Over-the-horizon active suspension preview control method based on high-precision positioning road surface state recognition

Abstract

The application discloses a super-range active suspension preview control method based on high-precision positioning road state recognition, a combined positioning system based on low-orbit satellite positioning, high-precision map and inertial navigation system is used to position a vehicle and obtain position information of the vehicle; road state recognition is carried out based on the position information of the vehicle, the road state includes road grade and pulse excitation; signal interaction between vehicles is established to realize interactive transmission of road state, suspension parameters and vehicle positioning information between the ego vehicle and surrounding vehicles; a super-range preview control strategy is formulated according to the received road state, suspension parameters and vehicle positioning information; if there is no pulse excitation in the road section, a fuzzy control is used for the vehicle suspension controller; if there is pulse excitation, an LQG control containing preview information is used for the vehicle suspension controller; the application can effectively solve the problems of limited sensing range in the preview control process and insufficient computing capacity of the vehicle-mounted electronic control unit.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle dynamics and control technology, and relates to a super-range active suspension preview control method based on high-precision positioning road state recognition. BACKGROUND

[0002] With the rapid development of the automobile industry, people's requirements for vehicle driving comfort and control stability are increasing, especially for freight vehicles, ensuring the integrity of the goods is the main purpose in the transportation process. The existing freight vehicles are mostly based on traditional passive suspensions, but due to their inherent characteristics, it is difficult to meet the smoothness requirements in different road conditions at the same time, so active suspension technology emerges as the times require. It can adjust the suspension parameters in real time according to the vehicle driving state and improve the driving experience.

[0003] However, the active suspension based on feedback control has a certain hysteresis. When facing complex and changeable road conditions, especially some sudden potholes, bumps or continuous bumping road surfaces, it is impossible to make accurate and effective adjustments in advance, which greatly reduces the vehicle driving comfort and even affects the control safety. At this time, the vehicle needs to know the road information in front in advance to ensure the real-time performance of the suspension control effect. However, most of the existing active suspension preview control is limited by the performance of the sensor technology, and it is difficult to accurately obtain the road attribute information in front of the vehicle, which limits the perception ability of the vehicle. On the other hand, with the increase of the data amount collected by cameras, laser radars and other sensors and the complexity of advanced application control strategies, a large amount of computing and storage resources are needed, but the storage and computing capacity of the vehicle-mounted electronic control unit is limited, which easily causes a bottleneck in road perception and data processing, and it is difficult to meet the demand of high-quality control of the active suspension system.

[0004] Therefore, in order to solve the problems of limited perception range and insufficient computing capacity of the vehicle-mounted electronic control unit in the preview control process, it is urgent to realize the super-range road state perception and efficient preview control of the active suspension. SUMMARY

[0005] In order to solve the problems in the prior art, the present application provides a super-range active suspension preview control method based on high-precision positioning road state recognition, which can effectively solve the problems of limited perception range and insufficient computing capacity of the vehicle-mounted electronic control unit in the preview control process.

[0006] The technical scheme adopted by the present application is as follows:

[0007] The super-range active suspension preview control method based on high-precision positioning road state recognition comprises the following steps:

[0008] Step 1: Based on the combined positioning system of low-orbit satellite positioning, high-precision map and inertial navigation system, the vehicle is positioned to obtain the position information of the vehicle;

[0009] Step 2: Based on the position information of the vehicle, the road surface state is identified, including the road surface grade and the pulse excitation; and the signal interaction between the vehicles is established to realize the interaction transmission between the ego vehicle and the surrounding vehicles about the road surface state, the suspension parameter and the vehicle positioning information;

[0010] Step 3: According to the received road surface state, suspension parameter and vehicle positioning information, the over-the-horizon preview control strategy is formulated; if there is no pulse excitation in the road section, the vehicle suspension controller adopts fuzzy control; if there is pulse excitation, the vehicle suspension controller adopts LQG control containing preview information.

[0011] Further, in the process of positioning the vehicle, the inertial navigation system obtains the vehicle mileage, the high-precision map provides the candidate road according to the vehicle mileage, and the low-orbit satellite provides the distance from the vehicle to the candidate road and the included angle between the vehicle heading and the direction of the candidate road, and the real positioning information of the vehicle is obtained in the map through fusion calculation.

[0012] Further, the method for determining the real positioning information of the vehicle by the fusion calculation is: after obtaining the distance d i from the vehicle to the candidate road and the included angle ξ i between the vehicle heading and the direction of the candidate road, the distribution probability corresponding to each candidate road is calculated by using Gaussian distribution respectively; the candidate road corresponding to the maximum distribution probability is selected, and the vehicle road and position are positioned by using the weighted matching value.

[0013] Further, the road surface grade identification method is:

[0014] ①Obtain the suspension response signal, including: collecting the vehicle vertical acceleration signal by the acceleration sensor, solving the suspension deflection based on the vehicle dynamics equation, and obtaining the vehicle body speed by the low-orbit satellite;

[0015] ②Suspension response signal preprocessing: the vehicle speed is divided into several grades, and the road surface grade is classified according to the road conditions, so as to traverse the driving conditions of the vehicle in different road surface grades under each vehicle speed grade and obtain the vehicle response;

[0016] ③Signal decomposition: the suspension deflection and the sprung mass acceleration signal in the vehicle response signal are decomposed by variational mode decomposition, and the characteristic components are extracted respectively; and the characteristic components which can obviously contain the road surface grade information are selected, so that the characteristic components can obviously contain the road surface information;

[0017] (4) Signal feature calculation: the screened feature components containing road information are reconstructed to form an initial feature matrix, and the singular value entropy of the matrix is calculated;

[0018] (5) Road grade classification: the corresponding relationship between the singular value entropy and the road grade is obtained by automatically establishing fuzzy rules through machine learning, and the road grade recognition is realized according to the response signal.

[0019] Further, the method for confirming the pulse excitation is:

[0020] The position of the pulse excitation generated by the high-precision map is confirmed, the position information is uploaded to the cloud for map correction after passing through the position of the front vehicle, and the position information is also sent to the rear vehicle simultaneously, and the vehicle ECU of the rear vehicle calculates the time for the vehicle to reach the pulse excitation position according to the vehicle speed information provided by the low-orbit satellite and the self-positioning information.

[0021] Further, the method for establishing signal interaction between vehicles is:

[0022] Based on the positioning information provided by the combined positioning system, the base station is searched in the area between the two vehicles based on the determination of the paired vehicle position and the distance between the two vehicles, and the information is transmitted by the base station.

[0023] Further, the fuzzy control process is as follows:

[0024] 1. The input of the controller is set as the difference e between the suspension deflection in the suspension system and the ideal value and the change rate ec;

[0025] 2. The basic domain and quantization factor of the input signal and output signal are selected respectively, the output signal can select the output upper limit of the suspension actuator as its domain range, the input signal domain selects the response range of the suspension deflection under the C-class road grade at a vehicle speed of 50 km / h, and the quantization factor value K under different road grades at each vehicle speed is calculated based on this standard i ;

[0026] 3. Design membership function: the input variables are e and ec, both of which adopt 7 language fuzzy subsets {NB (negative big), NM (negative medium), NS (negative small), Z (zero), PS (positive small), PM (positive medium), PB (positive big)}, and the membership functions are all trimf-shaped;

[0027] 4. Design fuzzy rules:

[0028] (4) When the values of e and ec are large and the directions are the same, the error of the system is large and the error is still increasing,

[0029] The system needs to provide a large control amount to reduce its error and ensure the stability of the system;

[0030] (5) When the values of e and ec are large and in opposite directions, it indicates that the error of the system is large but is decreasing, so the system needs to ensure that the control amount is not too large and that the system will not produce large oscillations,

[0031] The output of the control force is stable;

[0032] (6) When the values of e and ec are small, the system starts to approach stability, and when the input values are in the same direction, a very small output force should be selected, and when the input values are in opposite directions, the output force can be kept at zero to prevent the system from producing oscillations.

[0033] Further, the LQG control method is as follows:

[0034] The vehicle dynamics equation is constructed and rewritten as a state equation, denoted as:

[0035]

[0036] y = Cx + Du

[0037] Let x1 = Z uf , x2 = Z sf , x3 = Z ur , x4 = Z sr , so

[0038] The state variables are:

[0039] The output variables are:

[0040] The control input is:

[0041] The noise input is: w = [x 01 x 02 ];

[0042] wherein A, B, C, D, and E are coefficient matrices, x 01 is the road input passed by the front wheels, x 02 is the road input passed by the rear wheels; Z uf , Z ur are the vertical displacements of the front and rear tires of the vehicle; Z sf , Z sr are the vertical displacements of the front and rear bodies of the vehicle; F f , F r are the output forces of the front and rear active suspensions;

[0043] The body acceleration and the front suspension deflection are used to evaluate the driver's ride comfort, the rear suspension vertical acceleration and the rear suspension deflection are used to evaluate the cargo integrity, the front and rear tire dynamic load are used to evaluate the road friendliness, and the performance index functional of the active suspension optimal controller is defined as:

[0044]

[0045] Wherein, q1, q2, q3, q4, q5, q6, r1 and r2 are the weighted coefficients of each index; is the vertical acceleration of the vehicle mass center, and t is the time variable of the time domain signal.

[0046] The output variable is introduced, and the index functional is further converted into:

[0047]

[0048] In the formula, X is the state variable of the system, Q is the state variable weight coefficient matrix, N is the cross term weight coefficient matrix; R is the control variable weight coefficient matrix; and U is the control vector of the system.

[0049] The optimal control vector of the system is:

[0050] U=-KX=-R -1 (N T +B T L)X

[0051] In the formula, K is the feedback gain matrix of the LQG control, B is the coefficient matrix of the system control, and L is the solution of the Riccati equation.

[0052] After the additional state vector is combined with the system state space equation, the system state equation with the additional state vector and the preview control is combined:

[0053]

[0054] In the formula, is the first order derivative of the state variable of the system, is the first order derivative of the additional state vector, A is the coefficient matrix of the system, F is the coefficient matrix of the additional state vector, A η , D η , E η , B η are the coefficient matrices of the system after the additional state vector, and are respectively a0 and a1 are coefficients, and are respectively denoted as coefficients coefficients

[0055] The optimal control gain matrix combined with the axle distance preview information is solved by solving the Riccati equation of the system state equation combined with the previous performance index functional equation J, and the axle distance preview optimal control force is solved:

[0056] U p (t)=K p [X(t)+η(t)]

[0057] Wherein, K p is the optimal control gain matrix combined with the axle distance preview information, X(t) is the system state variable in the time domain, and η(t) is the additional state vector in the time domain.

[0058] An electronic device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements the above-mentioned long-range active suspension preview control method based on high-precision positioning road surface state recognition when executing the computer program.

[0059] A computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned long-range active suspension preview control method based on high-precision positioning road surface state recognition.

[0060] The beneficial effects of the present application are:

[0061] (1) In the present application, low-orbit satellite positioning, high-precision map and inertial navigation system fusion technology are used to realize real-time accurate positioning of the vehicle, so that the positioning accuracy reaches centimeter level or even higher. Not only the detailed road surface information in front of the vehicle is obtained, but also the positions of the front and rear vehicles are accurately positioned. At the same time, the accurate position of the vehicle is fed back to the roadbed base station in real time, so that the roadbed base station can transmit signals according to the distribution of vehicles, optimize the allocation of communication resources, and improve the communication efficiency. In addition, combined with the driving trajectory and time sequence information of the vehicle, the pulse excitation position information generated by the front and rear vehicles in the driving process due to the factors such as deceleration zone and potholes is analyzed, which provides key data support for subsequent road surface state recognition and suspension control strategy making.

[0062] (2)The application establishes signal interaction between vehicles, on the one hand, the vehicle sends its own road surface level, suspension parameters, self positioning and other information to the rear vehicle in real time, and receives similar information of adjacent vehicles, so as to more comprehensively understand the road surface feedback state under local road conditions; on the other hand, it maintains close communication with road end facilities (such as intelligent traffic base station, roadside unit, etc.), receives more detailed road surface monitoring data and traffic flow information from the road end sensor, corrects its own state, and uploads the data of the road surface it has passed to upgrade the map of the region by the road end. With the help of the road end facility, the above data is transmitted and shared in all vehicles in the region in real time, and the actual working state of the suspension is fed back to the control center, so as to optimize and adjust the control strategy in real time, form a closed loop control, ensure the stability and reliability of the system, and complete the intelligent monitoring and control management of vehicles and roads on the whole road section.

[0063] (3) The application obtains the feedback information of the road surface passed by the front vehicle through communication with the front vehicle, including detailed data such as road surface level and pulse excitation position. On the one hand, the information is transmitted to the rear suspension system in real time, so that the rear vehicle can know the road surface condition in front in advance, so as to adjust the suspension parameters in time and improve the driving comfort; on the other hand, the real-time collected data are used to dynamically update and enrich the high-precision map, making up for the information lag or missing problem that may exist in the map. Especially in special environments such as tunnels, due to the limitation of satellite signals, the accuracy of high-precision map information may be reduced, at this time, the road surface information collected by the front vehicle by the road surface state recognition module is particularly important, which can accurately correct the road surface information in the tunnel, such as identifying newly appeared potholes, water accumulation areas and the like in the tunnel, to ensure the safe and smooth driving of vehicles in complex special environments. For the road surface state related information obtained from different sources, an adaptive weighted fusion algorithm is adopted, the weight is dynamically allocated according to the real-time accuracy, reliability and relevance of the information to the current vehicle driving scene, and the accuracy of road surface state recognition is improved.

[0064] (4) The application uses vehicle sensors and high-precision map fusion to realize the beyond-visual-range recognition of the front road surface state while reducing the cost of the vehicle, to provide sufficient road surface information for the active suspension control in advance, overcome the problems of limited perception range and insufficient computing power of the vehicle-mounted electronic control unit in the preview control process, significantly improve the adaptability of the vehicle under various road conditions, and provide reliable data for the control of the subsequent vehicles passing through the road section.

[0065] (5)The present application considers the integrity of the goods, adjusts the control strategy according to the actual working condition of the suspension and the road condition in front, and considers the random road and the pulse excitation, the influence of the vehicle chassis under the two excitations is very different, separate processing can better use the information obtained by the preview system, and the preview control effect is better. Ensure the control effect and real-time performance of the active suspension system, keep the whole system in the best working state at all times, prolong the service life of the system, reduce the maintenance cost, and at the same time ensure the efficiency of goods transportation. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a combined positioning flow chart.

[0067] Figure 2 is a vehicle-base station data interaction system.

[0068] Figure 3 is a 1 / 2 vehicle dynamics model schematic diagram.

[0069] Figure 4 is a road surface grade identification method flow chart.

[0070] Figure 5 is a super-horizon preview signal transmission schematic diagram.

[0071] Figure 6 is a super-horizon preview control strategy schematic diagram. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0073] In combination with the drawings Figures 1-5 , the super-horizon active suspension preview control method based on high-precision positioning road surface state identification proposed by the present application has the following specific steps:

[0074] Step 1: The vehicle is positioned by a combined positioning system of low-orbit satellite positioning, high-precision map and inertial navigation system, which can be used to obtain the specific position of the front vehicle, rear vehicle or surrounding vehicle. Specifically, in the combined positioning system, the inertial navigation system provides the vehicle mileage, the high-precision map provides the candidate road according to the vehicle mileage calculation result, and the low-orbit satellite provides the coordinate point, vehicle heading, vehicle speed and other information; the real positioning information of the vehicle can be obtained in the map through the fusion calculation of the three; and the output of the inertial navigation system can be corrected by using the obtained real positioning information to eliminate errors. The combined positioning flow chart is shown in Figure 1 , and the specific steps are as follows:

[0075] (1) The specific process of determining the vehicle mileage by the inertial navigation system is as follows:

[0076] After the vehicle starts, the relative position of the vehicle relative to the starting point is reckoned according to the dead reckoning (DR) by using the attitude, heading and mileage information of travel. The signal output by the odometer is generally the distance increment of the vehicle in a short period of time, and the output of the odometer can be regarded as the instantaneous speed. The DR using the data obtained by the odometer can be described in the following manner:

[0077]

[0078] wherein x0 and y0 are respectively set as the initial coordinates of the x-axis and the y-axis, θ0 is the initial heading angle output by the inertial navigation system of the vehicle at this time, x k is the coordinate position of the vehicle in the x-axis direction after passing through k displacement segments, y k is the coordinate position of the vehicle in the y-axis direction after passing through k displacement segments; Δθ j is the difference of the vehicle heading angle in the adjacent two displacement segments, θ k is the vehicle heading angle after k displacement segments, wherein Δθ k-1 is the difference of the vehicle heading angle from the k-1 displacement segment to the k displacement segment; S i is the displacement of the vehicle in the i-th time segment (in a period of time). S i is calculated by the pulse signal obtained by the non-steering wheel of the vehicle:

[0079]

[0080] wherein ΔN i is the increment of the odometer output in the i-th sampling period, R is the resolution of the odometer, and D is the diameter of the wheel.

[0081] However, the error of the dead reckoning will gradually increase with the accumulation of the distance, in order to reduce the maximum error and the average error of the dead reckoning and improve the positioning accuracy of the vehicle, the high-precision map and the low-orbit satellite are introduced to correct the position information of the vehicle.

[0082] (2) A plurality of candidate roads of the position of the vehicle are provided by the high-precision map, in order to determine the candidate road range of the area where the vehicle is located, the number and length of the to-be-matched roads are reduced as much as possible while ensuring that the correct path is contained, so as to ensure the matching accuracy and efficiency, all paths in the surrounding range are divided according to the vehicle position obtained by the dead reckoning, the roads with larger errors are eliminated in combination with the heading angle output by the dead reckoning, and finally a plurality of candidate roads are obtained.

[0083] (3) The distance d iand the angle ξ between the vehicle's heading and the candidate road direction i The specific process is as follows:

[0084] The coordinates M of two consecutive points on the road are provided by low-Earth orbit satellites. i1 (x i1 ,y i1 ) and N i1 (x i2 ,y i2 Let the vehicle's current position be P(x0,y0), and the distance from the vehicle to the candidate road can be expressed as:

[0085]

[0086] Vehicle heading β P With candidate road direction The included angle can be expressed as:

[0087]

[0088] (4) The information obtained from low-orbit satellite positioning, high-precision map and inertial navigation system is fused and calculated to obtain the vehicle’s real positioning information in the map; the obtained positioning information can also be used to correct the output of the inertial navigation system itself.

[0089] The method for determining the vehicle's true location information through the aforementioned fusion calculation is as follows: after obtaining the distance d from the vehicle to the candidate road... i and the angle ξ between the vehicle's heading and the candidate road direction i Then, the probability distribution p(d) for each candidate road is calculated using the Gaussian distribution. i |r i i ) and p(ξ i |r i i Since a larger distribution probability p represents a higher degree of matching, the candidate road with the highest distribution probability is selected and denoted as the candidate road with the highest degree of matching.

[0090] Based on the selected candidate roads, the weighted matching value Θ = α1·p(d) is finally used. i |r i i )+α2·p(ξ i |r i i The vehicle's location is determined by its road and position. Here, α1 and α2 are the distance and angle weighting coefficients, respectively.

[0091] Step 2: Based on the position information of the vehicle, the road surface state recognition is performed, the road surface state includes the road surface grade and the pulse excitation; and the signal interaction between the vehicles is established to realize the interaction transmission between the ego vehicle and the surrounding vehicles about the road surface state, the suspension parameter and the vehicle positioning information.

[0092] In the embodiment, in order to reduce the cost of road surface state recognition, the high-precision map, the low-orbit satellite and the vehicle dynamics response inverse measurement method are fused to perform the road surface state recognition, and the road surface state mainly includes the road surface grade and the pulse excitation.

[0093] In the embodiment, the high-precision map, the low-orbit satellite and the vehicle dynamics response inverse measurement method are combined to perform the road surface state recognition. Figure 4 As shown in the figure, the specific process of the road surface grade recognition method of the application is as follows:

[0094] ①Obtaining the suspension response signal: the vertical acceleration signal of the vehicle is collected through the acceleration sensor, the suspension deflection is solved based on the vehicle dynamics equation, and the vehicle body speed and other signals are obtained by the low-orbit satellite.

[0095] In the embodiment, taking a four-wheel truck as an example, a 1 / 2 vehicle model is established as shown in the figure, so the vehicle dynamics equation is constructed, and the vehicle dynamics equation includes the vertical motion dynamics differential equation at the vehicle body mass center, the vehicle body pitch motion dynamics differential equation and the vertical motion dynamics differential equation of the un-sprung mass (front and rear), which are as follows respectively: Figure 3

[0096] The vertical motion dynamics differential equation at the vehicle body mass center is as follows:

[0097]

[0098] The vehicle body pitch motion dynamics differential equation is as follows:

[0099]

[0100] The front wheel vertical motion dynamics differential equation is as follows:

[0101]

[0102] The rear wheel vertical motion dynamics differential equation is as follows:

[0103]

[0104] Wherein, M s is the sprung mass of the vehicle, M uf is the front wheel mass of the vehicle; M ur is the rear wheel mass of the vehicle; θ is the vehicle body pitch angle; is the vehicle body pitch angle acceleration; I is the vehicle body rotational inertia; a and b are the distances from the front and rear axles of the vehicle to the vehicle mass center; K tf , K​tr K is the stiffness of the front and rear tires, ignoring damping f K r K is the stiffness of the front and rear suspensions f K r C is the damping of the front and rear suspensions f C r F is the output force of the front and rear active suspension actuators s Z is the vertical displacement of the vehicle mass center Z is the vertical acceleration of the vehicle mass center sf Z sr Z is the vertical displacement of the vehicle body Z is the vertical velocity of the vehicle body uf Z ur Z is the vertical displacement of the vehicle tires Z is the vertical velocity of the vehicle tires Z is the vertical acceleration of the vehicle tires f Z r Z is the front and rear road excitation

[0105] 2. Suspension response signal preprocessing: the vehicle speed is divided into 10 km / h, and the vehicle speed of 0-100 km / h is divided into 10 grades, and the road level is divided into A-E according to the road conditions, so as to traverse the actual situation of the vehicle driving on different road levels at each grade of vehicle speed.

[0106] 3. Signal decomposition: the suspension deflection and sprung mass acceleration signals are decomposed by variational modal decomposition, and the characteristic components (BIMF components) are extracted. There are many types of characteristic components, such as mean, root mean square, root amplitude, variance, peak value and standard deviation; but part of the statistical characteristics is not sensitive to the time-frequency domain information caused by different road levels, at this time, the statistical characteristics of this part are removed, and the difference characteristics that are most obvious due to different road levels are retained; therefore, the screened BIMF components obviously contain road level information.

[0107] 4. Signal feature calculation: the screened BIMF components containing road information are reconstructed to form an initial feature matrix, and the singular value entropy of the matrix is calculated.

[0108] 5. Road level classification: the calculated singular value entropy result is imported into ANFIS, and the fuzzy rules are automatically established by machine learning to obtain the corresponding relationship between singular value entropy and road level. Finally, the road level can be identified according to the response signal, and the final road level is output. At the same time, the information is also uploaded to the cloud to correct the map.

[0109] In this embodiment, the confirmation method for pulse excitation such as potholes and speed bumps is as follows:

[0110] According to the high-precision map, the position of the pulse excitation with obvious features such as the speed bump can be confirmed, and the front vehicle transmits the position information to the cloud for updating the map after passing the position, and the information is also transmitted to the rear vehicle, and the vehicle ECU of the rear vehicle calculates the time when the vehicle reaches the position of the pulse excitation according to the vehicle speed information provided by the low-orbit satellite and the positioning information of the vehicle.

[0111] In the embodiment, the vehicles transmit signals by relying on the communication system, transmit the road level, suspension parameters, and the own positioning information to the rear vehicle in real time, and receive similar information of the adjacent vehicle, so that the road feedback state under the local road condition is known more comprehensively, and the suspension control parameters are matched by the rear vehicle.

[0112] The existing data communication mode between the coupled vehicles generally adopts the WI-FI or Bluetooth mode, and the communication has poor penetration and short effective distance.

[0113] In view of the deficiency, based on the positioning information provided by the combined positioning system in step 1, the position of the coupled vehicles and the distance between the two vehicles are determined, a suitable base station, for example, a base station close to the two vehicles, is found between the two vehicles, and the base station is used for information transmission. The whole process is as shown in Figure 2 In the high-precision map, the front vehicle passes a certain position, obtains the information of the position, and sends a communication request to the rear vehicle through the base station, the rear vehicle responds, and then the two vehicles establish a TCP connection, the front vehicle sends the position coordinate information, the suspension response parameter, and the data packet of the passing road information to the rear vehicle at this time, and the vehicle group also establishes a TCP connection with the cloud server to update the real-time road section information, and sends the data packet to other vehicles following behind to complete the intelligent monitoring and control management of the vehicles and the road on the whole road section.

[0114] Step 3: over-the-horizon preview control strategy: as shown in Figure 6 As shown in the figure, the control force output by the suspension actuator corresponding to the two cases of random road and pulse excitation is considered respectively, under the random road, the suspension actuator still matches the fuzzy control quantization factor according to the road level of the front vehicle, and outputs the fuzzy control force, and under the pulse excitation, the output of the suspension actuator becomes the LQG control force. According to the received road state, suspension parameters, and vehicle positioning information, the over-the-horizon preview control strategy is formulated.

[0115] Firstly, it should be confirmed that the control target of the active suspension system is to ensure the vehicle ride comfort, and the handling stability requirement is met, at this time, the vehicle should ensure that:

[0116] ① The output saturation of the active suspension system actuator exists, and the control force generated by the algorithm should be less than the output limit of the actuator:

[0117] F≤F max (10)

[0118] ②Due to the mechanical structure of the suspension itself, the suspension dynamic deflection should be limited within a certain range, in order to ensure that most vehicles are suitable, try to design according to the minimum dynamic deflection of most vehicles:

[0119] Z s -Z t ≤Z max (11)

[0120] ③During the driving process of the vehicle, the dynamic load between the tire and the ground at any time should be less than the static load when the vehicle is stationary, to ensure the handling stability of the vehicle, which can be:

[0121] K t (Z t -Z g )≤(M u +M t )g (12)

[0122] On this basis, the look-ahead preview control of the vehicle is designed, and the whole vehicle suspension control process is shown in Figure 5 When the vehicle passes through the road, the front vehicle provides the road grade information that will pass through the road, and the vehicle suspension controller switches the quantization factor parameter in time according to the front road information, in order to reduce the working pressure of the controller.

[0123] If there is no pulse excitation in the road passed by the front vehicle, the vehicle suspension controller adopts fuzzy control, that is, a set of fuzzy rules is designed to deal with the whole control process, and only the quantization factor before the fuzzy control input changes with the road information.

[0124] The fuzzy control process is as follows:

[0125] ②In order to better follow the effect of road grade identification algorithm, the input of the controller is set as the difference e and the change rate ec between the ideal value of the suspension dynamic deflection in the suspension system.

[0126] ③Select the basic domain and quantization factor of the input and output signals. The output signal can limit the output of the actuator to its domain range, and the input signal domain selects the response range of the suspension dynamic deflection under the C-class road grade at a speed of 50km / h, and the quantization factor value K i is calculated under different road grades at different speeds.

[0127] ④Design of membership function: the input variables are e and ec, both of which adopt 7 language fuzzy subsets {NB (negative big), NM (negative medium), NS (negative small), Z (zero), PS (positive small), PM (positive medium), PB (positive big)} To ensure that the output of the algorithm can be smoothly transitioned, the membership functions are all trimf-shaped.

[0128] (5) Design of fuzzy rules: According to the relationship between input and output, combined with the demand of the system to the fuzzy controller, the fuzzy reasoning rules are designed, so the basic principles of designing fuzzy control rules are:

[0129] (1) When the values of e and ec are large and the directions are the same, the error of the system is large and the error is still increasing, which requires the system to provide a large control amount to reduce its error and ensure the stability of the system;

[0130] (2) When the values of e and ec are large and the directions are opposite, this state indicates that the error of the system is large but the error is decreasing, which requires the system to try to ensure that the control amount is not too large, to ensure that the system will not produce large oscillation, and to ensure the stability of the control force output;

[0131] (3) When the values of e and ec are small, the system starts to approach stability, the input value is in the same direction, a small output force should be selected, and the output force can be kept to zero when the direction is opposite, to prevent the system from producing oscillation.

[0132] The fuzzy control rule table can be obtained:

[0133]

[0134]

[0135] If there is a pulse excitation, the LQG control method containing preview information is as follows:

[0136] When the vehicle passes through the pulse excitation, in order to better cope with such instantaneous large impact and ensure the integrity of the goods, the LQG algorithm containing preview information can realize more accurate force tracking control. The specific process is shown in Figure 6 .

[0137] The calculation process of the LQG algorithm containing preview information is as follows:

[0138] Rewrite formulas (5)-(9) as state equations:

[0139]

[0140] Where x is the state variable, is the first derivative of the state variable, y is the output variable, u is the control input, w is the noise input, and A, B, E, C and D are the coefficient matrices of each term.

[0141] Let x1 = Z uf , x2 = Z sf , x3 = Z ur , x4 = Z sr , the road input x 01provided by the front vehicle, since the input at the rear wheel has only a time lag compared to the front wheel ( wheelbase / vehicle speed), so the input of the rear wheel can be considered as x 02 = x 01 (t-τ).

[0142] State variables are:

[0143]

[0144] Output variables are:

[0145]

[0146] Control inputs are:

[0147] u = [F f F r ]

[0148] Noise inputs are:

[0149] w = [x 01 x 02 ]

[0150] Let

[0151] Coefficient matrix:

[0152]

[0153] The performance index functional of the optimal controller of the active suspension is defined as:

[0154]

[0155] where q1, q2, q3, q4, q5, q6, r1 and r2 are weighting coefficients of each index. Then the index functional can be transformed as:

[0156]

[0157] where q is a vector composed of q1, q2, q3, q4, q5 and q6, denoted as q = diag(q1, q2, q3, q4, q5, q6); r is a vector composed of r1 and r2, denoted as r = diag(r1, r2); Y is an output vector matrix, U is the optimal control vector of the system, and t is the time variable of the time domain signal.

[0158] The output variable is introduced, and the index functional is further transformed as:

[0159]

[0160] In the formula, X is the state variable of the system, Q is the state variable weight coefficient matrix, and Q=C T qC;N is the cross term weight coefficient matrix; R is the control variable weight coefficient matrix, and R=r+D T qD;N=C T qD

[0161] At this time, the optimal control vector of the system is:

[0162] U=-KX=-R -1 (N T +B T L)X (17)

[0163] In the formula, K is the feedback gain matrix of LQG control, B is the coefficient matrix of system control, and L is the solution of Riccati equation:

[0164] LA+A T L-LBR -1 B T L+Q=0 (18)

[0165] Wherein, A is the state coefficient matrix of the system.

[0166] The relationship between the road inputs at the front and rear wheels is represented by the Laplace transfer function and its 2nd order Pade approximation:

[0167]

[0168] In the formula, s is the Laplace space complex variable, the coefficient The coefficient The coefficient a2=1;

[0169] Take an additional state vector η=[η1η2], and convert the above formula (19) into a state equation form:

[0170]

[0171] In the formula,

[0172] After combining formula (20) with the system state space equation (13), the system state equation combined with preview control and with an additional state vector is obtained as:

[0173]

[0174] In the formula, the matrix The matrix

[0175] The optimal control gain matrix combined with the previous performance index functional equation J is solved to the Riccati equation, and the optimal control gain matrix combined with the wheelbase preview information is obtained, so that the optimal control force of the wheelbase preview can be obtained:

[0176] U p (t)=K p [X(t)+η(t)] (22)

[0177] Wherein, K p is the optimal control gain matrix combined with the wheelbase preview information.

[0178] Through the above implementation mode, the road surface state under the over-the-horizon can be recognized, and the active suspension can be precisely previewed and controlled, so that the driving comfort of the vehicle under the complex road condition is effectively improved.

[0179] The above examples are only used to illustrate the design idea and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and to implement it, and the protection scope of the present application is not limited to the above examples. Therefore, any equivalent changes or modifications made according to the principles and design ideas disclosed by the present application are within the protection scope of the present application.

Claims

1. A method for over-the-horizon active suspension preview control based on high-precision positioning road surface state recognition, characterized in that, The method comprises the following steps: Step 1: positioning the vehicle based on a combined positioning system of low-orbit satellite positioning, high-precision map and inertial navigation system, and obtaining position information of the vehicle; Step 2: identifying road surface state based on the position information of the vehicle, wherein the road surface state comprises road surface grade and pulse excitation; and establishing signal interaction between vehicles to realize the interaction transmission between the vehicle and surrounding vehicles about road surface state, suspension parameters and vehicle positioning information; and confirming the method of pulse excitation: confirming the position of the road surface generating the pulse excitation through the high-precision map, uploading the position information to the cloud to modify the map by the preceding vehicle passing through the position, and synchronously sending the position information to the following vehicle, and the vehicle ECU of the following vehicle calculating the time of the vehicle reaching the pulse excitation position according to the vehicle speed information provided by the low-orbit satellite and the self-positioning information; Step 3: formulating the over-the-horizon preview control strategy according to the received road surface state, suspension parameters and vehicle positioning information; if there is no pulse excitation in the road section, the vehicle suspension controller adopts fuzzy control; if there is pulse excitation, the vehicle suspension controller adopts LQG control containing preview information.

2. The super-vision active suspension preview control method based on high-precision positioning road surface state recognition according to claim 1, characterized in that, In the process of positioning the vehicle, the inertial navigation system obtains the vehicle mileage, the high-precision map provides the candidate road according to the vehicle mileage, the low-orbit satellite provides the distance of the vehicle to the candidate road and the included angle between the vehicle heading and the direction of the candidate road, and the real positioning information of the vehicle is obtained in the map through fusion calculation.

3. The super-vision active suspension preview control method based on high-precision positioning road surface state recognition according to claim 2, characterized in that, The method for fusing the calculation of the real positioning information of the vehicle is: obtaining the distance from the vehicle to the candidate road and the included angle between the vehicle heading and the direction of the candidate road After that, the distribution probability corresponding to each candidate road is calculated by using the Gaussian distribution respectively; the candidate road corresponding to the maximum distribution probability is selected, and the weighted matching value is used to locate the road and position of the vehicle.

4. The super-vision active suspension preview control method based on high-precision positioning road surface state recognition according to claim 1, characterized in that, The road surface grade identification method is as follows: ① obtaining the suspension response signal, comprising: collecting the vehicle vertical acceleration signal through the acceleration sensor, solving the suspension dynamic deflection based on the vehicle dynamics equation, and obtaining the vehicle body speed by the low-orbit satellite; ② suspension response signal preprocessing: classifying the vehicle speed, and classifying the road surface grade according to the road condition, so as to traverse the driving conditions of the vehicle in different road surface grades under each vehicle speed gear, and obtain the vehicle response; ③ signal decomposition: performing variational modal decomposition on the suspension dynamic deflection and the sprung mass acceleration signal in the vehicle response signal to extract feature components; and selecting feature components that can obviously contain road surface grade information, so that the feature components can obviously contain road surface information; ④ signal feature calculation: reconstructing the feature components containing road surface information selected to form an initial feature matrix, and calculating the singular value entropy of the matrix; ⑤ road surface grade classification: establishing fuzzy rules automatically by machine learning to obtain the corresponding relationship between the singular value entropy and the road surface grade, and realizing the road surface grade identification according to the response signal.

5. The super-vision active suspension preview control method based on high-precision positioning road surface state recognition according to claim 1, characterized in that, The method for establishing signal interaction between vehicles is as follows: Based on the positioning information provided by the combined positioning system, the base station is searched in the area between the two vehicles on the basis of determining the positions of the two vehicles and the distance between the two vehicles, and the information is transmitted by the base station.

6. The super-vision active suspension preview control method based on high-precision positioning road surface state recognition according to claim 1, characterized in that, The fuzzy control process is as follows: S1, the input of the controller is the difference e between the suspension dynamic deflection in the suspension system and the ideal value and the change rate ec; S2, select the basic domain and quantization factor of input signal and output signal respectively, the output signal can select the output upper limit of the suspension actuator as its domain range, the input signal domain selects the response range of suspension deflection under C level road surface at 50 km / h vehicle speed, and the quantization factor value under different level road surfaces at each vehicle speed is calculated based on this standard ; S3, design membership function: input variable is e and ec, both of which adopt 7 language fuzzy subsets {NB (negative big), NM (negative medium), NS (negative small), Z (zero), PS (positive small), PM (positive medium), PB (positive big)}, membership function is trimf; S4, design fuzzy rule: (1) when e and ec value is large and the same direction, the system error is large and the error is still increasing, the system needs to provide a large control amount to reduce its error and ensure the stability of the system; (2) when e and ec value is large and the opposite direction, it indicates that the system error is large but the error is decreasing, the system needs to ensure that the control amount is not too large, to ensure that the system will not produce large oscillation, to ensure the stability of the output of the control force; (3) when e and ec value is small, the system begins to approach to stable, the input value is the same direction, the output force should be selected to be very small, the opposite direction, the output force can be kept to be zero, to prevent the system from producing oscillation.

7. The super-vision active suspension preview control method based on high-precision positioning road surface state recognition according to claim 1, characterized in that, LQG control method is as follows: The vehicle dynamics equation is constructed and rewritten as a state equation, denoted as: ; Let , , , ; The state variable is therefore: ; The output variables are: ; the control inputs are: ; the noise inputs are: ; wherein, A , B , C , D , E are coefficient matrices, is the road input to the front wheels, is the road input to the rear wheels; , are the vertical displacements of the front and rear tires of the vehicle; , are the vertical displacements of the front and rear bodies of the vehicle; , are the output forces of the front and rear active suspensions. The vehicle body acceleration and the front suspension dynamic deflection are used to evaluate the driver ride comfort, the rear suspension vertical acceleration and the rear suspension dynamic deflection are used to evaluate the cargo integrity, the front and rear tire dynamic load is used to evaluate the road friendliness, and the performance index functional of the active suspension optimal controller is defined as: ; wherein, and are the weighting coefficients of each index; is the vehicle mass center vertical acceleration, and t is the time variable of the time domain signal. The output variable is introduced, and the index functional is further transformed as: ; wherein, X is the state variable of the system, Q is the state variable weight coefficient matrix, N is the cross term weight coefficient matrix; R is the control variable weight coefficient matrix; U is the control vector of the system; The optimal control vector of the system is: ; In the formula, K is the feedback gain matrix of LQG control, B is the coefficient matrix of system control, and L is the solution of Riccati equation. The system state space equation combined with the additional state vector is as follows: ; wherein, is the first derivative of the state variable of the system, is the first derivative of the additional state vector, A is the coefficient matrix of the system, and F is the coefficient matrix of the additional state vector, are the coefficient matrices of the system after the additional state vector, and are respectively ; are coefficients, and are respectively denoted as coefficients and coefficients ; The above system state equation is solved in combination with the Riccati equation of the previous performance index functional equation J, and the optimal control gain matrix combined with the wheelbase preview information, so that the wheelbase preview optimal control force can be obtained: ; wherein, is the optimal control gain matrix combined with the wheelbase preview information, is the system state variable in the time domain, is the additional state vector in the time domain.

8. An electronic device, comprising: The computer readable storage medium has a computer program stored thereon, and the computer program is executed by the processor to realize the super-long-distance active suspension preview control method based on high-precision positioning road surface state recognition.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium has a computer program stored thereon, and the computer program is executed by the processor to realize the super-long-distance active suspension preview control method based on high-precision positioning road surface state recognition.

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