A road condition prediction adaptive control system for electromagnetic dampers based on vehicle-road collaboration

Abstract

The present invention relates to the field of automobile chassis dynamics and active control technology, and in particular to an electromagnetic damper road condition prediction adaptive control system based on vehicle-road collaboration. An electromagnetic damper road condition prediction adaptive control system based on vehicle-road collaboration, comprising: a CDC sensing module, a CDC shock absorber, a CDC controller and a vehicle-road collaboration device; the CDC sensing module is communicatively connected with the CDC controller; the CDC controller comprises: a signal conditioning module, an MCU and a solenoid valve drive circuit. Based on the traditional CDC electromagnetic damping shock absorber, the present invention utilizes target detection technology based on multi-source heterogeneous information fusion and vehicle-road collaboration technology to predict the road conditions ahead, detect road speed bumps, puddles, damaged road surfaces, etc. in advance, and actively control the damping of the CDC electromagnetic damping shock absorber, thereby improving the vehicle's operational stability and comfort.

Description

Technical Field

[0001] The present invention relates to the field of automobile chassis dynamics and active control technology, and in particular to an electromagnetic damper road condition prediction adaptive control system based on vehicle-road collaboration. Background Art

[0002] CDC electromagnetic damping shock absorber is a new type of damping adjustable shock absorber improved based on the traditional hydraulic shock absorber. The shock absorber is filled with oil and has two inner and outer chambers. The oil can flow through the pores connecting the two chambers. When the wheel bumps, the piston in the shock absorber will move up and down in the sleeve, and the oil in the chamber will flow back and forth between the two chambers under the action of the reciprocating motion of the piston. The mutual friction between the oil molecules and the friction between the oil and the hole wall form resistance to the movement of the piston, converting the kinetic energy of the vibration into heat, which is dissipated into the air through the shock absorber casing, thus realizing the shock absorption process of the shock absorber.

[0003] The CDC system can control the size of the pore cross-sectional area between the chambers, and change the cross-sectional area of ​​the connecting part between the two chambers through the electronically controlled solenoid valve. When the flow rate is constant, the size of the cross-sectional area is inversely proportional to the resistance of the fluid, which changes the resistance of the oil in the chambers, thereby changing the damping of the shock absorber. The core components of the CDC continuous shock absorption control system are composed of a central control unit, a CDC shock absorber, a body acceleration sensor, a body height sensor, a wheel speed sensor, and a CDC control valve. The control principle is that the CDC control system determines the driving state of the vehicle based on the data of the body acceleration sensor, body height sensor, wheel speed sensor, and lateral acceleration sensor on the vehicle, and the central control unit ECU performs calculations, and then the ECU issues corresponding instructions to the CDC control valve on the shock absorber to control the valve opening to provide damping that adapts to the current state.

[0004] The disadvantage is that the adjustment is only made after the vehicle passes an obstacle and generates vertical vibration, which affects the comfort of the driver and passengers. Summary of the invention

[0005] The purpose of the present invention is to provide an electromagnetic damper road condition prediction adaptive control system based on vehicle-road collaboration, which is used to solve the problem of the prior art: adjustment is only performed after the vehicle passes an obstacle and generates vertical vibration, which affects the comfort of the driver and passengers.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A road condition prediction adaptive control system for an electromagnetic damper based on vehicle-road collaboration, the system comprising:

[0008] CDC sensing module, CDC shock absorber, CDC controller and vehicle-road cooperative equipment;

[0009] The CDC sensing module is communicatively connected with the CDC controller;

[0010] The CDC controller includes: a signal conditioning module, an MCU and a solenoid valve driving circuit.

[0011] Further preferably, the CDC perception module includes at least one of the following:

[0012] Vehicle-mounted cameras, vehicle-mounted millimeter-wave radars, and vehicle-mounted lidars.

[0013] Further preferably, the CDC shock absorber is an adjustable damping shock absorber with a linear solenoid valve.

[0014] Further preferably, the signal conditioning module is used to collect the voltage signal of the vehicle height sensor, the voltage signal of the vehicle acceleration sensor, the pulse signal of the wheel speed sensor and the signal input by each sensor in the CDC sensing module;

[0015] The MCU is internally provided with:

[0016] A multi-source information fusion algorithm is used to process the data collected by the CDC perception module to obtain fused images and point cloud data; the data processing includes: raw data filtering, image binarization processing, time synchronization, and spatial coordinate transformation;

[0017] The complex yolo algorithm is used to detect the image and point cloud data once to obtain the detection result 1;

[0018] The clustering algorithm is used to perform secondary detection on the image and point cloud data to obtain detection result 2;

[0019] The judgment module determines whether there is an obstacle target in front of the road according to the detection results 1 and 2, and determines the type, size and position of the obstacle target;

[0020] The solenoid valve driving circuit is used to control the duty cycle of the solenoid valve according to the calculation result of the MCU to adjust the damping force of the shock absorber.

[0021] Further preferably, the solenoid valve driving circuit is used to control the solenoid valve duty cycle according to the calculation result of the MCU, and the CDC shock absorber solenoid valve duty cycle is controlled by the H∞ controller so that the vehicle can output the vertical acceleration. The specific method steps are:

[0022] The vehicle dynamics model and CDC shock absorber are uniformly expressed in augmented matrix form, and the state space expression of the entire vehicle system can be obtained as follows:

[0023]

[0024] A, B, C are the state matrices of the suspension system; u(t) is the duty cycle of the solenoid valve, and ω(t) is the impact force from the road surface;

[0025] Citing Schur's lemma, for a given pairing matrix:

[0026]

[0027] S11∈R r×r , then the following three conditions are equivalent:

[0028] 1.S<0

[0029] 2.S 11 <0,

[0030] 3.S 22 <0,

[0031] Assuming the state feedback control law is u(t) = -kx, the corresponding state space equation can be written as:

[0032]

[0033] k is the state feedback control law, ω(t) is the impact disturbance from the road surface, z=(βω r z) T , D1 and D2 are zero matrices, and the following Riccati equation needs to be introduced to obtain the control law k:

[0034]

[0035] If the Riccati equation has a positive solution P, then the system is asymptotically stable and there exists a full-state feedback controller u(t) = -kx, so that the H∞ norm of the closed-loop transfer function from the disturbance input to the regulated output satisfies;

[0036] ||T wz (s)|| ∞ =||C1[sI-(A+B2K)] -1 B1||<γ

[0037] Where: γ is a positive number set in advance, H∞ control law

[0038] The design of the state H∞ feedback controller is transformed into solving the Riccati equation, which is transformed into the following linear matrix inequality problem according to Schur's lemma:

[0039]

[0040] Solving the above matrix inequality, there exists a feasible solution W and P, and the H∞ controller k of the system can be obtained.

[0041] Further preferably, the vehicle-road cooperative device includes at least one of the following:

[0042] RSU, OBU, millimeter wave radar, lidar, camera.

[0043] The present invention has at least the following beneficial effects:

[0044] Based on the traditional CDC electromagnetic damping shock absorber, the present invention utilizes target detection technology and vehicle-road collaborative technology based on multi-source heterogeneous information fusion to predict the road conditions ahead and detect road speed bumps, puddles, damaged road surfaces, etc. in advance, so as to actively control the damping of the CDC electromagnetic damping shock absorber, thereby effectively improving the vehicle's operating stability and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0046] Figure 1 It is a schematic diagram of the system of the present invention;

[0047] Figure 2 This is a schematic diagram of the time synchronization between the camera and the lidar;

[0048] Figure 3 This is the camera pinhole imaging model diagram;

[0049] Figure 4 This is the camera intrinsic calibration diagram. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] See also Figure 1 The present invention is a road condition prediction adaptive control system of an electromagnetic damper based on vehicle-road collaboration.

[0052] include:

[0053] It consists of four parts: CDC sensing module, CDC shock absorber, CDC controller and vehicle-road cooperative equipment.

[0054] (1) CDC perception module

[0055] The CDC sensing module includes an on-board camera, an on-board millimeter-wave radar, and an on-board laser radar. Each sensor communicates with the CDC controller via Ethernet, and the data collected by each sensor is sent to the CDC controller respectively;

[0056] (2) CDC shock absorber

[0057] The CDC shock absorber is an adjustable damping shock absorber with a linear solenoid valve. The damping force is changed by adjusting the duty cycle of the linear solenoid valve, thereby changing the shock absorber oil flow connecting the inner and outer chambers of the shock absorber.

[0058] (3) CDC controller

[0059] The CDC controller consists of three parts: signal conditioning module, MCU and solenoid valve drive circuit, specifically:

[0060] A. The signal conditioning module collects the voltage signal of the vehicle acceleration sensor, the voltage signal of the vehicle height sensor, the pulse signal of the wheel speed sensor, and the signal input by the CDC sensing module via Ethernet;

[0061] B. Based on the multi-source information fusion technology, MCU performs data filtering, image binarization, time synchronization, spatial coordinate transformation and other multi-source heterogeneous data fusion on the data collected by the CDC perception module, and unifies the data of various sensors into the vehicle body coordinate system; then, based on the complex yolo algorithm, the image and point cloud data are detected respectively, and the clustering algorithm is used to detect the target of the millimeter wave radar data, and the detection results of the two are judged to determine whether there are obstacles such as speed bumps and potholes in front of the road, and to determine the type, size and position of the obstacle target.

[0062] Specifically, it includes the following:

[0063] 3.1 Time Synchronization

[0064] Time synchronization is an existing method. Figure 2 Briefly explain: Set the MCU system time node as the benchmark time standard, and solve the lidar data frame time node and camera data frame time node based on this time node. The solution process is as follows: Figure 2 If the difference between the radar data frame time node and the camera data frame time node is within the set threshold of 0.001s, the multi-source heterogeneous sensor time synchronization data stream is obtained. The time synchronization method for other sensors is similar. (The computing industrial computer system in the figure is the MCU of this application)

[0065] 3.2 Camera Calibration

[0066] (1) Camera model establishment

[0067] See also Figure 3 The process represented by the camera pinhole imaging model is the process of the camera mapping the coordinate points in the three-dimensional space to the two-dimensional image plane. This process can be expressed by a simplified geometric model.

[0068] In the camera pinhole model, O′-x′-y′ is the physical imaging plane, Oxyz is the camera coordinate system, and O w -x w -y w -z w is the world coordinate system. Assume that any target point P(x1, y1, z1) in the world coordinate system is P′(x1′, y1′, z1′) in the image coordinate system, and f is the focal length of the camera. According to the characteristics of visible light propagating along a straight line and the principle of similar triangles, it can be simplified to:

[0069]

[0070] Where: f represents the focal length, z1 represents the coordinate in the z-axis direction of the world coordinate system, x1 represents the coordinate in the x-axis direction of the world coordinate system, y1 represents the coordinate in the y-axis direction of the world coordinate system, x1′ is the coordinate in the x-axis direction of the image coordinate system, and y1′ is the coordinate in the y-axis direction of the image coordinate system.

[0071] The ultimate goal of camera imaging is to obtain pixels, which requires quantization and sampling of pixels on the physical imaging plane. For the process of converting physical light into image pixels, assume that a pixel plane on the physical imaging plane is Ouv. The pixel coordinates P′ of point P on the pixel plane are [u, v] T The origin of the pixel plane o′ is located in the upper left corner of the image, the u axis is parallel to the x axis to the right, and the v axis is parallel to the y axis downward. The difference between the pixel coordinate system and the physical imaging is a translation of the origin and a scaling. Assume that the pixel coordinate is scaled α times on the u axis and β times on the v axis, and the intersection of the optical axis centerline and the image plane is recorded as (u0, v0). Then, the coordinates of P′ are the same as the pixel coordinates [u, v] T The relationship is:

[0072]

[0073] Substitute Formula 1 into Formula 2 and express it in matrix form:

[0074]

[0075] Where: f is the focal length, α is the scaling factor on the u-axis of the image coordinate system, β is the scaling factor on the v-axis of the image coordinate system, the units of u0 and v0 are pixels, [x1 / z1, y1 / z1, 1] T is the normalized camera physical coordinate, T M It is the camera intrinsic parameter matrix composed of intermediate quantities.

[0076] The functional relationship between the camera coordinate system and the world coordinate system is as shown in Formula 4

[0077]

[0078] Where: [x1, y1, z1] is the coordinate of a point in the camera coordinate system, [x w ,y w ,z w ] is the coordinate of the point in the world coordinate system, R is the rotation matrix to be solved, and P is the translation vector obtained by the solution.

[0079] The process of projecting physical light from the camera lens into an image will produce distortion. The shape of the lens will cause distortion in the propagation of light. Mechanical assembly makes it impossible for the lens and the imaging plane to be completely parallel, which will also cause distortion when the position of the physical light projected through the lens to the physical imaging plane changes. The distortion of this process can be expressed and corrected using quadratic terms and high-order polynomial functions related to the center distance. The radial distortion function is as follows:

[0080]

[0081] Where: x, y are the horizontal and vertical coordinates of the uncorrected point, respectively. c and c They are the horizontal and vertical coordinates of the corrected points, and all of them are coordinate points on the normalized plane.

[0082] For the central area of ​​the image with less distortion, k1 is the main factor affecting the distortion correction, while k2 mainly affects the edge area with greater distortion. For tangential distortion, the two parameters P1 and P2 can be used for correction, as shown in the following formula:

[0083]

[0084] By jointly solving Equation 5 and Equation 6, for any point P (x, y, z) in the camera coordinate system, the correct position of the point on the pixel plane can be found through the five distortion coefficients.

[0085]

[0086] Where: r represents the distance between the camera coordinate point P and the origin of the coordinate system.

[0087] Table 2-2 Camera calibration process

[0088]

[0089]

[0090] According to this calibration process, by collecting the corner point selection results and 20-30 frames of original data sets, the position relationship between the camera and the calibration plate is shown in the figure below. Figure 4 .

[0091] 3.3 Joint calibration of camera and lidar

[0092] To obtain the coordinate transformation matrix of the visual, radar and vehicle coordinate systems, a multi-source sensor joint calibration method must be used. The data of the visual and laser sensors can only be converted after completing the spatial registration operation. Assume that the radar coordinate system is O L -X L Y L Z L , vehicle body coordinate system O V -X V Y V Z V , camera coordinate system O C -X C Y C Z C .

[0093] (1) Lidar coordinate system O L -X L Y L Z L :X L is the front position of the vehicle, Z L is the vertical position of the vehicle itself, Y L It is the left direction of the origin of the laser radar coordinate system.

[0094] (2) Vehicle camera coordinate system O C -X C Y C Z C :According to the camera installation situation, the front view angle of the vehicle is taken as Z C The vertical direction is Y C axis, and define the vehicle's left position as X C。

[0095] (3) Vehicle coordinate system O V -X V Y V Z V :Through the calibration based on the coordinate system position and the intelligent vehicle on-board sensor installation positioning function, the projection vertical direction of the vehicle on the level surface is Z V , horizontal direction is XV Axis, horizontal to the left is Y V axis.

[0096] In order to detect the calibration plate, the installation height of the laser radar and camera needs to be adjusted to a certain extent, and then the optimization target is determined, and the iterative optimization or closed-form solution is completed. After the calibration is completed, the point cloud data is filtered. The planes of the calibration plate and the radar point cloud are fitted, and the distance θ from the origin of the radar coordinate system and the normal vector of the fitting plane to the model fitting plane is calculated respectively. L ,s L In addition, it is assumed that the feature vector on the calibration plane relative to the camera coordinate system and the length from the origin of the camera coordinate system to the calibration plate are θ c ,s c .

[0097]

[0098] Where: θ L is the normal vector of the calibration plate plane fitted to the radar point cloud, s L is the distance from the origin of the radar coordinate system to the calibration plate; θ c is the normal vector of the calibration plate plane in the image, s c is the distance from the origin of the image coordinate system to the plane of the calibration plate.

[0099] For the same space, the distance between a point and the calibration plate plane observed in the radar coordinate system and the camera coordinate system should be consistent. When the point is used as the origin of the radar coordinate system, the objective function is defined as:

[0100]

[0101] Where: n is the number of data pairs, S L,i is the observed distance from the radar origin to the calibration plate plane in the radar coordinate system, S c,i -θ T c,i t is the observed distance from the origin of the camera coordinate system to the calibration plate plane. And the result of the function T0 is obtained:

[0102]

[0103] The closed-form solution to this function is:

[0104]

[0105] Similarly, for any vector in space, we need to observe and transfer it from the laser radar coordinate system to the physical coordinate system of the camera, and make the observation results consistent to ensure the maximum inner product. When selecting the plane normal vector, if the objective function is determined, the result of the function must be greater than R0:

[0106]

[0107] Where: R c L is the rotation matrix from radar coordinate system to camera coordinate system, θ c,i is the normal vector of the plane in the camera coordinate system, θ L,i is the plane normal vector in the radar coordinate system.

[0108] It can be seen that the rotation matrix can satisfy the conditions of the orthogonal matrix under the premise of the maximum inner product. Suppose the rotation matrix R satisfies R T R = I, the objective function is defined as:

[0109]

[0110] The closed-form solution of this function is:

[0111] R l =VU T (14)

[0112] Where: V, U is θ L θ T c =USV T Singular value decomposition matrix.

[0113] Select a point from the closed-form solution obtained by the rotation matrix and translation vector in the radar and project it into the camera coordinate system. By doing this, the observation result can be made the same as the result observed directly in the camera coordinate system. Thus, the optimization function is constructed:

[0114]

[0115] Where: x (j) r,i ∈R 3×1 is any point on the calibration plate plane, m(i) is the number of points selected in the i-th fitting plane, R is the rotation matrix, t is the translation vector, θ T c,i is the normal vector of the plane in the camera coordinate system, s c,i is the distance from the origin of the camera coordinate system to the plane. Taking the result of the first step as the initial value, the iterative optimization design is realized through the step reduction method.

[0116] The collected offline data is marked, and then the spatial transformation matrix is ​​calculated. Then the spatial point cloud is added to the image space coordinate system and switched to the vehicle space coordinate system. The conversion formula is as follows:

[0117]

[0118] Where: PV , P c is the coordinates in the vehicle coordinates and the camera coordinates; R V L ,t V L is the rotation matrix and time translation vector between the radar coordinate system and the vehicle coordinate system; R c L ,t c L It is the rotation matrix and time translation vector between the radar coordinate system and the camera coordinate system.

[0119] Thus, the transformation matrix from the radar coordinate system to the camera coordinate system is obtained. Finally, after the matrix is ​​projected to the camera image coordinate system, the spatial registration of multi-sensor fusion can be realized.

[0120] 3.4 Speed ​​bump and pothole detection

[0121] The target types and locations belong to existing mature target detection applications. The complex yolo algorithm is used in this article. The basic principle is to use labeled speed bumps, potholes and other target images or point cloud datasets to train a deep learning complex yolo network and then perform target detection.

[0122] Distance judgment uses similar triangles to calculate the distance from the camera to a known object or target. Similar triangles are: assuming that the target or object with a width of W is at a distance of D from the camera, take a picture of the object with the camera and measure the pixel width P of the object. The formula for the focal length of the camera is: F = (PxD) / W. When the camera continues to move closer to or farther away from the object or target, the distance of the object from the camera can be calculated using similar triangles: D' = (WxF) / P. In this way, the detection of obstacles is achieved and the type, size and position of obstacles can be determined.

[0123] C. The solenoid valve drive circuit controls the solenoid valve duty cycle according to the MCU calculation results to adjust the shock absorber damping force.

[0124] The basic principle of the algorithm is: when the vehicle is driving, sudden impacts from speed bumps, potholes, etc. on the road are very likely to cause the vehicle to have a large vertical acceleration or even lose control stability. By designing an H∞ controller to control the duty cycle of the CDC shock absorber solenoid valve, the vehicle can output vertical acceleration. The basic steps of the algorithm are:

[0125] ① The vehicle dynamics model and CDC shock absorber are uniformly expressed in the form of augmented matrix, and the state space expression of the entire vehicle system can be obtained as follows:

[0126]

[0127] A, B, and C are the state matrices of the suspension system; u(t) is the duty cycle of the solenoid valve, and ω(t) is the impact force from the road surface, which is related to the type, size, position, and current vehicle speed of the obstacle and is stored in the CDC controller in the form of a spectrum.

[0128] Citing Schur's lemma, for a given pairing matrix:

[0129]

[0130] S11∈R r×r , then the following three conditions are equivalent:

[0131] 1.S<0

[0132] 2.S 11 <0,

[0133] 3.S 22 <0,

[0134] Assuming the state feedback control law is u(t) = -kx, the corresponding state space equation can be written as:

[0135]

[0136] k is the state feedback control law, ω(t) is the impact disturbance from the road surface, the disturbance is uncertain, but the range is bounded. z=(βω r z) T , D1 and D2 are zero matrices. To obtain the control law k, the following Riccati equation needs to be introduced:

[0137]

[0138] If the Riccati equation has a positive solution P, then the system is asymptotically stable and there exists a full-state feedback controller u(t) = -kx such that the H∞ norm of the closed-loop transfer function from the disturbance input to the regulated output satisfies

[0139] ||T wz (s)|| ∞ =||C1[sI-(A+B2K)] -1 B1||<γ

[0140] Where: γ is a positive number set in advance, H∞ control law

[0141] The design of the state H∞ feedback controller is transformed into solving the Riccati equation, which is transformed into the following linear matrix inequality problem according to Schur's lemma.

[0142]

[0143] Solving the above matrix inequality, there exists a feasible solution W and P, and the H∞ controller k of the system can be obtained.

[0144] (4) Vehicle-Road Collaborative Equipment

[0145] Vehicle-road collaboration equipment includes roadside units (RSUs) and onboard communication devices (OBUs); roadside sensing devices such as millimeter-wave radars, lidars, and cameras;

[0146] The roadside perception device can send obstacle information outside the blind spot detected by the vehicle's sensors to the vehicle's OBU through the RSU device via LTE-V / 5G signals. The information received by the OBU is sent to the CDC controller via Ethernet to improve the CDC road condition prediction range.

[0147] From the above, we can know that:

[0148] Based on the traditional CDC electromagnetic damping shock absorber, the present invention utilizes target detection technology based on multi-source heterogeneous information fusion and vehicle-road collaborative technology to predict the road conditions ahead and detect road speed bumps, puddles, damaged road surfaces, etc. in advance, so as to actively control the damping of the CDC electromagnetic damping shock absorber, thereby improving the vehicle's operating stability and comfort.

[0149] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A road condition prediction adaptive control system for electromagnetic dampers based on vehicle-road collaboration, characterized in that: The system comprises: CDC sensing module, CDC shock absorber, CDC controller and vehicle-road cooperative equipment; The CDC sensing module is communicatively connected with the CDC controller; The CDC controller includes: a signal conditioning module, an MCU and a solenoid valve driving circuit; The signal conditioning module is used to collect the voltage signal of the vehicle body acceleration sensor, the voltage signal of the vehicle body height sensor, the pulse signal of the wheel speed sensor and the signal input by each sensor in the CDC sensing module; The MCU is internally provided with: A multi-source information fusion algorithm is used to process the data collected by the CDC perception module to obtain fused images and point cloud data; the data processing includes: raw data filtering, image binarization processing, time synchronization, and spatial coordinate transformation; The complex yolo algorithm is used to detect the image and point cloud data once to obtain the detection result 1; The clustering algorithm is used to perform secondary detection on the image and point cloud data to obtain detection result 2; The judgment module determines whether there is an obstacle target in front of the road according to the detection results 1 and 2, and determines the type, size and position of the obstacle target; The solenoid valve driving circuit is used to control the duty cycle of the solenoid valve according to the calculation result of the MCU to adjust the damping force of the shock absorber; The solenoid valve driving circuit is used to control the solenoid valve duty cycle according to the calculation result of the MCU, and the CDC shock absorber solenoid valve duty cycle is controlled by the H∞ controller so that the vehicle can output the vertical acceleration. The specific method steps are as follows: The vehicle dynamics model and CDC shock absorber are uniformly expressed in augmented matrix form, and the state space expression of the entire vehicle system can be obtained as follows: u(t) is the duty cycle of the solenoid valve, ω(t) is the impact force from the road surface; Citing Schur's lemma, for a given pairing matrix: S11∈ Rr×r , then the following three conditions are equivalent: 1.S＜0 2.S 11 ＜0, 3.S 22 ＜0, Assuming the state feedback control law is u(t) = -kx, the corresponding state space equation can be written as: k is the state feedback control law, ω(t) is the impact disturbance from the road surface, z=( β ω r z) T , D1 and D2 are zero matrices, and the following Riccati equation needs to be introduced to obtain the control law k: If the Riccati equation has a positive solution P, then the system is asymptotically stable and there exists a full-state feedback controller u(t) = -kx, so that the H∞ norm of the closed-loop transfer function from the disturbance input to the regulated output satisfies; ||T wz (s)|| ∞ =||C1[sI-(A1+B2K)] -1 B1||<γ Where: γ is a positive number set in advance, H∞ control law The design of the state H∞ feedback controller is transformed into solving the Riccati equation, which is transformed into the following linear matrix inequality problem according to Schur's lemma: Solving the above matrix inequality, there exists a feasible solution W and P, and the H∞ controller k of the system can be obtained.

2. The electromagnetic damper road condition prediction adaptive control system based on vehicle-road collaboration according to claim 1 is characterized in that: The CDC perception module includes at least one of the following: Vehicle-mounted cameras, vehicle-mounted millimeter-wave radars, and vehicle-mounted lidars.

3. The electromagnetic damper road condition prediction adaptive control system based on vehicle-road collaboration according to claim 1 is characterized in that: The CDC shock absorber is an adjustable damping shock absorber with a linear solenoid valve.

4. The electromagnetic damper road condition prediction adaptive control system based on vehicle-road collaboration according to claim 1 is characterized in that: The vehicle-road cooperative device includes at least one of the following: RSU, OBU, millimeter wave radar, lidar, camera.

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