CN105539449B - A kind of coefficient of road adhesion real-time estimating method under damped condition - Google Patents
A kind of coefficient of road adhesion real-time estimating method under damped condition Download PDFInfo
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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
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- 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
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Abstract
A kind of coefficient of road adhesion real-time estimating method under damped condition, including two-wheel vehicles Brake Dynamics model, ideal brake force square controller and coefficient of road adhesion observer.Wherein, two-wheel vehicles Brake Dynamics model is made of whole vehicle model and tire model.Based on two-wheel vehicles Brake Dynamics model, sliding mode controller is redesigned using saturation function and integration diverter surface, jitter problem is eliminated, establishes ideal brake force square controller.On the basis of two-wheel vehicles Brake Dynamics model and ideal brake force square controller, using second-order linearity extended state observer, coefficient of road adhesion observer, observation and the relevant expansion state amount of attachment coefficient are designed, and then completes the real-time estimation of road pavement attachment coefficient.Design parameter of the present invention is few, and computational efficiency is high, and eliminating Sliding mode variable structure control using saturation function and integration diverter surface mode buffets problem, and coefficient of road adhesion observer uses second-order linearity extended state observer, strong robustness.
Description
Technical Field
The invention relates to the field of automobile active safety control, in particular to a real-time estimation method for a road adhesion coefficient under a braking working condition.
Background
Currently, most of the existing automobiles are equipped with advanced driver assistance systems, such as an emergency avoidance system (ECA), an adaptive cruise control system (ACC), an anti-lock braking system (ABS), a driving force control system (TCS), an Electronic Stability Program (ESP), and the like, which can greatly improve the safety and stability of the driving of the automobile. The advanced driver assistance system can automatically adjust the control logic according to the change of the road adhesion coefficient, thereby exerting the performance of the control system to the maximum extent, and accurately obtaining the road adhesion coefficient in real time is a necessary premise for realizing active safety control.
For the method for obtaining the road adhesion coefficient, there are two methods, namely a direct detection method and an estimation method, mainly at home and abroad. The direct detection method mainly adopts an optical sensor to measure the absorption and scattering conditions of the road surface to light, and carries out road surface adhesion coefficient identification according to the road surface form and physical characteristics. The estimation method estimates the magnitude of the road adhesion coefficient by measuring the vehicle or tire dynamic response related to the road adhesion coefficient, and the method can fully utilize the vehicle-mounted sensor and reduce the cost. At present, the main estimation methods include three methods, namely a kalman filter algorithm, a double-extended kalman filter algorithm and an extended state observer method. Compared with a Kalman filtering algorithm and a double-extended Kalman filtering algorithm, the extended state observer method can avoid solving a complicated Jacobian matrix on the premise of ensuring higher calculation accuracy, but does not consider load transfer under the braking condition, has more design parameters and is not high in calculation efficiency.
Disclosure of Invention
Aiming at the problems and defects of the prior art, the method for estimating the road adhesion coefficient in real time considering the front and rear axle load transfer under the braking working condition is provided.
The invention adopts the following technical scheme for solving the technical problems:
a real-time estimation method for a road adhesion coefficient under a braking condition comprises a two-wheel vehicle braking dynamic model, an ideal braking torque controller and a road adhesion coefficient observer. The braking dynamics model of the two-wheel vehicle consists of a whole vehicle model and a tire model. Based on a two-wheel vehicle braking dynamics model, tracking an ideal slip ratio by the front wheel slip ratio and the rear wheel slip ratio as a control target, establishing a sliding mode controller, redesigning the sliding mode controller by adopting a saturation function and an integral switching surface, eliminating the problem of jitter, and establishing an ideal braking torque controller. And finally, on the basis of a two-wheel vehicle braking dynamic model and an ideal braking torque controller, designing a road adhesion coefficient observer by using a wheel speed signal and a braking torque signal as observer inputs and using the adhesion coefficient between the tire and the road as the output of the observer by using a second-order linear expansion state observer, and observing the items related to the adhesion coefficient as expansion state quantities by using the road adhesion coefficient observer so as to complete the real-time estimation of the road adhesion coefficient.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. the estimation method considers the axle load transfer under the braking working condition, the design parameters are few, and the calculation efficiency is high;
2. the problem of buffeting control of the sliding mode variable structure is solved by adopting a saturation function and integral switching surface mode, and the road surface attachment coefficient observer adopts a second-order linear expansion state observer and has strong robustness.
Drawings
Fig. 1 is a process diagram of the road surface adhesion coefficient estimation method.
In the figure, 1-a whole vehicle model, 2-a tire model, 3-a two-wheel vehicle braking dynamics model, 4-a sliding mode controller, 5-an ideal braking torque controller and 6-a road surface adhesion coefficient observer.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
as shown in FIG. 1, the invention discloses a real-time estimation method of road adhesion coefficient under braking condition, comprising a two-wheel vehicle braking dynamic model 3, an ideal braking torque controller 5 and a road adhesion coefficient observer 6. The two-wheel vehicle braking dynamics model 3 is composed of a whole vehicle model 1 and a tire model 2.
Establishing a finished automobile model 1:
let x be the displacement of the vehicle during driving, mf、mrFront and rear unsprung masses, h, respectively, of the vehiclef、hrRespectively the unsprung mass height, m, of the vehiclesFor suspending masses, hsIs the sprung mass height, Fzf、FzrNormal ground reaction forces respectively experienced by the front and rear wheels,/f、lrThe distances from the center of mass to the front and rear shafts are respectively provided with a dynamic equation set:
wherein,
in the formula: mu (lambda)f) And μ (λ)r) Respectively, the adhesion coefficients between the front and rear wheels and the road surface, m is the mass of the whole vehicle, V is the longitudinal speed of the mass center of the vehicle, and T isbf、TbrBraking torques of front and rear wheels, J, respectivelyf、JrRespectively, front and rear wheel rotational inertia, omegaf、ωrRespectively front and rear wheel angular velocity, RωIs the wheel radius.
Building a tire model 2:
based on a Magic Formula model, a Burckhardt model can be obtained through theoretical deformation and simulation analysis, and the road adhesion coefficient of the Burckhardt model is related to the tire slip ratio lambda and the vehicle speed V:
in the formula: c1、C2、C3As a characteristic parameter of adhesion of the tire, C4The influence parameter of the driving speed of the automobile on the adhesion characteristic is shown.
Based on a two-wheel vehicle braking dynamics model 3, a sliding mode controller 4 is established by taking the front wheel slip rate and the rear wheel slip rate to track the ideal slip rate as a control target:
in the formula: lambda [ alpha ]f、λrActual slip rates of the front wheel and the rear wheel are respectively; lambda [ alpha ]fd、λrdRespectively, the target slip rates of the front and rear wheels.
The equivalent control torque can be obtained by derivation:
the ideal braking torque of the front and rear wheels is:
wherein:
Ff(λf，λr)＝F2(1-λf)+RωF3
Fr(λf，λr)＝F2(1-λr)+RωF4
in the formula η1and η2Are all positive numbers.
To eliminate the buffeting problem, the saturation function sat (b)) The method is applied to sliding mode control, and an integral switching surface is adopted to redesign the sliding mode controller, so that an ideal braking torque controller 5 is established:
S1＝λf-λfd+ξ1∫(λf-λfd)dt
S2＝λr-λrd+ξ2∫(λr-λrd)dt
xi in the formula1and xi2Is a constant.
The ideal braking torques of the front wheel and the rear wheel are respectively as follows:
in the formula:andis a constant.
And finally, establishing a road surface adhesion coefficient observer 6 which takes a wheel speed signal and a braking torque signal as observer inputs and an adhesion coefficient between the tire and the road surface as observer outputs by adopting a second-order linear extended state observer:
from the two-wheeled vehicle brake dynamics model 3,
regarding the road adhesion coefficient term contained in the above formula as the disturbance of the system, and taking the disturbance as the expansion state variable of the system, let:
ωf＝x1；ωr＝x3； Tbf＝u1；Tbr＝u2。
then two integrator series systems are obtained:
taking the first integrator series system as an example, the state x can be observed by the following second-order linear extended state observer1And an expanded state x2：
Wherein: omega0To be configured by polesObtaining the bandwidth of the linear extended state observer; u. of1And y1Are input signals of an observer respectively; z is a radical of1And z2Respectively, the output signals of the linear extended state observer, respectively, are states x1And an expanded state x2The observed value of (a); b0To control the gain b1An estimate of (d). The state x of the second integrator series system can be obtained by the same method3And an expanded state x4The observed value of (1).
Then for the first two integrator series systems there are:
wherein: z is a radical of1And z2Is a state x1(front wheel speed) and expanded state x2An observed value of z3And z4Is a state x3(rear wheel speed) and expanded state x4The observed value of (1).
Based on the established road surface adhesion coefficient observer 6, the term related to the road surface adhesion coefficient is observed as the expansion state quantity, and the adhesion coefficient between the front and rear wheels and the road surface can be conveniently estimated in real time by using the expression.
Claims (2)
1. A road adhesion coefficient real-time estimation method under a braking working condition is characterized by comprising the following steps: the system comprises a two-wheel vehicle brake dynamic model (3), an ideal brake torque controller (5) and a road adhesion coefficient observer (6); the braking dynamics model (3) of the two-wheeled vehicle comprises a whole vehicle model (1) and a tire model (2); the whole vehicle model (1) meets the following relational expression:
<mrow> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mo>=</mo> <mi>V</mi> </mrow>
<mrow> <mover> <mi>V</mi> <mo>&CenterDot;</mo> </mover> <mo>=</mo> <mo>-</mo> <mi>g</mi> <mfrac> <mrow> <mi>&mu;</mi> <mrow> <mo>(</mo> <msub> <mi>&lambda;</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>1</mn> </msub> <mo>+</mo> <mi>&mu;</mi> <mrow> <mo>(</mo> <msub> <mi>&lambda;</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> </mrow> <mrow> <mi>m</mi> <mo>-</mo> <mi>&mu;</mi> <mrow> <mo>(</mo> <msub> <mi>&lambda;</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>3</mn> </msub> <mo>+</mo> <mi>&mu;</mi> <mrow> <mo>(</mo> <msub> <mi>&lambda;</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>3</mn> </msub> </mrow> </mfrac> </mrow>
<mrow> <msub> <mover> <mi>&omega;</mi> <mo>&CenterDot;</mo> </mover> <mi>f</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <msub> <mi>J</mi> <mi>f</mi> </msub> </mrow> </mfrac> <mrow> <mo>(</mo> <mo>-</mo> <msub> <mi>T</mi> <mrow> <mi>b</mi> <mi>f</mi> </mrow> </msub> <mo>+</mo> <mi>&mu;</mi> <mo>(</mo> <msub> <mi>&lambda;</mi> <mi>f</mi> </msub> <mo>)</mo> <msub> <mi>m</mi> <mn>1</mn> </msub> <msub> <mi>R</mi> <mi>&omega;</mi> </msub> <mi>g</mi> <mo>-</mo> <mi>&mu;</mi> <mo>(</mo> <msub> <mi>&lambda;</mi> <mi>f</mi> </msub> <mo>)</mo> <msub> <mi>m</mi> <mn>3</mn> </msub> <msub> <mi>R</mi> <mi>&omega;</mi> </msub> <mover> <mi>x</mi> <mo>&CenterDot;&CenterDot;</mo> </mover> <mo>)</mo> </mrow> </mrow>
<mrow> <msub> <mover> <mi>&omega;</mi> <mo>&CenterDot;</mo> </mover> <mi>r</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <msub> <mi>J</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mrow> <mo>(</mo> <mo>-</mo> <msub> <mi>T</mi> <mrow> <mi>b</mi> <mi>r</mi> </mrow> </msub> <mo>+</mo> <mi>&mu;</mi> <mo>(</mo> <msub> <mi>&lambda;</mi> <mi>r</mi> </msub> <mo>)</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> <msub> <mi>R</mi> <mi>&omega;</mi> </msub> <mi>g</mi> <mo>+</mo> <mi>&mu;</mi> <mo>(</mo> <msub> <mi>&lambda;</mi> <mi>r</mi> </msub> <mo>)</mo> <msub> <mi>m</mi> <mn>3</mn> </msub> <msub> <mi>R</mi> <mi>&omega;</mi> </msub> <mover> <mi>x</mi> <mo>&CenterDot;&CenterDot;</mo> </mover> <mo>)</mo> </mrow> </mrow>
wherein,
<mrow> <msub> <mi>m</mi> <mn>1</mn> </msub> <mo>=</mo> <mfrac> <msub> <mi>l</mi> <mi>r</mi> </msub> <mrow> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mi>m</mi> </mrow>
<mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>=</mo> <mfrac> <msub> <mi>l</mi> <mi>f</mi> </msub> <mrow> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mi>m</mi> </mrow>
<mrow> <msub> <mi>m</mi> <mn>3</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>m</mi> <mi>f</mi> </msub> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>m</mi> <mi>s</mi> </msub> <msub> <mi>l</mi> <mi>s</mi> </msub> <mo>+</mo> <msub> <mi>m</mi> <mi>r</mi> </msub> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> <mrow> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> </mfrac> </mrow>
in the formula: x is the displacement of the vehicle during running, mf、mrFront and rear unsprung masses, h, respectively, of the vehiclef、hrRespectively the unsprung mass height, m, of the vehiclesFor suspending masses, hsIs the sprung mass height, Fzf、FzrNormal ground reaction forces respectively experienced by the front and rear wheels,/f、lrRespectively, the distance from the center of mass to the front and rear axes, mu (lambda)f) And μ (λ)r) Respectively, the adhesion coefficients between the front and rear wheels and the road surface, m is the mass of the whole vehicle, V is the longitudinal speed of the mass center of the vehicle, and T isbf、TbrBraking torques of front and rear wheels, J, respectivelyf、JrRespectively, front and rear wheel rotational inertia, omegaf、ωrRespectively front and rear wheel angular velocity, RωIs the wheel radius;
the tire model (2) satisfies the following relation:
<mrow> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>&lambda;</mi> <mo>,</mo> <mi>V</mi> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>(</mo> <mrow> <mn>1</mn> <mo>-</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> <mi>&lambda;</mi> </mrow> </msup> </mrow> <mo>)</mo> <mo>-</mo> <msub> <mi>C</mi> <mn>3</mn> </msub> <mi>&lambda;</mi> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>C</mi> <mn>4</mn> </msub> <mi>&lambda;</mi> <mi>V</mi> </mrow> </msup> </mrow>
in the formula: c1、C2、C3As a characteristic parameter of adhesion of the tire, C4The influence parameter of the automobile running speed on the adhesion characteristic is lambda, the tire slip rate and the automobile speed.
2. The method for estimating the road adhesion coefficient under the braking condition in real time according to claim 1, wherein: the ideal braking torque controller (5) is obtained by redesigning the sliding mode controller (4) by adopting a saturation function and an integral switching surface;
the ideal braking torque controller (5) satisfies the following relation:
S1＝λf-λfd+ξ1∫(λf-λfd)dt
S2＝λr-λrd+ξ2∫(λr-λrd)dt
in the formula: lambda [ alpha ]f、λrActual slip rates, λ, of the front and rear wheels, respectivelyfd、λrdtarget slip rates, xi, of the front and rear wheels, respectively1and xi2Is a constant.
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| CN109131336B (en) * | 2017-06-15 | 2020-07-28 | 华为技术有限公司 | Method and system for acquiring road adhesion coefficient |
| CN108454597B (en) * | 2018-01-03 | 2020-01-24 | 江苏大学 | Vehicle anti-lock control system based on LQG controller and slip rate jitter suppression method |
| CN108528419B (en) * | 2018-01-31 | 2019-12-03 | 江苏大学 | A kind of bicyclic forecast Control Algorithm of the vehicle line control brake system towards full application of brake operating condition |
| CN109131306B (en) * | 2018-08-31 | 2020-10-30 | 北京新能源汽车股份有限公司 | Brake control method and brake control system of electric automobile and automobile |
| CN109733410A (en) * | 2018-12-21 | 2019-05-10 | 浙江万安科技股份有限公司 | A kind of real-time pavement identification method of ABS and system |
| CN110597064B (en) * | 2019-09-24 | 2021-04-16 | 燕山大学 | Active suspension output feedback control method based on nonlinear and uncertain models |
| CN110884496A (en) * | 2019-10-30 | 2020-03-17 | 北京理工大学 | Method and device for identifying road adhesion coefficient suitable for tracked vehicle |
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| CN101419456A (en) * | 2008-11-25 | 2009-04-29 | 长安大学 | Pavement adhesion coefficient simulator control system based on rack |
| CN101480946B (en) * | 2009-02-16 | 2010-06-02 | 华南理工大学 | Wheel load-based type intelligent sensing wheel brake performance monitoring methods |
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