CN112526880A - Real-time estimation method for road surface height in vehicle driving process - Google Patents

Real-time estimation method for road surface height in vehicle driving process Download PDF

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CN112526880A
CN112526880A CN202011345674.7A CN202011345674A CN112526880A CN 112526880 A CN112526880 A CN 112526880A CN 202011345674 A CN202011345674 A CN 202011345674A CN 112526880 A CN112526880 A CN 112526880A
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suspension
observer
road surface
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surface height
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赵林辉
高士金
刘志远
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Harbin Institute of Technology
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Abstract

The invention discloses a real-time estimation method for road surface height in the running process of a vehicle, which comprises the following steps: firstly, establishing a quarter suspension model considering a suspension geometrical structure; secondly, converting the road surface height estimation problem into an unknown input reconstruction problem; establishing an estimation model considering the nonlinear characteristic of the suspension damper; fourthly, designing a state observer to estimate the state of the system; step five, calculating an error equation of the observer; designing an observer gain matrix to ensure that an observer error equation is stable; and seventhly, the estimation of the road surface height is realized through unknown input reconstruction. The method considers the influence of the geometrical structure of the suspension and the nonlinear characteristic of the damper, establishes a linear variable parameter model of the suspension system, designs a sliding mode observer, and realizes the real-time estimation of the road height under different road conditions. Meanwhile, the sensors required by the invention are all sensors existing on the vehicle, and the cost of the system can be reduced on the premise of ensuring the estimation precision of the road surface height.

Description

Real-time estimation method for road surface height in vehicle driving process
Technical Field
The invention belongs to the technical field of automobile control, relates to a real-time road height estimation method in the vehicle running process, and particularly relates to a real-time road height estimation method by utilizing suspension dynamics and considering the influence of a suspension geometrical structure and the nonlinear characteristic of a damper.
Background
With the rapid development of vehicle control technology, people increasingly demand vehicle handling stability and riding comfort. When a vehicle runs on an impact road or a continuously bumpy road, the impact generated by the height change of the road brings discomfort to passengers, influences the riding comfort of the vehicle, and even influences the operation stability and the running safety of the vehicle. Therefore, it is desirable for an automotive motion control system to be able to accurately estimate the height of the road surface during vehicle travel and use the information to improve the automotive motion control effect. For example: in the suspension control, if the road height can be accurately identified, the damping coefficient or the acting force of the suspension can be actively adjusted according to the road height, and the suspension control effect is optimized. Therefore, in order to improve the safety and comfort of the vehicle running, it is important to accurately estimate the road surface height during the vehicle running.
In the prior art, the method for acquiring the road surface height mainly comprises a direct measurement method, an indirect measurement method based on an image and an estimation method based on dynamic response. Among them, CN202511783U discloses a direct measurement method based on a road surface profile measuring instrument, which can measure longitudinal and transverse road surface profiles by using data collected by displacement sensors, but each component of the measuring instrument needs to be integrated on a cart, has a large volume, and cannot be installed in a vehicle for use. CN109564682A discloses a road surface shape estimation method based on images shot by a camera, and CN108955584A discloses a method and apparatus for estimating the undulation of a road surface according to the vertical height and horizontal distance between a laser radar and a scanned point, but the above method requires a camera or a laser radar mounted on a vehicle to acquire road surface information, and is relatively high in cost. CN110001335A proposes a road surface identification technology based on suspension dynamic stroke, and CN106985627A proposes a road surface identification technology based on suspension dynamic stroke and suspension sprung and unsprung mass acceleration signals, but all the above methods are based on statistical rules, and are only suitable for identifying the grade of a section of continuous road surface, and for discrete impact road surfaces and continuous long-wave road surfaces similar to deceleration strips, the accurate height of the road surface cannot be estimated in real time.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the real-time road surface height estimation method in the vehicle running process, which has the advantages of mature theory, wide application range and high precision. The method considers the influence of the geometrical structure of the suspension and the nonlinear characteristic of the damper, establishes a linear variable parameter model of the suspension system, designs a sliding mode observer, and realizes the real-time estimation of the road height under different road conditions. Meanwhile, the sensors required by the invention are all sensors existing on the vehicle, and the cost of the system can be reduced on the premise of ensuring the estimation precision of the road surface height.
The purpose of the invention is realized by the following technical scheme:
a real-time estimation method for road surface height in the running process of a vehicle comprises the following steps:
step one, establishing a quarter suspension model considering the suspension geometry.
And step two, converting the road surface height estimation problem into an unknown input reconstruction problem.
(1) Defining the system state according to the suspension system dynamic model given in the step one
Figure BDA0002799745320000021
Wherein z issIn order to displace the sprung mass of the suspension,
Figure BDA0002799745320000031
is the velocity of the sprung mass displacement of the suspension, theta is the angular displacement of the stabilizer bar of the suspension relative to the equilibrium position,
Figure BDA0002799745320000032
is the angular velocity of the suspension stabilizer bar relative to the equilibrium position; selecting system outputs
Figure BDA0002799745320000033
Wherein
Figure BDA0002799745320000034
Is the sprung mass acceleration, and Δ l is the relative displacement of the sprung and unsprung masses;
(2) based on the above definition, linearizing the model at the balance point in the first step to obtain:
Figure BDA0002799745320000035
wherein z isrIs the road surface height, faControl force applied for active suspension, the control force in semi-active suspension being taken to be 0, fdThe variable quantity of the vehicle body load is A, B, G, H, C, D, E and F are respectively corresponding coefficient matrixes;
in the above-described linearized model, the model,
Figure BDA0002799745320000036
the system measurement value can be obtained by the measurement of a sensor;
Figure BDA0002799745320000037
the system state quantity can be observed by a state observer; f. ofa，fdThe changes in the forces applied to the suspension and the body load, respectively, may be known quantities; z is a radical ofrWhich is an unknown road height, can be considered as an unknown input. Therefore, the problem of estimating the unknown road height can be converted into the unknown input z in the modelrThe reconstruction problem of (1).
Step three, establishing an estimation model considering the nonlinear characteristic of the suspension damper:
considering the linearized model in step two, the elements in the coefficient matrices a and C contain the suspension damping coefficient CpIn the actual dynamic process cpIs a variable with speed, so that the coefficient matrices A and C are CpAlternatively, the system can be written as follows:
Figure BDA0002799745320000038
wherein, A (c)p)、C(cp) Representation matrix with parameter cpMay vary.
In order to deal with the problem of model variation due to the variation parameters,the model was adapted to the form of linear parametric system multicellular bodies. In a linear parametric system, a variable parameter c is selectedpFor scheduling variables, according to cpThe value range of (a) is selected as the vertex of the multicellular body (c)pmax,cpminWherein c ispmax,cpminRespectively is a variable parameter cpMaximum and minimum values of. C is topmax,cpminRespectively substituting the matrix A and the matrix C of the model to obtain a multicellular form of the linear variable parameter model:
Figure BDA0002799745320000041
wherein the content of the first and second substances,
Figure BDA0002799745320000042
is through a scheduling variable cpCalculated weight coefficient, AcpmaxIs cp＝cpmaxValue of the time matrix A, AcpminIs cp＝cpminThe value of the time matrix A, CcpmaxIs cp＝cpmaxValue of the time matrix C, CcpminIs cp＝cpminThe value of the time matrix C.
Step four, designing a state observer to estimate the state of the system:
the following form of state observer is designed:
Figure BDA0002799745320000043
wherein L iscpmaxIs cp＝cpmaxTime observer feedback gain matrix, LcpminIs cp＝cpminTime observer feedback gain matrix, ρcpmaxIs cp＝cpmaxTime observer sliding mode gain matrix, ρcpminIs cp＝cpminThe time observer sliding mode gain matrix.
To avoid the effect of buffeting on state estimation, use is made of
Figure BDA0002799745320000044
And (e) replacing a sign function sign (e) in an observer, wherein e is an estimation error output by the system, eta is a small positive number, and the slope of the function near the zero-value estimation error can be adjusted to influence the reconstruction accuracy of unknown input of the system.
Step five, calculating an error equation of the observer:
defining state estimation errors
Figure BDA0002799745320000051
The error equation is written in the form:
Figure BDA0002799745320000052
wherein:
Figure BDA0002799745320000053
Figure BDA0002799745320000054
designing an observer gain matrix to ensure that an observer error equation is stable, wherein the error equation is as follows:
Figure BDA0002799745320000055
wherein
Figure BDA0002799745320000056
Due to alpha in the above formula1,α2All values of are equal to cpIn relation, the above formula can be abbreviated as:
Figure BDA0002799745320000057
will be provided with
Figure BDA0002799745320000058
Viewed as a disturbance, the observer design problem can be converted to (A (c)p)-K(cp)C(cp) ) convergence on 0. At the same time, for interference
Figure BDA0002799745320000059
Since it is related to the road height and the road height is bounded, the disturbance is bounded.
According to the LPV (linear variable parameter) system stability theory: for a given positive tunable parameter γ ∈ R, if a symmetric positive definite matrix P (c) existsp) The matrix Y (c)p) And the identity matrix I and a positive definite factor epsilon R meet the following conditions:
P(cp)＝PT(cp)，ε＞0
Figure BDA0002799745320000061
wherein:
Π(cp)＝P(cp)A(cp)+AT(cp)P(cp)-Y(cp)C(cp)-CT(cp)Y(cp)+εγI；
the designed LPV observer is stable.
Simultaneously, an observer gain matrix is obtained:
K(cp)＝P-1(cp)Y(cp)；
according to the formula, the gain of the sliding mode observer can be obtained.
Step seven: and (3) realizing the estimation of the road height through unknown input reconstruction:
once the estimation error equation reaches the sliding mode surface and the estimated system state converges to the true state, the sliding mode term in the observer
Figure BDA0002799745320000062
The road surface height can be approximated, namely the road surface height can be reconstructed as:
Figure BDA0002799745320000063
compared with the prior art, the invention has the following advantages:
1. the road surface height estimation method adopts a quarter suspension model which is more consistent with the real geometrical structure of the suspension, further, the change of model parameters is considered and processed, and the model is more accurate;
2. the stability of the road surface height estimation method is ensured by the LPV observer stability theory, and the observer estimation error designed by the method is bounded theoretically;
3. the road surface height estimation method can accurately estimate the specific numerical value of the road surface height;
4. the road surface height estimation method utilizes the measurement information of the relative displacement of the sprung mass and the unsprung mass and the sprung mass acceleration of the suspension system, and the used sensors are all common sensors on the vehicle body, so that the road surface height estimation method has the advantage of low cost;
5. the road surface height estimation method is high in calculation efficiency and can meet the real-time requirement of a suspension control system;
6. the road surface height estimation method can be suitable for different suspension systems such as a semi-active suspension, an active suspension and the like.
Drawings
FIG. 1 is a schematic diagram of a Macpherson suspension geometry;
FIG. 2 is a block diagram of a method for estimating road surface height;
FIG. 3 is a schematic diagram of the comparison between the estimated value and the actual value of the road surface height on the deceleration strip road surface;
FIG. 4 is a schematic representation of estimated values of road surface height over a sinusoidally varying road surface compared to actual values;
fig. 5 is a schematic diagram showing the comparison between the estimated value and the actual value of the road surface height on the random road surface.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the protection scope of the present invention.
The invention provides a real-time estimation method for road surface height in the running process of a vehicle, which comprises the following steps:
the method comprises the following steps: a quarter suspension model is built that takes into account suspension geometry.
In order to establish a more accurate quarter suspension model, the influence of the real geometrical structure of the suspension is considered, and the suspension model is established by taking the Macpherson suspension as an example. A suspension taking into account the real geometry is shown in fig. 1, ignoring the effect of wheel-ground coupled damping, and from the lagrangian equation, the suspension system dynamics model can be derived as follows:
Figure BDA0002799745320000081
Figure BDA0002799745320000082
wherein:
D1＝mslC+mulCsin2(θ-θ0)；
Figure BDA0002799745320000083
Figure BDA0002799745320000084
b＝2lAlB；
c＝a2-ab.cosα′；
d＝ab-b2.cosα′；
α′＝α+θ0。
in the formula: z is a radical ofs、
Figure BDA0002799745320000085
Respectively suspension sprung mass displacement, velocity and acceleration; theta, theta,
Figure BDA0002799745320000086
Angular displacement, angular velocity, angular acceleration (positive counterclockwise) of OC relative to equilibrium position in fig. 1, respectively; theta0And alpha is the angle between OC and the horizontal line and the angle between OA and the horizontal line in the steady state in FIG. 1, respectively; z is a radical ofrIs the road surface height; m issIs a quarter of the sprung mass of the suspension, muIs a quarter of the suspension unsprung mass, ksIs the suspension stiffness coefficient, cpIs the damping coefficient, k, of the suspension dampertIs a wheel-ground coupling stiffness coefficient; lA、lB、lCOA, OB, OC lengths in FIG. 1, respectively; f. ofaThe control force applied for the active suspension of fig. 1, in a semi-active suspension, the value is taken to be 0; f. ofdIs the change in body load in fig. 1.
Step two: and converting the road height estimation problem into an unknown input reconstruction problem.
In the quarter suspension model established in the first step, the road height is coupled with the suspension state and cannot be directly used for estimating the road height. For this purpose, the model in step one is linearized.
Defining the system state as the suspension system dynamic model given in the step one
Figure BDA0002799745320000091
Output is as
Figure BDA0002799745320000092
Wherein:
Figure BDA0002799745320000093
to sprung mass acceleration, Δ l is the amount of change in the length of AB in FIG. 1, i.e., the relative displacement of the sprung and unsprung masses. As can be seen from the geometrical relationship in fig. 1:
Figure BDA0002799745320000094
in the formula: l and l' are the lengths of AB at steady state and at dynamic state, respectively.
As can be seen from equations (1) and (2), in the suspension model derived from the lagrange equation, the state variable and the road height zrCoupled together, make it difficult to estimate road height using the model, so it is considered to linearize the model at the equilibrium point.
At the equilibrium point
Figure BDA0002799745320000095
And (5) carrying out linearization processing on the model in the step one. Wherein z issIn order to displace the sprung mass,
Figure BDA0002799745320000096
theta is the angular displacement relative to the equilibrium position, i.e., the amount of angular change relative to the equilibrium position,
Figure BDA0002799745320000097
is the corresponding angular velocity. f. ofaControl force applied for active suspension, in semi-active suspension this value takes the value 0, fdIs the amount of change in body load, zrIs the road surface height. In a steady state condition at the equilibrium point, the above variables are all apparently 0.
Obtaining a linearized equation of state:
Figure BDA0002799745320000101
wherein:
Figure BDA0002799745320000102
Figure BDA0002799745320000103
W1＝(mslC+mulCsin2(-θ0))2；
Figure BDA0002799745320000104
Figure BDA0002799745320000105
Figure BDA0002799745320000111
Figure BDA0002799745320000112
Figure BDA0002799745320000113
Figure BDA0002799745320000114
Figure BDA0002799745320000115
Figure BDA0002799745320000116
Figure BDA0002799745320000117
Figure BDA0002799745320000118
Figure BDA0002799745320000119
obtaining a linearized measurement equation:
Figure BDA00027997453200001110
wherein:
Figure BDA0002799745320000121
in expressions (3) and (4), the control force f exerted by the active suspension is taken into accountaAnd change of vehicle body load fdAre all known inputs, and the road surface excitation zrIs an unknown input. In particular, when using semi-active dynamic suspensions, f a0; when the vehicle body load is not changed, fd＝0。
For the systems described in the expressions (3) and (4), the system output is selected by comprehensively considering the cost and the road height estimation effect
Figure BDA0002799745320000125
And Δ l, the sprung mass acceleration of the suspension system and the relative displacement of the sprung and unsprung masses, are measured quantities. The invention designs an observer to estimate the state of the system and linearizes the model by reconstructing the unknown input zrAnd the estimation of the road surface height is realized. Thereby converting the road height estimation problem into the state estimation of the system described by expressions (3) and (4) and as an unknown input zrThe reconstruction problem of (1).
Step three: and establishing an estimation model considering the nonlinear characteristic of the suspension damper.
Consider the linearized model in step two, where parameter cpIs the damping coefficient of the suspension damper, which is a variable with speed in the practical process, and the parameter c is noticedpOnly at a24、a44Then the system can be written as follows:
Figure BDA0002799745320000122
wherein the content of the first and second substances,
Figure BDA0002799745320000123
is a state variable selected by the system;
Figure BDA0002799745320000124
is a system measurement; z is a radical ofrInputting unknown road surface; f. ofa,fdThe amount of change in the control force and vehicle load, respectively, applied to the active suspension is a known input. A (c)p)、C(cp) Is dependent on the parameter cpThe changed matrix, B, G, H, D, E, F, is the corresponding coefficient matrix, namely:
Figure BDA0002799745320000131
Figure BDA0002799745320000132
to deal with this variation, the model is rewritten to the form of a linear parametric system multicellular body. In a linear parametric system, a variable parameter c is selectedpFor scheduling variables, according to cpThe value range of (a) is selected as the vertex of the multicellular body (c)pmax,cpminWherein c ispmax,cpminRespectively is a variable parameter cpMaximum and minimum values of. C is topmax,cpminRespectively substituting the A matrix and the C matrix of the model to obtain the form of the multicellular body of the linear variable parameter model:
Figure BDA0002799745320000133
wherein the content of the first and second substances,
Figure BDA0002799745320000134
is through a scheduling variable cpCalculated weight coefficient, zrFor unknown road surface input, fa,fdThe control force and vehicle load, respectively, applied to the active suspension are known inputs. A. thecpmax,Acpmin,Ccpmax,CcpminTo change the parameter cpTaking the coefficient matrix at the boundary value, namely:
Figure BDA0002799745320000135
Figure BDA0002799745320000136
b, G, H, D, E and F are corresponding coefficient matrixes respectively.
Step four: a state observer is designed to estimate the system state.
For the linear parametric system described in expression (6), measurable system output is utilized
Figure BDA0002799745320000141
As the observed quantity, a state observer is designed. Wherein
Figure BDA0002799745320000142
Is the sprung mass acceleration and Δ l is the sprung unsprung mass relative displacement.
Consider faAnd fdFor known amount, respectively for cp＝cpmaxAnd cp＝cpminTwo cases design the state observer.
When c is going top＝cpmaxThe time design state observer is as follows:
Figure BDA0002799745320000143
wherein: l1cpmax、l2cpmax、l3cpmax、l4cpmaxFor observer feedback gain, ρ1cpmax、ρ2cpmax、ρ3cpmax、ρ4cpmaxIn order to obtain the sliding-mode gain of the observer,
Figure BDA0002799745320000144
respectively represent the state x1、x2、x3、x4Is determined by the estimated value of (c),
Figure BDA0002799745320000145
represents the output y1、y2An estimate of (d).
Similarly, when cp＝cpminThe time design state observer is as follows:
Figure BDA0002799745320000146
wherein: l1cpmin、l2cpmin、l3cpmin、l4cpminFor observer feedback gain, ρ1cpmin、ρ2cpmin、ρ3cpmin、ρ4cpminIn order to obtain the sliding-mode gain of the observer,
Figure BDA0002799745320000147
respectively represent the state x1、x2、x3、x4Is determined by the estimated value of (c),
Figure BDA0002799745320000148
represents the output y1、y2An estimate of (d).
The state observer is written in the form of a multicellular body as follows:
Figure BDA0002799745320000149
wherein:
Figure BDA0002799745320000151
respectively as feedback gain matrixes of the observer at the top of the multicellular body;
Figure BDA0002799745320000152
respectively, observer sliding mode gain matrixes at the top of the multicellular body.
In order to avoid the influence of buffeting on state estimation, the invention adopts the following equivalent symbolic functions to replace the symbolic function sign (e) in the observer designed by the expression (9):
Figure BDA0002799745320000153
in the formula:
Figure BDA0002799745320000154
is the estimated error of the output; η is a small positive number that can adjust the slope of the function around the zero-valued estimation error, affecting the reconstruction accuracy for unknown inputs to the system.
Step five: an observer error equation is calculated.
Defining the state estimation error:
Figure BDA0002799745320000155
are respectively paired with cp＝cpmaxAnd cp＝cpminThe estimation error equation of the observer is calculated for both cases. When c is going top＝cpmaxThe observer error equation is then as follows:
Figure BDA0002799745320000161
wherein:
Figure BDA0002799745320000162
Figure BDA0002799745320000163
Figure BDA0002799745320000164
Figure BDA0002799745320000165
similarly, when cp＝cpminThe observer error equation is then as follows:
Figure BDA0002799745320000166
wherein:
Figure BDA0002799745320000167
Figure BDA0002799745320000168
Figure BDA0002799745320000169
Figure BDA00027997453200001610
writing the error equation to the form of a multicellular body is as follows:
Figure BDA0002799745320000171
wherein:
Figure BDA0002799745320000172
Figure BDA0002799745320000173
step six: and designing an observer gain matrix to ensure that an observer error equation is stable.
The error equation is as follows:
Figure BDA0002799745320000174
in the formula (I), the compound is shown in the specification,
Figure BDA0002799745320000175
representing the road height estimation error.
Due to alpha in the above formula1,α2All values of are equal to cpIn relation, the above formula can be abbreviated as:
Figure BDA0002799745320000176
will be provided with
Figure BDA0002799745320000177
As a disturbance, the observer gain design can be converted to (A (c)p)-K(cp)C(cp) ) convergence on 0. At the same time, for interference
Figure BDA0002799745320000178
Due to the fact that
Figure BDA0002799745320000179
In relation to road height, and road height is bounded, then the disturbance is known to be bounded, then (A (c)p)-K(cp)C(cp) The estimated error of the road surface height is also bounded at convergence.
According to the LPV (linear variable parameter) system stability theorem: for a given positive tunable parameter γ ∈ R, if a symmetric positive definite matrix P (c) existsp) The matrix Y (c)p) And the identity matrix I and a positive definite factor epsilon R meet the following conditions:
P(cp)＝PT(cp)，ε＞0；
Figure BDA0002799745320000181
wherein:
П(cp)＝P(cp)A(cp)+AT(cp)P(cp)-Y(cp)C(cp)-CT(cp)Y(cp)+εγI；
the designed LPV observer is stable.
Simultaneously, an observer gain matrix is obtained:
K(cp)＝P-1(cp)Y(cp) (17)；
and proper sliding mode gain can be obtained through the stability theorem of the LPV observer.
Step seven: and the estimation of the road surface height is realized through unknown input reconstruction.
Once the estimation error equation reaches the sliding mode surface and the estimated system state converges to the true state, the sliding mode term in the observer
Figure BDA0002799745320000182
The road surface height can be approximated, namely the road surface height can be reconstructed as:
Figure BDA0002799745320000183
it can be seen that the deviation between the measured output and the estimated output is used to reconstruct the road height.
According to the invention, the measurement information of two sensors, namely the measurement information of the relative displacement of the sprung mass and the unsprung mass of the suspension system and the measurement information of the sprung mass acceleration are utilized, and when the error of an observer converges to zero, the estimation of the road surface height can be realized; the suspension model used by the road surface height estimation method of the invention is more in line with the actual suspension structure, and the change of model parameters is considered. Therefore, the road surface height estimation method has the advantages of low cost, high precision and real-time performance.
Example (b):
and designing simulation operation parameters and the feedback gain and sliding mode gain of the observer according to the design requirements of the vehicle suspension and the expected simulation operation result.
The designed simulation operation related parameters and observer gains are as follows:
ms＝283.7kg,mu＝37.6kg,ks＝18500N/m,kt＝180000N/m,
cpmin＝1500N/m,cpmax＝3000N/m,α′＝70.5°,θ0＝2°,
lA＝0.6257m,lB＝0.3232m,lC＝0.3742m,η＝0.001。
Figure BDA0002799745320000191
Figure BDA0002799745320000192
the embodiment verifies the estimation effect of the method on deceleration strip road surfaces, sine variation road surfaces and random road surfaces respectively.
Fig. 3 is a schematic diagram of comparison between the estimated road surface height and the actual road surface height on the road surface of the speed bump in the embodiment. The road surface is a trapezoidal bulge road surface with an upper bottom of 10cm, a lower bottom of 30cm and a height of 5cm on a flat road surface, and is used for simulating the situation that a vehicle passes through a speed bump; fig. 4 is a schematic diagram of the comparison of the estimated road surface height with the actual road surface height on a sinusoidally varying road surface as mentioned in the example. The road surface is a road surface with sine variation in height, the amplitude is 0.05m, the frequency is 1rad/s, and the road surface is used for simulating the situation that a vehicle passes through the road with sine variation; fig. 5 is a schematic diagram showing the comparison between the estimated road surface height and the actual road surface height on the random road surface mentioned in the example. The road surface is used for simulating the situation that vehicles pass through a random road surface.
As can be seen from fig. 3, 4, and 5: the road surface height estimation method can obtain better estimation effect under different road surface conditions.

Claims (3)

1. A real-time estimation method for road surface height in the running process of a vehicle is characterized by comprising the following steps:
step one, establishing a quarter suspension model considering a suspension geometrical structure;
step two, converting the road surface height estimation problem into an unknown input reconstruction problem:
(1) defining the system state according to the suspension system dynamic model given in the step one
Figure FDA0002799745310000011
Wherein z issIn order to displace the sprung mass of the suspension,
Figure FDA0002799745310000012
is the velocity of the sprung mass displacement of the suspension, theta is the angular displacement of the stabilizer bar of the suspension relative to the equilibrium position,
Figure FDA0002799745310000013
is the angular velocity of the suspension stabilizer bar relative to the equilibrium position; selecting system outputs
Figure FDA0002799745310000014
Wherein
Figure FDA0002799745310000015
Is the sprung mass acceleration, and Δ l is the relative displacement of the sprung and unsprung masses;
(2) based on the above definition, linearizing the model at the balance point in the first step to obtain:
Figure FDA0002799745310000016
wherein z isrIs the road surface height, faControl force applied for active suspension, fdThe variable quantity of the vehicle body load is A, B, G, H, C, D, E and F are respectively corresponding coefficient matrixes;
step three, establishing an estimation model considering the nonlinear characteristic of the suspension damper:
Figure FDA0002799745310000017
wherein the content of the first and second substances,
Figure FDA0002799745310000018
is through a scheduling variable cpCalculated weight coefficient, AcpmaxIs cp＝cpmaxValue of the time matrix A, AcpminIs cp＝cpminThe value of the time matrix A, CcpmaxIs cp＝cpmaxValue of the time matrix C, CcpminIs cp＝cpminValue of the time matrix C, Cpmax,cpminAre respectively cpMaximum and minimum values of;
step four, designing a state observer to estimate the state of the system:
Figure FDA0002799745310000021
wherein L iscpmaxIs cp＝cpmaxTime observer feedback gain matrix, LcpminIs cp＝cpminTime observer feedback gain matrix, ρcpmaxIs cp＝cpmaxTime observer sliding mode gain matrix, ρcpminIs cp＝cpminA time observer sliding mode gain matrix;
step five, calculating an error equation of the observer:
defining state estimation errors
Figure FDA0002799745310000022
The error equation is written in the form:
Figure FDA0002799745310000023
designing an observer gain matrix to ensure the stability of an observer error equation, wherein the observer gain matrix is as follows:
K(cp)＝P-1(cp)Y(cp)；
the error equation is:
Figure FDA0002799745310000024
and seventhly, estimating the road height through unknown input reconstruction, wherein the road height reconstruction comprises the following steps:
Figure FDA0002799745310000025
2. the method according to claim 1, wherein in the fifth step,
Figure FDA0002799745310000031
Figure FDA0002799745310000032
3. the method according to claim 1, wherein in the sixth step,
Figure FDA0002799745310000033
in the formula (I), the compound is shown in the specification,
Figure FDA0002799745310000034
representing the road height estimation error.
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