Multi-working-condition humanoid intelligent control method and system for magnetorheological suspension of passenger vehicle

Abstract

The invention relates to the technical field of magnetorheological suspension multi-working-condition humanoid intelligent control, in particular to a passenger vehicle magnetorheological suspension multi-working-condition humanoid intelligent control method and system. The multi-working-condition humanoid intelligent control method for the magneto-rheological suspension of the passenger vehicle comprises the steps of working condition division, parameter self-adaption, executive-level controller design and control current calculation, and by dividing the driving working conditions of the vehicle, selecting an advantage control algorithm and matching corresponding controller parameters, the control current of the vehicle is calculated. Controller parameters are input into the corresponding executive-level controllers, the executive-level controllers calculate executive control force, finally, the calculated executive control force is converted into control current, and the magnetorheological damper is driven to generate coulomb damping force; and a multi-working-condition humanoid intelligent control system for the magnetorheological suspension of the passenger vehicle is built according to the multi-working-condition humanoid intelligent control method for the magnetorheological suspension of the passenger vehicle. The method has the effect of improving the comfort and the handling stability of the vehicle under different working conditions.

Description

Technical Field

[0001] The present application relates to the technical field of multi-operating-condition human-simulating intelligent control of magnetorheological suspension, and in particular to a multi-operating-condition human-simulating intelligent control method and system for magnetorheological suspension of a passenger vehicle. Background Art

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

[0003] At present, in the research field of magnetorheological suspension of passenger vehicles, human-like intelligent control theory is widely used. This theory simulates human control behavior to achieve intelligent control of the vehicle suspension system, so as to improve the smoothness and stability of the vehicle and enhance the ride comfort.

[0004] In the prior art, there are various methods for controlling magnetorheological suspension, but different control methods have their own advantages and disadvantages, and the applicable working conditions are relatively single, and it is impossible to take into account multiple working conditions at the same time; therefore, how to improve the adaptability of magnetorheological suspension to multiple working conditions is a problem that needs to be solved urgently. Summary of the invention

[0005] In order to cope with different operating conditions of the vehicle at the same time, the present application provides a multi-operating condition humanoid intelligent control method for a magnetorheological suspension of a passenger vehicle.

[0006] In the first aspect, the present application provides a multi-operating-condition humanoid intelligent control method for a magnetorheological suspension of a passenger vehicle, which adopts the following technical solution: A multi-operating-condition human-like intelligent control method for a magnetorheological suspension of a passenger vehicle comprises the following steps: Working condition division: including information acquisition, model calculation, and driving condition division; Information acquisition: obtaining the vertical velocity of the center of mass of the vehicle body, the pitch angular velocity of the center of mass of the vehicle body, the roll angular velocity of the center of mass of the vehicle body, the speed of the top of the shock absorber, the relative velocity of the shock absorber, the vehicle speed and the steering wheel angle information; Model calculation: input the information obtained 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: , in: is the vertical velocity of the center of mass of the vehicle body; is the pitch angular velocity of the vehicle body center of mass; is the body center of mass roll angular velocity; is the speed at the top of the shock absorber; is the relative movement speed of the shock absorber; is the vehicle speed; is the steering wheel angle; ; Driving condition classification: Based on the seven-degree-of-freedom model variables of the suspension and information fusion theory, the driving conditions are classified into Class A road conditions, Class B road conditions, cornering conditions, acceleration and deceleration conditions, and speed bump conditions; Parameter adaptation: According to the existing driving conditions, the dominant algorithm and the dominant controller parameter combination under the corresponding conditions are preset in advance, and the dominant control algorithm and the corresponding controller parameter combination are selected according to the vehicle driving conditions divided in the condition division step; Execution level controller design: According to different vehicle driving conditions, the corresponding execution level controller is designed. The execution level controller receives the controller parameter combination obtained from the controller parameter adaptation step, combines the vehicle body acceleration and the relative displacement of the suspension, and calculates the output execution control force of a single controller. The output control force of a single controller The calculation model is as follows: , in, for The corresponding weights, The control force required for a single shock absorber to improve comfort; The control force required for a single shock absorber to improve safety; The calculation model is as follows: , in, and is the distance between the center of mass of the vehicle body and the front and rear axles, and is the horizontal distance from the center of mass to the left and right shock absorbers, , , are the control forces required to suppress the vertical, pitch and roll vibrations of the vehicle body. , , Calculate according to the control algorithm used under different working conditions; Increased safety Control forces required for a single shock absorber The calculation formula is: , in, The value of the floor shed damping coefficient, Indicates 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 a control current through the corresponding damper inverse model, and input into the current driver to make the magnetorheological damper generate the Coulomb damping force required for vibration suppression.

[0007] 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, and then the dominant control algorithm is selected according to the division results, 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. The controller parameters are then input into the corresponding execution-level controller, and the execution-level controller calculates the execution control force in combination with the vehicle body acceleration and the relative displacement of the suspension. Finally, the calculated execution control force is converted into a control current through the damper inverse model, and the magnetorheological damper is driven to generate the Coulomb damping force required for vibration suppression under different working conditions, thereby reducing the vibration of the vehicle during driving and improving the comfort and handling stability of the vehicle under different working conditions.

[0008] Optionally, in the parameter adaptation step, for Class A road surface and Class B road surface, the selection of the dominant control algorithm includes the selection of vehicle speed acquisition, vehicle speed judgment and control algorithm; Vehicle speed acquisition: Get the vehicle speed through the vehicle speedometer And set the speed threshold ; Speed ​​judgment: When the vehicle is running at a Less than or equal to the vehicle speed threshold When the vehicle is traveling at a low speed, Greater than the speed threshold When the vehicle is judged to be traveling at high speed; Choice of control algorithm: Class A road: When driving at low speed, the ceiling-ground-shed hybrid control algorithm is adopted; when driving at high speed, the ADD-ground-shed hybrid control algorithm is adopted; Class B road surface: When driving at low speed, a fuzzy ground-shelf hybrid control algorithm is used; when driving at high speed, a finite frequency robust and ground-shelf hybrid control algorithm is used.

[0009] By adopting the above technical solution, in view of the difference in flatness between Class A and Class B roads, and the inconsistency of vibration amplitude of the same grade of road at different vehicle speeds, it is first necessary to detect the current driving speed of the vehicle. , and set the corresponding speed threshold By dividing the vehicle speed into two intervals, high speed and low speed, and then selecting the corresponding advantageous control algorithm according to the characteristics of different levels of road surfaces under high-speed and low-speed driving conditions, it can adapt to various driving conditions and enhance the stability and safety during driving.

[0010] Optionally, in the step of selecting the control algorithm, the coupling conditions of the turning condition, the acceleration / deceleration condition and the speed bump condition with the Class A road condition and the Class B road condition are also included; the coupling conditions include at least two different conditions; When a turning condition occurs, a turning control algorithm is added to the current control algorithm; When acceleration and deceleration conditions occur, an acceleration and deceleration control algorithm is added to the current algorithm; When encountering speed bump conditions, a fuzzy control algorithm is used or a fuzzy control algorithm designed specifically for speed bump conditions is added to the current algorithm.

[0011] By adopting the above technical solution, the vehicle driving conditions can be classified and comprehensively analyzed in detail, various situations that may be encountered during vehicle driving and the coupling between them can be fully considered, and the advantageous control algorithms can be selected and comprehensively applied in a targeted manner. By adjusting the parameters of each controller, the performance of the magnetorheological suspension under multiple working conditions can be optimized.

[0012] Optionally, in the parameter adaptation step, The weight value of is dynamically adjusted, and the comfort and safety balance matrix dynamically adjusts the weighted values ​​of the sprung acceleration restraining force and the unsprung acceleration restraining force according to the vehicle speed; wherein, Weight The calculation formula is: , in, is the vehicle speed.

[0013] By adopting the above technical solution, the weighted values ​​of the sprung acceleration suppression force and the unsprung acceleration suppression force can be adjusted in time according to the 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.

[0014] Optionally, the dominant control algorithm includes skyhook control, groundhook control, ADD control, fuzzy control, finite frequency robust control, turning control and acceleration / deceleration control.

[0015] By adopting the above technical solution, the single or combined control of the above advantageous control algorithms can cope with a variety of common working conditions. The advantageous control algorithms corresponding to common vehicle driving conditions can be stored in advance. When the corresponding conditions occur, the controller parameter combination under the corresponding conditions can be directly called to reduce the reaction time and effectively improve the response efficiency of the controller.

[0016] Optionally, the required control current is obtained according to the output control force of the controller, the relative displacement of the damper and the relative movement speed, specifically: Based on the corresponding magnetorheological damper, the force-velocity and force-displacement performance parameters under different currents are obtained through MTS testing. The polynomial inverse model of the magnetorheological damper is established according to the obtained parameters. Combined with the relative displacement of the damper, the control force calculated by the controller can be converted into the driving current. The expression of the polynomial inverse model is: , in, is the output control force of a single controller, is the relative displacement of the damper, is the relative motion speed of the damper.

[0017] By adopting the above technical solution, the output control force of the controller 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, thereby realizing dynamic adjustment of the vehicle suspension system, which can effectively reduce the vibration and impact of the vehicle during driving, not only improving the ride comfort of the vehicle, but also enhancing the handling stability of the vehicle under different driving conditions.

[0018] In the second aspect, the present application provides a multi-operating-condition humanoid intelligent control system for a passenger vehicle magnetorheological suspension, which adopts the following technical solutions: A multi-operating-condition humanoid intelligent control system for a magnetorheological suspension of a passenger vehicle, comprising: Information acquisition module: including acceleration sensor, relative displacement sensor, three-axis gyroscope, speedometer, steering wheel angle sensor, intelligent controller and current driver; used to obtain vehicle driving information such as vehicle speed, acceleration, relative displacement and steering wheel angle; Working condition classification module: the input end is connected to the output end of the information acquisition module by electrical signals; 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 the information fusion theory, the driving conditions are divided into Class A road conditions, Class B road conditions, turning conditions, acceleration and deceleration conditions, and speed bump conditions; Parameter adaptive module: the input end is connected to the output end of the information acquisition module and the working condition division module by electrical signals; it is used to select the dominant control algorithm and the corresponding controller parameter combination according to the vehicle driving information and the vehicle driving condition, and adjust the controller parameters through the damping force distribution matrix and the comfort and safety balance matrix; Execution level controller module: the input end is connected to the output end electrical signal 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 according to the vehicle driving conditions divided in the working condition division module, and the execution level controller receives the controller parameter combination obtained from the controller parameter adaptation step, and calculates the execution control force in combination with the acceleration and relative displacement in the information acquisition module; Current drive module: The input end is connected to the output end of the executive-level controller module with an electrical signal; the executive control force calculated in the executive-level controller module 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, thereby achieving the suppression of vehicle body vibration.

[0019] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application divides the driving conditions of the vehicle, selects the dominant control algorithm according to the division results, matches the corresponding controller parameters, and adjusts the controller parameters through the damping force distribution matrix and the comfort and safety balance matrix. The controller parameters are input into the corresponding execution-level controller, and the execution-level controller calculates the execution control force in combination with the vehicle body acceleration and the relative displacement of the suspension. Finally, the calculated execution control force is converted into a control current through the damper inverse model, and the magnetorheological damper is driven to generate the Coulomb damping force required for vibration suppression under different working conditions, thereby reducing the vibration of the vehicle during driving and improving the comfort and handling stability of the vehicle.

[0020] 2. This application addresses the differences in smoothness of different grades of road surfaces and the inconsistency of vibration amplitude of the same grade of road surfaces at different vehicle speeds. By dividing the vehicle speed into two intervals, high speed and low speed, the corresponding advantageous control algorithm is then selected based on the characteristics of different grades of road surfaces under high-speed and low-speed driving conditions, thereby adapting to various driving conditions and enhancing stability and safety during driving.

[0021] 3. This application can classify and comprehensively analyze the vehicle driving conditions in detail, comprehensively consider various situations that may be encountered during vehicle driving and their coupling conditions, select and comprehensively apply advantageous control algorithms in a targeted manner, and optimize the performance of the magnetorheological suspension under multiple working conditions by adjusting the parameters of each controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of Example 1 of the present application; Figure 2 is a flow chart of Example 2 of the present application; Figure 3 This is a flow chart of Example 3 of the present application. DETAILED DESCRIPTION

[0023] The following combination Figures 1 to 3 This application is described in further detail.

[0024] This embodiment discloses a multi-operating-condition human-simulated intelligent control method for a magnetorheological suspension of a passenger vehicle. Example

[0025] Reference Figure 1 , a multi-operating-condition human-like intelligent control method for a magnetorheological suspension of a passenger vehicle comprises the following steps: Working condition division: including information acquisition, model calculation, and driving condition division.

[0026] Information acquisition: The vertical movement speed of the vehicle center of mass, the pitch angular velocity of the vehicle center of mass, the roll angular velocity of the vehicle center of mass, the top speed of the shock absorber, the relative movement speed of the shock absorber, the vehicle driving speed and the steering wheel angle information are obtained during the vehicle driving process through the gyroscope at the vehicle center of mass, the acceleration sensor and relative displacement sensor at the shock absorber, the speedometer and the steering wheel angle.

[0027] Model calculation: input the vehicle information obtained in the information acquisition step into the suspension seven-degree-of-freedom model. The suspension seven-degree-of-freedom model includes seven degrees of freedom, including the vertical movement of the vehicle body, roll angle displacement, pitch angle displacement, and vertical bounce of four wheels, and can fully 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: , in: is the vertical velocity of the center of mass of the vehicle body; is the pitch angular velocity of the vehicle body center of mass; is the body center of mass roll angular velocity; is the speed at the top of the shock absorber; is the relative movement speed of the shock absorber; is the vehicle speed; is the steering wheel angle; .

[0028] Driving condition classification: Based on the suspension seven-degree-of-freedom model variables and referring to the "Mechanical Vibration Road Pavement Spectrum Measurement Data Report" (standard number: GB / T 7031-2005), the magnitude and influence of various variable values ​​are comprehensively considered, and combined with the DS evidence information fusion theory, the vehicle driving condition is divided into Class A road condition, Class B road condition, turning condition, acceleration and deceleration condition, and speed bump condition.

[0029] Class A road conditions: The road surface is relatively flat, the body vibration is small during driving, and only occasional large vibrations need to be suppressed; Class B road conditions: The road surface is relatively complex, the body vibrates greatly during driving, and the vibration is random and continuous; Cornering condition: When the vehicle turns, the body will tilt to a certain extent due to the centrifugal force; Acceleration and deceleration conditions: When the vehicle accelerates or decelerates, the vehicle body will have changes in pitch attitude, which will change the position of the center of gravity of the vehicle body; Speed ​​bump condition: Under speed bump condition, the posture of the vehicle body is mainly manifested in the bounce in the vertical direction and the change of pitch posture.

[0030] Parameter adaptation: According to the existing driving conditions, the dominant algorithm and the parameter combination of the dominant controller under the corresponding conditions are preset in advance, and the dominant control algorithm and the corresponding controller parameter combination are selected according to the vehicle driving conditions divided in the condition division step; the dominant control algorithm usually includes skyhook control, groundhook control, ADD control, fuzzy control, finite frequency robust control, turning control and acceleration and deceleration control; Specifically, skyhook control senses the vertical motion state of the vehicle and the external road conditions, and adjusts the stiffness and damping of the suspension system in real time, which has a significant effect on reducing body vibration; floorhook control senses the lateral tilt and lateral motion state of the vehicle, and adjusts the stiffness and damping of the suspension system in real time to improve the lateral stability and cornering performance of the vehicle, focusing on improving the handling safety and stability of the vehicle; ADD control uses the acceleration of the vehicle body to calculate the reference damping, and has a significant improvement effect in high-frequency vibration; fuzzy control designs a fuzzy controller, performs fuzzy reasoning based on the vehicle motion state and road conditions, and adjusts the stiffness and damping of the suspension system based on the reasoning results; finite-frequency robust control can improve the robustness and stability of the system and is suitable for a variety of uncertainties and interference situations; turning control refers to controlling the suspension system when the vehicle turns to improve the vehicle's handling and stability; acceleration and deceleration control controls the pitching motion of the vehicle during acceleration or deceleration by adjusting the damping characteristics of the suspension to improve ride comfort and vehicle handling stability.

[0031] Execution-level controller design: According to different vehicle driving conditions, the corresponding execution-level controller is designed. The execution-level controller includes ceiling controller, floor controller, ADD controller, fuzzy controller, finite frequency robust controller, turning controller, acceleration and deceleration controller; the execution-level controller receives the controller parameter combination obtained from the controller parameter adaptation step, combines the vehicle body acceleration and the relative displacement of the suspension, and calculates the optimal control force based on the vehicle damping force distribution matrix and the comfort and safety balance matrix.

[0032] The vehicle damping force distribution matrix distributes the control gains of various controllers as a whole according to the pitch and roll postures of the vehicle body in different driving conditions to suppress the body posture; the comfort and safety balance matrix is ​​used to adjust the balance between the sprung acceleration suppression force and the unsprung acceleration suppression force.

[0033] The expression of the vehicle damping force distribution matrix is: , in, and is the distance between the center of mass of the vehicle body and the front and rear axles, and is the horizontal distance from the center of mass to the left and right shock absorbers, , , are the control forces required to suppress the vertical, pitch and roll vibrations of the vehicle body, , , Calculations are performed based on the control algorithms used under different working conditions.

[0034] For example, under acceleration and deceleration conditions, the vehicle will produce a large pitch. In order to reduce the pitch angle of the vehicle body and improve ride comfort, it is necessary to enhance the control of pitch vibration. , to reduce the pitch angle of the vehicle body and improve ride comfort; the control force of pitch vibration The calculation is to input the vehicle driving parameters into the corresponding acceleration and deceleration control algorithm to obtain the control force .

[0035] Increased safety Control forces required for a single shock absorber The calculation formula is: , in, The value of the damping coefficient of the ground shed, Indicates the vibration velocity of the unsprung mass; and The values ​​of can be obtained directly or indirectly without calculation; Output control force of a single controller The calculation formula is: , in, is the adjustment function determined based on experience and experimental data, The control force required for a single shock absorber to improve comfort; The control force required for a single shock absorber to improve safety is the output force of the ground shelf control algorithm.

[0036] The ground shelf control algorithm can sense the lateral tilt and lateral movement of the vehicle and adjust the stiffness and damping of the suspension system in real time to generate a force in the opposite direction of the movement of the unsprung mass, thereby suppressing wheel bounce, improving wheel grip, and reducing the occurrence of roll and yaw. Under extreme driving conditions such as high-speed driving or sharp turns, the improvement in wheel grip can significantly improve the stability and safety of the vehicle. In complex and changeable road environments, the improvement in stability and safety helps the driver to better control the vehicle, avoid accidents, and improve the safety of the vehicle. It is suitable as a control algorithm to improve safety.

[0037] Control current calculation: The optimal control force calculated in the executive controller design step is converted into a control current through the corresponding damper inverse model, and input into the current driver to make the magnetorheological damper generate the Coulomb damping force required for vibration suppression.

[0038] Specifically, based on the corresponding magnetorheological damper, the force-velocity and force-displacement performance parameters under different currents are obtained through MTS testing. The polynomial inverse model of the magnetorheological damper is established according to the obtained parameters. Combined with the relative displacement and relative motion speed of the damper, the control force calculated by the controller can be converted into the driving current. The expression of the polynomial inverse model is:

[0039] in, is the output control force of a single controller, is the relative displacement of the damper, is the relative motion speed of the damper.

[0040] The implementation principle of Example 1 of the present application is: first, based on the seven-degree-of-freedom model of the suspension, different working conditions during vehicle driving are distinguished, different advantage control algorithms and corresponding controller parameters are matched according to different working conditions, and the controller parameters are adjusted through the damping force distribution matrix and the comfort and safety balance matrix, and then the corresponding controller parameters are input into the corresponding execution-level controller, and the execution-level controller calculates the execution control force based on the vehicle body acceleration and the relative displacement at the suspension, and finally the calculated execution control force is input into the damper inverse model, and the damper inverse model converts the execution control force into a control current, and the control current drives the magnetorheological damper to generate the Coulomb damping force required for vibration suppression of the vehicle under different driving conditions, thereby reducing the vibration of the vehicle during driving. Example:

[0041] Reference Figure 2 , the difference between this embodiment and embodiment 1 is that, in the parameter adaptation step, for Class A road surface and Class B road surface, the selection of the dominant control algorithm includes the selection of vehicle speed acquisition, vehicle speed judgment and control algorithm; Vehicle speed acquisition: Get the vehicle speed through the vehicle speedometer And set the speed threshold ; Based on relevant regulations and actual experience, the vehicle speed threshold is set to 30km / h.

[0042] Speed ​​judgment: When the vehicle is moving Speed ​​is less than or equal to the vehicle speed threshold When the vehicle is traveling at a low speed, Greater than the speed threshold When the vehicle is judged to be traveling at high speed; Choice of control algorithm: Class A road surface: When driving at low speeds, the ceiling-and-ground-shed hybrid control algorithm is adopted. The ceiling-and-ground-shed hybrid control algorithm combines the advantages of ceiling control and ground-shed control, and adjusts the ratio of the two through a weighted coefficient, thereby achieving simultaneous improvement of vehicle smoothness and handling stability; when driving at high speeds, the ADD-and-ground-shed hybrid control algorithm is adopted. The ADD-and-ground-shed hybrid control algorithm combines the advantages of ceiling control and ground-shed control, and can take into account the vehicle's comfort and handling stability at the same time.

[0043] Class B road surface: When driving at low speed, a fuzzy ground-shelf hybrid control algorithm is adopted. The fuzzy ground-shelf hybrid control algorithm combines the fuzzy control method with the ground-shelf control method, takes the vibration speed and acceleration of the vehicle body and wheels as input, and obtains the optimal shock absorber damping control force through the joint control of fuzzy control and ground-shelf, thereby significantly improving the performance of the suspension to adapt to the real-time changing vehicle operating conditions; when driving at high speed, a finite-frequency robust and ground-shelf hybrid control algorithm is adopted. The combination of finite-frequency robust and ground-shelf hybrid control algorithms provides a vehicle suspension system with an effective control method that can not only ensure robustness, but also adaptively adjust according to road conditions, while improving ride comfort and handling stability.

[0044] Specifically, in the step of selecting the control algorithm, the coupling conditions of the turning condition, the acceleration / deceleration condition, the speed bump condition, the A-level road condition, and the B-level road condition are also included; the coupling conditions include at least two different conditions; When a turning condition occurs, a turning control algorithm is added to the current control algorithm; the turning control algorithm can monitor the steering angle and lateral acceleration of the vehicle in real time, and effectively reduce the roll during turning by adjusting the stiffness and damping of the suspension, thereby improving the vehicle's handling performance; When acceleration and deceleration conditions 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 acceleration and deceleration of the vehicle to reduce the pitching movement during acceleration and deceleration to ensure the smooth driving of the vehicle. When encountering speed bump conditions, the fuzzy algorithm is used directly or a fuzzy control algorithm designed specifically for speed bump conditions is added to the current algorithm; when encountering speed bump conditions, 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.

[0045] The implementation principle of Example 2 of the present application is as follows: according to the problem that the vibration amplitude of the vehicle is inconsistent when driving on different levels of roads and at different speeds on the same level of road, the current driving speed of the vehicle is first detected, and a corresponding speed threshold is set to distinguish between high-speed and low-speed driving of the vehicle. Then, different advantage control algorithms are adopted according to the characteristics of different levels of road surfaces under high-speed and low-speed driving conditions, so as to adapt to different driving conditions, reduce vibration during driving, enhance vehicle stability during driving, and improve riding comfort; the vehicle driving conditions and possible coupling conditions are classified and comprehensively analyzed in detail, and the advantage control algorithms are selected and comprehensively 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 the intelligent control of various conditions and coupling conditions, the vehicle suspension system can more accurately adapt to various complex driving environments and ensure the stability and comfort of the vehicle under different conditions.

[0046] In another embodiment, in the parameter adaptation step, The weight value of is dynamically adjusted, and the comfort and safety balance matrix dynamically adjusts the weighted values ​​of the sprung acceleration restraining force and the unsprung acceleration restraining force according to the vehicle speed; wherein, Weight The calculation formula is: , in, is the vehicle speed.

[0047] By dynamically adjusting controller parameters through the comfort and safety balance matrix, the energy feedback efficiency and driving efficiency can be improved, and the smoothness and safety of vehicle driving can be improved.

[0048] In other embodiments, based on common vehicle driving conditions, advantageous control algorithms corresponding to common vehicle driving conditions are stored in advance. When such conditions occur, the controller parameter combination under the corresponding conditions in the controller parameter adaptation step is directly called, and the corresponding controller is used. This can reduce the reaction time and effectively improve the response efficiency of the controller. Example

[0049] Reference Figure 3 The embodiment of the present application also discloses a multi-operating-condition humanoid intelligent control system for a magnetorheological suspension of a passenger vehicle, comprising the following modules: Information acquisition module: acceleration sensor, relative displacement sensor, three-axis gyroscope, speedometer, steering wheel angle sensor, intelligent controller and current driver; used to obtain key vehicle driving information such as vehicle speed, acceleration, relative displacement and steering wheel angle in real time; through the precise measurement of these sensors, the vehicle's driving conditions can be accurately monitored.

[0050] Working condition division module: the input end is connected to the output end of the information acquisition module by electrical signals; 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 the information fusion theory, the driving conditions are divided into different categories, including Class A road conditions, Class B road conditions, turning conditions, acceleration and deceleration conditions, and speed bump conditions. Through the working condition division, the dynamic response of the vehicle under different driving conditions can be more accurately simulated and analyzed, thereby providing data support for the optimization of the suspension system.

[0051] Parameter adaptive module: The input end is connected to the output end of the working condition division module with electrical signals; it is used to select the most appropriate control algorithm and automatically adjust the corresponding controller parameter combination according to the specific needs of different driving conditions of the vehicle, and adjust the controller parameters through the damping force distribution matrix and the comfort and safety balance matrix to ensure that the performance of the vehicle is always in the best state.

[0052] Execution-level controller module: The input end is electrically connected to the output end 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 according to 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 in combination with the acceleration and relative displacement data obtained in the information acquisition module.

[0053] Current drive module: The input end is connected to the output end of the executive-level controller module through an electrical signal; it receives the optimal control force calculated in the executive-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 into the current driver, thereby causing the magnetorheological damper to generate the Coulomb damping force required for vibration suppression, thereby suppressing vehicle body vibration.

[0054] Furthermore, in the parameter adaptation module, for Class A and Class B roads, the corresponding vehicle driving speeds are obtained and the vehicle speed threshold is set to distinguish between high speed and low speed. When driving on Class A roads, a sky-shed and ground-shed hybrid control algorithm is adopted at low speeds; an ADD ground-shed hybrid control algorithm is adopted at high speeds; when driving on Class B roads, a fuzzy ground-shed hybrid control algorithm is adopted at low speeds, and a finite-frequency robust and ground-shed hybrid control algorithm is adopted at high speeds.

[0055] Furthermore, the selection module of the control algorithm also includes turning conditions, acceleration and deceleration conditions, and coupling conditions of speed bump conditions with Class A road conditions and Class B road conditions; and when a turning condition occurs, it is necessary to add a turning control algorithm to the current control algorithm; when acceleration and deceleration conditions occur, it is necessary to add an acceleration and deceleration control algorithm to the current algorithm; when a speed bump condition occurs, it is necessary to add a fuzzy control algorithm designed specifically for the speed bump condition to the current algorithm.

[0056] Furthermore, the comfort and safety balance matrix can dynamically adjust the weighted values ​​of the sprung acceleration restraint force and the unsprung acceleration restraint force according to the vehicle speed. Through this dynamic adjustment mechanism, the system can better cope with the vehicle status under different working conditions and improve the smoothness and safety of vehicle driving.

[0057] Furthermore, according to common vehicle driving conditions, the corresponding advantageous control algorithm is stored in advance. When the condition occurs, the controller parameter combination under the corresponding condition in the controller parameter adaptation step is directly called, and the corresponding controller is used, which can reduce the reaction time and effectively improve the response efficiency of the controller.

[0058] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A multi-operating-condition human-like intelligent control method for a magnetorheological suspension of a passenger vehicle, characterized in that: The following steps are involved: Working condition division: including information acquisition, model calculation, and driving condition division; Information acquisition: obtaining the vertical velocity of the center of mass of the vehicle body, the pitch angular velocity of the center of mass of the vehicle body, the roll angular velocity of the center of mass of the vehicle body, the speed of the top of the shock absorber, the relative velocity of the shock absorber, the vehicle speed and the steering wheel angle information; Model calculation: input the information obtained 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: ， in: is the vertical velocity of the center of mass of the vehicle body; is the pitch angular velocity of the vehicle body center of mass; is the body center of mass roll angular velocity; is the speed at the top of the shock absorber; is the relative movement speed of the shock absorber; is the vehicle speed; is the steering wheel angle; ; Driving condition classification: Based on the seven-degree-of-freedom model variables of the suspension and information fusion theory, the driving conditions are classified into Class A road conditions, Class B road conditions, cornering conditions, acceleration and deceleration conditions, and speed bump conditions; Parameter adaptation: According to the existing driving conditions, the dominant algorithm and the dominant controller parameter combination under the corresponding conditions are preset in advance, and the dominant control algorithm and the corresponding controller parameter combination are selected according to the vehicle driving conditions divided in the condition division step; Execution level controller design: According to different vehicle driving conditions, the corresponding execution level controller is designed. The execution level controller receives the controller parameter combination obtained from the controller parameter adaptation step, combines the vehicle body acceleration and the relative displacement of the suspension, and calculates the output execution control force of a single controller. The output control force of a single controller The calculation model is as follows: ， in, for The corresponding weights, The control force required for a single shock absorber to improve comfort; The control force required for a single shock absorber to improve safety; The calculation model is as follows: ， in, and is the distance between the center of mass of the vehicle body and the front and rear axles, and is the horizontal distance from the center of mass to the left and right shock absorbers, , , are the control forces required to suppress the vertical, pitch and roll vibrations of the vehicle body. , , Calculate according to the control algorithm used under different working conditions; Increased safety Control forces required for a single shock absorber The calculation formula is: ， in, The value of the floor shed damping coefficient, Indicates 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 a control current through the corresponding damper inverse model, and input into the current driver to make the magnetorheological damper generate the Coulomb damping force required for vibration suppression.

2. The multi-operating-condition human-simulated intelligent control method for a magnetorheological suspension of a passenger vehicle according to claim 1, characterized in that: In the parameter adaptation step, for Class A and Class B roads, the selection of the dominant control algorithm includes vehicle speed acquisition, vehicle speed judgment and the selection of the control algorithm; Vehicle speed acquisition: Get the vehicle speed through the vehicle speedometer And set the speed threshold ; Speed ​​judgment: When the vehicle is running at a Less than or equal to the vehicle speed threshold When the vehicle is traveling at a low speed, Greater than the speed threshold When the vehicle is judged to be traveling at high speed; Choice of control algorithm: Class A road: When driving at low speed, the ceiling-ground-shed hybrid control algorithm is adopted; when driving at high speed, the ADD-ground-shed hybrid control algorithm is adopted; Class B road surface: When driving at low speed, a fuzzy ground-shelf hybrid control algorithm is used; when driving at high speed, a finite frequency robust and ground-shelf hybrid control algorithm is used.

3. The multi-operating-condition human-simulated intelligent control method for a magnetorheological suspension of a passenger vehicle according to claim 2, characterized in that: The step of selecting the control algorithm also includes coupling conditions of turning conditions, acceleration and deceleration conditions, and speed bump conditions with Class A road conditions and Class B road conditions; the coupling conditions include at least two different conditions; When a turning condition occurs, a turning control algorithm is added to the current control algorithm; When acceleration and deceleration conditions occur, an acceleration and deceleration control algorithm is added to the current algorithm; When encountering speed bump conditions, a fuzzy control algorithm is used or a fuzzy control algorithm designed specifically for speed bump conditions is added to the current algorithm.

4. The multi-operating-state human-simulated intelligent control method for a magnetorheological suspension of a passenger vehicle according to claim 1, characterized in that: In the parameter adaptation step, The weight value of is dynamically adjusted, and the comfort and safety balance matrix dynamically adjusts the weighted values ​​of the sprung acceleration restraining force and the unsprung acceleration restraining force according to the vehicle speed; wherein, Weight The calculation formula is: ， in, is the vehicle speed.

5. The multi-operating-condition human-simulated intelligent control method for a magnetorheological suspension of a passenger vehicle according to claim 1, characterized in that: The advantageous control algorithms include skyhook control, groundhook control, ADD control, fuzzy control, finite frequency robust control, turning control and acceleration / deceleration control.

6. The multi-operating-state human-simulated intelligent control method for a magnetorheological suspension of a passenger vehicle according to claim 1, characterized in that: The required control current is obtained according to the output control force of the controller, the relative displacement of the damper and the relative movement speed, specifically: Based on the corresponding magnetorheological damper, the force-velocity and force-displacement performance parameters under different currents are obtained through MTS testing. The polynomial inverse model of the magnetorheological damper is established according to the obtained parameters. Then, the control force calculated by the controller is converted into the driving current by combining the relative displacement and relative velocity of the damper. The expression of the polynomial inverse model is: ， in, is the output control force of a single controller, is the relative displacement of the damper, is the relative motion speed of the damper.

7. A multi-operating-state human-like intelligent control system for a magnetorheological suspension of a passenger vehicle, the control system being applicable to the control method according to any one of claims 1 to 6, characterized in that: include: Information acquisition module: including acceleration sensor, relative displacement sensor, three-axis gyroscope, speedometer, steering wheel angle sensor, intelligent controller and current driver; Used to obtain vehicle driving information such as vehicle speed, acceleration, relative displacement and steering wheel angle; Working condition classification module: the input end is connected to the output end of the information acquisition module by electrical signals; 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 the information fusion theory, the driving conditions are divided into Class A road conditions, Class B road conditions, turning conditions, acceleration and deceleration conditions, and speed bump conditions; Parameter adaptive module: the input end is connected to the output end of the information acquisition module and the working condition division module by electrical signals; it is used to select the dominant control algorithm and the corresponding controller parameter combination according to the vehicle driving information and the vehicle driving condition, and adjust the controller parameters through the damping force distribution matrix and the comfort and safety balance matrix; Execution level controller module: the input end is respectively connected with the output end electrical signals of the information acquisition module, the working condition division module and the parameter adaptation module; Designing a corresponding execution-level controller according to the vehicle driving conditions divided in the working condition division module, the execution-level controller receives the controller parameter combination obtained in the controller parameter adaptation step, and calculates the execution control force in combination with the acceleration and relative displacement in the information acquisition module; Current driving module: the input end is connected with the output end electrical signal of the executive level controller module; The execution control force calculated in the execution-level controller module 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, thereby suppressing the vehicle body vibration.

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