Active suspension preview control method for a vehicle

Abstract

The application provides a kind of active suspension preview control method of vehicle, by obtaining the road surface point cloud data of target road point, longitudinal position information and the vehicle state information of vehicle;According to road surface point cloud data and longitudinal position information, determine the road surface unevenness function, and determine the multi-scale road surface feature vector according to road surface unevenness function;Road event identification is carried out based on multi-scale road surface feature vector, and road event information is obtained;According to road event information and vehicle state information, determine the vehicle active suspension actuation power expected value;According to vehicle active suspension actuation power expected value, determine the actual control force of vehicle suspension, and the active suspension preview control of vehicle is carried out based on actual control force of vehicle suspension.The technical scheme of the application, based on the road surface point cloud data of target road point, longitudinal position information and the vehicle state information of vehicle, etc., flexibly determines the active suspension preview control of vehicle suspension actual control force to vehicle.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to an active suspension anti-aiming control method for vehicles. Background Technology

[0002] The suspension is a crucial component of a vehicle's driving system, playing a vital role in mitigating road impacts and damping its own vibrations, thus affecting the vehicle's ride comfort and handling. The road is the primary excitation source for the suspension. With the development of autonomous driving technology, sensor-based road perception systems are being applied to intelligent vehicle control, enhancing the dynamic characteristics of the suspension. Therefore, by using visual sensors to perceive the road surface elevation in front of the vehicle as a reference for adjusting suspension system parameters, a time-varying suspension controller can be designed. This is crucial for improving the vehicle's ride comfort, handling, and handling under specific operating conditions.

[0003] Currently, in existing technologies, traditional active suspension controllers mostly employ global optimal algorithms, which have the following characteristics: (1) Steady characteristics. In order to reduce the computational load of the system, traditional controllers are mostly steady systems, that is, their system parameters are fixed values; (2) Global characteristics. The coefficient parameters of traditional controllers are mostly optimized based on the global range to improve the vibration characteristics of the suspension in the global range. Based on the above characteristics, suspension systems using traditional controllers can improve the ride comfort and other characteristics of the vehicle in the global range, but they cannot switch the control coefficients of the suspension in a timely manner according to road conditions, and cannot make the suspension controller always remain optimal.

[0004] Therefore, there is an urgent need for an active suspension anti-aiming control method for vehicles to solve the technical problem that traditional active suspension controllers cannot switch control coefficients according to road conditions. Summary of the Invention

[0005] This invention provides an active suspension anti-aiming control method for vehicles, addressing the shortcomings of existing technologies where traditional controllers optimize coefficients based on a global scope to improve suspension vibration characteristics across the entire range. While traditional controller-based suspension systems can improve ride comfort and other characteristics globally, they suffer from limitations in adapting to road conditions and maintaining optimal control. This invention enables active suspension anti-aiming control by flexibly determining the actual suspension control force based on target road point cloud data, longitudinal position information, and vehicle state information.

[0006] This invention provides an active suspension anti-aiming control method for vehicles, comprising the following steps.

[0007] Acquire surface point cloud data, longitudinal location information, and vehicle status information of the target road point; The road surface roughness function is determined based on the road surface point cloud data and longitudinal position information, and then the multi-scale road surface feature vector is determined based on the road surface roughness function. Road event identification is performed based on multi-scale road surface feature vectors to obtain road event information; Determine the expected value of the vehicle's active suspension power based on road event information and vehicle status information; The actual control force of the vehicle suspension is determined based on the expected value of the active suspension action, and the active suspension pre-aiming control of the vehicle is performed based on the actual control force of the vehicle suspension.

[0008] According to the present invention, an active suspension anti-aiming control method for a vehicle determines a road surface roughness function based on road surface point cloud data and longitudinal position information, including: The road surface point cloud data is processed by ground segmentation, outlier removal, attitude compensation, and coordinate unification to obtain a road surface point cloud set; the road surface point cloud set includes at least one elevation value. The road surface roughness function is determined based on the elevation values ​​and longitudinal position information in the road surface point cloud set.

[0009] According to the present invention, an active suspension anti-aiming control method for a vehicle determines a multi-scale road feature vector based on a road surface roughness function, including: The discrete Fourier transform of the road surface roughness function is used to obtain the road surface spatial frequency domain power spectral density. Obtain the spectral parameters of the target road points; Multi-scale pavement feature vectors are determined based on the pavement spatial frequency domain power spectral density and spectral parameters.

[0010] According to the present invention, an active suspension anti-aiming control method for a vehicle determines a multi-scale road feature vector based on the road surface spatial frequency domain power spectral density and spectral parameters, including: The signal energy in the frequency band region is determined based on the power spectral density in the spatial frequency domain of the road surface; wherein, the signal energy in the frequency band region includes the signal energy in the low-frequency region, the signal energy in the mid-frequency region, and the signal energy in the high-frequency region; The proportions of low-frequency energy, mid-frequency energy, and high-frequency energy are determined based on the signal energy in the low-frequency region, mid-frequency region, and high-frequency region. The multi-scale road feature vector is determined based on spectral parameters, signal energy in the low-frequency region, signal energy in the mid-frequency region, signal energy in the high-frequency region, the proportion of low-frequency energy, the proportion of mid-frequency energy, and the proportion of high-frequency energy.

[0011] According to the present invention, an active suspension anti-aiming control method for a vehicle includes road event information, which includes event confidence and event intensity; determining the expected value of the vehicle's active suspension action based on the road event information and vehicle state information includes: The vehicle control mode is determined based on the event confidence level and event intensity. Determine the set of weighting coefficients based on the vehicle control mode; Determine the optimal control gain matrix based on the set of weighting coefficients; The expected value of the vehicle's active suspension dynamics is determined based on the optimal control gain matrix and vehicle state information.

[0012] According to the present invention, an active suspension anti-aiming control method for a vehicle includes a vehicle control mode comprising an impact mode and a continuous road surface mode; the method determines the vehicle control mode based on event confidence and event intensity, including: If the event confidence level is greater than or equal to the confidence threshold and the event intensity is greater than or equal to the intensity threshold, the vehicle control mode is determined to be the impact mode. If the event confidence level is less than the confidence level threshold and / or the event intensity is less than the intensity threshold, the vehicle control mode is determined to be the continuous road mode.

[0013] According to the active suspension anti-aiming control method for a vehicle provided by the present invention, the road event information further includes road event categories; the weight coefficient set includes a performance index weight coefficient set or a shock suppression weight coefficient set; determining the weight coefficient set according to the vehicle control mode includes: When the vehicle control mode is determined to be impact mode, an impact suppression weight coefficient set is generated based on the road event category and event intensity. When the vehicle control mode is determined to be a continuous road mode, the set of performance index weight coefficients is determined based on the multi-scale road feature vector.

[0014] According to the active suspension anti-aiming control method for a vehicle provided by the present invention, after determining the set of weight coefficients based on the vehicle control mode, the method further includes: Perform stability constraint verification on the set of weight coefficients; If the stability constraint verification of the set of weight coefficients is passed, continue to the step of determining the optimal control gain matrix based on the set of weight coefficients; If the stability constraint verification of the set of weight coefficients fails, the feasible region of the set of weight coefficients is corrected to obtain the target set of weight coefficients. The optimal control gain matrix is ​​determined based on the target weight coefficient set, and the process continues to execute the step of determining the expected value of the vehicle's active suspension based on the optimal control gain matrix and vehicle state information.

[0015] According to the present invention, an active suspension anti-aiming control method for a vehicle determines the actual control force of the vehicle suspension based on the expected value of the active suspension action force, including: The target suspension dynamics expectation value is determined based on the vehicle's active suspension dynamics expectation value and the expectation value threshold. Determine the vehicle suspension vibration speed based on vehicle status information; The actual control force of the vehicle suspension is determined based on the target suspension dynamic expectation value and the vehicle suspension vibration speed.

[0016] According to the present invention, an active suspension anti-aiming control method for a vehicle determines the actual control force of the vehicle suspension based on the target suspension action force expectation value and the vehicle suspension vibration velocity, including: The instantaneous power is determined based on the target suspension dynamics expectation and the vehicle suspension vibration speed; The actual control force of the vehicle suspension is determined based on the instantaneous power and the preset power limit.

[0017] The present invention also provides an active suspension anti-aiming control device for a vehicle, comprising the following modules: The information acquisition module is used to acquire road surface point cloud data, longitudinal location information, and vehicle status information of the target road point; The vector determination module is used to determine the road surface roughness function based on the road surface point cloud data and longitudinal position information, and to determine the multi-scale road surface feature vector based on the road surface roughness function; The event recognition module is used to identify road events based on multi-scale road surface feature vectors to obtain road event information; The expected value determination module is used to determine the expected value of the vehicle's active suspension action based on road event information and vehicle status information. The anti-suspension control module is used to determine the actual control force of the vehicle suspension based on the expected value of the vehicle's active suspension action, and to perform active suspension anti-suspension control on the vehicle based on the actual control force of the vehicle suspension.

[0018] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the active suspension anti-aiming control method for any of the above-described vehicles.

[0019] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the active suspension anti-aiming control method for any of the above-described vehicles.

[0020] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements an active suspension anti-aiming control method for any of the above-described vehicles.

[0021] This invention provides a method for active suspension preview control of a vehicle. It acquires road surface point cloud data, longitudinal position information, and vehicle state information of a target road point; determines a road surface roughness function based on the road surface point cloud data and longitudinal position information, and determines a multi-scale road surface feature vector based on the road surface roughness function; identifies road events based on the multi-scale road surface feature vector to obtain road event information; determines the expected value of the vehicle's active suspension action based on the road event information and vehicle state information; determines the actual control force of the vehicle's suspension based on the expected value of the vehicle's active suspension action; and performs active suspension preview control of the vehicle based on the actual control force of the vehicle's suspension. The technical solution of this invention addresses the problem that the coefficient parameters of traditional controllers in the prior art are mostly optimized based on a global range, thus improving the vibration characteristics of the suspension in the global range. Based on the above characteristics, suspension systems using traditional controllers can improve vehicle ride comfort and other characteristics globally. However, they have the drawback of not being able to switch the control coefficient of the suspension in a timely manner according to road conditions, and not being able to keep the suspension controller always optimal. The solution is to achieve active suspension pre-aiming control of the vehicle by flexibly determining the actual control force of the vehicle suspension based on road surface point cloud data, longitudinal position information, and vehicle status information of the target road point. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is one of the flowcharts of the active suspension anti-aiming control method for vehicles provided by the present invention.

[0024] Figure 2 This is one of the smoothness comparison diagrams provided by the present invention.

[0025] Figure 3 This is the second schematic diagram showing the smoothness comparison provided by the present invention.

[0026] Figure 4 This is the third schematic diagram showing the smoothness comparison provided by the present invention.

[0027] Figure 5 This is the second flowchart of the active suspension anti-aiming control method for vehicles provided by the present invention.

[0028] Figure 6 This is a schematic diagram of the structure of the active suspension anti-aiming control device for vehicles provided by the present invention.

[0029] Figure 7This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0031] The following is combined with Figure 1 The present invention describes the active suspension anti-aiming control method for vehicles provided by the present invention. The active suspension anti-aiming control method for vehicles provided by the present invention is applicable to active suspension anti-aiming control of vehicles based on road features and events. The execution subject of this method can be an electronic device or an active suspension anti-aiming control device for vehicles installed in the electronic device. The active suspension anti-aiming control device for vehicles can be implemented by software, hardware or a combination of both. Figure 1 This is one of the flowcharts illustrating the active suspension anti-aiming control method for vehicles provided by the present invention, such as... Figure 1 As shown, the method includes the following steps 101, 102, 103, 104 and 105.

[0032] Step 101: Obtain the road surface point cloud data, longitudinal position information, and vehicle status information of the target road point.

[0033] In this step, the road surface point cloud data is the point cloud data of the target road points obtained by the vehicle-mounted LiDAR, and the longitudinal position information is the position information of the target road points obtained by the vehicle-mounted LiDAR. There is at least one target road point, but this embodiment does not limit this.

[0034] Specifically, it is based on the road surface point cloud data and longitudinal position information of the target road points obtained by the vehicle-mounted LiDAR.

[0035] Step 102: Determine the road surface roughness function based on the road surface point cloud data and longitudinal position information, and determine the multi-scale road surface feature vector based on the road surface roughness function.

[0036] Specifically, the road surface roughness function is determined based on the road surface point cloud data and longitudinal position information. The road surface roughness function is then subjected to Fourier transform, and the proportions of low-frequency, mid-frequency, and high-frequency energy of the signal are extracted as multi-scale road surface feature vectors. This embodiment does not limit this step.

[0037] In one specific embodiment, determining the road surface roughness function based on road surface point cloud data and longitudinal position information includes: performing ground segmentation processing, outlier removal processing, attitude compensation processing, and coordinate unification processing on the road surface point cloud data to obtain a road surface point cloud set; wherein, the road surface point cloud set includes at least one elevation value; and determining the road surface roughness function based on each elevation value in the road surface point cloud set and the longitudinal position information.

[0038] In this step, ground segmentation specifically refers to separating ground points and non-ground points in the road surface point cloud data, greatly simplifying the detection and analysis of obstacles in the target road points; outlier removal specifically refers to removing interference points in the road surface point cloud data caused by sensor noise, dust, rain, insects, or distant sparse and unreliable points, improving the data quality of the road surface point cloud data; attitude compensation specifically refers to correcting the point cloud distortion in the road surface point cloud data caused by the vehicle's own movement (acceleration, deceleration, turning, bumping) at the moment of acquisition; coordinate unification specifically refers to converting the road surface point cloud data collected at different times and by different sensors (e.g., multiple vehicle-mounted LiDARs) to the same, stable coordinate system, improving the accuracy of subsequent processing. This embodiment does not limit this aspect.

[0039] Specifically, after obtaining the road surface point cloud data, the road surface point cloud data is processed by ground segmentation, outlier removal, attitude compensation, and coordinate unification to obtain a road surface point cloud set. Then, the road surface unevenness function is determined based on the elevation values ​​and longitudinal position information in the road surface point cloud set.

[0040] For example, the combination of at least one target road point constructs the road surface roughness function. Road surface roughness function middle This represents the elevation value of the target road point. This indicates the longitudinal position information of the target road point relative to the vehicle, but this embodiment does not limit this.

[0041] In one specific embodiment, determining the multi-scale road feature vector based on the road surface roughness function includes: performing a discrete Fourier transform on the road surface roughness function to obtain the road surface spatial frequency domain power spectral density; obtaining the spectral parameters of the target road point; and determining the multi-scale road feature vector based on the road surface spatial frequency domain power spectral density and spectral parameters.

[0042] In this step, spectral parameters Spectral parameters used to characterize road surface roughness The road surface power spectral density corresponding to a pre-set reference spatial frequency. Indicates the reference space frequency, generally speaking. This embodiment does not limit this aspect.

[0043] Among them, multi-scale road surface feature vectors It includes at least one of the following: energy indexes of different frequency bands, frequency band energy ratio, root mean square, kurtosis / kurtosis, local slope extrema, local energy mutation index or wavelet packet energy distribution, etc., but this embodiment does not limit it.

[0044] Specifically, a discrete Fourier transform is performed on the road surface roughness function to obtain the road surface spatial frequency domain power spectral density. The calculation is shown in formula (1).

[0045] (1) In formula (1), Represents spatial frequency, in units of ; This indicates the equidistant sampling interval along the vehicle's direction of travel. This indicates the number of luminaire sampling intervals within the pre-aiming distance; Indicates the number of the target road point; Represents the imaginary number operator.

[0046] Determining the power spectral density in the spatial frequency domain of the road surface Subsequently, multi-scale pavement feature vectors are determined based on the pavement spatial frequency domain power spectral density and spectral parameters.

[0047] In one specific embodiment, determining a multi-scale road feature vector based on the road surface spatial frequency domain power spectral density and spectral parameters includes: determining the signal energy in a frequency band region based on the road surface spatial frequency domain power spectral density; wherein the signal energy in the frequency band region includes low-frequency signal energy, mid-frequency signal energy, and high-frequency signal energy; determining the proportion of low-frequency energy, mid-frequency energy, and high-frequency energy based on the low-frequency signal energy, mid-frequency signal energy, and high-frequency signal energy; and determining the multi-scale road feature vector based on the spectral parameters, low-frequency signal energy, mid-frequency signal energy, high-frequency signal energy, and the proportions of low-frequency, mid-frequency, and high-frequency energy.

[0048] In this step, the spatial frequency is pre-divided into several spatial frequency bands. Taking the low-frequency, mid-frequency, and high-frequency bands as an example, the b-th frequency band is set as... The regional signal energy of the sampled signal within this frequency band is defined as shown in formula (2).

[0049] (2) In formula (2), The power spectral density in the spatial frequency domain of the road surface. This represents the lower limit of the b-th frequency band. Let represent the upper limit of the b-th frequency band, and Less than .

[0050] For example, since ISO 8608 recommends the spatial frequency band for road surface unevenness as [0.011, 2.83], with the unit being cycle / m, according to the octave band division method, the three frequency bands can be [0.011, 0.125], [0.125, 0.25], and [0.25, 2.83], with the unit being cycle / m. This embodiment does not limit this.

[0051] Therefore, the signal energy in the low-frequency region is determined sequentially using formula (2). Mid-frequency signal energy and high frequency region signal energy .

[0052] Specifically, after determining the signal energy in the low-frequency, mid-frequency, and high-frequency regions, the proportions of low-frequency, mid-frequency, and high-frequency energy are determined based on these proportions. The calculation is shown in formula (3), and the proportion of mid-frequency energy is... The calculation is shown in formula (4), where the high-frequency energy ratio is... The calculation is shown in formula (5).

[0053] (3) (4) (5) Determining the proportion of low-frequency energy Medium frequency energy ratio and the proportion of high-frequency energy Subsequently, multi-scale road surface feature vectors are determined based on spectral parameters, signal energy in the low-frequency region, signal energy in the mid-frequency region, signal energy in the high-frequency region, and the proportions of low-frequency, mid-frequency, and high-frequency energy. The calculation is shown in formula (6).

[0054] (6) Step 103: Recognize road events based on multi-scale road surface feature vectors to obtain road event information.

[0055] In this step, road event information includes road event category e and event confidence level. and event intensity This embodiment does not limit this aspect.

[0056] The types of road incidents can include, for example, speed bumps, manhole covers, potholes, or road surface joints, but this embodiment does not limit them.

[0057] Specifically, based on multi-scale road surface feature vector road event recognition, the results include road event category e and event confidence. and event intensity Road incident information.

[0058] For example, taking short-pulse events such as speed bumps, manhole covers, potholes, or road joints as examples, let 0 represent non-events (normal road surface), 1 represent speed bumps, 2 represent potholes, and 3 represent joints / manhole covers. Then, the road event category e can be defined as: .

[0059] Event confidence The calculation is shown in formula (7).

[0060] (7) In formula (7), Indicates the slope coefficient. ; This represents the event comprehensive scoring function; the event comprehensive scoring function The calculation is shown in formula (8).

[0061] (8) In formula (8), , , , , , and All of these represent scoring weights. These scoring weights need to be obtained through parameter tuning during simulation and actual control. As an example, this invention recommends the following combination: 0.09, 0.27, 0.54, 0.95, 0.96, 0.15, 0.97. This embodiment does not limit this.

[0062] The classification of road event category e is based on actual road labels. The calculation is shown in formula (9).

[0063] (9) In formula (9), This represents the event comprehensive scoring function. , , and All of these represent the range of the comprehensive scoring function for the calibrated event, which is preset and not limited in this embodiment.

[0064] Event intensity The calculation is shown in formula (10).

[0065] (10) In formula (10), This represents the maximum road surface roughness function; Represents the smallest This embodiment does not limit this aspect.

[0066] Step 104: Determine the expected value of the vehicle's active suspension power based on road event information and vehicle status information.

[0067] The vehicle body, frame, and load-bearing components are abstracted into a degree-of-freedom suspension model. This model includes sprung mass elements, unsprung mass elements, suspension elastic elements, actuator elements, and tire elastic elements. The mass of the sprung mass element is denoted as... The mass of the unsprung mass element is denoted as The stiffness coefficient of the suspension elastic element is denoted as The driving force of the actuator element is denoted as The stiffness coefficient of the tire's elastic element is denoted as... The displacement of the spring-loaded mass is denoted as The unsprung mass displacement is denoted as Road vibration amplitude is recorded as .

[0068] In this step, vehicle status information Vehicle state information is obtained by collecting signals from vehicle suspension-related sensors (including but not limited to vehicle acceleration, suspension displacement / velocity, tire travel, etc.) and using a state observer or filter to perform state estimation. For example, it could be as shown in formula (11).

[0069] (11) In formula (11), Indicates the displacement of the spring-loaded mass. Indicates the displacement of the unsprung mass. Indicates the amplitude of road vibration. Indicates the displacement of the spring-loaded mass First derivative.

[0070] Specifically, after determining the road event information, the expected value of the vehicle's active suspension power is determined based on the road event information and vehicle status information.

[0071] In one specific embodiment, the road event information includes event confidence and event intensity; determining the expected value of the vehicle's active suspension power based on the road event information and vehicle state information includes: determining the vehicle control mode based on the event confidence and event intensity; determining a set of weight coefficients based on the vehicle control mode; determining the optimal control gain matrix based on the set of weight coefficients; and determining the expected value of the vehicle's active suspension power based on the optimal control gain matrix and vehicle state information.

[0072] In one specific embodiment, the vehicle control mode includes an impact mode and a continuous road surface mode; determining the vehicle control mode based on event confidence and event intensity includes: determining the vehicle control mode as an impact mode when the event confidence is greater than or equal to a confidence threshold and the event intensity is greater than or equal to an intensity threshold; and determining the vehicle control mode as a continuous road surface mode when the event confidence is less than a confidence threshold and / or the event intensity is less than an intensity threshold.

[0073] In this step, the confidence threshold is a pre-set threshold used to determine the confidence level of an event, and the intensity threshold is a pre-set threshold used to determine the intensity of an event. This embodiment does not limit these thresholds.

[0074] Specifically, if the event confidence level is greater than or equal to the confidence threshold and the event intensity is greater than or equal to the intensity threshold, the vehicle control mode is determined to be the impact mode; if the event confidence level is less than the confidence threshold and / or the event intensity is less than the intensity threshold, the vehicle control mode is determined to be the continuous road mode.

[0075] For example, when and In the case of impact, the vehicle control mode is determined to be impact mode; otherwise, the vehicle control mode is determined to be continuous road mode.

[0076] In one specific embodiment, the activation conditions for the impact mode are also set, when a real road label... In the case that, when the following conditions are met and At that time, the impact mode is activated; among them, Indicates the current moment of vehicle control. This indicates the moment when the vehicle's final impact mode ends. Indicates the minimum dwell time of the vehicle. This indicates the moment when the vehicle's final impact mode was activated. This indicates the duration of the vehicle's impact mode, but this embodiment does not limit this duration.

[0077] In one specific embodiment, the road event information further includes road event categories; the weight coefficient set includes a performance index weight coefficient set or an impact suppression weight coefficient set; determining the weight coefficient set according to the vehicle control mode includes: when the vehicle control mode is determined to be an impact mode, generating an impact suppression weight coefficient set according to the road event category and event intensity; when the vehicle control mode is determined to be a continuous road surface mode, determining the performance index weight coefficient set according to a multi-scale road surface feature vector.

[0078] The determination of the performance index weight coefficient set based on the multi-scale road feature vector is mainly done through a lookup table-like method. First, offline optimization is performed to obtain the weight coefficient combination (q1, q2, q3 combination) under typical working conditions and construct a "table". During actual control, the weight coefficient combination is selected by interpolation based on the multi-scale road feature vector to determine the performance index weight coefficient set, thereby realizing the continuous scheduling of control weight coefficients. This embodiment does not limit this process.

[0079] For example, for continuous road surfaces, the specific control method is similar to the lookup table method. First, offline optimization is performed to obtain the weight coefficient combination (q1, q2, q3 combination) under each typical working condition (such as the road grade of ISO ABCD). A "table" is constructed based on the power spectral density Gq(n0) value of the road (as shown in Table 1 below). During actual control, the weight coefficient combination is selected by interpolation method according to the road characteristics (Gq(n0) value) to determine the set of performance index weight coefficients, so as to realize the continuous scheduling of control weight coefficients. This embodiment does not limit this.

[0080] Table 1

[0081] Specifically, when the vehicle control mode is determined to be impact mode, based on the road event category e and the event intensity... Generate a set of shock suppression weight coefficients; set of shock suppression weight coefficients At least one of the following conditions must be met: (1) Increase the weight of tire dynamic travel. To mitigate the risk of tire lift-off / limit stroke; (2) based on the intensity of the event Adaptive increase of input weights To control energy consumption and saturation risk; (3) Weighting of vehicle body acceleration Adopting the intensity of the event The relevant piecewise or continuous functions are adjusted. The impact event is determined to end under impact mode and the impact mode holding time for the vehicle is satisfied. Then, a first-order filter or weighted transition method is used to smoothly transition the weight coefficients from the impact mode to the normal roughness mode to avoid secondary impacts when exiting.

[0082] For example, during the smooth transition process, a first-order discrete low-pass method is used to smooth the weighting coefficients. For example, at time k, we get The calculation is shown in formula (12). (12) In formula (12), express The smoothing coefficient.

[0083] Specifically, when the vehicle control mode is determined to be a continuous road mode, based on the multi-scale road feature vector... Through continuous scheduling mapping function The set of performance index weights corresponding to the performance index of the output linear quadratic regulator (LQR). ,in, This represents the suspension dynamic deflection weighting coefficient; This represents the weighting coefficient for vehicle body acceleration; This represents the tire travel weighting coefficient; This represents the weighting coefficient for the control input.

[0084] For example, active suspension control is used as the force, according to the mechanical principle as shown in formula (13).

[0085] (13) In formula (12), Represents the displacement of the spring-loaded mass. The second derivative, Represents unsprung mass displacement The second derivative is obtained. By generalizing formula (13), the state-space equation is obtained as formula (14).

[0086] (14) In formula (14), express First-order derivative, vehicle state information As shown in formula (11); Represents observation information, observation information As shown in formula (15).

[0087] (15) In formula (13), B, G, C, and E are all coefficients. As shown in formula (16); B is as shown in formula (17); G is as shown in formula (18); C is as shown in formula (19); E is as shown in formula (20).

[0088] (16) (17) (18) (19) (20) When using an LQR-based controller, construct the target matrix. As shown in formula (21).

[0089] (twenty one) In formula (21), , to serve as a driving force; The calculation is shown in formula (22). The calculation is shown in formula (23). The calculation is shown in formula (24).

[0090] (twenty two) (twenty three) (twenty four) In the above formula, It is the suspension dynamic deflection weighting coefficient. It is the vehicle body acceleration weighting coefficient. It is the tire travel weighting coefficient, when If the value is less than or equal to a preset threshold, determine a reasonable value. Vehicle body acceleration weighting coefficient and tire travel weighting factor This improves vehicle performance under specific road surface conditions.

[0091] For example, a comparison of mode switching smoothness (suspension dynamic deflection weighting coefficients during control on ISOA-BCD level roads). Vehicle body acceleration weighting coefficient and tire travel weighting factor The image shows the suspension dynamic deflection weighting coefficient obtained by the smooth transition processing in this invention. Vehicle body acceleration weighting coefficient and tire travel weighting factor It can effectively suppress high-frequency chattering of the controller weight coefficient. Figure 2This is one of the smoothness comparison diagrams provided by the present invention, such as... Figure 2 As shown, Figure 2 (a) indicates that the control process did not undergo smooth transition processing. , Figure 2 (a) The horizontal axis represents the acquisition time, in seconds (s); Figure 2 (a) The ordinate represents ; Figure 2 (b) indicates that a smooth transition is used in the control process. , Figure 2 (b) The horizontal axis represents the acquisition time, in seconds (s); Figure 2 (a) The ordinate represents . Figure 3 This is the second schematic diagram of smoothness comparison provided by the present invention, such as... Figure 3 As shown, Figure 3 (a) indicates that the control process did not undergo smooth transition processing. , Figure 3 (a) The horizontal axis represents the acquisition time, in seconds (s); Figure 3 (a) The ordinate represents ; Figure 3 (b) indicates that a smooth transition is used in the control process. , Figure 3 (b) The horizontal axis represents the acquisition time, in seconds (s); Figure 3 (a) The ordinate represents . Figure 4 This is the third schematic diagram of smoothness comparison provided by the present invention, such as... Figure 4 As shown, Figure 4 (a) indicates that the control process did not undergo smooth transition processing. , Figure 4 (a) The horizontal axis represents the acquisition time, in seconds (s); Figure 4 (a) The ordinate represents ; Figure 4 (b) indicates that a smooth transition is used in the control process. , Figure 4 (b) The horizontal axis represents the acquisition time, in seconds (s); Figure 4 (a) The ordinate represents .

[0092] In one specific embodiment, an offline traversal optimization method can also be used to calculate the optimal set of performance index weight coefficients under different road surface excitations. Specifically, this is done according to the spectral parameters. Different road incentive models are established for different road conditions. The optimal solution of the performance index weight coefficient set for that road is calculated through simulation optimization. The optimal solution of the performance index weight coefficient set is ( Due to spectral parameters It may change abruptly, resulting in a different set of weighting coefficients for the corresponding performance metrics ( The weight coefficients can also jump, which can cause the vehicle system to become unstable. To suppress the jumps in the weight coefficients, a hysteresis and dwell time mechanism is introduced.

[0093] In one specific embodiment, for Vehicle body acceleration weighting coefficient and tire travel weighting factor All are updated to introduce hysteresis and dwell time mechanisms, that is, when the "scale road surface feature vector" is satisfied... Greater than the feature vector threshold "And "the minimum dwell time for the vehicle has exceeded the time since the last update." Updates are only allowed when [the specified time is specified]. Vehicle body acceleration weighting coefficient and tire travel weighting factor Otherwise, keep the previous one. Vehicle body acceleration weighting coefficient and tire travel weighting factor The combination remains unchanged, thereby suppressing control chattering caused by frequent switching.

[0094] For example, let the set of performance index weighting coefficients used by the control system at time k be . ,by For example, let's assume The transition threshold is Then at time k, if If established, switch. , , Similarly, this embodiment does not limit this aspect.

[0095] In one specific embodiment, during the generation of the impact suppression weight coefficient set, since the impact road surface exhibits short-term, rapid road excitation, it has a significant impact on vehicle comfort. Therefore, on the impact road surface, the vehicle's suspension control system primarily aims to reduce vehicle acceleration and improve ride comfort. The baseline weights in the continuous road surface mode are modulated according to the frequency band energy ratio of the impact road surface. Since the main energy of the impact road surface is concentrated in the high-frequency range, the high-frequency energy ratio is used as the basis for the weighting. The modulation coefficients are based on the spectral parameters of the impacted road surface. right( In ) and Modulation processing is performed, that is, for impacted road surfaces, the set of impact suppression weighting coefficients used is as follows: .

[0096] The advantage of this setting is that it reduces... and It can increase the dynamic deflection of the suspension and the dynamic travel of the tires, thereby reducing the vehicle's acceleration and improving the vehicle's comfort under the excitation of impact road conditions.

[0097] In one specific embodiment, after determining the set of weight coefficients based on the vehicle control mode, the method further includes: performing stability constraint verification on the set of weight coefficients; if the stability constraint verification of the set of weight coefficients passes, continuing to execute the step of determining the optimal control gain matrix based on the set of weight coefficients; if the stability constraint verification of the set of weight coefficients fails, correcting the feasible region of the set of weight coefficients to obtain a target set of weight coefficients; determining the optimal control gain matrix based on the target set of weight coefficients, and continuing to execute the step of determining the expected value of the vehicle's active suspension based on the optimal control gain matrix and vehicle state information.

[0098] Specifically, the set of weight coefficients undergoes stability constraint verification. For example, stability constraint verification can employ an online verification method based on the unified Lyapunov function. A feasible region satisfying the unified Lyapunov function or linear matrix inequality (LMI) constraints is pre-constructed offline. Then, it is determined online whether the current set of weight coefficients falls within the feasible region. If the set of weight coefficients falls within the feasible region, the stability constraint verification is considered successful, and the step of determining the optimal control gain matrix based on the set of weight coefficients continues. If the stability constraint verification fails, the feasible region of the set of weight coefficients is corrected, trimming it to a feasible region satisfying the unified Lyapunov / LMI conditions to obtain the target set of weight coefficients. The optimal control gain matrix is ​​then determined based on the target set of weight coefficients, and the step of determining the expected power value of the vehicle's active suspension based on the optimal control gain matrix and vehicle state information continues.

[0099] For example, controller parameters are determined based on a set of weighting coefficients. And obtain the closed-loop equivalent state matrix of the vehicle. The common Lyapunov matrix is ​​obtained offline in advance. right Perform stability constraint verification. If the following formula (25) is met, the stability constraint verification is deemed to pass. Otherwise, the stability constraint verification is deemed to fail and feasible region correction is triggered.

[0100] (25) In formula (24), express transpose, and These are pre-defined constant coefficients.

[0101] When the set of weight coefficients does not meet the stability constraint verification, a feasible region correction strategy is executed to project or trim the set of weight coefficients to a feasible region that satisfies the stability of the unified system, thereby ensuring the stability of the controller under continuous scheduling and switching conditions.

[0102] In one specific embodiment, boundary value correction can also be used, and a set of weight boundaries that satisfy stability can be prepared offline in advance. As shown in formula (26).

[0103] (26) If the set of weight coefficients calculated online does not meet the stability constraint check, then the set of weight coefficients shall be selected as shown in formula (27) in the following manner.

[0104] (27) The advantage of this setting is that it ensures the stability of the weight coefficient set under continuous scheduling and switching conditions.

[0105] In one specific embodiment, the vehicle's active suspension is used as the desired dynamic value. ,in, This represents the optimal control gain matrix. This indicates vehicle status information.

[0106] Specifically, in obtaining vehicle status information And determine the optimal control gain matrix. Afterwards, the vehicle's active suspension is used to determine the desired power value. .

[0107] Step 105: Determine the actual control force of the vehicle suspension based on the expected value of the vehicle's active suspension power, and perform active suspension pre-aiming control on the vehicle based on the actual control force of the vehicle suspension.

[0108] Specifically, after determining the expected value of the vehicle's active suspension power, the expected value of the vehicle's active suspension power is constrained to obtain the actual control force of the vehicle's suspension. Based on the actual control force of the vehicle's suspension, the vehicle's active suspension is pre-aimed at control.

[0109] Specifically, constraint processing involves actuator constraint processing. Actuator constraints may include, for example, force amplitude saturation constraints, control input change rate constraints, power constraints, or thermal protection constraints. This embodiment does not limit these constraints.

[0110] In one specific embodiment, determining the actual control force of the vehicle suspension based on the expected value of the vehicle's active suspension action includes: determining the target expected value of the suspension action based on the expected value of the vehicle's active suspension action and the expected value threshold; determining the vehicle suspension vibration speed based on vehicle state information; and determining the actual control force of the vehicle suspension based on the target expected value of the suspension action and the vehicle suspension vibration speed.

[0111] In this step, the expected value threshold The threshold value for constraining the vehicle's active suspension is a pre-set threshold, but this embodiment does not limit it.

[0112] Specifically, the target suspension as the expected value of dynamics The calculation is shown in formula (28).

[0113] (28) In formula (28), This represents the expected value of the target suspension dynamics. This indicates the expected value of the vehicle's active suspension power. Indicates the expected value threshold. This represents the absolute value of the expected power of the vehicle's active suspension. Indicates the time.

[0114] Determine the target suspension as the desired dynamic value. Next, the vehicle suspension vibration speed is determined based on the vehicle status information. ,in, Indicates vehicle status information The derivative of the second term in the equation is the sprung mass displacement. Indicates vehicle status information The derivative of the unsprung mass displacement of the fourth term in the equation.

[0115] Finally, the desired dynamic value is based on the target suspension. and vehicle suspension vibration speed Determine the actual control force of the vehicle suspension.

[0116] In one specific embodiment, determining the actual control force of the vehicle suspension based on the target suspension working power expectation value and the vehicle suspension vibration speed includes: determining the instantaneous power based on the target suspension working power expectation value and the vehicle suspension vibration speed; and determining the actual control force of the vehicle suspension based on the instantaneous power and a preset power upper limit.

[0117] In this step, the preset power limit is set. This is the pre-set power limit for active vehicle suspension control.

[0118] Specifically, the expected dynamic value is based on the target suspension. and vehicle suspension vibration speed Determine instantaneous power The actual control force of the vehicle suspension is determined based on the instantaneous power and the preset power upper limit. The calculation is shown in formula (29).

[0119] (29) The advantage of this setup is that it is based on the actual control force of the vehicle suspension. Active suspension anti-aiming control is implemented for the vehicle.

[0120] In one specific embodiment, it can also be based on the actual control force of the vehicle suspension. Constructing energy consumption indicators When energy consumption index When the energy consumption exceeds the threshold, the weighting coefficient is adaptively adjusted. This is to achieve a dynamic balance between vehicle performance and energy consumption.

[0121] For example, When E is greater than the energy consumption threshold, the weighting coefficient is adaptively adjusted. This is to achieve a dynamic balance between vehicle performance and energy consumption.

[0122] In one specific embodiment, Figure 5 This is the second flowchart of the active suspension anti-aiming control method for vehicles provided by the present invention, as shown below. Figure 5 As shown, the active suspension anti-aiming control method for a vehicle includes steps 501, 502, 503, 504, 505, 506, 507, 508, 509, and 510.

[0123] Step 501: Obtain the road surface point cloud data, longitudinal position information, and vehicle status information of the target road point.

[0124] Specifically, it is based on the road surface point cloud data, longitudinal position information, and vehicle status information of the target road points obtained by the vehicle-mounted LiDAR.

[0125] Step 502: Perform ground segmentation, outlier removal, attitude compensation, and coordinate unification on the road surface point cloud data to obtain a road surface point cloud set.

[0126] In this step, the road surface point cloud set includes at least one elevation value.

[0127] Specifically, after obtaining the road surface point cloud data, the road surface point cloud data is processed by ground segmentation, outlier removal, attitude compensation, and coordinate unification to obtain a road surface point cloud set.

[0128] Step 503: Determine the road surface unevenness function based on the elevation values ​​and longitudinal position information in the road surface point cloud set.

[0129] Step 504: Perform a discrete Fourier transform on the road surface roughness function to obtain the road surface spatial frequency domain power spectral density.

[0130] Specifically, the road surface roughness function is subjected to Fourier transform, and the proportions of low-frequency, mid-frequency, and high-frequency energy of the signal are extracted as multi-scale road surface feature vectors. This embodiment does not limit this.

[0131] Step 505: Obtain the spectral parameters of the target road points; determine the multi-scale road feature vector based on the road surface spatial frequency domain power spectral density and spectral parameters.

[0132] Step 506: Based on multi-scale road surface feature vectors, road event identification is performed to obtain road event information; the road event information includes event confidence and event intensity.

[0133] Step 507: Determine the vehicle control mode based on the event confidence and event intensity.

[0134] Specifically, if the event confidence level is greater than or equal to the confidence threshold and the event intensity is greater than or equal to the intensity threshold, the vehicle control mode is determined to be the impact mode; if the event confidence level is less than the confidence threshold and / or the event intensity is less than the intensity threshold, the vehicle control mode is determined to be the continuous road mode.

[0135] Step 508: Determine the set of weight coefficients based on the vehicle control mode.

[0136] Specifically, the set of weighting coefficients includes a set of impact suppression weighting coefficients and a set of performance index weighting coefficients. When the vehicle control mode is determined to be impact mode, the set of impact suppression weighting coefficients is generated based on the road event category and event intensity; when the vehicle control mode is determined to be continuous road surface mode, the set of performance index weighting coefficients is determined based on multi-scale road surface feature vectors.

[0137] Step 509: Determine the optimal control gain matrix based on the set of weight coefficients; determine the expected value of the vehicle's active suspension power based on the optimal control gain matrix and vehicle state information.

[0138] Step 510: Determine the actual control force of the vehicle suspension based on the expected value of the vehicle's active suspension power, and perform active suspension pre-aiming control on the vehicle based on the actual control force of the vehicle suspension.

[0139] Table 2 Controller Performance Comparison Table

[0140] Specifically, after determining the expected value of the vehicle's active suspension power, the expected value of the vehicle's active suspension power is constrained to obtain the actual control force of the vehicle's suspension. Based on the actual control force of the vehicle's suspension, the vehicle's active suspension is pre-aimed at control.

[0141] For example, the control performance of the active suspension anti-aiming control method for vehicles proposed in this invention is compared with the performance of existing fixed weight coefficient LQR and passive suspension, as shown in Table 2 above. Table 2 is a controller performance comparison table. The objects compared in Table 2 include items, road grade, this invention, fixed weight coefficient LQR, and passive suspension; among them, items include the root mean square value of vehicle acceleration / ( The values ​​are: root mean square value of suspension dynamic deflection (m) and root mean square value of tire dynamic deformation (mm); the road grades include A, B, C and D, which are not limited in this embodiment.

[0142] The control coefficients of the fixed weight coefficient LQR are selected from the combination of q1, q2, and q3 of ISO Class A, but this embodiment does not limit this.

[0143] The active suspension anti-aiming control method for vehicles proposed in this invention has better control performance than existing fixed weight coefficient LQR and passive suspension.

[0144] This invention, based on the aforementioned active suspension anti-aiming control method for vehicles, introduces multi-scale road feature vectors and road event recognition to achieve continuous scheduling of weight coefficients and targeted control of impact modes. Compared to traditional switching schemes based on single index / discrete level lookup tables, this method can more accurately match different road conditions and significantly reduce switching shudder and control shock. Simultaneously, it suppresses frequent boundary switching through hysteresis and minimum dwell time mechanisms, and combines unified Lyapunov / LMI stability constraints to verify the weight coefficient set and correct the feasible region, providing provable switching stability guarantees. Furthermore, it superimposes actuator saturation / rate / power constraints and adaptive energy consumption adjustment to effectively avoid actuator overload and energy consumption runaway, improving the vehicle system's comfort, handling stability, and engineering reliability under non-stationary excitation conditions such as potholes and speed bumps.

[0145] This invention provides a method for active suspension preview control of a vehicle. It acquires road surface point cloud data, longitudinal position information, and vehicle state information of a target road point; determines a road surface roughness function based on the road surface point cloud data and longitudinal position information, and determines a multi-scale road surface feature vector based on the road surface roughness function; identifies road events based on the multi-scale road surface feature vector to obtain road event information; determines the expected value of the vehicle's active suspension action based on the road event information and vehicle state information; determines the actual control force of the vehicle's suspension based on the expected value of the vehicle's active suspension action; and performs active suspension preview control of the vehicle based on the actual control force of the vehicle's suspension. The technical solution of this invention addresses the problem that the coefficient parameters of traditional controllers in the prior art are mostly optimized based on a global range, thus improving the vibration characteristics of the suspension in the global range. Based on the above characteristics, suspension systems using traditional controllers can improve vehicle ride comfort and other characteristics globally. However, they have the drawback of not being able to switch the control coefficient of the suspension in a timely manner according to road conditions, and not being able to keep the suspension controller always optimal. The solution is to achieve active suspension pre-aiming control of the vehicle by flexibly determining the actual control force of the vehicle suspension based on road surface point cloud data, longitudinal position information, and vehicle status information of the target road point.

[0146] The active suspension anti-aiming control device for vehicles provided by the present invention will be described below. The active suspension anti-aiming control device for vehicles described below can be referred to in correspondence with the active suspension anti-aiming control method for vehicles described above.

[0147] Figure 6 This is a schematic diagram of the active suspension anti-aiming control device for vehicles provided by the present invention, with reference to... Figure 6 As shown, the vehicle's active suspension anti-aiming control device 600 includes: an information acquisition module 601, a vector determination module 602, an event recognition module 603, an expected value determination module 604, and an anti-aiming control module 605; wherein, The information acquisition module 601 is used to acquire road surface point cloud data, longitudinal position information and vehicle status information of the target road point.

[0148] The vector determination module 602 is used to determine the road surface roughness function based on the road surface point cloud data and longitudinal position information, and to determine the multi-scale road surface feature vector based on the road surface roughness function.

[0149] The event recognition module 603 is used to recognize road events based on multi-scale road surface feature vectors to obtain road event information.

[0150] The expected value determination module 604 is used to determine the expected value of the vehicle's active suspension action based on road event information and vehicle status information.

[0151] The anti-suspension control module 605 is used to determine the actual control force of the vehicle suspension based on the expected value of the vehicle's active suspension action, and to perform active suspension anti-suspension control on the vehicle based on the actual control force of the vehicle suspension.

[0152] In one example embodiment, the vector determination module 602 determines the road surface roughness function based on the road surface point cloud data and longitudinal position information. Specifically, it is used to: perform ground segmentation processing, outlier removal processing, attitude compensation processing, and coordinate unification processing on the road surface point cloud data to obtain a road surface point cloud set; wherein, the road surface point cloud set includes at least one elevation value; and determine the road surface roughness function based on each elevation value and longitudinal position information in the road surface point cloud set.

[0153] In one example embodiment, the vector determination module 602 determines multi-scale road feature vectors based on the road surface roughness function. Specifically, it is used to: perform a discrete Fourier transform on the road surface roughness function to obtain the road surface spatial frequency domain power spectral density; obtain the spectral parameters of the target road point; and determine the multi-scale road feature vectors based on the road surface spatial frequency domain power spectral density and spectral parameters.

[0154] In one example embodiment, the vector determination module 602 determines a multi-scale road feature vector based on the road surface spatial frequency domain power spectral density and spectral parameters. Specifically, it is used to: determine the signal energy in the frequency band region based on the road surface spatial frequency domain power spectral density; wherein the signal energy in the frequency band region includes low-frequency region signal energy, mid-frequency region signal energy, and high-frequency region signal energy; determine the proportion of low-frequency energy, mid-frequency energy, and high-frequency energy based on the low-frequency region signal energy, mid-frequency region signal energy, and high-frequency region signal energy; and determine the multi-scale road feature vector based on the spectral parameters, low-frequency region signal energy, mid-frequency region signal energy, high-frequency region signal energy, low-frequency energy proportion, mid-frequency energy proportion, and high-frequency energy proportion.

[0155] In one example embodiment, road event information includes event confidence and event intensity.

[0156] In one example embodiment, the expectation value determination module 604 is specifically used to: determine the vehicle control mode based on the event confidence and event intensity; determine the weight coefficient set based on the vehicle control mode; determine the optimal control gain matrix based on the weight coefficient set; and determine the expected value of the vehicle's active suspension power based on the optimal control gain matrix and vehicle state information.

[0157] In one example embodiment, the vehicle control modes include an impact mode and a continuous road surface mode.

[0158] In one example embodiment, the expected value determination module 604 determines the vehicle control mode based on the event confidence level and the event intensity, specifically: when the event confidence level is greater than or equal to a confidence threshold and the event intensity is greater than or equal to an intensity threshold, the vehicle control mode is determined to be an impact mode; when the event confidence level is less than a confidence threshold and / or the event intensity is less than an intensity threshold, the vehicle control mode is determined to be a continuous road surface mode.

[0159] In one example embodiment, the road event information further includes road event categories; the weight coefficient set includes a performance index weight coefficient set or an impact suppression weight coefficient set.

[0160] In one example embodiment, the expected value determination module 604 determines a set of weight coefficients based on the vehicle control mode. Specifically, it is used to: generate a set of impact suppression weight coefficients based on the road event category and event intensity when the vehicle control mode is determined to be an impact mode; and determine a set of performance index weight coefficients based on multi-scale road feature vectors when the vehicle control mode is determined to be a continuous road surface mode.

[0161] In one example embodiment, the device further includes a constraint verification module. The constraint verification module is configured to: after determining the set of weight coefficients based on the vehicle control mode, perform stability constraint verification on the set of weight coefficients; if the stability constraint verification of the set of weight coefficients passes, continue to execute the step of determining the optimal control gain matrix based on the set of weight coefficients; if the stability constraint verification of the set of weight coefficients fails, perform feasible region correction on the set of weight coefficients to obtain a target set of weight coefficients; determine the optimal control gain matrix based on the target set of weight coefficients, and continue to execute the step of determining the expected value of the vehicle's active suspension based on the optimal control gain matrix and vehicle state information.

[0162] In one example embodiment, the pre-aiming control module 605 determines the actual control force of the vehicle suspension based on the expected value of the vehicle's active suspension action. Specifically, it is used to: determine the target suspension action expectation value based on the expected value of the vehicle's active suspension action and the expectation value threshold; determine the vehicle suspension vibration speed based on the vehicle state information; and determine the actual control force of the vehicle suspension based on the target suspension action expectation value and the vehicle suspension vibration speed.

[0163] In one example embodiment, the pre-aiming control module 605 determines the actual control force of the vehicle suspension based on the target suspension working power expectation value and the vehicle suspension vibration speed. Specifically, it is used to: determine the instantaneous power based on the target suspension working power expectation value and the vehicle suspension vibration speed; and determine the actual control force of the vehicle suspension based on the instantaneous power and the preset power upper limit.

[0164] The apparatus of this embodiment can be used to execute the method of any embodiment in the side embodiment of the active suspension preview control method for vehicles. Its specific implementation process and technical effects are similar to those in the side embodiment of the active suspension preview control method for vehicles. For details, please refer to the detailed description in the side embodiment of the active suspension preview control method for vehicles, which will not be repeated here.

[0165] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 7 As shown, the electronic device may include a processor 710, a communications interface 720, a memory 730, and a communication bus 740. The processor 710, communications interface 720, and memory 730 communicate with each other via the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute an active suspension anti-aiming control method for the vehicle. This method includes: acquiring road surface point cloud data, longitudinal position information, and vehicle state information of a target road point; determining a road surface roughness function based on the road surface point cloud data and longitudinal position information, and determining a multi-scale road surface feature vector based on the road surface roughness function; identifying road events based on the multi-scale road surface feature vector to obtain road event information; determining the expected value of the vehicle's active suspension action based on the road event information and vehicle state information; determining the actual control force of the vehicle's suspension based on the expected value of the vehicle's active suspension action; and performing active suspension anti-aiming control on the vehicle based on the actual control force of the vehicle's suspension.

[0166] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0167] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the active suspension preview control method for a vehicle provided by the above methods. The method includes: acquiring road surface point cloud data, longitudinal position information, and vehicle state information of a target road point; determining a road surface roughness function based on the road surface point cloud data and longitudinal position information, and determining a multi-scale road surface feature vector based on the road surface roughness function; identifying road events based on the multi-scale road surface feature vector to obtain road event information; determining the expected value of the vehicle's active suspension action based on the road event information and vehicle state information; determining the actual control force of the vehicle's suspension based on the expected value of the vehicle's active suspension action, and performing active suspension preview control on the vehicle based on the actual control force of the vehicle's suspension.

[0168] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the active suspension preview control method for a vehicle provided by the methods described above. This method includes: acquiring road surface point cloud data, longitudinal position information, and vehicle state information of a target road point; determining a road surface roughness function based on the road surface point cloud data and longitudinal position information, and determining a multi-scale road surface feature vector based on the road surface roughness function; identifying road events based on the multi-scale road surface feature vector to obtain road event information; determining the expected value of the vehicle's active suspension action based on the road event information and vehicle state information; determining the actual control force of the vehicle's suspension based on the expected value of the vehicle's active suspension action; and performing active suspension preview control on the vehicle based on the actual control force of the vehicle's suspension.

[0169] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0170] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for active suspension anti-aiming control of a vehicle, characterized in that, include: Acquire surface point cloud data, longitudinal location information, and vehicle status information of the target road point; The road surface roughness function is determined based on the road surface point cloud data and the longitudinal position information, and a multi-scale road surface feature vector is determined based on the road surface roughness function. Road event identification is performed based on the multi-scale road surface feature vectors to obtain road event information; The expected value of the vehicle's active suspension power is determined based on the road event information and the vehicle status information. The actual control force of the vehicle suspension is determined based on the expected value of the vehicle's active suspension action, and the active suspension pre-aiming control of the vehicle is performed based on the actual control force of the vehicle suspension.

2. The active suspension anti-aiming control method for vehicles according to claim 1, characterized in that, The step of determining the road surface roughness function based on the road surface point cloud data and the longitudinal position information includes: The road surface point cloud data is subjected to ground segmentation processing, outlier removal processing, attitude compensation processing, and coordinate unification processing to obtain a road surface point cloud set; wherein, the road surface point cloud set includes at least one elevation value; The road surface unevenness function is determined based on the elevation values ​​and longitudinal position information in the road surface point cloud set.

3. The active suspension anti-aiming control method for vehicles according to claim 1, characterized in that, The step of determining the multi-scale road feature vector based on the road surface roughness function includes: The discrete Fourier transform of the road surface roughness function is performed to obtain the road surface spatial frequency domain power spectral density. Obtain the spectral parameters of the target road points; The multi-scale road feature vector is determined based on the road surface spatial frequency domain power spectral density and the spectral parameters.

4. The active suspension anti-aiming control method for vehicles according to claim 3, characterized in that, The step of determining the multi-scale road feature vector based on the road surface spatial frequency domain power spectral density and the spectral parameters includes: The signal energy in the frequency band region is determined based on the power spectral density of the road surface in the frequency domain; wherein, the signal energy in the frequency band region includes low-frequency signal energy, mid-frequency signal energy, and high-frequency signal energy; The proportions of low-frequency energy, mid-frequency energy, and high-frequency energy are determined based on the signal energy in the low-frequency region, the signal energy in the mid-frequency region, and the signal energy in the high-frequency region. The multi-scale road surface feature vector is determined based on the spectral parameters, the signal energy in the low-frequency region, the signal energy in the mid-frequency region, the signal energy in the high-frequency region, the proportion of low-frequency energy, the proportion of mid-frequency energy, and the proportion of high-frequency energy.

5. The active suspension anti-aiming control method for vehicles according to claim 1, characterized in that, The road event information includes event confidence and event intensity; determining the expected value of the vehicle's active suspension action based on the road event information and the vehicle state information includes: The vehicle control mode is determined based on the event confidence level and the event intensity. Determine the set of weighting coefficients based on the vehicle control mode; The optimal control gain matrix is ​​determined based on the set of weighting coefficients. The expected value of the vehicle's active suspension power is determined based on the optimal control gain matrix and the vehicle state information.

6. The active suspension anti-aiming control method for vehicles according to claim 5, characterized in that, The vehicle control modes include an impact mode and a continuous road surface mode; determining the vehicle control mode based on the event confidence level and the event intensity includes: If the confidence level of the event is greater than or equal to a confidence threshold and the intensity of the event is greater than or equal to an intensity threshold, the vehicle control mode is determined to be the impact mode. If the confidence level of the event is less than the confidence threshold and / or the intensity of the event is less than the intensity threshold, the vehicle control mode is determined to be the continuous road mode.

7. The active suspension anti-aiming control method for vehicles according to claim 6, characterized in that, The road event information also includes road event categories; the weighting coefficient set includes a performance index weighting coefficient set or an impact suppression weighting coefficient set; determining the weighting coefficient set according to the vehicle control mode includes: When the vehicle control mode is determined to be the impact mode, the impact suppression weight coefficient set is generated according to the road event category and the event intensity. When the vehicle control mode is determined to be the continuous road surface mode, the set of performance index weight coefficients is determined based on the multi-scale road surface feature vector.

8. The active suspension anti-aiming control method for a vehicle according to claim 7, characterized in that, After determining the set of weighting coefficients based on the vehicle control mode, the method further includes: The stability constraint of the set of weight coefficients is verified. If the stability constraint verification of the set of weight coefficients is passed, continue to execute the step of determining the optimal control gain matrix based on the set of weight coefficients; If the stability constraint verification of the weight coefficient set fails, the feasible region of the weight coefficient set is corrected to obtain the target weight coefficient set. The optimal control gain matrix is ​​determined based on the target weight coefficient set, and the step of determining the expected value of the vehicle's active suspension based on the optimal control gain matrix and the vehicle state information is then performed.

9. The active suspension anti-aiming control method for a vehicle according to any one of claims 1-8, characterized in that, The step of determining the actual control force of the vehicle suspension based on the expected value of the vehicle's active suspension includes: The target suspension performance expectation value is determined based on the vehicle active suspension performance expectation value and the expectation value threshold. The vehicle suspension vibration speed is determined based on the vehicle status information. The actual control force of the vehicle suspension is determined based on the expected value of the target suspension action and the vibration speed of the vehicle suspension.

10. The active suspension anti-aiming control method for a vehicle according to claim 9, characterized in that, The step of determining the actual control force of the vehicle suspension based on the target suspension dynamic expectation value and the vehicle suspension vibration speed includes: The instantaneous power is determined based on the expected value of the target suspension and the vibration speed of the vehicle suspension. The actual control force of the vehicle suspension is determined based on the instantaneous power and the preset power upper limit.

Images