CN111873744A - Active suspension pre-aiming control method based on camera sensor road surface information identification - Google Patents

Active suspension pre-aiming control method based on camera sensor road surface information identification Download PDF

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CN111873744A
CN111873744A CN202010677973.4A CN202010677973A CN111873744A CN 111873744 A CN111873744 A CN 111873744A CN 202010677973 A CN202010677973 A CN 202010677973A CN 111873744 A CN111873744 A CN 111873744A
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CN111873744B (en
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陈志勇
***强
于远彬
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Jilin University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
    • B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
    • B60G17/019—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the type of sensor or the arrangement thereof
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2401/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60G2401/14—Photo or light sensitive means, e.g. Infrared
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention discloses an active suspension pre-aiming control method based on camera sensor road surface identification, which comprises the following steps: acquiring the pre-aiming information of the front road surface through a camera sensor; carrying out data processing on the pre-aiming information of the front road surface to obtain an unevenness value of the front road surface, and determining the grade of the unevenness of the road surface according to an unevenness dividing criterion; receiving a front road surface unevenness grade instruction, and establishing an active suspension model based on front road surface preview information; design of active suspension H with multiple performance index constraints∞An optimal controller; obtaining H using a multi-objective constrained optimization algorithm∞And optimizing the weight value in the controller to realize the performance balance of various performance indexes of the suspension. The invention utilizes the camera sensor to acquire the road surface preview information in combination with H∞Optimal control is achieved, the time lag phenomenon in the process of adjusting the damping parameters of the active suspension is improved, and multi-performance index balance control of the active suspension is achieved.

Description

Active suspension pre-aiming control method based on camera sensor road surface information identification
Technical Field
The invention relates to parameter control of an active suspension, in particular to a pre-aiming control method of the active suspension based on camera sensor road surface identification.
Background
When the vehicle runs, the excitation provided by the uneven road surface may cause the vehicle to bump, so that the vehicle generates vertical vibration, and the smoothness and comfort of the vehicle are affected. In order to improve the ride comfort of the vehicle, it is necessary to provide a new method for improving the suspension performance. With the continuous development of machine vision, the traditional 2D camera sensor has been gradually transformed into a 3D camera sensor, which can not only represent two-dimensional plane information but also represent three-dimensional stereo coordinate information in the photographed image, such as: kinect, ZED 2KStereo Camera, and Bumble Bee, among others. If the stereoscopic vision sensor can be applied to road surface unevenness detection, pre-aiming information is provided for suspension control, and the suspension is preset with parameters to complete the vibration reduction function, so that the suspension performance is greatly improved.
The road heading information may allow the vehicle suspension to adjust to the most appropriate setting before receiving the road excitation input. For example, when the camera sensor scans a concave-convex road surface, the suspension controller is adjusted to be in a soft damping state in advance to absorb road surface excitation so as to reduce vehicle body vibration and improve comfort, and is timely switched to be in a hard damping state after passing through the concave-convex road surface so as to inhibit subsequent oscillation of a vehicle body.
The existing research of active suspension control through preview information only stays at the theoretical level of wheelbase preview control. In the document [ Madau D P, Khaykin B l. continuous variable semi-active suspension system using centrally located road surface rate and accelerometer sensors: US 2003 ], road surface information fed back from a front axle is applied to the adjustment of a rear axle suspension according to a travel time difference between the front axle and the rear axle. This method is generally time-lag and only works after the front wheels of the vehicle have run for a period of time on the road, but the driver has experienced vibrations from the undulations. In the literature [ study on a laser radar-based road unevenness reconstruction method ] road height information in front of a vehicle is obtained by a laser radar, but the method is not generally implemented due to the cost and computational complexity of the laser radar.
Therefore, the active suspension pre-aiming control method based on camera sensor road surface identification is designed and developed, not only can the road surface pre-aiming information be accurately obtained, the time lag phenomenon in the active suspension control process is improved, but also the implementation cost of the method is reduced due to the use of the low-cost camera sensor.
Disclosure of Invention
Aiming at the technical problems, the invention provides an active suspension pre-aiming control method based on camera sensor road surface identification, which can realize road surface information identification at low cost and is easy to realize active suspension pre-aiming control.
Acquiring road surface pre-aiming information according to a Kinect camera sensor, calculating road surface unevenness, adding the unevenness information into a suspension system model, and establishing a suspension system dynamic model based on the vehicle front pre-aiming control; secondly, a suspension controller is designed to realize optimization of multiple performance indexes of a suspension model, improve a time lag phenomenon in a suspension parameter adjusting process and reduce adverse effects caused by road excitation.
In order to realize the functions, the invention adopts the technical scheme that:
an active suspension pre-aiming control method based on camera sensor road surface identification comprises the following steps:
acquiring front road surface preview information through a camera sensor;
secondly, performing data processing on the front road surface preview information to obtain a front road surface unevenness value, and determining a road surface unevenness grade according to an unevenness dividing criterion;
step three, receiving the front road surface unevenness grade instruction determined in the step two, and establishing an active suspension model based on front road surface preview information;
step four, designing an active suspension H with multiple performance index constraints∞An optimal controller;
step five, obtaining H by using a multi-objective constraint optimization algorithm∞And optimizing the weight value in the controller to realize the performance balance of various performance indexes of the suspension.
Further, in the first step, before the front road surface preview information is collected, the camera sensor is calibrated and calibrated first, so that the accuracy of the collected road surface information is ensured.
Further, in the second step, the data processing of the forward road preview information includes the following processes:
performing noise reduction processing on an image acquired by a camera sensor by adopting a bilateral filter algorithm;
using an image splicing algorithm based on features to reconstruct a scene, and reducing overlapped contents in the same scene in the front and back continuous frame images: detecting key feature points of two RGB images of adjacent road parts by an SURF algorithm; then matching the feature points between the images by using a k-nearest neighbor algorithm, and removing abnormal values by using a RANSAC algorithm; and finally, estimating a homography transformation matrix between the two images according to the matched feature points, and splicing the continuous frame images by using homography transformation to obtain a new fusion image.
Further, according to claim 1, in the second step, after the image processing, the method for active suspension pre-aiming control based on the camera sensor road surface recognition is performed, the calculation of the IRI value is performed on the pre-aiming information of the front road surface collected by the camera sensor, so as to obtain the value of the front road surface unevenness, and the road surface unevenness grade is determined according to a preset formula, which includes the following steps:
transferring the depth data output by the camera sensor depth camera to an RGB image;
acquiring an aligned RGB image (3D point cloud data containing a longitudinal contour of a road surface), importing the RGB image into ProVAL software, and selecting a semi-vehicle model to calculate an IRI value;
determining the grade value alpha of the road surface unevenness according to the grade division rule in the international standard ISO 8608r。
Further, the transferring the depth data output by the camera sensor depth camera to the RGB image comprises the following steps:
firstly, the depth data acquired by the camera sensor is converted from image coordinates to world coordinates, and the calculation process is as follows:
Figure BDA0002584679530000031
Figure BDA0002584679530000032
ZIR＝ZD
in the formula (X)IR,YIR,ZIR) World coordinates of three-dimensional points on the camera sensor; (u)D,vD) Pixel coordinates of the depth image;
Figure BDA0002584679530000033
is the distortion center coordinate of the camera sensor plane;
Figure BDA0002584679530000034
is the focal length of the sensor portion of the IR camera; zDRepresenting depth values, obtained by a camera sensor; intrinsic parameters of remaining camera sensors
Figure BDA0002584679530000035
Obtaining through camera sensor calibration;
then, converting the depth coordinate information of the camera sensor into RGB image coordinates, wherein a calculation formula is as follows;
Figure BDA0002584679530000036
wherein (X)RGB,YRGB,ZRGB) World coordinates relative to the RGB camera sensor; (X)IR,YIR,ZIR) World coordinates relative to the IR camera sensor; the rotation matrix R and the translation matrix t are external parameters obtained through calibration and estimation of a camera sensor;
finally, the world coordinate (X) is determined according toRGB,YRGB,ZRGB) Pixel coordinates (u) mapped to RBG imageRGB,vRGB) The above step (1);
Figure BDA0002584679530000037
Figure BDA0002584679530000038
wherein the content of the first and second substances,
Figure BDA0002584679530000039
are all intrinsic parameters of the RGB camera sensor obtained by camera sensor calibration.
Further, the step three of establishing the active suspension model based on the front road aiming information comprises the following processes:
the front suspension and the rear suspension adopt Newton's second law to obtain a suspension model dynamic equation as follows:
Figure BDA00025846795300000310
wherein M is the sprung mass of the vehicle body, I is the moment of inertia of the vehicle body relative to the center of mass, and Mf、mrFor unsprung masses, x, of the front and rear wheelssIs the absolute displacement of the vehicle body, xf、xrRespectively, the unsprung mass displacement of the front and rear wheels, theta is the pitch angle, ksf、ksrFor the rigidity of the front and rear suspensions, cf、crIntrinsic damping coefficient, k, of front and rear suspensions, respectivelytf、ktrStiffness of front and rear tires, uf、urIs according to H∞Controlling the damping force generated by the strategy, wherein a and b are respectively the front and rear wheelbases, and L is the pre-aiming distance;
the state variables that define the suspension system are:
Figure BDA0002584679530000041
the disturbance inputs are:
Figure BDA0002584679530000042
wherein w (t) is the front wheel input; w (t-tau) is the rear wheel input; τ ═ (a + b)/v is the time delay between the front and rear wheels;
control input is U ═ Uf,ur]T；
Rewriting the system equation into the form of a state equation:
X＝AX+BU+D1w(t)+D2w(t-τ)
Figure BDA0002584679530000043
Nvthe number of state variables of the model;
Figure BDA0002584679530000044
Figure BDA0002584679530000045
Figure BDA0002584679530000046
Figure BDA0002584679530000047
Figure BDA0002584679530000048
further, the step four designs the active suspension H with multiple performance index constraints∞The optimal controller comprises the following processes:
considering system control targets as system sprung mass vertical acceleration, system pitch angle acceleration, front and rear suspension dynamic travel and front and rear wheel tire deformation, determining a system performance objective function as follows:
Figure BDA0002584679530000051
in the formula, T is control cycle time;
Figure BDA0002584679530000052
J3＝E[(zs+aθ-zf)2]；J4＝E[(zs-bθ-zr)2]；J5＝E[(zf-hf)2]；J6＝E[(zr-hr)2]；
Figure BDA0002584679530000053
Figure BDA0002584679530000054
is according to H∞Optimally controlling the generated suspension damping force;
the above formula is rewritten as follows:
Figure BDA0002584679530000055
in the formula, the performance index is restricted; q ═ Cyu TCyuIs a symmetric semi-positive definite matrix; f ═ Dyu TDyu、N＝Cyu TDyuIs a positive definite matrix; cyu＝Cyu1Cyu2；
Figure BDA0002584679530000056
Figure BDA0002584679530000057
Figure BDA0002584679530000058
Using H∞Control, assume that the measurement vector ψ is:
ψ＝Cmx+ξ
wherein, CmA conversion matrix of state and measurement is adopted, and xi is a measurement error;
to minimize the performance indicator function, the system control input U is solved for:
Figure BDA0002584679530000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002584679530000062
for the best estimation vector of the system state, r (t) is a vector containing the preview information, including the pre-axial and wheelbase preview information, and the formula is as follows:
Figure BDA0002584679530000063
in the formula, tpIs the preview time; ca、CbControlling gain for feedback and feedforward; wherein, Ca＝F-1(NT+BTS)；Ca＝-F- 1NT(ii) a S is a steady state solution obtained by the Riccati equation:
S(A-BF-1NT)+(A-BF-1NT)TS-S(BF-1BT--2DDT)S+(Q-NF-1NT)＝0
estimation of system state vector
Figure BDA0002584679530000064
Comprises the following steps:
Figure BDA0002584679530000065
wherein L is H∞Gain vector of optimal controller: l ═ PCm TR-1(ii) a R is a positive definite matrix of measurement errors; p is the covariance matrix of the estimation error:
Figure BDA0002584679530000066
the invention has the following beneficial effects:
1) a Kinect camera sensor with low cost is selected as a pavement information acquisition sensor, so that the development cost of the method is reduced while the acquisition precision is ensured, and the method is easy to realize;
2) the method comprises the steps that front road information is used as input of pre-aiming control, an active suspension pre-aiming control model based on the front road information is established, road interference can be prevented in advance, and a suspension system is adjusted to the most appropriate control parameters in advance before a vehicle inputs road excitation response;
3) designing H constrained by multiple performance indicators∞The predictive controller generates a feedforward term using the front road surface information on the basis of feedback control to form more effective control input.
Drawings
Fig. 1 is a flow chart of an active suspension pre-aiming control method based on camera sensor road surface recognition.
Fig. 2 is a schematic view of a camera sensor mounting position.
Fig. 3 is an active suspension semi-vehicle model.
Fig. 4 is a vehicle body acceleration transfer characteristic curve.
Fig. 5 is a tire dynamic deformation transfer characteristic curve.
Fig. 6 is a process diagram of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and examples:
examples
The time lag phenomenon existing in the control process of the traditional vehicle active suspension is considered, and the wheelbase preview scheme and the H are combined∞The predictive control method provides an active suspension pre-aiming control method based on camera sensor road surface identification, which identifies front road surface information to enable a vehicle suspension system to carry out pre-aiming control before an axle with parameter adjustment aiming at unknown road interference in advance, and comprises the following steps as shown in figure 1:
the method comprises the following steps of firstly, acquiring the preview information of the front road surface through a camera sensor:
1.1) calibrating and calibrating a camera sensor before information acquisition:
the camera sensor selects a Kinect V2 depth camera, and the internal and external calibration parameters are determined by a CameraCalibration Toolbox calibration tool box in MATLAB.
The camera sensor probably has the error in the installation, needs to carry out camera calibration, guarantees accurate collection road surface information, for this reason, compares the data of camera sensor collection with the true height in ground, calibrates the data accuracy of camera sensor collection, eliminates the camera because the installation is not hard up the error that appears, and its concrete step is as follows: .
Firstly, mounting a Kinect camera to the height of 600-1300 mm from the ground, acquiring 10 depth images at the interval of 100mm, and randomly extracting 2000 points from each image to calculate the measurement height between the camera and the ground by taking the average value;
then, determining the real height between the camera sensor and the ground by using a laser range finder, and fitting by using a least square method to obtain the relation between the real height value and the Kinect camera measurement value:
Zgt＝1.0016Zm+5.5122
in the formula, ZgtIndicating the true value, Z, obtained by the laser rangefindermRepresenting the depth value directly measured by Kinect;
1.2) set up the road surface information acquisition system, carry out the road surface of the place ahead and aim at information acquisition:
the road surface information acquisition system comprises a Kinect V2 camera sensor and a GPS sensor, wherein the Kinect V2 camera sensor is connected into a vehicle industrial personal computer through a special PCI-E USB card, and the GPS sensor is used for recording real-time vehicle speed information and creating timestamps on acquired images in sequence. In order to manage different programs and enable data acquisition software to be easy to operate, an independent data acquisition process is integrated through a LabVIEW virtual environment, wherein the data acquisition software of Kinect is written by C + +, an OpenNI library is used for simultaneously recording RGB data streams and depth data streams, the resolution ratio is 640 multiplied by 480 pixels, the frame rate is 30fps, and GPS data acquisition software is developed by C/C + + and Garmin Software Development Kit (SDK).
Step two, carrying out data processing on the front road surface information to obtain a front road surface unevenness value, and determining the road surface unevenness grade according to a preset formula:
2.1) carrying out image processing on the front road surface data collected by the camera sensor:
2.1.1) the original color difference image collected by the camera sensor contains a plurality of noise points which can affect the accuracy of depth data and need to be subjected to noise reduction treatment, therefore, a bilateral filter algorithm which can not only keep the edge information of the image but also achieve the smooth noise reduction effect is selected to carry out noise reduction treatment on the image;
2.1.1) during the camera sensor collection process, about 30% of overlapping content appears in the front and back continuous frame images, and the image mosaic algorithm based on the characteristics is used for scene reconstruction, so that the overlapping content in the same scene is reduced, the depth data processing speed is increased, and the implementation steps are as follows:
firstly, detecting key feature points of two RGB images of adjacent road parts by an SURF algorithm; then matching the feature points between the images by using a k-nearest neighbor algorithm, and removing abnormal values by using a RANSAC algorithm; finally, according to the matched feature points, a homography transformation matrix between the two images is estimated, and the continuous frame images are spliced by using homography transformation to obtain a new fusion image;
2.2) calculating the front road surface unevenness value, and determining the road surface unevenness grade according to a preset formula:
after image processing is carried out on front pavement data acquired by a camera sensor, IRI (international flatness index) value calculation is carried out, which requires that RGB camera output and depth camera output of a Kinect camera sensor are spatially aligned, so that in order to correctly combine the RGB image and the depth data, the depth data are transferred to the RGB image by the method, and the method comprises the following steps;
firstly, converting depth data acquired by a Kinect camera sensor from image coordinates into world coordinates, wherein the calculation process is as follows:
Figure BDA0002584679530000081
Figure BDA0002584679530000082
ZIR＝ZD
in the formula (X)IR,YIR,ZIR) World coordinates of three-dimensional points on the Kinect camera sensor, (u)D,vD) Is the pixel coordinates of the depth image,
Figure BDA0002584679530000083
is the distorted center coordinates of the camera sensor plane,
Figure BDA0002584679530000084
is the focal length of the sensor portion of the IR camera. ZDRepresenting depth values obtained by the Kinect camera sensor, intrinsic parameters of the remaining camera sensors
Figure BDA0002584679530000085
Obtaining through camera sensor calibration;
then, converting the depth coordinate information of the camera sensor into RGB image coordinates, wherein a calculation formula is as follows;
Figure BDA0002584679530000086
wherein (X)RGB,YRGB,ZRGB) (X) world coordinates relative to the RGB Camera sensorIR,YIR,ZIR) The rotation matrix R and the translation matrix t are extrinsic parameters estimated by Kinect camera sensor calibration for world coordinates relative to the IR camera sensor.
Finally, the world coordinate (X) is determined according toRGB,YRGB,ZRGB) Pixel coordinates (u) mapped to RBG imageRGB,vRGB) The above step (1);
Figure BDA0002584679530000087
Figure BDA0002584679530000088
wherein the content of the first and second substances,
Figure BDA0002584679530000089
all are intrinsic parameters of the RGB camera sensor obtained by calibrating the camera sensor;
in conclusion, 3D point cloud data of the longitudinal profile of the road surface on the aligned RGB images are obtained, ProVAL software is introduced, a semi-vehicle model is selected to calculate an IRI value, and the road surface unevenness grade value alpha is determined according to the grade division rule in the international standard ISO 8608r；
In this example, the ground identification information is output: alpha is alphar＝2rad/m,σ2＝3×10-4m2；
Step three, receiving the front road surface unevenness grade instruction determined in the step two, and establishing an active suspension model based on the front road surface preview information:
firstly, as shown in fig. 3, a suspension model dynamic equation obtained by applying newton's second law to front and rear suspensions is as follows:
Figure BDA0002584679530000091
wherein M is the sprung mass of the vehicle body, I is the moment of inertia of the vehicle body relative to the center of mass, and Mf、mrFor unsprung masses, x, of the front and rear wheelssIs the absolute displacement of the vehicle body, xf、xrRespectively, the unsprung mass displacement of the front and rear wheels, theta is the pitch angle, ksf、ksrFor the rigidity of the front and rear suspensions, cf、crIntrinsic damping coefficient, k, of front and rear suspensions, respectivelytf、ktrStiffness of front and rear tires, uf、urIs according to H∞Controlling the damping force generated by the strategy, wherein a and b are respectively the front and rear wheelbases, and L is the pre-aiming distance;
the state variables that define the suspension system are:
Figure BDA0002584679530000092
the disturbance is input as
Figure BDA0002584679530000093
Figure BDA0002584679530000094
Wherein w (t) is a front wheel; road surface input, w (t- τ) is rear wheel input, τ ═ a + b/v is the time delay between the front and rear wheels; control input is U ═ Uf,ur]T；
Rewriting the system equation into the form of a state equation:
X＝AX+BU+D1w(t)+D2w(t-τ)
Figure BDA0002584679530000095
Nvnumber of state variables modeled as 8
Figure BDA0002584679530000096
Figure BDA0002584679530000097
Figure BDA0002584679530000098
In summary, the road surface information-based front axle pre-aiming control only needs to input road surface unevenness information equivalent to a virtual front axle, input an actual front axle equivalent to a virtual rear axle, form new inter-axle pre-aiming control by the virtual front axle and measure the front road surface unevenness information input by the virtual front axle to control the action of the front suspension.
Step four, designing an active suspension H with multiple performance index constraints∞An optimal controller:
considering the system control targets as system sprung mass vertical acceleration, system pitch angle acceleration, front and rear suspension dynamic travel and front and rear wheel tire deformation, the system performance objective function can be determined as follows:
Figure BDA0002584679530000101
in the formula, T is control cycle time;
Figure BDA0002584679530000102
J3＝E[(zs+aθ-zf)2]；J4＝E[(zs-bθ-zr)2]；J5＝E[(zf-hf)2]；J6＝E[(zr-hr)2]；
Figure BDA0002584679530000103
Figure BDA0002584679530000104
is according to H∞Optimally controlling the generated suspension damping force;
the above formula is further rewritten as follows:
Figure BDA0002584679530000105
wherein, for performance index constraint, Q ═ Cyu TCyuIs a symmetric semi-positive definite matrix, F ═ Dyu TDyu、N＝Cyu TDyuIs a positive definite matrix, Cyu＝Cyu1Cyu2；
Figure BDA0002584679530000106
Figure BDA0002584679530000107
Figure BDA0002584679530000108
Using H∞Control, assuming a measurement vector psi of：
ψ＝Cmx+ξ
Wherein, CmA conversion matrix of state and measurement is adopted, and xi is a measurement error;
it is believed that the relative displacement and velocity between the sprung and unsprung masses can be H∞Optimal controller estimation, in order to minimize the performance indicator function, the system control input U is solved:
Figure BDA0002584679530000111
in the formula (I), the compound is shown in the specification,
Figure BDA0002584679530000112
for the best estimation vector of the system state, r (t) is a vector containing the preview information, including the pre-axial and wheelbase preview information, and the formula is as follows:
Figure BDA0002584679530000113
in the formula, tpFor preview time, Ca、CbControlling gain for feedback and feedforward; wherein C isa＝F-1(NT+BTS)；Ca＝-F- 1NT(ii) a S is a steady state solution obtained by the Riccati equation:
S(A-BF-1NT)+(A-BF-1NT)TS-S(BF-1BT--2DDT)S+(Q-NF-1NT)＝0
estimation of system state vector
Figure BDA0002584679530000114
Comprises the following steps:
Figure BDA0002584679530000115
wherein L is H∞Gain vector of optimal controller: l ═ PCm TR-1R is a positive definite matrix of measurement errors; p is the covariance matrix of the estimation error:
Figure BDA0002584679530000116
step five, obtaining H by using a multi-objective constraint optimization algorithm∞Weighting parameters p in an optimal controller1,ρ2,…,ρ8And the weight value is used for realizing the performance balance of various performance indexes (namely the mass acceleration of the spring, the pitching acceleration, the dynamic stroke of the front and rear suspensions, the adhesion force with reasonable damping force under different vehicle speeds and running speeds) of the suspension.
In this example, the vehicle parameters are selected as follows: the total mass of the vehicle body is as follows: m is 1200 kg; the unsprung masses of the front and rear suspensions are respectively: m isf＝75kg，mr80 kg; moment of inertia: i is 1800kg m2(ii) a Front and rear spring linear stiffness: k is a radical ofsf＝30kN/m,ksr30 kN/m; inherent damping coefficient of front and rear wheel suspensions: c. Cf＝400Ns/m,cr450 Ns/m; front and rear tire elastic stiffness: k is a radical oftf＝300kN/m,ktr300 kN/m; the wheelbase front and rear: a is 1.011m, b is 1.803 m;
in this example, the fixed constants of the weight parameters ρ are selected in accordance with the spring mass acceleration, the pitch acceleration and the R value1＝1，ρ2＝1,R＝10-3Obtaining the optimized value as follows: rho3＝31.623，ρ4＝98.958，ρ5＝5551.6，ρ6＝1224.1，ρ7＝1.64×10-6，ρ8＝2.11×10-8；
FIG. 4 is a vehicle acceleration transfer characteristic curve predicted by the pre-aiming control of the front road information; fig. 5 is a tire dynamic deformation transfer characteristic curve.
As can be seen from fig. 4 and 5, the active suspension pre-aiming control method based on the camera sensor road surface information recognition provided by the invention has an obvious improvement effect compared with the conventional suspension control method. Road surface pre-aiming identification is realized through a Kinect camera sensor, and road surface identification information and an active suspension H are combined∞The optimal controller is organically combined, so that the vehicle faces to an unknown road surfaceSuspension parameters are adjusted in advance with a low amplitude during interference, so that a time lag phenomenon in the parameter adjusting process is avoided, and meanwhile, the Kinect camera sensor with low cost is easy to implement.

Claims (7)

1. An active suspension pre-aiming control method based on camera sensor road surface identification is characterized by comprising the following steps:
acquiring front road surface preview information through a camera sensor;
secondly, performing data processing on the front road surface preview information to obtain a front road surface unevenness value, and determining a road surface unevenness grade according to an unevenness dividing criterion;
step three, receiving the front road surface unevenness grade instruction determined in the step two, and establishing an active suspension model based on front road surface preview information;
step four, designing an active suspension H with multiple performance index constraints∞An optimal controller;
step five, obtaining H by using a multi-objective constraint optimization algorithm∞And optimizing the weight value in the controller to realize the performance balance of multiple suspension performance indexes.
2. The active suspension pre-aiming control method based on the camera sensor road surface recognition is characterized in that in the first step, before the collection of the front road surface pre-aiming information, the camera sensor is calibrated and calibrated to ensure the accuracy of the collected road surface information.
3. The active suspension pre-aiming control method based on the camera sensor road surface recognition is characterized in that in the second step, the data processing of the front road surface pre-aiming information comprises the following processes:
performing noise reduction processing on an image acquired by a camera sensor by adopting a bilateral filter algorithm;
using an image splicing algorithm based on features to reconstruct a scene, and reducing overlapped contents in the same scene in the front and back continuous frame images: detecting key feature points of two RGB images of adjacent road parts by an SURF algorithm; then matching the feature points between the images by using a k-nearest neighbor algorithm, and removing abnormal values by using a RANSAC algorithm; and finally, estimating a homography transformation matrix between the two images according to the matched feature points, and splicing the continuous frame images by using homography transformation to obtain a new fusion image.
4. The active suspension pre-aiming control method based on camera sensor road surface recognition as claimed in claim 1, wherein in the second step, the calculation of the IRI value is performed after the image processing of the pre-aiming information of the front road surface collected by the camera sensor, so as to obtain the front road surface unevenness value, and the road surface unevenness grade is determined according to a preset formula, including the following processes:
transferring the depth data output by the camera sensor depth camera to an RGB image;
acquiring an aligned RGB image, importing the aligned RGB image into ProVAL software, and selecting a half car model to calculate an IRI value;
determining the grade value alpha of the road surface unevenness according to the grade division rule in the international standard ISO 8608r。
5. The active suspension pre-aiming control method based on camera sensor pavement recognition is characterized in that the step of transferring the depth data output by the camera sensor depth camera to an RGB image comprises the following steps:
firstly, the depth data acquired by the camera sensor is converted from image coordinates to world coordinates, and the calculation process is as follows:
Figure FDA0002584679520000021
Figure FDA0002584679520000022
ZIR＝ZD
in the formula (X)IR,YIR,ZIR) World coordinates of three-dimensional points on the camera sensor; (u)D,vD) Pixel coordinates of the depth image;
Figure FDA0002584679520000027
is the distortion center coordinate of the camera sensor plane;
Figure FDA0002584679520000028
is the focal length of the sensor portion of the IR camera; zDRepresenting depth values, obtained by a camera sensor; intrinsic parameters of remaining camera sensors
Figure FDA0002584679520000029
Obtaining through camera sensor calibration;
then, converting the depth coordinate information of the camera sensor into RGB image coordinates, wherein a calculation formula is as follows;
Figure FDA0002584679520000023
wherein (X)RGB,YRGB,ZRGB) World coordinates relative to the RGB camera sensor; (X)IR,YIR,ZIR) World coordinates relative to the IR camera sensor; the rotation matrix R and the translation matrix t are external parameters obtained through calibration and estimation of a camera sensor;
finally, the world coordinate (X) is determined according toRGB,YRGB,ZRGB) Pixel coordinates (u) mapped to RBG imageRGB,vRGB) The above step (1);
Figure FDA0002584679520000024
Figure FDA0002584679520000025
wherein the content of the first and second substances,
Figure FDA00025846795200000210
are all intrinsic parameters of the RGB camera sensor obtained by camera sensor calibration.
6. The active suspension pre-aiming control method based on the camera sensor road surface recognition is characterized in that the step three of establishing an active suspension model based on the front road surface pre-aiming information comprises the following processes:
the front suspension and the rear suspension adopt Newton's second law to obtain a suspension model dynamic equation as follows:
Figure FDA0002584679520000026
wherein M is the sprung mass of the vehicle body, I is the moment of inertia of the vehicle body relative to the center of mass, and Mf、mrFor unsprung masses, x, of the front and rear wheelssIs the absolute displacement of the vehicle body, xf、xrRespectively, the unsprung mass displacement of the front and rear wheels, theta is the pitch angle, ksf、ksrFor the rigidity of the front and rear suspensions, cf、crIntrinsic damping coefficient, k, of front and rear suspensions, respectivelytf、ktrStiffness of front and rear tires, uf、urIs according to H∞Controlling the damping force generated by the strategy, wherein a and b are respectively the front and rear wheelbases, and L is the pre-aiming distance;
the state variables that define the suspension system are:
Figure FDA0002584679520000037
the disturbance inputs are:
Figure FDA0002584679520000038
wherein w (t) is the front wheel input; w (t-tau) is the rear wheel input; τ ═ (a + b)/v is the time delay between the front and rear wheels;
control input is U ═ Uf,ur]T；
Rewriting the system equation into the form of a state equation:
X＝AX+BU+D1w(t)+D2w(t-τ)
Figure FDA0002584679520000031
Nvthe number of state variables of the model;
Figure FDA0002584679520000032
Figure FDA0002584679520000033
Figure FDA0002584679520000034
Figure FDA0002584679520000035
Figure FDA0002584679520000036
7. the active suspension pre-aiming control method based on camera sensor road surface recognition is characterized in that the step four is used for designing an active suspension H with multiple performance index constraints∞The optimal controller comprises the following processes:
considering system control targets as system sprung mass vertical acceleration, system pitch angle acceleration, front and rear suspension dynamic travel and front and rear wheel tire deformation, determining a system performance objective function as follows:
Figure FDA0002584679520000041
in the formula, T is control cycle time;
Figure FDA0002584679520000042
J3＝E[(zs+aθ-zf)2]；J4＝E[(zs-bθ-zr)2]；J5＝E[(zf-hf)2]；J6＝E[(zr-hr)2]；
Figure FDA0002584679520000043
Figure FDA0002584679520000044
Figure FDA0002584679520000045
is according to H∞Optimally controlling the generated suspension damping force;
the above formula is rewritten as follows:
Figure FDA0002584679520000046
in the formula, the performance index is restricted; q ═ Cyu TCyuIs a symmetric semi-positive definite matrix; f ═ Dyu TDyu、N＝Cyu TDyuIs a positive definite matrix; cyu＝Cyu1Cyu2；
Figure FDA0002584679520000047
Figure FDA0002584679520000048
Figure FDA0002584679520000049
According to H∞Control law, assuming that the measurement vector ψ is:
ψ＝Cmx+ξ
wherein, CmA conversion matrix of state and measurement is adopted, and xi is a measurement error;
to minimize the performance indicator function, the system control input U is solved for:
Figure FDA0002584679520000051
in the formula (I), the compound is shown in the specification,
Figure FDA0002584679520000055
for the best estimation vector of the system state, r (t) is a vector containing the preview information, including the pre-axial and wheelbase preview information, and the formula is as follows:
Figure FDA0002584679520000052
in the formula, tpIs the preview time; ca、CbControlling gain for feedback and feedforward; wherein, Ca＝F-1(NT+BTS)；Ca＝-F-1NT(ii) a S is a steady state solution obtained by the Riccati equation:
S(A-BF-1NT)+(A-BF-1NT)TS-S(BF-1BT--2DDT)S+(Q-NF-1NT)＝0
estimation of system state vector
Figure FDA0002584679520000056
Comprises the following steps:
Figure FDA0002584679520000053
wherein L is H∞Gain vector of optimal controller: l ═ PCm TR-1(ii) a R is a positive definite matrix of measurement errors; p is the covariance matrix of the estimation error:
Figure FDA0002584679520000054
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