Vehicle stability control method

Abstract

The present application is applicable to the field of vehicle control technology, and provides a vehicle stability control method, which includes: obtaining the driving condition data of the vehicle during the driving of the vehicle; determining the driving state data of the vehicle according to the driving condition data; establishing a group of dynamic equations of the magnetorheological air suspension system according to the system structure: placing the vehicle in the stable area according to the driving state data and the preset progressive stability zone control strategy of the magnetorheological air suspension system; if the vehicle is not in the stable area, adjusting the control parameters to adjust the vehicle to the stable area. This invention combines the advantages of the dual-chamber air spring system and the magnetorheological damper, improves the robustness, accuracy and anti-interference ability of the suspension control system, reduces the amplitude of vehicle bumps when the vehicle is driving under complex conditions, and improves the driving stability and comfort of the vehicle.

Description

Technical Field

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

[0002] As the main vibration isolation unit of the vehicle, the vibration reduction effect of the suspension system has a huge impact on the vehicle's ride smoothness, driving safety, and handling comfort. In the previous suspension system design, there is a problem of single optimization control. The vibration reduction of the suspension is achieved only by using air springs or magnetorheological dampers (MRD). This solution cannot fully utilize the advantages of each vibration reduction module in the suspension system at the same time. Although it can achieve partial vibration reduction and anti-interference effects, when the vehicle encounters complex working conditions or unbounded interference, it will cause the system to lose robustness, resulting in reduced vehicle driving performance. Summary of the invention

[0003] The embodiment of the present application provides a vehicle stability control method, which can solve the problem in the prior art that when the vehicle encounters complex working conditions or unbounded interference, the system loses robustness, resulting in reduced vehicle driving performance.

[0004] In a first aspect, an embodiment of the present application provides a vehicle stability control method, comprising:

[0005] During the driving of the vehicle, obtaining driving condition data of the vehicle;

[0006] Determining driving state data of the vehicle according to the driving condition data;

[0007] According to the system structure, the dynamic equations of the magnetorheological air suspension system are established:

[0008] According to the driving state data and the preset progressive stability zone control strategy of the magnetorheological air suspension system, the vehicle is placed in the progressive stability zone; if the vehicle is not in the progressive stability zone, the control parameters are adjusted to adjust the vehicle to the progressive stability zone.

[0009] Furthermore, the method further comprises:

[0010] The equivalent mechanical model of the dual-chamber air spring is established. According to the laws of thermodynamics, the frequency domain relationship between the suspension dynamic stroke and the additional air chamber parameters is obtained. The effective area part of the related equation is differentiated to obtain the total stiffness of the dual-chamber air spring:

[0011]

[0012] The output force F generated by the double-chamber air spring ASDivided into three parts, the air spring force when the contact area changes , Air spring force affected by the pressure difference between the inside and outside of the air spring and the air spring force generated by the system damping , which is specifically expressed by the following formula:

[0013]

[0014] in, is the excitation angular frequency; is the equivalent stiffness of the effective area to the output force of the air spring; is the first spring stiffness; is the second spring stiffness; is the first damping unit; is the second damping unit; is the unsprung wheel displacement, The first damping unit and the second damping unit The displacement between.

[0015] Furthermore, the method further comprises:

[0016] The double-chamber air spring is equivalent to the following three links in series, which are the first spring, the first damping unit, the second spring and the second damping unit in parallel. By setting the excitation frequency, the real part and the imaginary part of the total stiffness of the double-chamber air spring are obtained, which can be expressed as follows:

[0017]

[0018] in, is the real part; Is the imaginary part.

[0019] Furthermore, the method further comprises:

[0020] Obtain the state space expression of the magnetorheological air suspension system;

[0021] Determining an uncertainty expression of the magnetorheological air suspension system according to the state space expression and external interference factors of the magnetorheological air suspension system;

[0022] Determining an asymptotic stability region expression of the magnetorheological air suspension system according to the uncertainty expression, a predefined system sliding surface and preset conditions;

[0023] The region represented by the asymptotic stability region expression is determined as the asymptotic stability region of the magnetorheological air suspension system, as specifically shown in the following formula:

[0024]

[0025] Where w is the range of the asymptotically stable region; For the matrix The minimum eigenvalue of ; For the matrix The largest eigenvalue of is the correlation coefficient matrix; is the first adjustment coefficient; is the second adjustment factor; is the system control input; M is the conversion matrix; A is the state input matrix; is the inverse matrix of M.

[0026] Furthermore, the method further comprises:

[0027] The asymptotic stability region expression is optimized by a preset rule to obtain an optimized asymptotic stability region expression of the magnetorheological air suspension system, which is specifically shown in the following formula:

[0028]

[0029] in, It is the system control input; For the matrix The minimum eigenvalue.

[0030] The method provided in this embodiment places the vehicle in a stable area according to the driving state data of the vehicle and the progressive stability control strategy of the preset magnetorheological air suspension system. The magnetorheological air suspension system includes a dual-chamber air spring system, a magnetorheological damper, a sprung unit, and an unsprung unit. It combines the advantages of the dual-chamber air spring system and the magnetorheological damper, improves the robustness, accuracy and anti-interference ability of the suspension control system, and reduces the amplitude of vehicle bumps when the vehicle is driving under complex working conditions, thereby improving the driving stability and comfort of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 is a flow chart for implementing a first embodiment of a vehicle stability control method provided by an embodiment of the present application;

[0033] Figure 2is a structural schematic diagram of a magnetorheological air suspension system in a vehicle stability control method provided in an embodiment of the present application;

[0034] Figure 3 A schematic diagram of a test device for MRD in a vehicle stability control method provided in an embodiment of the present application;

[0035] Figure 4 It is an equivalent structural block diagram of a double-chamber air spring system in a vehicle stability control method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0037] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0038] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0039] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0040] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0041] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0042] See also Figure 1 , Figure 1 A flow chart of a vehicle stability control method provided by an embodiment of the present application is shown, including:

[0043] Step S10: Acquire driving condition data of the vehicle while the vehicle is driving.

[0044] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, or an electronic device, vehicle stability control device, computer, tablet computer, etc. capable of realizing the above functions. Taking the vehicle stability control device as an example, this embodiment and the following embodiments are illustrated.

[0045] It is understood that driving condition data can be a collection of various vehicle status and environmental data, which can be used to analyze and optimize the performance, stability and safety of the vehicle. Driving condition data includes vehicle speed, acceleration, body posture (including pitch angle, roll angle and yaw angle), steering angle, suspension travel, tire pressure and tire temperature, etc.

[0046] In one example, the vehicle stability control device collects the vehicle's driving condition data in real time through sensors and data acquisition systems on the vehicle. These data are transmitted to the central processing unit (ECU) for storage and analysis.

[0047] Step S20: Determine the driving state data of the vehicle according to the driving condition data.

[0048] It is understood that the driving state data can be the current dynamic state information of the vehicle analyzed and extracted from the driving condition data, describing the behavior and performance of the vehicle under specific conditions. The driving state data includes the body posture, suspension load and vehicle stability. The body posture can be the tilt angle or pitch angle of the body, reflecting the tilt or tilt change of the body. The suspension load can be the load on each wheel, indicating the weight borne by the suspension system. Vehicle stability can be to determine whether the vehicle remains within a predetermined posture range, such as whether there is excessive tilt or swing.

[0049] In one example, the vehicle stability control device uses the driving condition data collected from the sensor to calculate the driving state data of the vehicle through algorithms and models. For example, to calculate the lateral tilt angle of the vehicle body, first, the acceleration of the vehicle in the lateral direction is obtained through the sensor. Then, the lateral tilt angle is calculated using the following formula:

[0050]

[0051] in, is the lateral acceleration. is the acceleration due to gravity.

[0052] Step S30: establishing a group of dynamic equations of the magnetorheological air suspension system according to the system structure.

[0053] The magnetorheological air suspension system includes a dual-chamber air spring system, a magnetorheological damper, a sprung unit, an unsprung unit and a suspension system.

[0054] It is understood that the magnetorheological air suspension system can be a high-performance suspension system that combines air springs and magnetorheological dampers to improve the comfort and handling performance of the vehicle. The system structure can be the structure of the magnetorheological air suspension system. The system dynamics equations can be used to describe the mathematical equations that describe the characteristics of the magnetorheological air suspension system. The dual-chamber air spring system includes two airbags (a main airbag and an auxiliary airbag) for adjusting the height and elasticity of the vehicle body. The magnetorheological damper can use magnetorheological fluid to adjust the damping force through an electromagnetic field in response to dynamic changes in the vehicle. The sprung unit is used to support the part of the vehicle body, including air springs and wheels. The unsprung unit includes the connection part of the suspension system, such as control arms and brackets. The suspension system includes air springs, dampers and their control systems for adjusting the relative movement between the wheels and the vehicle body. The progressive stability zone can be a stability range based on the design of the magnetorheological air suspension system.

[0055] Step S40: placing the vehicle in the progressive stability zone according to the driving state data and the preset progressive stability zone control strategy of the magnetorheological air suspension system; if the vehicle is not in the progressive stability zone, adjusting the control parameters to adjust the vehicle to the progressive stability zone.

[0056] It is understandable that the control system parameters include the damping force and damping coefficient of the magnetorheological damper, the air pressure and spring stiffness of the air spring, etc. For example, the damping force can be increased and the tilt and swing of the vehicle body can be reduced by increasing the current of the magnetorheological damper.

[0057] In one example, for example, the vehicle stability control device collects the real-time driving condition data of the vehicle during the driving process of the vehicle, and obtains the driving state data of the vehicle according to the driving condition data, and the driving state data includes: vehicle acceleration (longitudinal, lateral, vertical), wheel load, vehicle tilt angle (roll angle and pitch angle), suspension height, speed and steering angle. According to the above driving state data, the actual area corresponding to the vehicle is searched through a preset mapping relationship table, wherein the preset mapping relationship table is obtained by pre-calibration. The actual area is compared with the progressive stability zone of the magnetorheological air suspension system. If the actual area is outside the progressive stability zone, it is determined that the vehicle is not in the stable area. If the actual area is within the progressive stability zone, it is determined that the vehicle is in the stable area. If the vehicle is outside the stable area, the damping coefficient and air pressure value of the magnetorheological air suspension of the vehicle are adjusted according to the preset adjustment method. During the adjustment process, the current area corresponding to the vehicle is compared with the progressive stability zone of the system until the vehicle is adjusted to the stable area, and the adjustment is terminated, wherein the adjustment frequency can be set according to the actual scene, and this embodiment is not limited here.

[0058] The method provided in this embodiment places the vehicle state in a stable region according to the driving state data and the preset control strategy of the vehicle's magnetorheological air suspension system. If the vehicle is not in the stable region, the control parameters of the system are adjusted, and the magnetorheological air suspension system includes a dual-chamber air spring system, a magnetorheological damper, a sprung unit, an unsprung unit and a suspension system, which combines the advantages of the dual-chamber air spring system and the magnetorheological damper, improves the robustness, accuracy and anti-interference ability of the suspension control system, reduces the amplitude of vehicle bumps when the vehicle is driving under complex working conditions, and improves the driving stability and comfort of the vehicle.

[0059] In an optional implementation of each embodiment of the present application, the vehicle stability control method further includes the following steps:

[0060] Step S001: constructing a system dynamics equation group corresponding to the magnetorheological air suspension system.

[0061] It is understood that the system dynamics equations can be a set of mathematical equations used to describe the dynamic behavior of a system with multiple degrees of freedom. In engineering and physics, a system with multiple independent motion parameters, each degree of freedom represents an independent direction or mode of motion in the system. The dynamics equations describe how these degrees of freedom change over time and are affected by external and internal forces.

[0062] Step S002: construct a state space expression of the dual-chamber air spring system.

[0063] It can be understood that the state-space expression is a mathematical model used to describe the behavior of a dynamic system. It describes how the state of the system changes over time through a set of first-order differential equations, and can be conveniently used for analysis and control design. The state-space expression is mainly used for linear time-invariant (LTI) systems, but can also be extended to nonlinear and time-varying systems.

[0064] Step S003: Determine an equivalent model of the dual-chamber air spring system according to the system dynamics equations, the state space expression and the random road excitation expression.

[0065] It can be understood that the random road excitation expression is a mathematical model used to describe the vehicle motion or suspension response caused by a random road surface. It characterizes the height fluctuation or irregularity of the road surface in the form of a random process and is usually used to analyze and design the vehicle suspension system to cope with random disturbances in actual road conditions.

[0066] In one example, based on the structure of the magnetorheological air suspension system, a system dynamics equation group corresponding to the magnetorheological air suspension system is constructed. Based on the structure of the dual-chamber air spring system, a state space expression corresponding to the dual-chamber air spring system is constructed. A random road surface excitation expression corresponding to the road impact using a random road surface as an anti-interference control strategy is determined. Based on the system dynamics equation group, the state space expression, and the random road surface excitation expression, an equivalent model corresponding to the dual-chamber air spring system is obtained through deduction and conversion, wherein the equivalent structural block diagram of the magnetorheological air suspension system can be referred to Figure 4 , the first spring (spring stiffness is ), the first damping unit , the second spring (spring stiffness is ) and the second damping unit Parallel. Assume that the equivalent stiffness of the effective area to the output force of the air spring is k a .

[0067] In some optional implementations, the above step S003 can be implemented by the following steps: establishing an equivalent mechanical model of the dual-chamber air spring, obtaining the frequency domain relationship between the suspension dynamic stroke and the additional air chamber parameters according to the laws of thermodynamics, differentiating the effective area part of the related equation, and obtaining the total stiffness of the dual-chamber air spring as:

[0068]

[0069] The output force generated by the double-chamber air spring F AS Divided into three parts, the air spring force when the contact area changes , Air spring force affected by the pressure difference between the inside and outside of the air spring and the air spring force generated by the system damping , which is specifically expressed by the following formula:

[0070]

[0071] in, is the excitation angular frequency; is the equivalent stiffness of the effective area to the output force of the air spring; is the first spring stiffness; is the second spring stiffness; is the first damping unit; is the second damping unit; is the unsprung wheel displacement, The first damping unit and the second damping unit The displacement between.

[0072] In one example, for example, step 1: according to Figure 2 , establish the system dynamics equations, the system dynamics equations are shown in formula 1:

[0073] (1)

[0074] Where: is the sprung body mass. is the unsprung wheel mass. is the air spring force. is the tire stiffness. Represents the displacement of vehicle body mass. Indicates wheel displacement. represents the displacement of the road surface excitation. Represents the output damping force of the magnetorheological damping unit. Represents the damping coefficient. Indicates the air bag pressure. p a Indicates external pressure. Represents the contact area. Step 2: Take the system state variable , the output variable is , input variable And make ,in For the time-varying equivalent stiffness of the double-chamber air spring, the above state system dynamic equations can be converted into the following state space expressions, as shown in Formula 2:

[0075] (2)

[0076] in,

[0077] (3)

[0078] (4)

[0079] (5)

[0080] (6)

[0081] This embodiment uses a random road surface as the road surface impact of the anti-interference control strategy. The random road surface excitation expression is shown in Formula 7:

[0082] (7)

[0083] in, v Indicates vehicle speed. Represents white noise. represents the road roughness coefficient. It can be obtained through parameter identification. Step 3: In order to test the damping characteristics of MRD, a Figure 3 The MRD test device shown mainly includes a DC regulated power supply, MRD, a stretching machine and an electro-hydraulic servo controller. The velocity characteristic curve of the MRD is obtained by processing the test data. When the magnetic viscosity of the magnetorheological fluid causes the piston speed to change direction, the inertial effect of the magnetorheological damping unit is aggravated. The mass of the inertial effect is introduced to describe it. By adding the accumulator stiffness, the adjustable Sigmoid mechanical model of the MRD can be expressed by Formula 8:

[0084] (8)

[0085] in, Output damping force for MRD. is the maximum yield stress, which is related to the size of the hysteresis loop. α is a coefficient related to the width of the velocity characteristic curve. h is a parameter related to the smoothness of the MRD mechanical characteristic curve. is the viscosity curve coefficient. k is the hysteresis loop width proportional factor. is the bias damping force. This mechanical model requires identification of 5 parameters, which are , , h and , this embodiment uses test data under different currents for parameter identification. The Levenberg-Marquardt (LM) optimization algorithm with fast convergence rate and global convergence is used to perform parameter identification on the adjustable Sigmoid model of MRD. Through this parameter identification method, parameter groups under different currents are obtained, and the average value of the parameter group is obtained as the parameter identification result. Since the difference between the test data and the simulation data is not large when the input current changes from 0.4A to 2.0A, the result of parameter identification of the adjustable Sigmoid model of MRD using the LM optimization algorithm in this embodiment can be effectively coupled to the controller. Step 4: As shown in Figure 3, the dual-chamber air spring is mainly composed of an additional air chamber, an air bag, a piston, and an air tank, wherein the additional air chamber is connected to the air bag through a throttle hole, and the piston is connected to the air bag through a sealing rubber ring. The opening and closing of the air tank can be controlled by the solenoid valve, thereby changing the gas capacity inside the air spring. According to the first law of thermodynamics, it can be obtained:

[0086] (9)

[0087] in, p is the gas pressure. T For temperature. m is the gas quality. V is the gas volume. R is the universal gas constant, R=287J·[(kg·K)]-1. Q is the amount of heat absorbed. W is the work done by the outside world on the system. ∆U is the added internal energy of the system. According to formula 9, we can get formula 10:

[0088] (10)

[0089] in, is the heat exchange coefficient. is the temperature of the gas tank. is the temperature of the additional air chamber. is the specific heat capacity at constant pressure. Cv is the specific heat capacity at constant volume. Combining Formula 9 and Formula 10, eliminating the derivative of the additional gas chamber temperature, the first gas mass flow rate can be obtained. The mathematical expression of the first gas mass flow rate is shown in Formula 11:

[0090] (11)

[0091] Where R represents the ideal gas state constant of air and R = 287 J / (kg.K). The specific heat ratio γ = 1.4. The first assumption is that the middle throttle hole uses a large aspect ratio and the load displacement is small. Assuming that the first assumption is true, the internal air flow can be regarded as laminar flow without mass lag, and the second gas mass flow rate can be obtained. The mathematical expression of the second gas mass flow rate is shown in formula 12:

[0092] (12)

[0093] The above formula can be used to express the dynamic equation of a double-chamber air spring with an additional air chamber. Based on the study of small hole flow and the gas state equation in aerodynamic theory, and considering the coefficient correction of the effective area of ​​the connecting hole, different pressures are defined according to the positive direction of the gas flow (considering the actual situation, only the case where the pressure ratio is greater than 0.5283 is considered), then Formula 13 can be obtained:

[0094] (13)

[0095] The second assumption is that: (1) the gas flow direction has the same correction coefficient on the effective area of ​​the throttle hole; (2) the temperature change of the gas in the chamber is much less than twice the atmospheric temperature, and the pressure ratio α = min { p b , p t} / max { p b , p t}≤ 1, the above relationship can be expressed by formula 14:

[0096] (14)

[0097] in, A c is the radial area of ​​the throttle hole, which can be obtained by measurement. The above formula can be expressed as the ratio of gas mass flow rate to pressure: α Differentiating the output force expression of the double-chamber air spring yields Formula 15:

[0098] (15)

[0099] From the above formula, we can know that the output force of the double-chamber air spring is mainly determined by the contact area and the pressure difference between the outside and the airbag. Considering that in the actual double-chamber air spring, the contact area changes little, the output force of the air spring is mainly determined by the pressure difference. Assuming that the heat exchange of the air spring occurs only in the airbag, formula 16 is valid:

[0100] (16)

[0101] In addition, it is assumed that the temperature difference between the airbag and the outside, the coupling term between the airbag pressure and the suspension vibration speed, and the inverse of the suspension system dynamic stroke meet the conditions expressed in formula 17:

[0102] (17)

[0103] in, is the initial dynamic travel of the suspension. , perform Fourier transform on the time domain variables of the above results and obtain:

[0104] (18)

[0105] By eliminating the Fourier transform of the additional air chamber P t (w), the frequency domain relationship between the suspension travel and the additional air chamber parameters can be obtained, which is shown in formula 19:

[0106] (19)

[0107] Convert the above formula into the following stiffness form

[0108] (20)

[0109] in,

[0110] (twenty one)

[0111] From formula 21, it can be seen that the dynamic characteristics of the air spring can be equivalent to the following three units connected in series: the first spring, the first damping unit, the second spring and the second damping unit in parallel. Considering that the change in effective area has a greater impact on the output of the air spring, the equivalent stiffness of the effective area for the output force of the air spring is set to k a , then differentiate the effective area part of the relevant formula to obtain:

[0112] (twenty two)

[0113] in The influence of the contact area change on the air spring is given by the total stiffness of the double-chamber air spring as shown in formula 23:

[0114] (twenty three)

[0115] The output force generated by the air spring is F AS ,in The influence of the pressure difference between the outside world and the air bag on the double-chamber air spring. F air,3is the damping force inside the air spring, and the output force expression is shown in Formula 24:

[0116] (twenty four)

[0117] This embodiment builds a dual-chamber air spring mechanical model in Simulink, selects a sine wave as the excitation of the dual-chamber air spring, and analyzes the dynamic characteristics of the air spring by changing the input excitation. , which makes Formula 25 valid:

[0118] (25)

[0119] Then Formula 23 can be simplified to Formula 26:

[0120] (26)

[0121] The dynamic stiffness K The real part of (w) and the imaginary part As shown in Formula 27:

[0122] (27)

[0123] This embodiment constructs an equivalent model of the dual-chamber air spring system through the system dynamics equations of the magnetorheological air suspension system and the state-space expression of the dual-chamber air spring system. It can combine the advantages of the dual-chamber air spring system with the magnetorheological damper, thereby improving the robustness of the suspension system and enhancing the driving stability of the vehicle.

[0124] In an optional implementation of each embodiment of the present application, the vehicle stability control method may further include the following steps:

[0125] Step S01: obtaining a state space expression of a magnetorheological air suspension system.

[0126] It can be understood that the state-space expression may be a mathematical expression for describing the dynamic behavior of the magnetorheological air suspension system.

[0127] Step S02: determining an uncertainty expression of the magnetorheological air suspension system according to the state space expression and external interference factors of the magnetorheological air suspension system.

[0128] It is understood that external interference factors can be influences from outside the system that interfere with or change the performance and behavior of the spring and the system in which it is located. These factors are usually not generated by the spring system itself, but come from the environment, operating conditions or other systems. The uncertainty expression can be a mathematical expression determined based on the state space expression and the expression corresponding to the external interference factor.

[0129] Step S03: determining the asymptotic stability region of the magnetorheological air suspension system according to the uncertainty expression, the predefined system sliding surface and preset conditions.

[0130] It is understood that the predefined system sliding surface may be a sliding surface of a predefined magnetorheological air suspension system. The preset condition may be a predefined assumption. The asymptotic stability region expression may be a mathematical expression for defining the asymptotic stability region of the magnetorheological air suspension system.

[0131] Step S04: determining the region represented by the asymptotic stability region expression as the asymptotic stability region of the magnetorheological air suspension system.

[0132] In some optional implementations, the above-mentioned step S03 can be implemented through the following steps: determining the state variables of the magnetorheological air suspension system according to the uncertainty expression; determining the dynamic characteristics of the magnetorheological air suspension system on a predefined system sliding surface according to the state variables and preset conditions; determining a characteristic matrix according to the dynamic characteristics; determining the asymptotic stability zone expression of the magnetorheological air suspension system according to the characteristic matrix, preset control laws, preset conditions and preset conversion matrix.

[0133] It is understood that the uncertainty expression of the system is used to describe the uncertainties present in the system model. These uncertainties may come from a variety of factors, such as inaccurate parameters, errors in model simplification, changes in the external environment, etc. The state variables of the system may be variables used to describe the internal state of the system at any given moment. They are key components of the dynamic behavior of the system, through which the current state of the system can be fully described and its future behavior can be predicted. The selection of state variables is very important for system modeling, analysis and control design. The predefined system sliding surface may be a predefined sliding surface of a magnetorheological air suspension system. The preset condition may be a condition under which a predefined assumption holds. The preset control law may be a predefined control law. The asymptotic stability region expression may be a mathematical expression used to represent the magnetorheological air suspension system.

[0134] In some optional implementations, the above-mentioned step S04 can be implemented by the following steps: determining the initial asymptotic stability zone expression through the Lyapunov equation according to the characteristic matrix, the preset control law, the preset conditions and the preset conversion matrix; determining the asymptotic stability zone expression of the magnetorheological air suspension system according to the initial asymptotic stability zone expression, the Lyapunov equation and the preset conditions.

[0135] In some optional implementations, the method further includes: optimizing the asymptotic stability region expression according to preset rules to obtain an optimized asymptotic stability region expression of the magnetorheological air suspension system.

[0136] In one example, according to the state space expression: , after considering the external factors and adding external interference, the uncertainty expression of the magnetorheological air suspension system can be obtained. The uncertainty expression is shown in formula 28:

[0137] (28)

[0138] in, belong n dimensional state vector. Input vector for the finite m-dimensional state of the system. is the bounded interference from the outside world. A and B are the characteristic matrices of the system, and matrix B is full rank. Assume that the predefined system sliding surface is as shown in formula 29:

[0139] (29)

[0140] in, The full rank is m. The preconditions are A1 to A5: (A1) Uncertain interference of the system For a known constant satisfy (A2) System Control There is a known upper norm bound (A3) Difference is correct, . (A4) Matrix It is controllable. (A5) is non-singular. Then we can design the predefined system sliding surface under the conditions of A1-A5 and estimate the asymptotic stability region of the system.

[0141] Since the sliding mode matrix S of the system has a rank of m, the remaining (n-m) system state variables can be fully expressed using m state vector matrices. Then in assumption (A4), there exists a characteristic matrix and Satisfies formula 30:

[0142] (30)

[0143] in, is a Jordan matrix, which determines the dynamic characteristics of the magnetorheological air suspension system on the sliding surface, that is, the simplified (n - m) order equivalent system dynamics. The inverse matrix of B and V can be found , So that formula 31 is established:

[0144] , , , (31)

[0145] Therefore, by choosing , the system can produce the expected system response on the predefined sliding surface. By choosing an appropriate control strategy, we can also make the trajectory of the magnetorheological air suspension system approach the sliding surface and keep the trajectory on the sliding surface at all subsequent times. This study is based on the bounded norm of the system control input Under the condition of , the designed preset control law is shown in formula 32:

[0146] (32)

[0147] The magnetorheological air suspension system can be controlled by the preset control law under the condition of the switching surface to obtain its asymptotic stability domain and sliding mode region. In order to simplify the system form, an n-dimensional conversion matrix is ​​defined as shown in the following formula:

[0148] (33)

[0149] The relevant transformation vector is defined as:

[0150] (34)

[0151] in, and From the above, we can see and Through the above transformation we can get

[0152] (35)

[0153] Formula 35 can also be rewritten as Formula 36:

[0154] (36)

[0155] in, is some positive definite matrix, and:

[0156] (37)

[0157] (38)

[0158] (39)

[0159] For the sake of simplicity, assume that ,in Then, the embodiment estimates the asymptotically stable region using the following lemma. Lemma: Consider the system:

[0160] (40)

[0161] in, , , are the state, input and output of the system respectively. Then there exists a positive definite matrix , so that , where P0 is the only positive solution of the Lyapunov equation, which is shown in Formula 41:

[0162] (41)

[0163] The Meyer-Kalman-Yakubovich theorem can fully prove that A0 is an asymptotically stable matrix. is a strictly positive real number. A0 can be converted into an upper triangular matrix, which can be expressed by formula 42:

[0164] (42)

[0165] The submatrices along the main diagonal and A0, the matrices J and are asymptotically stable matrices, so A0 is a stable matrix. Similarly, since Multiply by S0 is of arrive The transfer function of Depend on Given.

[0166] set up ,in:

[0167] (43)

[0168] is a positive definite matrix such that and , where any All satisfy the above equations, for example , where α is a positive constant such that .

[0169] Consider the Lyapunov function shown in Equation 44:

[0170] (44)

[0171] The derivative of formula 44 yields formula 45:

[0172] (45)

[0173] According to the lemma and the presuppositions, we can get formula 46:

[0174] (46)

[0175] Then formula 47 holds:

[0176] (47)

[0177] in, , ,and:

[0178] (48)

[0179] Since Q is positive definite, and , then formula 49 holds:

[0180] (49)

[0181] Therefore, the asymptotic stability zone of the magnetorheological air suspension system can be obtained, and the asymptotic stability zone is shown in formula 50:

[0182] (50)

[0183] Because the Lyapunov function Satisfies the bounded condition shown in Formula 51:

[0184] (51)

[0185] Therefore, we can get Equation 52:

[0186] (52)

[0187] In the following area, namely the estimation of the ASR of the closed-loop control system, is exponentially stable, we can get:

[0188] (53)

[0189] Where w is the range of the asymptotically stable region; For the matrix The minimum eigenvalue of ; For the matrix The largest eigenvalue of is the correlation coefficient matrix; is the first adjustment coefficient; is the second adjustment factor; is the system control input; M is the conversion matrix; A is the state input matrix; is the inverse matrix of M.

[0190] On the other hand, Under the condition of , the sliding mode domain can be estimated by formula 53. Because the relevant formula can be rewritten as

[0191] (54)

[0192] An estimate of the sliding domain is given by According to the above derivation, we can get: Assuming that (A1)-(A5) are satisfied, if ,but It is exponentially stable over a large range. Otherwise is locally exponentially stable, at which point, by simple choice, we can obtain Equation 55:

[0193] (55)

[0194] From this we can get , and , so we can further get the restriction conditions of the derivative of the Lyapunov function:

[0195] (56)

[0196] Define the following parameters, then you can get:

[0197] (57)

[0198] because , , It is worth noting that in the region In the above equation, the system response satisfies inequality 58:

[0199] (58)

[0200] And, the estimated value of the sliding mode area is determined by Formula 59, and the area represented by Formula 59 can be set as the asymptotic stability area of ​​the magnetorheological air suspension system.

[0201] (59)

[0202] In another example, the asymptotic stability region can be further optimized. Since another estimate of ASR is more conservative than the previous estimate, it is easy to obtain formula 60:

[0203] (60)

[0204] in, It is the system control input; For the matrix The minimum eigenvalue.

[0205] according to , and The choice of matrix , estimates the degree of conservatism of ASR differently. Currently there is no systematic method to select , and , so that the estimated stability bound is maximized. However, the default rule for selecting a suitable matrix is ​​as follows: First, select for ,in , so that The value exists and is as small as possible, and then the selected Find and , the last search ,in , so that and There is a maximum value.

[0206] because exist The peak value at is 1, and by setting , the expression of the optimized asymptotically stable region is obtained from the above formula as shown in formula 61:

[0207] (61)

[0208] This embodiment obtains an uncertainty expression based on the state space expression of the dual-chamber air suspension system and the external interference factors of the magnetorheological air suspension system. According to the uncertainty expression, the predefined sliding surface and the preset conditions, the asymptotic stability zone of the magnetorheological air suspension system is obtained. The stability of the vehicle is controlled during the driving process based on the asymptotic stability zone. The advantages of the dual-chamber air spring system and the magnetorheological damper are combined to improve the robustness of the system and help to improve the stability of the vehicle during driving.

Claims

1. A vehicle stability control method, characterized in that: The method comprises: During the driving of the vehicle, obtaining driving condition data of the vehicle; Determining driving state data of the vehicle according to the driving condition data; According to the system structure, a group of dynamic equations of the magnetorheological air suspension system is established, and the group of dynamic equations is converted into a state space expression of the magnetorheological air suspension system; placing the vehicle in the progressive stability zone according to the driving state data and a preset progressive stability zone control strategy of the magnetorheological air suspension system; if the vehicle is not in the progressive stability zone, adjusting the control parameters to adjust the vehicle to the progressive stability zone; Placing the vehicle in the progressive stability zone according to the driving state data and a preset progressive stability zone control strategy of the magnetorheological air suspension system includes: According to the driving state data, searching for an actual area corresponding to the vehicle through a preset mapping relationship table, comparing the actual area with the progressive stability area of ​​the magnetorheological air suspension system, and if the actual area is outside the progressive stability area, adjusting the damping coefficient and air pressure value of the magnetorheological air suspension system according to a preset adjustment method to place the vehicle within the progressive stability area; The method further comprises: Obtain the state space expression of the magnetorheological air suspension system; Determining an uncertainty expression of the magnetorheological air suspension system according to the state space expression and external interference factors of the magnetorheological air suspension system; Determining an asymptotic stability region expression of the magnetorheological air suspension system according to the uncertainty expression, a predefined system sliding surface and preset conditions; The region represented by the asymptotic stability region expression is determined as the asymptotic stability region of the magnetorheological air suspension system, as specifically shown in the following formula: Among them, w is the range of the asymptotically stable region; λ min (P0) is the minimum eigenvalue of the matrix P0; λ max (P0) is the largest eigenvalue of the matrix P0; S0 is the correlation coefficient matrix; ε is the first adjustment coefficient; β is the second adjustment coefficient; μ is the system control input; M is the conversion matrix; A is the state input matrix; M -1 is the inverse matrix of M; P0 is the only positive definite solution of the Lyapunov equation; The method further includes: determining the state variables of the magnetorheological air suspension system according to the uncertainty expression; determining the dynamic characteristics of the magnetorheological air suspension system on a predefined system sliding surface according to the state variables and preset conditions; determining a characteristic matrix according to the dynamic characteristics; determining an asymptotic stability region expression of the magnetorheological air suspension system according to the characteristic matrix, a preset control law, a preset condition and a preset conversion matrix; According to the characteristic matrix, preset control law, preset conditions and preset conversion matrix, the initial asymptotic stability zone expression is determined by Lyapunov equation; according to the initial asymptotic stability zone expression, the Lyapunov equation and the preset conditions, the asymptotic stability zone expression of the magnetorheological air suspension system is determined.

Images