Vehicle control method, control method and vehicle

Abstract

The embodiment of the invention discloses a vehicle control method, a control method and a vehicle, and belongs to the field of vehicle control. The vehicle control method comprises the steps that current state data of a vehicle are obtained, and the current state data comprise at least two types of data representing the state of the vehicle; a preset quadratic form control algorithm is used for processing the current state data, adjusting parameters for the air spring are obtained, and the quadratic form control algorithm comprises a quadratic form performance index and / or a quadratic integral algorithm; and the running state of the air spring is adjusted according to the adjusting parameters. The embodiment of the invention has the technical effects that the vertical dynamic balance of the vehicle is quickly adjusted, and the driving comfort and safety of the vehicle are improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control, and in particular, to a vehicle control method, a control method and a vehicle. Background Art

[0002] In the field of transportation, ensuring the stability and driving comfort of vehicles under various road conditions and load conditions is an important part of optimizing overall transportation efficiency and safety. As a key technology to improve vehicle suspension performance, the performance of the air spring system during vehicle dynamic driving directly affects the stability and comfort of the vehicle.

[0003] However, current vehicles rely on traditional PID control algorithms to control air spring systems, which results in the vehicle being unable to ensure driving stability in complex road environments or under conditions of easily changing loads due to performance limitations of the PID control algorithm. Summary of the invention

[0004] The embodiments of the present application provide a vehicle control method, a control method and a vehicle, so as to at least solve the technical problem of poor driving stability of the vehicle.

[0005] According to a first aspect of an embodiment of the present application, a vehicle control method is provided, wherein the vehicle comprises a frame and a wheel bracket, an air spring for changing the distance between the frame and the wheel bracket is provided between the frame and the wheel bracket, and the method comprises:

[0006] Acquiring current state data of the vehicle, wherein the current state data includes at least two types of data representing the state of the vehicle;

[0007] Processing the current state data using a preset quadratic control algorithm to obtain adjustment parameters for the air spring, wherein the quadratic control algorithm includes an algorithm having a quadratic performance index and / or performing a quadratic integration;

[0008] The operating state of the air spring is adjusted according to the adjustment parameter.

[0009] With this embodiment, when the state data of the vehicle is obtained, the current state data obtained includes at least two types of data, which provides more comprehensive data support for the control of the vehicle, so that when the current state data is used to adjust the operating state of the air spring in the future, it is easier to adjust the vehicle to a steady state under complex road environments and load conditions that are easy to change, thereby improving the driving stability of the vehicle. At the same time, the current state data is processed using a quadratic control algorithm, so that the obtained adjustment parameters can accurately respond to changes in the vehicle state, realize rapid adjustment of the vertical dynamic balance of the vehicle, and improve the driving comfort and safety of the vehicle.

[0010] In combination with the first aspect, in an optional implementation of the embodiment of the present application, the current state data is processed using a preset quadratic control algorithm to obtain adjustment parameters for the air spring, including:

[0011] The current state data and the preset expected index are calculated by the quadratic control algorithm to obtain a target control strategy, wherein the expected index includes an index when the vehicle state meets the preset conditions, and the target control strategy includes a strategy for converging the vehicle state to the expected index;

[0012] The adjustment parameters are determined according to the target control strategy.

[0013] By adopting this implementation method, by presetting the expected index, the quadratic control algorithm can obtain the target control strategy when processing the current state data. Therefore, when the air spring is controlled according to the target control strategy, the state of the vehicle can be adjusted in the direction of convergence to the expected index, thereby improving the stability of the vehicle.

[0014] In combination with the first aspect, in an optional implementation of the embodiment of the present application, the current state data and the preset expected index are calculated by the quadratic control algorithm to obtain a target control strategy, including:

[0015] updating a preset vehicle dynamics model according to the current state data, wherein the vehicle dynamics model is generated using historical state data of the vehicle, and the historical state data includes state data of the vehicle obtained before the current state data;

[0016] The quadratic control algorithm is used to calculate the updated vehicle dynamic model and the expected index to obtain the target control strategy.

[0017] By adopting this implementation method, a vehicle dynamic model is preset, so that the quadratic control algorithm can directly use the data in the vehicle dynamic model when it needs to determine the target control strategy, which makes it easier for the quadratic control algorithm to obtain more comprehensive vehicle status data, improve the accuracy and quality of the target control strategy, and thus improve the stability of the vehicle.

[0018] In combination with the first aspect, in an optional implementation of the embodiment of the present application, the obtaining of the current state data of the vehicle includes:

[0019] Acquire raw state data collected by at least two sensors of the vehicle at a target time, wherein different sensors are used to collect different types of raw state data;

[0020] The original state data is processed by Kalman filtering to obtain the current state data.

[0021] By adopting this implementation method, Kalman filtering can effectively denoise the original state data, thereby improving the data quality of the current state data, facilitating subsequent calculations, improving the accuracy of the adjustment parameters, and improving the driving stability of the vehicle.

[0022] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, before performing Kalman filtering on the original state data to obtain the current state data, the method further includes:

[0023] The raw state data is preprocessed, wherein the preprocessing includes at least one of correcting errors and synchronizing a time baseline.

[0024] By adopting this implementation method, the original state data is preprocessed first, which helps to improve the quality of the original state data after preprocessing, thereby improving the accuracy of subsequent data processing.

[0025] In combination with the first aspect, in an optional implementation manner of the embodiment of the present application, the method further includes:

[0026] When the original state data is processed by Kalman filtering, the vehicle state after the target time is predicted by using the Kalman filtering to obtain predicted state data;

[0027] Processing the predicted state data using the quadratic control algorithm to obtain predicted adjustment parameters for the air spring;

[0028] The operating state of the air spring is adjusted according to the predicted adjustment parameter.

[0029] By adopting this implementation method, the vehicle state is predicted and adjusted, actively preventing the vehicle from being in an unstable state under complex road conditions, and enhancing the response capability to emergencies.

[0030] In combination with the first aspect, in an optional implementation of the embodiment of the present application, the air spring is connected to an air circuit, and a solenoid valve is provided on the air circuit;

[0031] The adjustment parameters include the opening degree and / or opening time of the solenoid valve;

[0032] The step of adjusting the operating state of the air spring according to the adjustment parameter includes:

[0033] The solenoid valve is controlled according to the opening degree and / or opening duration to adjust the flow rate and / or pressure of the compressed air in the air spring.

[0034] By adopting this implementation method, the electromagnetic valve is controlled to change the operating state of the air spring. The control process is simple and convenient, and it is easy to improve the efficiency of restoring the stable operation of the vehicle.

[0035] In combination with the first aspect, in an optional implementation of the embodiment of the present application, the current state data includes multiple types of parameters, wherein the types of parameters include at least two of displacement, vehicle speed, vehicle acceleration, and an inclination angle between the vehicle and a horizontal plane;

[0036] The quadratic control algorithm includes at least one of a linear quadratic regulation algorithm and an H-infinity control algorithm.

[0037] According to a second aspect of an embodiment of the present application, a vehicle control method is provided, which adopts the control method described above.

[0038] According to a third aspect of an embodiment of the present application, a vehicle is provided, the vehicle comprising a frame and a wheel bracket, an air spring for changing the distance between the frame and the wheel bracket being provided between the frame and the wheel bracket;

[0039] The vehicle also includes a controller, which adopts the control method described above.

[0040] In combination with the third aspect, in an optional implementation of the embodiment of the present application, the air spring can be controllably extended or compressed, one end of the air spring is fixedly connected to the vehicle frame, and the other end is fixedly connected to the wheel bracket;

[0041] The vehicle further comprises a gas tank for storing gas for the air spring;

[0042] An air circuit, connecting the air storage tank and the air inlet end of the air spring;

[0043] The solenoid valve is arranged on the air circuit and is used to control the on-off of the air circuit to control the inflation and deflation of the air spring, wherein the air spring is extended or compressed when inflating and deflation.

[0044] According to a fourth aspect of an embodiment of the present application, a suspension system of a vehicle is provided, comprising a parameter acquisition module, a suspension controller, a linear quadratic regulator and a solenoid valve, wherein the solenoid valve is used to control the inflation and deflation of an air spring in the suspension system;

[0045] The parameter acquisition module is used to collect at least two types of current state data representing the vehicle state, and the parameter acquisition module is connected to the suspension controller and is used to transmit the current state data to the suspension controller;

[0046] The suspension controller is connected to the linear quadratic regulator and is used to process the current state data using the linear quadratic regulator to obtain adjustment parameters for the solenoid valve;

[0047] The linear quadratic regulator is connected to the solenoid valve and is used to adjust the duty cycle of the solenoid valve according to the adjustment parameter;

[0048] Wherein, the parameter acquisition module includes an inertial measurement unit, a positioner and an encoder.

[0049] The technical effects obtained in the above-mentioned second to fourth aspects are similar to the technical effects obtained by the corresponding technical means in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of a vehicle control method provided by an embodiment of the present application;

[0051] Figure 2 is a flow chart of a vehicle control method provided by an embodiment of the present application in a specific application;

[0052] Figure 3 It is a structural block diagram of the control part of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0054] It should be understood that the "plurality" mentioned in this article refers to two or more. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second" and so on are used to distinguish between the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the words "first", "second" and so on do not limit the quantity and execution order, and the words "first", "second" and so on do not limit certain different

[0055] In addition, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus.

[0056] First, the terms involved in the embodiments of the present application are introduced.

[0057] LQR: LQR (linear quadratic regulator) is a linear quadratic regulator. LQR can obtain the optimal control law of state linear feedback and is easy to form closed-loop optimal control. LQR optimal control can make the original system achieve better performance indicators at a low cost (in fact, it can also adjust unstable systems), and the method is simple and easy to implement. At the same time, it is easy to simulate the system using the powerful function system of Matlab. Specifically, the role of LQR includes giving a linear system (the control quantity and the state quantity show a linear relationship), and then establishing a cost function (cost function, an indicator for evaluating the quality of control) for this system. Within a certain constraint range, a control sequence is obtained to make the system stable (stability can be understood as maintaining a certain value or tracking a certain curve).

[0058] H-infinity control algorithm: also known as H∞ optimal control, is a metric-based optimization design that uses the H∞ norm as the objective function.

[0059] LQG: The full name of LQG Regulator is Linear-Quadratic-Gaussian Regulator, which is a controller obtained by combining an optimal quadratic linear regulator LQR and an optimal state estimator (Kalman filter).

[0060] Kalman filtering: Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through the system input and output observation data. Since the observation data includes the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process. It performs state estimation in two main steps: prediction and update. In the prediction step, the Kalman filter uses the system's state transition equation to predict the state and covariance matrix at the next moment; in the update step, it compares the observed value with the predicted value, combines the observation noise and the uncertainty of the system model, and updates the system's state estimate and covariance.

[0061] IMU: Also known as inertial measurement unit, it is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object. Generally, an IMU contains three single-axis accelerometers and three single-axis gyroscopes. The accelerometer detects the acceleration signal of the object in the carrier coordinate system in three independent axes, while the gyroscope detects the angular velocity signal of the carrier relative to the navigation coordinate system, measures the angular velocity and acceleration of the object in three-dimensional space, and uses this to calculate the object's attitude.

[0062] In the field of modern commercial transportation, ensuring the stability and driving comfort of vehicles under various road conditions and load conditions is an important part of optimizing overall transportation efficiency and safety. As a key technology to improve vehicle suspension performance, the performance of the air spring system during dynamic driving directly affects the driver's experience and the integrity of the cargo. However, the current commercial vehicle air spring system still has certain limitations when facing complex road environments and load changes, especially in real-time adjustment of suspension parameters to cope with emergencies. Traditional control methods often find it difficult to achieve accurate and efficient performance optimization.

[0063] Limitations of existing technologies

[0064] 1. Due to the limitation of single sensor control, traditional air spring control systems mostly rely on data from a single sensor (such as an accelerometer or pressure sensor) for adjustment. Under complex road conditions, such as bumpy roads, sharp turns or load changes, it is unable to fully capture vehicle dynamics, resulting in slow response or insufficient adjustment, affecting driving stability.

[0065] 2. Lack of predictive adjustment capabilities. When encountering emergencies (such as emergency braking or sudden changes in load), the current system can only respond passively, but lacks active predictive and rapid adjustment capabilities, thereby reducing driving comfort and safety.

[0066] 3. Limitations of the control algorithm. Although the traditional PID (proportional-integral-differential) control algorithm is effective within a certain range, its performance and adaptability are limited when faced with nonlinear and dynamically changing system states, making it difficult to achieve dynamically optimized control effects.

[0067] In order to overcome the above limitations and achieve a higher level of vehicle dynamic control and driving experience, innovative control technology and sensor fusion strategies have become the focus of research and development. In particular, the comprehensive application of advanced algorithms and multi-sensor fusion technology provides a feasible solution to the shortcomings of traditional control methods.

[0068] This application proposes an advanced control strategy that integrates multi-sensor fusion (including IMU, GPS, wheel speed sensor, etc.) and LQR (linear quadratic control), aiming to significantly improve the performance, stability and adaptability of commercial air spring systems to road conditions.

[0069] Based on this, the embodiments of the present application provide a vehicle control method, a control method and a vehicle, which at least solve the following problem:

[0070] 1. Improved response speed and accuracy: Traditional control strategies have limited response speed and accuracy to changes in road surface or vehicle load. Traditional ECAS commercial vehicle systems use classic control, which often cannot respond to load disturbances in a timely manner and is difficult to quickly adjust to the optimal vertical balance state during dynamic driving of the vehicle.

[0071] 2. Optimization of safety and stability: When faced with complex road conditions, such as curved sections, uneven roads or emergency braking situations, the existing air spring system lacks effective adaptive capabilities, which may increase the risk of vehicle instability or rollover.

[0072] At least the following effects:

[0073] This application can monitor and analyze vehicle status and road conditions in real time, automatically adjust air spring parameters, provide accurate and real-time vertical dynamic balance control, and significantly improve vehicle driving stability and driving comfort. At the same time, by integrating intelligent prediction algorithms, the system can make adjustments in advance based on sensor data trend analysis to avoid potential unstable conditions, enhance safety, and reduce response time in emergency situations.

[0074] Have at least one of the following characteristics:

[0075] 1. Abandon the original single sensor input, based on the attitude capture principle of drones, small aircraft and other equipment, adopt multi-sensor fusion technology, utilize the real-time fusion of multiple sensor data, use IMU, GPS, encoder and other aircraft sensor equipment to obtain more comprehensive and accurate vehicle status information, including position, speed, acceleration, inclination, etc., to provide high-precision data support for the control system;

[0076] 2. Integration of LQR control algorithm: Apply LQR control theory to the parameter adjustment of the air spring system. By optimizing the control law, the system can actively and accurately respond to changes in driving conditions, achieve rapid adjustment of the vehicle's vertical dynamic balance, and improve driving comfort and safety.

[0077] 3. Intelligent prediction and preventive control, combined with trend analysis of sensor data, makes predictive adjustments through LQR control, proactively guards against vehicle instability under complex road conditions, such as roll, excessive vibration, etc., and enhances the system's ability to respond to emergencies.

[0078] Next, the vehicle control method provided in the embodiment of the present application is further described. Figure 1The flowchart of the vehicle control method shown is a schematic diagram, wherein the vehicle includes a frame and a wheel bracket, and an air spring for changing the distance between the frame and the wheel bracket is provided between the frame and the wheel bracket, and the method includes the following processing process.

[0079] S100: Acquire current status data of the vehicle.

[0080] The current state data includes at least two types of data representing the state of the vehicle. Specifically, different types of data representing the state of the vehicle can be acquired through different sensors. For example, in an application scenario, the current state data includes the speed of the vehicle, the acceleration of the vehicle, the levelness of the vehicle, etc. The speed of the vehicle can be acquired using a speed sensor, the acceleration can be acquired using an IMU inertial measurement unit, and the levelness can be measured by a gyroscope. It should be noted that the vehicle state refers to a state related to whether the vehicle is stable, such as the horizontal state of the vehicle, the acceleration state, the distance state between the vehicle chassis and the ground, etc.

[0081] S102: Process the current state data using a preset quadratic control algorithm to obtain adjustment parameters for the air spring.

[0082] The quadratic control algorithm includes an algorithm having a quadratic performance index and / or performing quadratic integration. Specifically, the quadratic control algorithm includes an algorithm of a linear quadratic regulator and an algorithm of an LQG controller.

[0083] Since the current state data can reflect the state of the vehicle, such as the distance between the frame and the wheel bracket, and the quadratic control algorithm can calculate the adjustment parameters according to the current state data, the stability of the vehicle can be improved by adjusting the vehicle according to the adjustment parameters. Therefore, this embodiment does not specifically limit the quadratic control algorithm, and at the same time, does not limit the specific calculation process of the adjustment parameters.

[0084] For ease of understanding, LQR is used as an example for explanation. Before the steady state of the vehicle is controlled, the vehicle's state data is collected, including the vehicle's displacement, speed, acceleration, and inclination angle with the horizontal plane. The collected state data is used to form a vehicle dynamic model in time sequence (i.e., the vehicle is simulated). The calculation parameters in LQR are cycled or optimized according to the vehicle dynamic model and preset desired indicators (such as minimizing vehicle body vibration), so that after receiving the vehicle's state data, LQR can output adjustment parameters that can minimize the vehicle's body vibration.

[0085] S104: Adjust the operating state of the air spring according to the adjustment parameter.

[0086] In one embodiment, the adjustment parameter may be a parameter of the air spring itself, such as the extension, contraction, elastic coefficient, etc. of the air spring. The adjustment parameter may also be a parameter of a control device used to control changes in the operating state of the air spring, such as a solenoid valve. The adjustment parameter includes the opening of the solenoid valve. When the opening of the solenoid valve changes, the flow rate and pressure of the compressed air entering the air spring change, thereby changing the operating state of the air spring. Specifically, the operating state of the air spring may include the stiffness and damping of the air spring.

[0087] With this embodiment, when the state data of the vehicle is obtained, the current state data obtained includes at least two types of data, which provides more comprehensive data support for the control of the vehicle, so that when the current state data is used to adjust the operating state of the air spring in the future, it is easier to adjust the vehicle to a steady state under complex road environments and load conditions that are easy to change, thereby improving the driving stability of the vehicle. At the same time, the current state data is processed using a quadratic control algorithm, so that the obtained adjustment parameters can accurately respond to changes in the vehicle state, realize rapid adjustment of the vertical dynamic balance of the vehicle, and improve the driving comfort and safety of the vehicle.

[0088] In a possible embodiment of the present application, the current state data is processed by a preset quadratic control algorithm to obtain adjustment parameters for the air spring, including:

[0089] The current state data and the preset expected index are calculated by the quadratic control algorithm to obtain a target control strategy, wherein the expected index includes an index when the vehicle state meets the preset conditions, and the target control strategy includes a strategy for converging the vehicle state to the expected index;

[0090] The adjustment parameters are determined according to the target control strategy.

[0091] In one embodiment, the preset condition may be set according to actual needs, for example, the preset condition may be that the vibration amplitude of the vehicle body does not exceed 1 cm. This embodiment does not make any specific limitation to this.

[0092] In one embodiment, the current body vibration amplitude of the vehicle may be 2 cm, and the target control strategy output by the quadratic control algorithm only needs to be able to adjust the vehicle in the direction of reducing the body vibration amplitude.

[0093] By adopting this implementation method, by presetting the expected index, the quadratic control algorithm can obtain the target control strategy when processing the current state data. Therefore, when the air spring is controlled according to the target control strategy, the state of the vehicle can be adjusted in the direction of convergence to the expected index, thereby improving the stability of the vehicle.

[0094] Optionally, in an implementation of this embodiment, the current state data and the preset expected index are calculated by the quadratic control algorithm to obtain a target control strategy, including:

[0095] updating a preset vehicle dynamics model according to the current state data, wherein the vehicle dynamics model is generated using historical state data of the vehicle, and the historical state data includes state data of the vehicle obtained before the current state data;

[0096] The quadratic control algorithm is used to calculate the updated vehicle dynamic model and the expected index to obtain the target control strategy.

[0097] In one embodiment, the vehicle dynamics model is constructed by simulation means and is generated based on historical status data of the vehicle.

[0098] In one embodiment, the vehicle dynamic model is updated using the current state data, so that the calculation parameters of the quadratic control algorithm are updated accordingly, thereby ensuring the accuracy of the target control strategy output next time.

[0099] By adopting this implementation method, a vehicle dynamic model is preset, so that the quadratic control algorithm can directly use the data in the vehicle dynamic model when it needs to determine the target control strategy, which makes it easier for the quadratic control algorithm to obtain more comprehensive vehicle status data, improve the accuracy and quality of the target control strategy, and thus improve the stability of the vehicle.

[0100] Optionally, in an implementation of this embodiment, the acquiring the current state data of the vehicle includes:

[0101] Acquire raw state data collected by at least two sensors of the vehicle at a target time, wherein different sensors are used to collect different types of raw state data;

[0102] The original state data is processed by Kalman filtering to obtain the current state data.

[0103] By adopting this implementation method, Kalman filtering can effectively denoise the original state data, thereby improving the data quality of the current state data, facilitating subsequent calculations, improving the accuracy of the adjustment parameters, and improving the driving stability of the vehicle.

[0104] Optionally, in an implementation of this embodiment, before performing Kalman filtering on the original state data to obtain the current state data, the method further includes:

[0105] The raw state data is preprocessed, wherein the preprocessing includes at least one of correcting errors and synchronizing a time baseline.

[0106] By adopting this implementation method, the original state data is preprocessed first, which helps to improve the quality of the original state data after preprocessing, thereby improving the accuracy of subsequent data processing.

[0107] Optionally, in an implementation of this embodiment, the method further includes:

[0108] When the original state data is processed by Kalman filtering, the vehicle state after the target time is predicted by using the Kalman filtering to obtain predicted state data;

[0109] Processing the predicted state data using the quadratic control algorithm to obtain predicted adjustment parameters for the air spring;

[0110] The operating state of the air spring is adjusted according to the predicted adjustment parameter.

[0111] By adopting this implementation method, the vehicle state is predicted and adjusted, actively preventing the vehicle from being in an unstable state under complex road conditions, and enhancing the response capability to emergencies.

[0112] Optionally, in an implementation of this embodiment, the air spring is connected to an air circuit, and a solenoid valve is provided on the air circuit;

[0113] The adjustment parameters include the opening degree and / or opening time of the solenoid valve;

[0114] The step of adjusting the operating state of the air spring according to the adjustment parameter includes:

[0115] The solenoid valve is controlled according to the opening degree and / or opening duration to adjust the flow rate and / or pressure of the compressed air in the air spring.

[0116] By adopting this implementation method, the electromagnetic valve is controlled to change the operating state of the air spring. The control process is simple and convenient, and it is easy to improve the efficiency of restoring the stable operation of the vehicle.

[0117] Optionally, in an implementation of this embodiment, the current state data includes multiple types of parameters, wherein the types of parameters include at least two of displacement, vehicle speed, vehicle acceleration, and an inclination angle between the vehicle and a horizontal plane;

[0118] The quadratic control algorithm includes at least one of a linear quadratic regulation algorithm and an H-infinity control algorithm.

[0119] An embodiment of the present application also provides a vehicle control method, which adopts the control method described above.

[0120] The embodiment of the present application further provides a vehicle, the vehicle comprising a frame and a wheel bracket, an air spring for changing the distance between the frame and the wheel bracket being provided between the frame and the wheel bracket;

[0121] The vehicle also includes a controller, which adopts the control method described above.

[0122] Optionally, in an implementation of this embodiment, the air spring can be controllably extended or compressed, one end of the air spring is fixedly connected to the vehicle frame, and the other end is fixedly connected to the wheel bracket;

[0123] The vehicle further comprises a gas tank for storing gas for the air spring;

[0124] An air circuit, connecting the air storage tank and the air inlet end of the air spring;

[0125] The solenoid valve is arranged on the air circuit and is used to control the on-off of the air circuit to control the inflation and deflation of the air spring, wherein the air spring is extended or compressed when inflating and deflation.

[0126] The present application also provides a vehicle suspension system, such as Figure 3 As shown, it includes a parameter acquisition module, a suspension controller (that is, an ECU suspension controller), a linear quadratic regulator (that is, an LQR integrated controller) and a solenoid valve, wherein the solenoid valve is used to control the charging and discharging of the air spring in the suspension system;

[0127] The parameter acquisition module is used to collect at least two types of current state data representing the vehicle state, and the parameter acquisition module is connected to the suspension controller and is used to transmit the current state data to the suspension controller;

[0128] The suspension controller is connected to the linear quadratic regulator and is used to process the current state data using the linear quadratic regulator to obtain adjustment parameters for the solenoid valve;

[0129] The linear quadratic regulator is connected to the solenoid valve and is used to adjust the duty cycle of the solenoid valve according to the adjustment parameter;

[0130] The parameter acquisition module includes an inertial measurement unit (ie, IMU), a locator (ie, GPS), and an encoder to detect the position, speed, acceleration, and inclination of the vehicle.

[0131] In the above-mentioned embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The steps shown in the relevant flow chart can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flow chart, in some cases, the steps shown or described can be executed in an order different from that here. In other words, the order of steps described in the foregoing embodiments is only an example, and it is also within the scope of protection of the embodiments of the present application to reasonably adjust the order of steps based on the content of the embodiments of the present application.

[0132] In a specific implementation of the embodiment of the present application, the vehicle control method includes the following processing:

[0133] like Figure 2 and 3 As shown, the purpose of this implementation method is to design a multi-sensor fusion air spring control system that can accurately and real-time adjust the vertical dynamic balance of the vehicle, especially under complex road conditions and dynamic load conditions, to ensure the maximum degree of driving safety and driving comfort of the vehicle. The system architecture mainly includes the following core modules:

[0134] 1. Sensor data acquisition and preprocessing module: This module is responsible for collecting real-time data from multiple sensors (such as IMU inertial measurement unit, GPS global positioning system, wheel speed sensor, etc.) and performing preliminary preprocessing, including noise removal, error correction, and time baseline synchronization.

[0135] 2. Data fusion and analysis module: It uses advanced multi-sensor data fusion algorithms, such as Kalman filtering, to fuse and analyze the pre-processed sensor data to obtain data including displacement, velocity, acceleration and inclination. In addition, it builds a comprehensive dynamic model of the vehicle.

[0136] 3. LQR control strategy generation module: Based on the fused and analyzed vehicle status data, as well as the preset comprehensive dynamic model and control performance indicators (such as minimizing body vibration), the LQR (Linear Quadratic Regulator) theory is used to calculate the optimal control strategy that makes the vehicle status converge to the desired target.

[0137] 4. Air spring dynamic adjustment module: This module adjusts the stiffness and damping of the air spring in real time according to the LQR control strategy to meet the vertical dynamic balance requirements of the vehicle. The elastic properties of the spring are changed by precisely controlling the pressure of the gas inside the air spring.

[0138] 5. Intelligent prevention and feedback control module: The system has a predictive algorithm that can predict potential changes in road conditions based on sensor data trends, make preventive control adjustments in advance, and reduce response time in emergency situations. At the same time, the system also has a self-detection function that can automatically identify and repair some problems, increasing the robustness and reliability of the system.

[0139] The specific steps include the following:

[0140] 1. Initialize the multi-sensor fusion module: Ensure that all sensors are connected properly, perform initial data calibration and deviation correction, and set data fusion algorithm parameters, such as the dynamic model of the Kalman filter and the noise statistics.

[0141] Real-time data acquisition and processing: The sensor module monitors the vehicle status in real time, including but not limited to three-dimensional acceleration, angular velocity, position, speed and other information. The data acquisition frequency must meet the real-time control requirements.

[0142] 2. Fusion data to generate vehicle model: The data fusion module processes real-time sensor data to generate a comprehensive model that reflects the current dynamic state of the vehicle.

[0143] 3. LQR control strategy calculation and output: Based on the real-time updated vehicle model, the LQR control strategy generation module calculates the optimal control strategy, including the adjustment of air spring parameters.

[0144] 4. Dynamic adjustment of air spring parameters: Apply the calculated control strategy to the air spring to achieve real-time dynamic adjustment of spring stiffness and damping by adjusting the flow and pressure of compressed air.

[0145] 5. Intelligent prevention and feedback control: The system continuously monitors data trends and takes preventive control measures in advance based on the prediction algorithm. At the same time, the self-detection module regularly checks the system status to ensure the long-term stability and effectiveness of the control system.

[0146] The sequence of serial numbers or introduction of the embodiments of the present application is for description only and does not represent the superiority or inferiority of the embodiments.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0148] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0149] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0150] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital versatile disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiment of the present application may be a non-volatile storage medium, in other words, a non-transient storage medium.

[0151] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. For example, the scene data of the current frame in the three-dimensional virtual scene, the device information of the client, and the scene interaction information involved in the embodiments of this application are all obtained with full authorization.

[0152] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A vehicle control method, characterized in that: The vehicle comprises a frame and a wheel bracket, an air spring for changing the distance between the frame and the wheel bracket is provided between the frame and the wheel bracket, and the method comprises: Acquiring current state data of the vehicle, wherein the current state data includes at least two types of data representing the state of the vehicle; Processing the current state data using a preset quadratic control algorithm to obtain adjustment parameters for the air spring, wherein the quadratic control algorithm includes an algorithm having a quadratic performance index and / or performing a quadratic integration; The operating state of the air spring is adjusted according to the adjustment parameter.

2. The vehicle control method according to claim 1, characterized in that: The method of processing the current state data using a preset quadratic control algorithm to obtain adjustment parameters for the air spring includes: The current state data and the preset expected index are calculated by the quadratic control algorithm to obtain a target control strategy, wherein the expected index includes an index when the vehicle state meets the preset conditions, and the target control strategy includes a strategy for converging the vehicle state to the expected index; The adjustment parameters are determined according to the target control strategy.

3. The vehicle control method according to claim 2, characterized in that: The current state data and the preset expected index are calculated by the quadratic control algorithm to obtain a target control strategy, including: updating a preset vehicle dynamics model according to the current state data, wherein the vehicle dynamics model is generated using historical state data of the vehicle, and the historical state data includes state data of the vehicle obtained before the current state data; The quadratic control algorithm is used to calculate the updated vehicle dynamic model and the expected index to obtain the target control strategy.

4. The vehicle control method according to claim 1, characterized in that: The obtaining of the current status data of the vehicle includes: Acquire raw state data collected by at least two sensors of the vehicle at a target time, wherein different sensors are used to collect different types of raw state data; The original state data is processed by Kalman filtering to obtain the current state data.

5. The vehicle control method according to claim 4, characterized in that: Before performing Kalman filtering on the original state data to obtain the current state data, the method further includes: The raw state data is preprocessed, wherein the preprocessing includes at least one of correcting errors and synchronizing a time baseline.

6. The vehicle control method according to claim 1, characterized in that: The air spring is connected to an air circuit, and a solenoid valve is provided on the air circuit; The adjustment parameters include the opening degree and / or opening time of the solenoid valve; The step of adjusting the operating state of the air spring according to the adjustment parameter includes: The solenoid valve is controlled according to the opening degree and / or opening duration to adjust the flow rate and / or pressure of the compressed air in the air spring.

7. The vehicle control method according to claim 1, characterized in that: The current state data includes multiple types of parameters, wherein the types of parameters include at least two of displacement, vehicle speed, vehicle acceleration, and an inclination angle between the vehicle and a horizontal plane; The quadratic control algorithm includes at least one of a linear quadratic regulation algorithm and an H-infinity control algorithm.

8. A vehicle control method, characterized in that: The control method according to any one of claims 1 to 7 is adopted.

9. A vehicle, characterized in that: The vehicle comprises a frame and a wheel bracket, wherein an air spring for changing the distance between the frame and the wheel bracket is provided between the frame and the wheel bracket; The vehicle also includes a controller, which adopts the control method described in any one of claims 1-7.

10. The vehicle according to claim 9, characterized in that The air spring can be controlled to extend or compress, one end of the air spring is fixedly connected to the vehicle frame, and the other end is fixedly connected to the wheel bracket; The vehicle further comprises a gas tank for storing gas for the air spring; An air circuit, connecting the air storage tank and the air inlet end of the air spring; The solenoid valve is arranged on the air circuit and is used to control the on-off of the air circuit to control the inflation and deflation of the air spring, wherein the air spring is extended or compressed when inflating and deflation.

11. A suspension system for a vehicle, characterized in that: It includes a parameter acquisition module, a suspension controller, a linear quadratic regulator and a solenoid valve, wherein the solenoid valve is used to control the inflation and deflation of the air spring in the suspension system; The parameter acquisition module is used to collect at least two types of current state data representing the vehicle state, and the parameter acquisition module is connected to the suspension controller and is used to transmit the current state data to the suspension controller; The suspension controller is connected to the linear quadratic regulator and is used to process the current state data using the linear quadratic regulator to obtain adjustment parameters for the solenoid valve; The linear quadratic regulator is connected to the solenoid valve and is used to adjust the duty cycle of the solenoid valve according to the adjustment parameter; Wherein, the parameter acquisition module includes an inertial measurement unit, a positioner and an encoder.

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