A multi-working-condition human-simulated intelligent control method and system for a magnetorheological suspension of a passenger vehicle

Abstract

The application relates to the technical field of magnetorheological suspension multi-working-condition human-simulated intelligent control, in particular to a passenger vehicle magnetorheological suspension multi-working-condition human-simulated intelligent control method and system, wherein the passenger vehicle magnetorheological suspension multi-working-condition human-simulated intelligent control method comprises the steps of working condition division, parameter self-adaptation, execution stage controller design and control current calculation. The driving working conditions of the vehicle are divided, an advantageous control algorithm is selected, corresponding controller parameters are matched, the controller parameters are input into corresponding execution stage controllers, the execution stage controllers calculate execution control forces, finally, the calculated execution control forces are converted into control currents to drive magnetorheological dampers to generate coulomb damping forces; and a passenger vehicle magnetorheological suspension multi-working-condition human-simulated intelligent control system is built according to the passenger vehicle magnetorheological suspension multi-working-condition human-simulated intelligent control method. The application has the effect of improving the comfort and steering stability of the vehicle under different working conditions.

Description

Technical Field

[0001] This application relates to the field of multi-condition humanoid intelligent control technology for magnetorheological suspension, and in particular to a multi-condition humanoid intelligent control method and system for magnetorheological suspension of passenger vehicles. Background Technology

[0002] Magnetorheological suspension is an advanced semi-active suspension system. Semi-active suspension has advantages such as low energy consumption, superior performance, simple and reliable structure, and moderate cost, and therefore it has been widely used in the automotive industry.

[0003] Currently, in the research field of magnetorheological suspension for passenger vehicles, human-like intelligent control theory is widely used. This theory achieves intelligent control of the vehicle suspension system by simulating human control behavior, thereby improving the ride smoothness and stability of the vehicle and enhancing the comfort of the ride.

[0004] In the existing technology, there are various control methods for magnetorheological suspension, but different control methods have their own advantages and disadvantages, and are applicable to relatively single working conditions, and cannot simultaneously take into account multiple working conditions; therefore, how to improve the adaptability of magnetorheological suspension to multiple working conditions is an urgent problem to be solved. Summary of the Invention

[0005] In order to simultaneously cope with different operating conditions of a vehicle, this application provides a multi-condition humanoid intelligent control method for magnetorheological suspension of passenger vehicles.

[0006] Firstly, this application provides a multi-condition anthropomorphic intelligent control method for magnetorheological suspension in passenger vehicles, which adopts the following technical solution:

[0007] A multi-condition anthropomorphic intelligent control method for magnetorheological suspension in passenger vehicles includes the following steps:

[0008] Operating condition classification: including information acquisition, model calculation, and driving condition classification;

[0009] Information Acquisition: Acquire vertical motion velocity of vehicle center of gravity, pitch velocity of vehicle center of gravity, roll velocity of vehicle center of gravity, damper top velocity, relative motion velocity of damper, vehicle speed, and steering wheel angle information;

[0010] Model calculation: The information obtained in the information acquisition step is input into the suspension seven-degree-of-freedom model to simulate the dynamic performance of the vehicle under various working conditions;

[0011] Based on the variables shown in the seven-degree-of-freedom model of the suspension, the input variables are selected as follows:

[0012] ,

[0013] in: The vertical velocity of the vehicle's center of gravity; The pitch rate of the vehicle's center of gravity; The angular velocity of the vehicle's center of gravity; The velocity at the top of the damper; The relative velocity of the damper; The vehicle's speed; The steering wheel angle; where the subscript ;

[0014] Driving conditions are classified as follows: Based on the seven-degree-of-freedom model variables of the suspension and the information fusion theory, driving conditions are classified into Class A road conditions, Class B road conditions, turning conditions, acceleration and deceleration conditions, and speed bump conditions.

[0015] Parameter Adaptation: Based on the existing driving conditions, the optimal algorithm and the optimal controller parameter combination for the corresponding driving conditions are preset in advance. Based on the vehicle driving conditions divided in the driving condition division step, the optimal control algorithm and the corresponding controller parameter combination are selected.

[0016] Execution-level controller design: Based on different vehicle driving conditions, corresponding execution-level controllers are designed. These controllers receive combinations of controller parameters obtained from the controller parameter adaptation process, and, combined with vehicle acceleration and relative suspension displacement, calculate the output execution control force of a single controller. The calculation model is as follows:

[0017] ,

[0018] in, for The corresponding weights, The control force required for a single damper to improve comfort; The control force required for a single damper to improve safety;

[0019] The calculation model is as follows:

[0020] ,

[0021] in, and This is the distance from the vehicle's center of gravity to the front and rear axles. and The distance from the center of mass to the horizontal distance between the left and right dampers. , , These are the control forces required to suppress vertical, pitch, and roll vibrations of the vehicle body, respectively. , , Calculations are performed based on the control algorithms used under different operating conditions;

[0022] Control force required to improve safety for a single damper The calculation formula is:

[0023] ,

[0024] in, This represents the numerical value of the ground damping coefficient. This indicates the vibration velocity of the unsprung mass;

[0025] Control current calculation: The execution control force calculated in the execution stage controller design step is converted into control current through the corresponding damper inverse model and input into the current driver so that the magnetorheological damper generates the Coulomb damping force required for vibration suppression.

[0026] By adopting the above technical solution, since different driving conditions are suitable for different control algorithms, the driving conditions of the vehicle must first be divided. Then, based on the division results, the dominant control algorithm is selected, the corresponding controller parameters are matched, and the controller parameters are adjusted through the damping force distribution matrix and the comfort and safety balance matrix. Then, the controller parameters are input into the corresponding execution level controller. The execution level controller then calculates the execution control force by combining the vehicle acceleration and the relative displacement of the suspension. Finally, the calculated execution control force is converted into a control current through the inverse model of the damper, which drives the magnetorheological damper to generate the Coulomb damping force required for vibration suppression under different conditions, thereby reducing the vibration during vehicle driving and improving the comfort and handling stability of the vehicle under different conditions.

[0027] Optionally, in the parameter adaptation step, for Class A and Class B road surfaces, the selection of the dominant control algorithm includes vehicle speed acquisition, vehicle speed judgment, and selection of the control algorithm;

[0028] Vehicle speed acquisition: Obtain vehicle speed via the vehicle speedometer. And set vehicle speed threshold ;

[0029] Speed ​​judgment: When the vehicle is traveling at a certain speed Less than or equal to the vehicle speed threshold When the vehicle is traveling at a low speed, it is determined that the vehicle is moving at a low speed; when the vehicle's speed is... Greater than the vehicle speed threshold At that time, it was determined that the vehicle was traveling at high speed;

[0030] Selection of control algorithm:

[0031] Class A road surface: When driving at low speed, a hybrid control algorithm of ceiling and floor is used; when driving at high speed, a hybrid control algorithm of ADD and floor is used.

[0032] For Class B roads: a fuzzy ground cover hybrid control algorithm is used when driving at low speeds; a finite frequency robust and ground cover hybrid control algorithm is used when driving at high speeds.

[0033] By adopting the above technical solution, given the differences in smoothness between Class A and Class B roads, and the inconsistency in vibration amplitude of the same class of road surface at different vehicle speeds, it is first necessary to detect the vehicle's current driving speed. And set the corresponding vehicle speed threshold. By dividing the vehicle speed into high-speed and low-speed ranges, and then selecting appropriate advantageous control algorithms based on the characteristics of different road surfaces under high-speed and low-speed driving conditions, the system can adapt to various driving conditions and enhance stability and safety during driving.

[0034] Optionally, the selection step of the control algorithm also includes coupled conditions of turning, acceleration / deceleration, and speed bump conditions with Class A road conditions and Class B road conditions; the coupled conditions include at least two different conditions.

[0035] When a turning condition occurs, add a turning control algorithm to the current control algorithm;

[0036] When acceleration or deceleration occurs, an acceleration / deceleration control algorithm is added to the current algorithm;

[0037] When encountering speed bump conditions, a fuzzy control algorithm is used, or a fuzzy control algorithm specifically designed for speed bump conditions is added to the current algorithm.

[0038] By adopting the above technical solutions, the vehicle driving conditions can be classified and analyzed in detail, and all kinds of situations that may be encountered during vehicle driving and their coupling can be fully considered. The superior control algorithms can be selected and applied in a targeted manner. By adjusting the parameters of each controller, the performance optimization of the magnetorheological suspension under multiple conditions can be achieved.

[0039] Optionally, in the parameter adaptation step, for The weights of the comfort and safety balance matrix are dynamically adjusted based on the vehicle speed, specifically by dynamically adjusting the weights of the sprung acceleration damping force and the unsprung acceleration damping force.

[0040] weight The calculation formula is:

[0041] ,

[0042] in, This refers to the vehicle's speed.

[0043] By adopting the above technical solution, the weighted values ​​of the sprung acceleration damping force and the unsprung acceleration damping force can be adjusted in a timely manner according to real-time road conditions and driving conditions, so as to achieve dynamic adjustment of the comfort and safety balance matrix, thereby ensuring the vehicle's handling stability and ride comfort under different driving conditions.

[0044] Optionally, the superior control algorithm includes ceiling control, floor control, ADD control, fuzzy control, finite frequency robust control, turning control, and acceleration / deceleration control.

[0045] By adopting the above technical solutions, the single or combined control of the above-mentioned advantageous control algorithms can cope with a variety of common operating conditions. The advantageous control algorithms corresponding to common vehicle driving conditions are stored in advance. When the corresponding operating condition occurs, the controller parameter combination under the corresponding operating condition is directly called, reducing the reaction time and effectively improving the controller's response efficiency.

[0046] Optionally, the required control current is obtained based on the controller's output control force, the damper's relative displacement, and the relative motion velocity, specifically as follows:

[0047] Based on the corresponding magnetorheological damper, force-velocity and force-displacement performance parameters under different currents are obtained through MTS testing. Based on the obtained parameters, a polynomial inverse model of the magnetorheological damper is established. Combined with the relative displacement of the damper, the control force calculated by the controller can be converted into the driving current.

[0048] The expression for the polynomial inverse model is:

[0049] ,

[0050] in, For the output control force of a single controller, This represents the relative displacement of the damper. The relative velocity of the damper.

[0051] By adopting the above technical solution, the output control force of the controller is converted into control current through the corresponding damper inverse model and input into the current driver, so that the magnetorheological damper generates the Coulomb damping force required for vibration suppression, thereby realizing the dynamic adjustment of the vehicle suspension system. This can effectively reduce the vibration and impact of the vehicle during driving, which not only improves the ride comfort of the vehicle, but also enhances the handling stability of the vehicle under different driving conditions.

[0052] Secondly, the multi-condition anthropomorphic intelligent control system for magnetorheological suspension of passenger vehicles provided in this application adopts the following technical solution:

[0053] A multi-condition anthropomorphic intelligent control system for magnetorheological suspension in passenger vehicles includes:

[0054] Information acquisition module: includes an acceleration sensor, a relative displacement sensor, a three-axis gyroscope, a speedometer, a steering wheel angle sensor, an intelligent controller, and a current driver; used to acquire vehicle speed, acceleration, relative displacement, and steering wheel angle information;

[0055] Working condition classification module: The input end is electrically connected to the output end of the information acquisition module; it is used to receive vehicle driving information and input the acquired vehicle driving information into the suspension seven-degree-of-freedom model, and based on information fusion theory, classify driving conditions into Class A road conditions, Class B road conditions, turning conditions, acceleration and deceleration conditions, and speed bump conditions.

[0056] Parameter adaptive module: The input end is electrically connected to the output end of the information acquisition module and the working condition division module; it is used to select the advantageous control algorithm and the corresponding controller parameter combination according to the vehicle driving information and vehicle driving conditions, and adjust the controller parameters through the damping force distribution matrix and the comfort and safety balance matrix.

[0057] The execution-level controller module has its input terminals connected to the output terminals of the information acquisition module, the working condition division module, and the parameter adaptation module, respectively. It is used to design the corresponding execution-level controller based on the vehicle driving conditions divided in the working condition division module. The execution-level controller receives the controller parameter combination obtained from the controller parameter adaptation step, and calculates the execution control force by combining the acceleration and relative displacement from the information acquisition module.

[0058] Current drive module: The input terminal is electrically connected to the output terminal of the execution level controller module; the execution control force calculated in the execution level controller module is converted into control current through the corresponding damper inverse model and input to the current driver, so that the magnetorheological damper generates the Coulomb damping force required for vibration suppression, thereby achieving the suppression of vehicle body vibration.

[0059] In summary, this application includes at least one of the following beneficial technical effects:

[0060] 1. This application divides the vehicle's driving conditions, selects the dominant control algorithm based on the division results, matches the corresponding controller parameters, and adjusts the controller parameters through a damping force distribution matrix and a comfort and safety balance matrix. The controller parameters are then input into the corresponding execution-level controller. The execution-level controller then calculates the execution control force by combining the vehicle acceleration and the relative displacement of the suspension. Finally, the calculated execution control force is converted into a control current through the inverse model of the damper, which drives the magnetorheological damper to generate the Coulomb damping force required for vibration suppression under different operating conditions, thereby reducing the vibration of the vehicle during driving and improving the vehicle's comfort and handling stability.

[0061] 2. This application addresses the differences in road surface smoothness across different grades and the inconsistency in vibration amplitude of the same grade of road surface at different vehicle speeds. By dividing the vehicle speed into high-speed and low-speed ranges, and then selecting appropriate advantageous control algorithms based on the characteristics of different grades of road surface under high-speed and low-speed driving conditions, this application adapts to various driving conditions and enhances stability and safety during driving.

[0062] 3. This application can classify and comprehensively analyze vehicle driving conditions in detail, fully consider various situations that may be encountered during vehicle driving and their coupling, select advantageous control algorithms in a targeted manner and apply them in a comprehensive manner, and achieve performance optimization of magnetorheological suspension under multiple working conditions by adjusting the parameters of each controller. Attached Figure Description

[0063] Figure 1 This is a flowchart of Embodiment 1 of this application;

[0064] Figure 2 This is a flowchart of Embodiment 2 of this application;

[0065] Figure 3 This is a flowchart of Embodiment 3 of this application. Detailed Implementation

[0066] The following combination Figures 1 to 3 This application will be described in further detail.

[0067] This embodiment discloses a multi-condition humanoid intelligent control method for magnetorheological suspension of passenger vehicles. Example

[0068] Reference Figure 1 A multi-condition humanoid intelligent control method for magnetorheological suspension in passenger vehicles includes the following steps:

[0069] Operating condition classification: This includes information acquisition, model calculation, and driving condition classification.

[0070] Information Acquisition: The vehicle's vertical velocity of the center of gravity, pitch velocity of the center of gravity, roll velocity of the center of gravity, damper top velocity, relative velocity of the damper, vehicle speed, and steering wheel angle are acquired through a gyroscope at the vehicle's center of gravity, an accelerometer and relative displacement sensor at the damper, a speedometer, and steering wheel angle information.

[0071] Model calculation: The vehicle information obtained in the information acquisition step is input into the suspension seven-degree-of-freedom model. The suspension seven-degree-of-freedom model includes seven degrees of freedom, such as the vertical motion of the vehicle body, roll angle displacement, pitch angle displacement, and vertical hop of the four wheels, which can comprehensively simulate the dynamic performance of the vehicle under various working conditions.

[0072] Based on the variables shown in the seven-degree-of-freedom model of the suspension, the input variables are selected as follows:

[0073] ,

[0074] in: The vertical velocity of the vehicle's center of gravity; The pitch rate of the vehicle's center of gravity; The angular velocity of the vehicle's center of gravity; The velocity at the top of the damper; The relative velocity of the damper; The vehicle's speed; The steering wheel angle; where the subscript .

[0075] Driving conditions are classified as follows: Based on the seven-degree-of-freedom model variables of the suspension and referring to the "Measuring Data Report of Road Surface Spectrum of Mechanical Vibration" (Standard No.: GB / T 7031-2005), the driving conditions of the vehicle are classified into Class A road conditions, Class B road conditions, turning conditions, acceleration and deceleration conditions, and speed bump conditions, taking into account the magnitude and influence of various variable values ​​and combining the DS evidence information fusion theory.

[0076] Class A road conditions: The road surface is relatively smooth, and the vehicle body vibration is small during driving. Only occasional large vibrations need to be suppressed.

[0077] Class B road conditions: The road surface is relatively complex, and the vehicle body vibrates greatly during driving, and the vibration is random and continuous.

[0078] Cornering conditions: When a vehicle turns, the body will tilt to a certain extent due to centrifugal force;

[0079] Acceleration and deceleration conditions: When a vehicle accelerates or decelerates, the vehicle body will undergo a pitch change, which will change the position of the vehicle's center of gravity.

[0080] Speed ​​bump condition: Under speed bump conditions, the vehicle's posture is mainly manifested in vertical bouncing and pitching changes.

[0081] Parameter Adaptation: Based on the existing driving conditions, the optimal algorithm and the optimal controller parameter combination for the corresponding driving conditions are preset in advance. Based on the vehicle driving conditions divided in the driving condition division step, the optimal control algorithm and the corresponding controller parameter combination are selected. The optimal control algorithms usually include ceiling control, floor control, ADD control, fuzzy control, frequency-limited robust control, turning control and acceleration / deceleration control.

[0082] Specifically, suspension control adjusts the stiffness and damping of the suspension system in real time by sensing the vehicle's vertical motion and external road conditions, significantly reducing vehicle vibration; floor control adjusts the stiffness and damping of the suspension system in real time by sensing the vehicle's lateral tilt and lateral motion to improve lateral stability and cornering performance, focusing on enhancing vehicle handling safety and stability; ADD control uses the vehicle's acceleration to calculate reference damping, showing significant improvement in high-frequency vibrations; fuzzy control uses a fuzzy controller to perform fuzzy inference based on the vehicle's motion and road conditions, and adjusts the stiffness and damping of the suspension system based on the inference results; finite-frequency robust control improves the system's robustness and stability, and is suitable for various uncertainties and disturbances; cornering control controls the suspension system when the vehicle is cornering to improve handling and stability; acceleration / deceleration control adjusts the damping characteristics of the suspension to control the vehicle's pitch motion during acceleration or deceleration, improving ride comfort and handling stability.

[0083] Execution-level controller design: Based on different vehicle driving conditions, corresponding execution-level controllers are designed. The execution-level controllers include ceiling controllers, floor controllers, ADD controllers, fuzzy controllers, limited-frequency robust controllers, cornering controllers, and acceleration / deceleration controllers. The execution-level controllers receive the controller parameter combinations obtained from the controller parameter adaptation step, and calculate the optimal control force based on the vehicle damping force distribution matrix and the comfort and safety balance matrix, combined with the vehicle acceleration and suspension relative displacement.

[0084] The vehicle damping force distribution matrix allocates the control gain of each controller according to the magnitude of the vehicle pitch and roll attitude under different vehicle driving conditions in order to suppress the vehicle attitude; the comfort and safety balance matrix is ​​used to adjust the balance between the sprung acceleration suppression force and the unsprung acceleration suppression force.

[0085] The expression for the vehicle damping force distribution matrix is:

[0086] ,

[0087] in, and This is the distance from the vehicle's center of gravity to the front and rear axles. and The distance from the center of mass to the horizontal distance between the left and right dampers. , , These are the control forces required to suppress vertical, pitch, and roll vibrations of the vehicle body, respectively. , , Calculations are performed based on the control algorithms used under different operating conditions.

[0088] For example, during acceleration and deceleration, a vehicle will experience significant pitch. To reduce the pitch angle and improve ride comfort, it is necessary to enhance the control of pitch vibration. To reduce the vehicle's pitch angle and improve ride comfort; pitch vibration control force The calculation involves inputting vehicle driving parameters into the corresponding acceleration / deceleration control algorithm to obtain the control force. .

[0089] Control force required to improve safety for a single damper The calculation formula is:

[0090] ,

[0091] in, This represents the numerical value of the ground damping coefficient. This indicates the vibration velocity of the unsprung mass; and The values ​​can be obtained directly or indirectly without calculation;

[0092] Single controller outputs control force The calculation formula is:

[0093] ,

[0094] in, The adjustment function is determined based on experience and experimental data. The control force required for a single damper to improve comfort; The control force required for a single damper to improve safety is the output force of the canopy control algorithm.

[0095] The suspension control algorithm can sense the vehicle's lateral tilt and lateral movement and adjust the stiffness and damping of the suspension system in real time to generate a force opposite to the direction of unsprung mass movement. This suppresses wheel bounce, improves wheel grip, and reduces roll and yaw. Under extreme driving conditions such as high-speed driving or sharp turns, the increased wheel grip can significantly improve vehicle stability and safety. In complex and changing road environments, improved stability and safety help drivers better control the vehicle, avoid accidents, and improve vehicle safety. Therefore, it is suitable as a control algorithm to enhance safety.

[0096] Control current calculation: The optimal control force calculated in the execution stage controller design step is converted into a control current through the corresponding damper inverse model and input into the current driver, so that the magnetorheological damper generates the Coulomb damping force required for vibration suppression.

[0097] Specifically, based on the corresponding magnetorheological damper, force-velocity and force-displacement performance parameters under different currents are obtained through MTS testing. Based on the obtained parameters, a polynomial inverse model of the magnetorheological damper is established. Then, combined with the relative displacement and relative motion velocity of the damper, the control force calculated by the controller can be converted into driving current.

[0098] The expression for the polynomial inverse model is:

[0099]

[0100] in, For the output control force of a single controller, This represents the relative displacement of the damper. The relative velocity of the damper.

[0101] The implementation principle of Embodiment 1 of this application is as follows: First, based on the seven-degree-of-freedom model of the suspension, different working conditions during vehicle driving are distinguished. Different advantageous control algorithms and corresponding controller parameters are matched according to different working conditions. The controller parameters are adjusted through the damping force distribution matrix and the comfort and safety balance matrix. Then, the corresponding controller parameters are input to the corresponding execution level controller. The execution level controller then calculates the execution control force by combining the vehicle acceleration and the relative displacement at the suspension. Finally, the calculated execution control force is input to the inverse model of the damper. The inverse model of the damper converts the execution control force into a control current. The control current drives the magnetorheological damper to generate the Coulomb damping force required for vehicle vibration suppression under different driving conditions, thereby reducing the vibration of the vehicle during driving. Example

[0102] Reference Figure 2 The difference between this embodiment and embodiment 1 is that, in the parameter adaptation step, for Class A road surfaces and Class B road surfaces, the selection of the dominant control algorithm includes vehicle speed acquisition, vehicle speed judgment, and selection of the control algorithm.

[0103] Vehicle speed acquisition: Obtain vehicle speed via the vehicle speedometer. And set vehicle speed threshold Based on relevant regulations and practical experience, the vehicle speed threshold is set to 30km / h.

[0104] Speed ​​judgment: When the vehicle is moving Speed ​​less than or equal to vehicle speed threshold When the vehicle is traveling at a low speed, it is determined that the vehicle is moving at a low speed; when the vehicle's speed is... Greater than the vehicle speed threshold At that time, it was determined that the vehicle was traveling at high speed;

[0105] Selection of control algorithm:

[0106] Class A road surface: At low speeds, a hybrid ceiling-floor control algorithm is used, which combines the advantages of ceiling control and floor control. By adjusting the ratio of the two through weighting coefficients, it simultaneously improves vehicle ride comfort and handling stability. At high speeds, an ADD-floor hybrid control algorithm is used, which combines the advantages of ceiling control and floor control, and can simultaneously consider vehicle comfort and handling stability.

[0107] Class B road surface: At low speeds, a fuzzy damping hybrid control algorithm is used. This algorithm combines fuzzy control and damping control methods, taking the vibration velocity and acceleration of the vehicle body and wheels as inputs. Through joint control of fuzzy control and damping, the optimal damper damping control force is obtained, significantly improving suspension performance to adapt to real-time changes in vehicle operating conditions. At high speeds, a finite-frequency robust and damping hybrid control algorithm is used. The combination of finite-frequency robust and damping hybrid control algorithms provides the vehicle suspension system with an effective control method that ensures robustness, adaptively adjusts according to road conditions, and improves ride comfort and handling stability.

[0108] Specifically, the selection step of the control algorithm also includes coupled conditions of turning, acceleration / deceleration, and speed bump conditions with Class A and Class B road conditions; the coupled conditions include at least two different conditions.

[0109] When a turning condition occurs, a turning control algorithm is added to the current control algorithm. The turning control algorithm can monitor the vehicle's steering angle and lateral acceleration in real time, and effectively reduce body roll during turning by adjusting the stiffness and damping of the suspension, thereby improving the vehicle's handling performance.

[0110] When acceleration and deceleration occur, an acceleration and deceleration control algorithm is added to the current algorithm. The acceleration and deceleration control algorithm will dynamically adjust the damping force of the suspension according to the vehicle's acceleration and deceleration to reduce pitch motion during acceleration and deceleration and ensure smooth vehicle driving.

[0111] When encountering speed bumps, the system directly uses a fuzzy algorithm or adds a fuzzy control algorithm specifically designed for speed bump conditions to the current algorithm. When encountering speed bumps, the system will identify the unevenness of the road surface and automatically adjust the stiffness and damping of the suspension to absorb the impact, reduce the impact on passengers, and thus improve ride comfort.

[0112] The implementation principle of Embodiment 2 of this application is as follows: Addressing the issue of inconsistent vehicle vibration amplitudes at different speeds on different road surfaces and even on the same road surface, the current vehicle speed is first detected, and a corresponding speed threshold is set to distinguish between high-speed and low-speed driving. Then, different advantageous control algorithms are adopted based on the characteristics of different road surfaces under high-speed and low-speed driving conditions, thereby adapting to different driving conditions, reducing vibration during driving, enhancing vehicle stability, and improving ride comfort. Furthermore, the vehicle driving conditions and potential coupled conditions are classified and comprehensively analyzed in detail, and advantageous control algorithms are selected and applied in a targeted manner. By adjusting the parameters of each controller, the performance optimization of the magnetorheological suspension under multiple conditions is achieved. Through intelligent control of various conditions and coupled conditions, the vehicle suspension system can more accurately adapt to various complex driving environments, ensuring vehicle stability and comfort under different conditions.

[0113] In another implementation, during the parameter adaptation step, for The weights of the comfort and safety balance matrix are dynamically adjusted based on the vehicle speed, specifically by dynamically adjusting the weights of the sprung acceleration damping force and the unsprung acceleration damping force.

[0114] weight The calculation formula is:

[0115] ,

[0116] in, This refers to the vehicle's speed.

[0117] By dynamically adjusting the controller parameters through a balance matrix of comfort and safety, energy feeding efficiency and drive efficiency can be improved, thereby enhancing the smoothness and safety of vehicle operation.

[0118] In other implementations, based on common vehicle driving conditions, advantageous control algorithms corresponding to common vehicle driving conditions are stored in advance. When such a condition occurs, the controller parameter combination for the corresponding condition is directly called in the controller parameter adaptation step, and the corresponding controller is used. This can reduce reaction time and effectively improve the controller's response efficiency. Example

[0119] Reference Figure 3 This application also discloses a multi-condition humanoid intelligent control system for magnetorheological suspension of passenger vehicles, including the following modules:

[0120] Information acquisition module: accelerometer, relative displacement sensor, three-axis gyroscope, speedometer, steering wheel angle sensor, intelligent controller and current driver; used to acquire key vehicle driving information in real time, such as vehicle speed, acceleration, relative displacement and steering wheel angle; through the precise measurement of these sensors, the vehicle's driving condition can be accurately monitored.

[0121] Operating condition classification module: The input end is electrically connected to the output end of the information acquisition module; it is used to receive vehicle driving information and input the acquired vehicle driving information into the suspension seven-degree-of-freedom model. Based on information fusion theory, the driving conditions are classified into different categories, including Class A road conditions, Class B road conditions, turning conditions, acceleration and deceleration conditions, and speed bump conditions. Through operating condition classification, the dynamic response of the vehicle under different driving conditions can be simulated and analyzed more accurately, thereby providing data support for the optimization of the suspension system.

[0122] Parameter Adaptive Module: The input terminal is electrically connected to the output terminal of the operating condition division module; it is used to select the most suitable control algorithm and automatically adjust the corresponding controller parameter combination according to the specific needs of different vehicle driving conditions, and adjust the controller parameters through the damping force distribution matrix and the comfort and safety balance matrix to ensure that the vehicle performance is always in the best state.

[0123] The execution-level controller module has its input terminals connected to the output terminals of the information acquisition module, the working condition division module, and the parameter adaptation module, respectively. It is used to design the corresponding execution-level controller based on the vehicle driving conditions divided in the working condition division module. The execution-level controller receives the controller parameter combination obtained from the controller parameter adaptation step and calculates the optimal control force by combining the acceleration and relative displacement data obtained from the information acquisition module.

[0124] Current drive module: The input terminal is connected to the output terminal of the actuator-level controller module; it receives the optimal control force calculated in the actuator-level controller module, and converts the calculated optimal control force into a control current through the corresponding damper inverse model. The control current is input to the current driver, which in turn causes the magnetorheological damper to generate the Coulomb damping force required for vibration suppression, so as to achieve the suppression of vehicle body vibration.

[0125] Furthermore, in the parameter adaptive module, for Class A and Class B roads, the corresponding vehicle speeds are obtained and speed thresholds are set to distinguish between high and low speeds. When driving on Class A roads, a hybrid control algorithm of ceiling and floor is used at low speeds and an ADD-floor hybrid control algorithm is used at high speeds. When driving on Class B roads, a fuzzy floor hybrid control algorithm is used at low speeds and a finite-frequency robust and floor hybrid control algorithm is used at high speeds.

[0126] Furthermore, the control algorithm selection module also includes coupled conditions for turning, acceleration / deceleration, and speed bump conditions with Class A and Class B road surface conditions. When a turning condition occurs, a turning control algorithm needs to be added to the current control algorithm; when an acceleration / deceleration condition occurs, an acceleration / deceleration control algorithm needs to be added to the current algorithm; and when a speed bump condition is encountered, a fuzzy control algorithm specifically designed for speed bump conditions needs to be added to the current algorithm.

[0127] Furthermore, the comfort and safety balance matrix can dynamically adjust the weighted values ​​of the sprung acceleration damping force and the unsprung acceleration damping force according to the vehicle speed. Through this dynamic adjustment mechanism, the system can better cope with the vehicle state under different operating conditions and improve the smoothness and safety of vehicle driving.

[0128] Furthermore, based on common vehicle driving conditions, corresponding advantageous control algorithms are stored in advance. When such a condition occurs, the controller parameter combination for the corresponding condition is directly called in the controller parameter adaptive step, and the corresponding controller is used. This can reduce reaction time and effectively improve the controller's response efficiency.

[0129] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A multi-condition anthropomorphic intelligent control method for magnetorheological suspension in passenger vehicles, characterized in that: The method comprises the following steps: Working condition division: including information acquisition, model calculation, and driving working condition division; Information acquisition: acquiring the vertical motion speed of the vehicle body mass center, the pitch angle speed of the vehicle body mass center, the roll angle speed of the vehicle body mass center, the top speed of the damper, the relative motion speed of the damper, the vehicle driving speed, and the steering wheel angle information; Model calculation: inputting the information acquired in the information acquisition step into the suspension seven-degree-of-freedom model to simulate the dynamic performance of the vehicle under various working conditions; According to the variables shown in the suspension seven-degree-of-freedom model, the input variables are selected as: ， wherein: is the vertical motion velocity of the vehicle body mass center; is the pitch angular velocity of the vehicle body mass center; is the roll angular velocity of the vehicle body mass center; is the damper top end velocity; is the damper relative motion velocity; is the vehicle running speed; is the steering wheel rotation angle; wherein subscript ; Driving working condition division: according to the variables of the suspension seven-degree-of-freedom model, based on the information fusion theory, the driving working condition is divided into A-class road working condition, B-class road working condition, turning working condition, acceleration and deceleration working condition, and deceleration zone working condition; Parameter self-adaptation: according to the existing driving working condition, the parameter combination of the advantage control algorithm and the advantage controller under the corresponding working condition is preset in advance, and according to the vehicle driving working condition divided in the working condition division step, the advantage control algorithm and the corresponding controller parameter combination are selected; The execution level controller is designed according to different vehicle driving conditions, and receives the controller parameter combination obtained from the controller parameter adaptive step, combines the vehicle body acceleration and the suspension relative displacement, and calculates the output execution control force of a single controller, wherein the output execution control force of the single controller is calculated according to the following calculation model: The calculation model is as follows: ， wherein is corresponding weight, is the control force required for a single damper to improve comfort; is the control force required for a single damper to improve safety; The computational model is as follows: ， wherein, and is the distance of the vehicle body mass center from the front and rear axles, and is the horizontal distance of the mass center from the left and right dampers, , , are the control forces required to suppress the vertical, pitch and roll vibrations of the vehicle body, respectively, , , are calculated according to the control algorithm used under different working conditions; Control force required for a single damper to improve safety The formula for calculating the control force required for a single damper to improve safety is: ， wherein denotes the value of the ground damping coefficient, denotes the vibration velocity of the unsprung mass; Control current calculation: the execution control force calculated in the execution level controller design step is converted into control current through the corresponding damper inverse model and input into the current driver to make the magneto-rheological damper generate the required Coulomb damping force.

2. The multiphase human-simulated intelligent control method for the magnetorheological suspension of the passenger vehicle according to claim 1, characterized in that: In the parameter self-adaptation step, for A-class road and B-class road, the selection of the advantage control algorithm includes vehicle speed acquisition, vehicle speed judgment, and control algorithm selection; Speed acquisition: the vehicle driving speed is acquired through a vehicle driving speed meter and a speed threshold is set ; Speed judgment: when the vehicle speed is less than or equal to a speed threshold , the vehicle is determined to be traveling at low speed; when the vehicle speed is greater than the speed threshold , the vehicle is determined to be traveling at high speed; Control algorithm selection: A-class road: when driving at low speed, the sky and ground hybrid control algorithm is adopted; when driving at high speed, the ADD ground hybrid control algorithm is adopted; B-class road: when driving at low speed, the fuzzy ground hybrid control algorithm is adopted; when driving at high speed, the finite frequency robust and ground hybrid control algorithm is adopted.

3. The multiphase human-simulated intelligent control method for the magnetorheological suspension of the passenger vehicle according to claim 2, characterized in that: In the control algorithm selection step, it also includes the coupling working condition of the turning working condition, the acceleration and deceleration working condition, and the deceleration zone working condition with the A-class road working condition and the B-class road working condition; the coupling working condition at least includes two different working conditions; When the turning working condition occurs, the turning control algorithm is added in the current control algorithm; When the acceleration and deceleration working condition occurs, the acceleration and deceleration control algorithm is added in the current algorithm; When the deceleration zone working condition is encountered, the fuzzy control algorithm or the fuzzy control algorithm specially designed for the deceleration zone working condition is added in the current algorithm.

4. The multiphase human-simulated intelligent control method for the magnetorheological suspension of the passenger vehicle according to claim 1, characterized in that: In the parameter adaptation step, for The weights of the comfort and safety balance matrix are dynamically adjusted based on the vehicle speed, specifically by dynamically adjusting the weights of the sprung acceleration damping force and the unsprung acceleration damping force. Weight The calculation formula is: ， wherein, is the vehicle travel speed.

5. The multiphase human-simulated intelligent control method for magnetorheological suspension of a passenger vehicle according to claim 1, characterized in that: The advantage control algorithm includes sky control, ground control, ADD control, fuzzy control, finite frequency robust control, turning control, and acceleration and deceleration control.

6. The multiphase human-simulated intelligent control method for magnetorheological suspension of a passenger vehicle according to claim 1, characterized in that: According to the output control force of the controller, the relative displacement and relative motion speed of the damper, the required control current is obtained, specifically: Based on the corresponding magneto-rheological damper, the force-velocity and force-displacement performance parameters under different currents are obtained through MTS test, a polynomial inverse model of the magneto-rheological damper is established according to the obtained parameters, and the control force calculated by the controller is converted into driving current in combination with the relative displacement and relative speed of the damper; The expression of the polynomial inverse model is: ， wherein, is the output control force of the single controller, is the relative displacement of the damper, is the relative velocity of the damper.

7. A passenger vehicle magnetorheological suspension multi-working-condition human-simulated intelligent control system, the control system being suitable for the control method as claimed in any one of claims 1-6, characterized in that, It includes: Information acquisition module: including acceleration sensor, relative displacement sensor, three-axis gyroscope, speedometer, steering wheel angle sensor, intelligent controller, and current driver; For obtaining the vehicle speed, acceleration, relative displacement and steering wheel angle information of the vehicle; The working condition division module: the input end is connected with the output end of the information acquisition module; it is used for receiving the vehicle driving information and inputting the obtained vehicle driving information into the suspension seven-degree-of-freedom model, and dividing the driving working condition into A-class road working condition, B-class road working condition, turning working condition, acceleration and deceleration working condition and deceleration zone working condition based on the information fusion theory; The parameter adaptive module: the input end is connected with the output end of the information acquisition module and the working condition division module; it is used for selecting the advantage control algorithm and the corresponding controller parameter combination according to the vehicle driving information and the vehicle driving working condition, and adjusting the controller parameters through the damping force distribution matrix and the comfort and safety balance matrix; The execution level controller module: the input end is connected with the output end of the information acquisition module, the working condition division module and the parameter adaptive module respectively; According to the vehicle driving working condition divided in the working condition division module, the corresponding execution level controller is designed, the execution level controller receives the controller parameter combination obtained in the controller parameter adaptive step, combines the acceleration and the relative displacement in the information acquisition module, and calculates the execution control force; The current driving module: the input end is connected with the output end of the execution level controller module; The execution control force calculated in the execution level controller module is converted into the control current through the corresponding damper inverse model, and is input into the current driver, so that the magneto-rheological damper generates the coulomb damping force required for vibration suppression, and the suppression of the vehicle body vibration is realized.

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