761A (en) * | 2020-09-28 | 2021-01-05 | 嘉兴汇智诚电子科技有限公司 | Automobile part signal estimation method based on data driving |
| CN112950812A (en) * | 2021-02-04 | 2021-06-11 | 南京航空航天大学 | Vehicle state fault-tolerant estimation method based on long-time and short-time memory neural network |
| CN114684159A (en) * | 2022-03-21 | 2022-07-01 | 潍柴动力股份有限公司 | Vehicle mass estimation method and device, electronic equipment and storage medium |
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| CN108594652A (en) * | 2018-03-19 | 2018-09-28 | 江苏大学 | A kind of vehicle-state fusion method of estimation based on observer information iteration |
| CN109606378A (en) * | 2018-11-19 | 2019-04-12 | 江苏大学 | Vehicle running state estimation method towards non-Gaussian noise environment |
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| CN108594652A (en) * | 2018-03-19 | 2018-09-28 | 江苏大学 | A kind of vehicle-state fusion method of estimation based on observer information iteration |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112182761A (en) * | 2020-09-28 | 2021-01-05 | 嘉兴汇智诚电子科技有限公司 | Automobile part signal estimation method based on data driving |
| CN112950812A (en) * | 2021-02-04 | 2021-06-11 | 南京航空航天大学 | Vehicle state fault-tolerant estimation method based on long-time and short-time memory neural network |
| CN112950812B (en) * | 2021-02-04 | 2022-07-26 | 南京航空航天大学 | Vehicle state fault-tolerant estimation method based on long-time and short-time memory neural network |
| CN114684159A (en) * | 2022-03-21 | 2022-07-01 | 潍柴动力股份有限公司 | Vehicle mass estimation method and device, electronic equipment and storage medium |
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