A shock absorber adaptive damping control method, device, medium and product

Abstract

The embodiment of the application relates to the technical field of shock absorber control, and discloses a shock absorber adaptive damping control method, equipment, medium and product. The method comprises the following steps: acquiring a multi-source sensing signal, performing frequency phase compensation on a road preview signal based on a time lead; fusing the compensated road preview signal, a vehicle body vibration signal and a suspension movement signal to obtain a full-band vibration representation signal; performing frequency band separation on the full-band vibration representation signal to obtain a low-frequency component representing vehicle body movement and a high-frequency component representing wheel movement; and inputting the low-frequency component and the high-frequency component into a dual-frequency decoupling control model to obtain a low-frequency damping force control instruction and a high-frequency damping force control instruction which are mutually decoupled. The method can be used to solve technical problems related to shock absorber control.

Description

Technical Field

[0001] This application relates to the field of vibration damper control technology, and in particular to a vibration damper adaptive damping control method, device, medium and product. Background Technology

[0002] As the automotive industry increasingly demands higher levels of driving comfort, handling stability, and safety, traditional passive hydraulic dampers are no longer sufficient to meet optimal performance requirements under various operating conditions. Therefore, adaptive or semi-active suspension systems capable of adjusting damping force in real time based on road conditions and vehicle status have become a focus of research and application. Currently, mainstream semi-active suspension systems primarily achieve control through a single adjustable damping element (such as a CDC continuously damped control damper or a single-coil magnetorheological damper) combined with sensor feedback. Their technological evolution and limitations are mainly reflected in the following aspects:

[0003] Early adaptive suspensions used two or three "soft / hard" settings, which could not achieve fine and continuous changes in damping force. Although the later developed CDC or magnetorheological dampers achieved continuous adjustment of damping force, their control strategies were mostly based on single or a few state feedbacks such as vehicle acceleration and speed, and their ability to handle the coupling problem of different physical sources in vehicle vibration was limited.

[0004] The classic "skylight damping" strategy can effectively suppress low-frequency resonance of the vehicle body (sprung mass) and improve comfort, but it will worsen the high-frequency ground contact of the wheels (unsprung mass); while the "ground damping" strategy has the opposite effect. Although a hybrid "skylight-ground damping" strategy has emerged, attempting to balance both in a single control algorithm, since its control command ultimately acts on the same damping actuator, it is essentially still responding to broadband excitation through a comprehensive force command. It is difficult to completely resolve the inherent contradiction between low-frequency comfort and high-frequency grip, and it is prone to compromise or mutual interference under complex excitation.

[0005] Therefore, there is an urgent need for a new generation of shock absorber control methods that can decouple low-frequency and high-frequency vibrations at the control strategy and execution hardware level and make accurate short-term predictions, so as to break through the current technical bottlenecks and improve the overall performance of vehicles under various complex driving conditions. Summary of the Invention

[0006] One object of this application is to provide a method, device, medium, and product for adaptive damping control of a vibration damper, at least to solve the technical problems of vibration damper control.

[0007] To achieve the above objectives, some embodiments of this application provide the following aspects:

[0008] In a first aspect, some embodiments of this application also provide an adaptive damping control method for a shock absorber. The method includes acquiring multi-source sensing signals, including road surface preview signals, vehicle body vibration signals, and suspension motion signals; calculating the time lead of the road surface preview signal relative to the vehicle body vibration signal based on the current vehicle speed; performing frequency and phase compensation on the road surface preview signal based on the time lead; fusing the compensated road surface preview signal, the vehicle body vibration signal, and the suspension motion signal to obtain a full-frequency vibration characterization signal; performing frequency band separation on the full-frequency vibration characterization signal to obtain a low-frequency component characterizing vehicle body motion and a high-frequency component characterizing wheel motion; and inputting the low-frequency component and the high-frequency component into a dual-frequency decoupled control model to obtain mutually decoupled low-frequency damping force control commands and high-frequency damping force control commands.

[0009] Secondly, some embodiments of this application also provide an electronic device, the electronic device comprising: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method described above.

[0010] Thirdly, some embodiments of this application also provide a computer-readable medium having computer program instructions stored thereon, which can be executed by a processor to implement the method described above.

[0011] Fourthly, some embodiments of this application also provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method described above.

[0012] Compared with related technologies, the solution provided in this application eliminates the inherent phase difference between the pre-aiming and real-time sensing signals through a phase compensation fusion algorithm, achieving advanced damping adjustment. Its dual-frequency decoupled control matrix and dual-coil magnetorheological actuator solve the control coupling problems of low-frequency comfort and high-frequency grounding from both algorithmic and hardware perspectives, achieving independent optimization. Simultaneously, the integrated LSTM prediction module and fault diagnosis degradation strategy enhance the system's intelligent prediction capability and operational robustness, enabling simultaneous improvements in comfort, stability, and safety under complex road conditions. Attached Figure Description

[0013] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0014] Figure 1This is a flowchart illustrating an adaptive damping control method for a vibration damper according to an embodiment of this application.

[0015] Figure 2 This is a schematic flowchart of a magnetic field closed-loop feedback correction method provided according to an embodiment of this application;

[0016] Figure 3 This is a schematic flowchart of a phase compensation method provided according to an embodiment of this application;

[0017] Figure 4 This is a flowchart illustrating a prediction mapping method based on a dynamic inverse model provided according to an embodiment of this application.

[0018] Figure 5 This is a flowchart illustrating another adaptive damping control method for a vibration damper provided according to an embodiment of this application.

[0019] Figure 6 This is a structural block diagram of a vibration damper system provided according to an embodiment of this application;

[0020] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] As the automotive industry increasingly demands higher levels of ride comfort, handling stability, and safety, traditional passive hydraulic shock absorbers are no longer sufficient to meet optimal performance requirements under various operating conditions. Therefore, adaptive or semi-active suspension systems capable of adjusting damping force in real time according to road conditions and vehicle status have become a key focus of research and application.

[0023] The dual-coil magnetorheological damper applied in this application decouples the magnetic circuit and drive structure of the traditional single-coil damper, forming a physically independent and functionally synergistic dual-channel damping generation unit. The damper mainly consists of the following key components: a piston assembly, on which two electrically isolated and magnetically independent excitation coils—a low-frequency coil and a high-frequency coil—are integrated. The main coil is typically located on the upper part of the piston, employing a high number of turns and a thin wire diameter winding method, resulting in a large inductance. Its generated magnetic field is primarily used to excite the basic yield stress of the magnetorheological fluid, providing a strong and gentle low-frequency damping force. The secondary coil is typically located on the lower part of the piston, employing a low number of turns and a thicker wire diameter winding method, resulting in a smaller inductance. Its design goal is to achieve rapid establishment and dissipation of the magnetic field, used for rapid and precise damping adjustment of high-frequency vibrations.

[0024] The piston and cylinder wall form a magnetic circuit. By optimizing the magnetic circuit structure, the magnetic flux generated by the main and auxiliary coils can be mainly concentrated in their respective adjacent working gaps, thereby physically reducing the direct coupling between the two magnetic fields and achieving relatively independent control of the dual-channel magnetic field. The damping channel is the key area where the magnetorheological fluid undergoes rheological effects under the action of a magnetic field, generating variable damping. The dual-coil structure allows for more precise and zoned control of the magnetic field in this gap. Two independent current drive circuits are provided, connected to the main and auxiliary coils respectively, to receive low-frequency and high-frequency current commands from the controller.

[0025] Based on the magnetorheological effect, when the coil is energized, a magnetic field is generated in the working gap. The solid particles of the magnetorheological fluid in the gap instantly form a chain structure, which causes a sharp increase in its apparent viscosity or shear yield stress, thereby hindering the flow of fluid and generating a controllable damping force. The magnitude of the damping force is nonlinearly positively correlated with the magnetic field strength.

[0026] The dual-coil collaborative working modes include: low-frequency high-damping control, when it is necessary to suppress large low-frequency body sway, the controller applies a large current to the main coil. Due to its large inductance, the current change of the main coil is relatively gradual, but it can generate a stable and high-intensity magnetic field, thereby outputting a strong basic damping force to effectively control the body posture; and high-frequency fast-damping adjustment, when it is necessary to deal with high-frequency minor bumps on the road, the controller applies a rapidly changing current to the secondary coil. The secondary coil has a small inductance and can achieve a microsecond-level current response, thereby enabling rapid damping fine-tuning to follow high-frequency vibrations and optimize tire contact.

[0027] The two drive circuits are independently adjustable and do not affect each other, allowing for decoupling settings of the low-frequency and high-frequency components of the damping force. The main coil focuses on extending the static range of the damping force, while the secondary coil focuses on extending the dynamic bandwidth of the system. The combination of the two overcomes the trade-off limitations of a single coil in terms of dynamic range and response speed. The current of the two coils can be dynamically distributed according to the actual vibration energy distribution, avoiding the need for a single coil to maintain an unnecessary large current for a long time to cope with high-frequency, small-amplitude vibrations, thereby improving system energy efficiency.

[0028] The dual-coil magnetorheological damper used in this application provides a physical platform for the adaptive damping control method of the damper. It assigns the strength and velocity dimensions of the damping force to the main and auxiliary coils respectively, supporting independent, precise, and rapid coordinated control of low-frequency body sway and high-frequency wheel bounce. It is a key component in this solution for improving the overall performance of the suspension.

[0029] First Embodiment

[0030] The first embodiment of this application relates to an adaptive damping control method for a vibration damper. For example... Figure 1 As shown, the method may include the following steps:

[0031] S101, acquire multi-source sensor signals, the multi-source sensor signals including road surface preview signal, vehicle body vibration signal and suspension motion signal.

[0032] A forward-facing millimeter-wave radar scans the road surface contour within a predetermined distance in front of the vehicle, generating a road surface pre-aiming signal containing elevation information. A three-axis accelerometer, installed near the vehicle's center of gravity, measures the vehicle's vertical, longitudinal, and lateral acceleration in real time, generating a vehicle vibration signal. Linear displacement sensors installed on each suspension strut monitor the relative displacement between the wheels and the vehicle body in real time, generating suspension motion signals. All signals are timestamped and synchronized via a unified clock source to avoid phase disturbances caused by timing deviations during subsequent signal fusion. They are digitized at a sampling rate of at least 100Hz to form the raw signal set for subsequent processing.

[0033] S102, calculate the time lead of the road surface preview signal relative to the vehicle body vibration signal based on the vehicle's current speed.

[0034] By combining the real-time acquired vehicle speed, the theoretical time lead Δt between the road surface preview signal and the vehicle body vibration signal is dynamically calculated. The physical essence of this lead is that the millimeter-wave radar detects road surface information at a specific distance ahead of the vehicle. The corresponding road surface excitation needs a time delay positively correlated with the current vehicle speed before it is transmitted to the vehicle body and reflected in the vibration signal. Its calculation expression is Δt = d / v (where d is the effective preview distance of the radar, and v is the current real-time vehicle speed). This time lead Δt is the core parameter for subsequent frequency domain phase compensation of the road surface preview signal. The goal is to achieve precise alignment of the forward-looking road surface information and the real-time vibration state of the vehicle body on the time axis, laying a reliable timing foundation for subsequent multi-source signal fusion and control decisions.

[0035] S103, perform frequency and phase compensation on the road surface preview signal based on the time lead; fuse the compensated road surface preview signal, the vehicle body vibration signal and the suspension motion signal to obtain a full-frequency vibration characterization signal.

[0036] First, the road surface pre-aiming signal is converted from the time domain to the frequency domain, and each frequency component is multiplied by a complex compensation factor exp( j 2π f Δt) (where f is the signal frequency and j is the imaginary unit) accurately corrects the signal phase lag caused by the delay in the transmission of road excitation, and finally obtains a compensated pre-aiming signal that is completely aligned with the vehicle vibration signal on the time axis.

[0037] Subsequently, a frequency-domain adaptive weighted fusion strategy was adopted to comprehensively process the compensated road surface anticipation signal, vehicle body vibration signal, and suspension motion signal. For low-frequency signals, the compensated anticipation signal was given higher weight to fully leverage its forward-looking prediction advantage and capture the low-frequency attitude change trend of the vehicle body in advance. For high-frequency signals, the more real-time vehicle body vibration signal and suspension motion signal were given higher weight to ensure accurate response to subtle high-frequency disturbances on the road surface. Finally, a full-frequency vibration characterization signal in the time domain was generated through inverse frequency domain transformation. This signal combines time synchronization and frequency domain optimization characteristics, completely replicating the entire vibration process from road surface excitation to suspension transmission to vehicle body response, providing high-fidelity and high-reliability core input data for subsequent frequency band separation and dual-frequency decoupling control decisions.

[0038] S104, perform frequency band separation on the full-band vibration characterization signal to obtain the low-frequency component characterizing the vehicle body motion and the high-frequency component characterizing the wheel motion.

[0039] This step employs an adaptive filter bank dynamically updated based on recursive least squares. Using the vehicle's current driving state (e.g., real-time speed, vehicle load) as a dynamic reference, it intelligently adjusts the cutoff frequency and filtering characteristics of the separation filter to ensure the separation adapts to the vibration signal characteristics under different operating conditions. Through this filter bank, the low-frequency energy in the full-band vibration signal is resolved into low-frequency components representing vehicle body motion. These low-frequency components reflect the macroscopic attitude movements of the sprung mass, such as pitch, roll, and sway. Simultaneously, the high-frequency energy is resolved into high-frequency components representing wheel motion. These high-frequency components mainly correspond to the local high-frequency vibrations of unsprung masses such as wheels and suspension links. This separation process successfully decouples the originally coupled and superimposed composite vibration signal into two independent components with clear physical meaning and well-defined control objectives, providing a targeted input foundation for the accurate calculation of the subsequent dual-frequency decoupling control model.

[0040] S105, the low-frequency component and the high-frequency component are input into the dual-frequency decoupled control model to obtain the mutually decoupled low-frequency damping force control command and high-frequency damping force control command.

[0041] The low-frequency and high-frequency components are used as input state vectors and fed into a preset dual-frequency decoupling control model for independent parallel computation. This model is constructed in state-space form, and its control gain matrix is ​​specifically designed with a block-diagonal dominant structure, which can calculate control quantities for the low-frequency vehicle body motion channel and the high-frequency wheel motion channel respectively, and effectively suppress dynamic coupling between channels.

[0042] The dual-frequency decoupled control model calculates low-frequency damping force control commands and high-frequency damping force control commands based on real-time component amplitudes, frequencies, and vehicle operating parameters through internal mapping relationships. These two commands are independent in both numerical and physical sense, and their direct control objectives are to suppress low-frequency vehicle body sway and optimize high-frequency wheel grounding, respectively, providing precise force setpoints for subsequent actuator-level independent drives.

[0043] It is not difficult to see that, compared with related technologies, the solution provided in this application, by introducing a phase compensation fusion algorithm, fundamentally solves the problem of time asynchrony between millimeter-wave radar pre-aiming signals and real-time vehicle vibration signals, and achieves accurate alignment between forward-looking information and real-time state; through the collaborative architecture of dual-frequency decoupled control model and dual-coil magnetorheological actuator, the control channels for low-frequency vehicle body sway and high-frequency wheel bounce are decoupled from both the control algorithm and physical hardware levels, overcoming the performance compromise and coupling interference of traditional single actuator solutions under wideband excitation.

[0044] Second Embodiment

[0045] The second embodiment of this application relates to an adaptive damping control method for a vibration damper. The second embodiment is an improvement upon the first embodiment, specifically in that:

[0046] Furthermore, the method further includes: mapping the low-frequency damping force control command and the high-frequency damping force control command to a first current signal for the low-frequency coil and a second current signal for the high-frequency coil in the damper, respectively; and adjusting the damper damping by current control of the dual coils of the damper based on the first current signal and the second current signal. Figure 2 As shown:

[0047] The low-frequency damping force control command and the high-frequency damping force control command are converted into a first current command signal for driving the low-frequency coil and a second current command signal for driving the high-frequency coil inside the vibration damper, respectively, based on a pre-stored damping force-current nonlinear mapping relationship. This mapping relationship is obtained through experimental calibration and model fitting, taking into account nonlinear factors such as magnetic circuit saturation and temperature effects.

[0048] Based on the first and second current command signals, independent real-time closed-loop current control is performed on the dual coils of the vibration damper. This control process can employ a fast current tracking algorithm (such as predictive current control or PID control with feedforward compensation) to drive two independent power amplifier circuits, ensuring that the actual current in the low-frequency and high-frequency coils accurately tracks their respective command values. Through this independent current drive, the magnetic field strength of the two operating regions is changed separately, thereby achieving partitioning, decoupling, and precise adjustment of the magnetorheological fluid damping characteristics, ultimately synthesizing the optimal total damping force that meets the full-frequency control objective.

[0049] Driven by independent currents from dual coils, the magnetic field strength of their respective working areas can be adjusted separately. The low-frequency coil constructs a strong magnetic field through stable current output, providing basic damping to suppress low-frequency vehicle body sway; the high-frequency coil generates a dynamic magnetic field through rapid current changes, precisely addressing the damping requirements of high-frequency wheel vibrations. This zoned control mode achieves decoupled and precise adjustment of the magnetorheological fluid damping characteristics, ultimately enabling the two damping forces to work together to output the optimal total damping force that meets the vibration control target across the entire frequency band, while simultaneously considering vehicle body attitude stability and wheel ground contact.

[0050] Furthermore, the obtained full-frequency vibration characterization signal is as follows: Figure 3 As shown, it includes:

[0051] The phase-compensated road surface preview signal, the vehicle body vibration signal, and the suspension motion signal are fused according to frequency-dependent weights to obtain the full-frequency vibration characterization signal.

[0052] For signal components with frequencies below the first threshold, the weight assigned to the phase-compensated road surface preview signal is greater than the weight assigned to the vehicle body vibration signal;

[0053] For signal components with frequencies higher than the second threshold frequency, the weight assigned to the vehicle vibration signal is greater than the weight assigned to the road surface preview signal after phase compensation.

[0054] The phase-compensated road surface preview signal, vehicle body vibration signal, and suspension motion signal are input into a frequency-dependent adaptive weighted fusion unit. This unit dynamically assigns fusion weights based on the instantaneous frequency components of the signals, allowing each signal to fully play its role in its suitable frequency band: forward-looking signals dominate low-frequency prediction, and real-time feedback signals dominate high-frequency response, achieving a fusion effect of frequency band adaptation and complementary advantages.

[0055] For components in the signal with frequencies below the first threshold frequency f1, their physical meaning mainly corresponds to slow-speed changes in vehicle body attitude (such as roll during steering, pitch during acceleration and deceleration, and heave on uneven road surfaces). These changes can be detected in advance by radar pre-aiming. Therefore, the fusion unit assigns higher weight to the phase-compensated road pre-aiming signal, making it dominant in the low-frequency fusion result. This allows for the prediction of vehicle body attitude trends in advance using forward-looking information, reserving adjustment space for subsequent low-frequency damping control.

[0056] For components in the signal with frequencies above the second threshold frequency f2, these mainly correspond to local vibrations of the wheels and suspension system caused by minor high-frequency bumps on the road surface (such as high-frequency impacts from gravel roads or jointed roads). These vibrations are transient and sudden, requiring extremely high real-time response. Therefore, the fusion unit assigns higher weights to the more real-time vehicle vibration signal (directly reflecting the vibration response) and the suspension motion signal (directly capturing the vibration transmission process), ensuring that the fusion result accurately and without delay reflects high-frequency transient impacts, providing a reliable basis for rapid adjustment of high-frequency damping.

[0057] The first threshold frequency f1 is lower than the second threshold frequency f2, together defining a mid-frequency transition range within which the signal weights transition smoothly. Within this range, the weights of each signal are not abruptly adjusted, but rather dynamically adjusted through a smooth transition algorithm (such as linear interpolation or exponential decay). As the frequency increases from f1 to f2, the weight of the road surface pre-aiming signal gradually decreases, while the weights of the vehicle body vibration signal and suspension motion signal gradually increase. This avoids abrupt changes in the fused signal caused by frequency band switching, ensuring the continuity and stability of the full-frequency signal.

[0058] The fusion unit performs weighted superposition calculations on the three signals according to the aforementioned weight allocation rules, and then generates a full-frequency vibration characterization signal in the time domain through inverse frequency-time domain transformation. This signal possesses three core characteristics: temporal synchronization, with phase compensation and timestamp calibration ensuring complete alignment of multi-source signals on the time axis; frequency band optimization, incorporating forward prediction in the low-frequency band and retaining real-time response in the high-frequency band, resulting in optimal information quality for each frequency band; and physical integrity, fully covering the entire chain from road excitation (pre-aiming signal) to vibration transmission (suspension signal) and then to vehicle body response (vibration signal), accurately characterizing the vehicle's full-frequency vibration state, and providing a high-fidelity, non-redundant core input for subsequent frequency band separation and dual-frequency decoupling control.

[0059] Furthermore, the dual-frequency decoupling control model includes:

[0060] The dual-frequency decoupling control model is expressed in state-space form, and its control law is: u=Kx

[0061] Where u = [u_low, u_high]^T, u_low is the output vector of the low-frequency damping force control command, u_high is the output vector of the high-frequency damping force control command, x is the state vector containing the low-frequency component and the high-frequency component, and K is the control gain matrix;

[0062] The control gain matrix K is configured as a block diagonalized structure, such that the absolute value of the coupling term coefficient between the control gain elements for low-frequency components and the control gain elements for high-frequency components is less than a specified proportion of the absolute value of their respective main diagonal elements.

[0063] This model, through structured design and parameter constraints, fundamentally eliminates unnecessary dynamic interactions and energy transfer between the low-frequency and high-frequency control loops. The low-frequency control channel can focus on ride comfort by optimizing the u_low command to suppress large low-frequency body sway. The high-frequency control channel can independently focus on handling stability by adjusting the u_high command to improve high-frequency wheel grounding, resolving the inherent contradiction of traditional single control channels that compromise on one aspect while addressing another. Although the two channels make independent decisions, based on a unified vehicle state input and global control objective, they can achieve a synergistic improvement in comfort and stability. This avoids both the deterioration of high-frequency grounding caused by large low-frequency damping and the interference of fast high-frequency adjustments on low-frequency attitude control, achieving a performance balance across all operating conditions in both theory and engineering practice.

[0064] To adapt to the complex and ever-changing driving scenarios, the control gain matrix K features online dynamic fine-tuning. Based on real-time vehicle operating conditions (such as real-time vehicle speed, vehicle load, and road surface assessment results), the parameters of each sub-block within the matrix are dynamically adjusted using preset adaptive adjustment rules or machine learning algorithms. For example, when driving at high speeds, the gain weight of the low-frequency control sub-block is appropriately increased to enhance vehicle attitude stability; when driving on bumpy roads, the response sensitivity of the high-frequency control sub-block is improved to optimize wheel contact, ensuring that the model maintains optimal decoupling control performance under different operating conditions and significantly improving system robustness.

[0065] Further, the signals are respectively mapped to a first current signal for the low-frequency coil and a second current signal for the high-frequency coil in the vibration damper, such as... Figure 4 As shown, it includes:

[0066] Establish a current-force dynamic inverse model of the vibration damper, using the coil current, piston speed and the expected damping force in the next cycle as inputs;

[0067] The optimal current value required to achieve the desired damping force is predicted and output through the current-force dynamic inverse model, and used as the corresponding current signal.

[0068] A dynamic inverse model of the current-force relationship is established and maintained: Based on the physical characteristics of the magnetorheological damper (including coil electromagnetic dynamics, magnetic circuit saturation characteristics, and constitutive relations of the magnetorheological fluid), a mathematical model is constructed to characterize the dynamic relationship between the coil input current, piston velocity, and output damping force. Subsequently, the dynamic inverse model of this forward model is obtained through theoretical derivation or system identification methods. This inverse model describes the dynamic relationship for solving the required input current given the current state and the desired output force.

[0069] Real-time current prediction based on an inverse model uses the measured low-frequency / high-frequency coil current values, the current piston speed, and the expected low-frequency / high-frequency damping force calculated by the controller for the next cycle as inputs to the inverse model within each control sampling cycle. This inverse model is then used for forward calculation to predict the optimal current values ​​required to be applied to each coil in the next control cycle to accurately achieve the desired damping force. This prediction process effectively compensates for current response hysteresis caused by coil inductance, magnetic eddy currents, etc.

[0070] The inverse model incorporates a compensation algorithm for the nonlinearity of yield stress caused by the magnetorheological fluid field, the hysteresis effect, and the temperature influence (if temperature sensing is integrated). The predicted optimal current value, after being processed by output limiting and protection logic, is ultimately determined as the first or second current command signal to be output in the current cycle to drive the corresponding coil.

[0071] By using the aforementioned predictive mapping method based on the dynamic inverse model, the damping force command from the upper layer is quickly and accurately converted into the current command from the lower layer actuator, which significantly improves the tracking accuracy and response speed of the current loop, thereby ensuring the final realization effect of dual-frequency damping force control.

[0072] Furthermore, the method also includes:

[0073] Before the low-frequency and high-frequency components are input into the dual-frequency decoupling control model, a feedforward optimization process based on time-series prediction is introduced.

[0074] The low-frequency component, the high-frequency component, and the vehicle state signal are input into a trained recurrent neural network prediction model.

[0075] The recurrent neural network prediction model outputs predicted vibration characteristics in a short time domain in the future.

[0076] Based on the predicted vibration characteristics, the parameters or output damping force control commands of the dual-frequency decoupled control model are optimized by feedforward compensation.

[0077] The low-frequency components representing vehicle body motion, the high-frequency components representing wheel motion, and related vehicle state signals (including at least real-time vehicle speed and steering wheel angle) are combined as input features to form a time-series data sample. This time-series data sample is then input into a pre-trained recurrent neural network prediction model (preferably a Long Short-Term Memory network, LSTM). This model learns the dynamic mapping relationship between historical vibration data and future states, outputting a predicted value for vibration characteristics in a short future time domain. This predicted value includes at least an estimate of the amplitude and trend of the future low-frequency and high-frequency components.

[0078] Based on the predicted short-time-domain vibration characteristics output by the recurrent neural network prediction model, the subsequent control process is optimized using feedforward compensation. Specifically, the optimization method involves one or a combination of the following two approaches: First, based on the predicted characteristics, the parameters of the control gain matrix K in the dual-frequency decoupled control model are fine-tuned in real time, allowing the controller parameters to adapt to anticipated road condition changes in advance. Second, based on the low-frequency and high-frequency damping force control commands output by the dual-frequency decoupled control model, a feedforward compensation command calculated based on the predicted characteristics is superimposed, thus forming a composite control command of feedforward and feedback to actively counteract the predicted upcoming vibration disturbance.

[0079] By introducing this feedforward optimization link based on deep learning time-series prediction, the evolution from real-time response to predictive active suppression reduces the control lag caused by the inherent delays in the sensing, computing, and execution links of the system. This allows the damping force to be adjusted in advance before the disturbance occurs, further improving the ride comfort and stability of the vehicle under transient conditions.

[0080] Furthermore, the current control adjustment of the damper's dual coils to regulate the damper's damping includes:

[0081] Independent closed-loop control of the dual coils is performed based on the first current signal and the second current signal;

[0082] The magnetic field strength signal is detected in real time by a magnetic field sensor installed at the working gap of the magnetorheological fluid and used as a feedback quantity.

[0083] The magnetic field strength signal is compared with the desired magnetic field strength obtained by mapping the first current signal and the second current signal to generate a current correction amount;

[0084] The first current signal and the second current signal are subjected to magnetic field closed-loop feedback correction based on the current correction amount.

[0085] like Figure 5 As shown, based on the first current signal and the second current signal, independent closed-loop current control is performed on the low-frequency coil and high-frequency coil of the vibration damper, respectively. Simultaneously, a magnetic field sensor (such as a Hall sensor array) pre-embedded in the working gap of the magnetorheological fluid is used to detect the actual magnetic field strength signal of the corresponding working area in real time, and this signal is used as a key physical quantity feedback.

[0086] The detected actual magnetic field strength signal is compared with a desired magnetic field strength signal. This desired magnetic field strength signal is calculated using the first current command signal and the second current command signal, based on a pre-calibrated static or dynamic mapping relationship between current and magnetic field. The difference between the two is the magnetic field strength error signal.

[0087] The magnetic field strength error signal is input to a magnetic field regulator (such as a PI controller), which outputs a current correction value. Subsequently, this current correction value is superimposed on the original first current command signal and the second current command signal (or independently corrected for the respective coil regions) to form the final current command after magnetic field closed-loop feedback correction.

[0088] Based on the corrected final current command, the corresponding power amplifier circuits are driven to generate a precise magnetic field in the coil, thereby achieving precise control of the damping characteristics of the magnetorheological fluid and ultimately outputting the target damping force.

[0089] By introducing closed-loop feedback based on direct magnetic field measurement, a magnetic field loop is constructed in the inner layer of the current loop. This effectively compensates for changes in the current-damping force relationship caused by factors such as magnetic circuit nonlinearity, temperature drift, and magnetorheological fluid aging. Thus, at the physical level, this ensures the long-term accuracy, high repeatability, and strong robustness of the damping force control.

[0090] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0091] Third Embodiment

[0092] The third embodiment of this application relates to a vibration damper system. The third embodiment is an improvement upon the first embodiment, specifically in that:

[0093] like Figure 6 As shown, the vibration damper system adopts a four-layer architecture design, including a sensing layer, a processing layer, a decision-making layer, and an execution layer. Data exchange and control command transmission are performed between the layers through a standard interface.

[0094] The perception layer includes a multi-source sensor network consisting of millimeter-wave radar, a triaxial accelerometer sensor group, a linear displacement sensor, a magnetic field sensor, and a vehicle status sensor. This network is used to collect road surface preview signals, vehicle body vibration signals, suspension motion signals, magnetic field strength signals, and vehicle status signals such as vehicle speed and steering angle. The time synchronization mechanism uses a combination of hardware timestamps and software synchronization to ensure that all sensor data have a unified time reference. Redundancy design includes dual-path redundancy configuration for key sensors (such as the vehicle body acceleration sensor). When the primary sensor fails, it automatically switches to the backup sensor, improving system reliability.

[0095] The processing layer includes: a phase compensation algorithm module, which calculates the time lead of the millimeter-wave radar signal relative to the vehicle vibration signal based on the vehicle's current speed, and performs phase compensation on the radar signal in the frequency domain; an adaptive fusion module, which fuses the phase-compensated road surface preview signal, vehicle vibration signal, and suspension motion signal according to frequency-dependent weights to generate a full-band vibration characterization signal; and an LSTM prediction module, which uses a trained long short-term memory network to predict future vibration characteristics based on historical vibration data sequences, and outputs prediction results that include both low-frequency and high-frequency components.

[0096] The decision-making layer includes: a decoupling control matrix module that implements dual-frequency decoupling control in state-space form, with a control law of u = Kx, where the control gain matrix K is configured as a block diagonalized structure, making the absolute value of the coupling term coefficients between the low-frequency control channel and the high-frequency control channel less than the absolute value of their respective main diagonal elements; a dynamic mapping model module containing a current-force dynamic inverse model, used to map low-frequency and high-frequency damping force control commands to corresponding coil current commands, with built-in compensation algorithms for nonlinear factors such as magnetic circuit saturation and temperature effects; and an adaptive parameter module that dynamically adjusts the parameters in the decoupling control matrix according to the real-time vehicle operating status (vehicle speed, load, road surface grade), and switches to a degraded control strategy when the system detects a sensor fault.

[0097] The execution layer includes: a dual current loop control module containing two independent current controllers, used to control the current of the low-frequency coil and the high-frequency coil respectively, employing a predictive current control algorithm to achieve fast and accurate current tracking; a magnetic field feedback loop module based on the actual magnetic field strength detected by the Hall sensor array set at the working gap, comparing it with the desired magnetic field strength, and generating a current correction amount through a magnetic field regulator (PI controller) to perform closed-loop correction of the current command; and a fast response drive module containing dual H-bridge power amplifier circuits, employing an interleaved parallel drive topology to improve the dynamic response speed and stability of the current output.

[0098] The sensing layer's sensors synchronously acquire raw data, which is then converted using an analog-to-digital converter (AD) and timestamped before being transmitted to the processing layer. The processing layer performs phase compensation, adaptive fusion, and frequency band separation on the multi-source signals to obtain low-frequency and high-frequency components, and performs short-time prediction using an LSTM model. The decision layer inputs the processed signals into a dual-frequency decoupled control model to generate decoupled low-frequency and high-frequency damping force control commands, which are then converted into current commands through a dynamic mapping model. The execution layer performs dual-path independent current control based on the current commands and performs real-time correction through a magnetic field feedback loop, ultimately driving the dual-coil magnetorheological damper to output precise damping force. The system continuously monitors the operating status of each module. When a fault is detected, the adaptive parameter module initiates a degradation control strategy to ensure the system's basic functionality under fault conditions.

[0099] It is easy to see that in the embodiments of this application, a closed-loop intelligent control method integrating perception, prediction, decision execution, and correction is constructed by introducing a series of refined control links, such as multi-source signal adaptive fusion, current prediction mapping based on dynamic inverse models, time-series feedforward optimization based on deep learning, and magnetic field closed-loop feedback correction. This not only decouples high- and low-frequency control at the algorithmic level, but also solves key technical challenges such as signal synchronization, response lag, model nonlinearity, and time-varying parameters at the engineering implementation level, thereby improving the overall smoothness, handling stability, and safety boundaries of the vehicle under complex operating conditions.

[0100] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0101] The electronic device includes: one or more processors; and a memory storing computer program instructions that, when executed, cause the processor to perform the steps of the methods provided in any one or more of the above embodiments. Figure 7 An exemplary structural diagram of the electronic device is disclosed. For example... Figure 7 As shown, the electronic device includes one or more processors 1101, a memory 1102, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0102] The electronic device may further include an input device 1103 and an output device 1104. The processor 1101, memory 1102, input device 1103, and output device 1104 may be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0103] Input device 1103 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 1104 may include a display device, auxiliary lighting device (e.g., LED), and haptic feedback device (e.g., vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0104] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0105] In this embodiment, a computer-readable medium stores a computer program / instructions that, when executed by a processor, implement the steps of the methods provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The aforementioned computer-readable medium carries one or more computer-readable instructions.

[0106] The memory 1102 can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 1102, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0107] The memory 1102 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 1102 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1102 may optionally include memory remotely located relative to the processor 1101, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0109] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0110] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, or similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0112] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0113] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0114] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0115] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method for adaptive damping control of a vibration damper, characterized in that, The method includes: Acquire multi-source sensor signals, including road surface preview signals, vehicle body vibration signals, and suspension motion signals; The time lead of the road surface preview signal relative to the vehicle body vibration signal is calculated based on the vehicle's current speed. Frequency and phase compensation is performed on the road surface preview signal based on the time lead; the compensated road surface preview signal, the vehicle body vibration signal, and the suspension motion signal are fused to obtain a full-frequency vibration characterization signal; The process of obtaining the full-frequency vibration characterization signal includes: fusing the phase-compensated road surface preview signal, the vehicle body vibration signal, and the suspension motion signal according to frequency-dependent weights to obtain the full-frequency vibration characterization signal; for signal components with frequencies below a first threshold, the weight assigned to the phase-compensated road surface preview signal is greater than the weight assigned to the vehicle body vibration signal; for signal components with frequencies above a second threshold, the weight assigned to the vehicle body vibration signal is greater than the weight assigned to the phase-compensated road surface preview signal. The full-band vibration characterization signal is subjected to frequency band separation to obtain the low-frequency component characterizing the vehicle body motion and the high-frequency component characterizing the wheel motion. The low-frequency and high-frequency components are input into the dual-frequency decoupled control model to obtain mutually decoupled low-frequency damping force control commands and high-frequency damping force control commands.

2. The method according to claim 1, characterized in that, The method further includes: The low-frequency damping force control command and the high-frequency damping force control command are respectively mapped to a first current signal for the low-frequency coil and a second current signal for the high-frequency coil in the vibration damper. Based on the first current signal and the second current signal, the damper damping is adjusted by current control of the dual coils of the damper.

3. The method according to claim 2, characterized in that, The dual-frequency decoupling control model includes: The dual-frequency decoupling control model is expressed in state-space form, and its control law is: u=Kx Where u = [u_low, u_high]^T, u_low is the output vector of the low-frequency damping force control command, u_high is the output vector of the high-frequency damping force control command, x is the state vector containing the low-frequency component and the high-frequency component, and K is the control gain matrix; The control gain matrix K is configured as a block diagonalized structure, such that the absolute value of the coupling term coefficient between the control gain elements for low-frequency components and the control gain elements for high-frequency components is less than a specified proportion of the absolute value of their respective main diagonal elements.

4. The method according to claim 3, characterized in that, The first current signal, respectively mapped to the low-frequency coil of the vibration damper, and the second current signal, respectively mapped to the high-frequency coil, include: Establish a current-force dynamic inverse model of the vibration damper, using the coil current, piston speed and the expected damping force in the next cycle as inputs; The optimal current value required to achieve the desired damping force is predicted and output through the current-force dynamic inverse model, and used as the corresponding current signal.

5. The method according to claim 4, characterized in that, The method further includes: Before the low-frequency and high-frequency components are input into the dual-frequency decoupling control model, a feedforward optimization process based on time-series prediction is introduced. The low-frequency component, the high-frequency component, and the vehicle state signal are input into a trained recurrent neural network prediction model. The recurrent neural network prediction model outputs predicted vibration characteristics in a short time domain in the future. Based on the predicted vibration characteristics, the parameters or output damping force control commands of the dual-frequency decoupled control model are optimized by feedforward compensation.

6. The method according to any one of claims 2 to 5, characterized in that, The method of adjusting the damper's damping by current control of the dual coils includes: Independent closed-loop control of the dual coils is performed based on the first current signal and the second current signal; The magnetic field strength signal is detected in real time by a magnetic field sensor installed at the working gap of the magnetorheological fluid and used as a feedback quantity. The magnetic field strength signal is compared with the desired magnetic field strength obtained by mapping the first current signal and the second current signal to generate a current correction amount; The first current signal and the second current signal are subjected to magnetic field closed-loop feedback correction based on the current correction amount.

7. An electronic device, characterized in that, The electronic device includes: One or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as described in any one of claims 1 to 6.

8. A computer-readable medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

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