Vehicle EMB single-wheel failure control method and device considering non-matching disturbance suppression

Abstract

The application provides a vehicle EMB single-wheel failure control method and device considering non-matching disturbance suppression, which inputs vehicle longitudinal speed, actual center of mass side slip angle and actual yaw rate in real-time state parameters of a vehicle into a non-matching disturbance observer and outputs a non-matching disturbance estimation value, and reconstructs distribution brake torque based on the non-matching disturbance estimation value in the case of single-wheel EMB failure, so as to solve the problems of insufficient control precision and robustness of the single-wheel EMB failure, and further realize stable control of the vehicle in the failure condition.

Description

Technical Field

[0001] This invention relates to the field of vehicle stability control, and specifically to a method and apparatus for controlling single-wheel failure of vehicle EMB while also suppressing mismatched disturbances. Background Technology

[0002] With the deep integration of vehicle electrification and intelligence, Electronic Mechanical Braking (EMB) systems, driven by electric motors and coupled with force amplification mechanisms, have become an important development direction for brake-by-wire technology due to their core advantages such as rapid response, precise control, and compact structure. However, EMB systems eliminate traditional hydraulic brake lines and use wiring harnesses to transmit control commands. Key components such as brake calipers, drive motors, and transmission mechanisms are susceptible to wear, fatigue, and impact, leading to performance degradation or functional failure. Therefore, controllable operating scenarios after EMB system failure, especially vehicle fault-tolerant control strategies under single-wheel EMB failure, have become a research hotspot and a key technological bottleneck urgently needing breakthroughs in the field of vehicle braking control.

[0003] Currently, the core idea of ​​fault-tolerant control schemes for single-wheel EMB failure in vehicles is to treat the braking force attenuation caused by EMB failure as a matching disturbance (a disturbance that can be directly canceled out by control input in control theory). By dynamically reconstructing the braking torque of the healthy wheel, a target additional yaw moment is generated to counteract the additional instability yaw moment caused by EMB failure. In other words, the influence of the matching disturbance is counteracted by directly adjusting the control input. For example, patent CN118722552A discloses an EMB braking system failure control method and system. By comparing the vehicle's target braking intensity with a preset threshold and combining the EMB failure type, the target braking torque of the non-failed wheel is dynamically adjusted to effectively suppress yaw moment disturbances. Patent CN116552485A proposes a single-wheel failure control method for distributed brake-by-wire systems. Based on the braking force balance principle, the braking force of each wheel is reconstructed. The additional yaw moment is solved through a sliding mode controller to achieve differential braking control and ensure vehicle driving stability.

[0004] However, under the condition of single-wheel EMB failure, the disturbances affecting vehicle stability include not only the matching disturbance of braking force attenuation caused by EMB failure, but also non-matching disturbances such as changes in road adhesion coefficient, lateral air resistance disturbances, and unmodeled terms of tire nonlinear characteristic deviations (disturbances that cannot be directly canceled by control input in control theory). These non-matching disturbances are uncertain and cannot be directly canceled, and cannot be eliminated by simply adjusting the braking torque of each wheel. However, the existing control schemes mentioned above only consider the matching disturbances caused by EMB failure during the design process, and do not fully consider the impact of non-matching disturbances. If there is a lack of effective disturbance observation and feedforward compensation mechanisms to accurately suppress non-matching disturbances, it is very easy to cause an increase in the vehicle's yaw rate deviation and the center of gravity sideslip angle to exceed the safety threshold, significantly affecting the vehicle's lateral stability and handling stability. In extreme cases, especially under complex conditions such as high-speed driving and low-adhesion road surfaces, it may also induce dangerous conditions such as vehicle fishtailing and rollover, seriously threatening the lives of passengers and road traffic safety. Summary of the Invention

[0005] This application provides a vehicle EMB single-wheel failure control method that takes into account both non-matching disturbance suppression. It estimates the accurate value of non-matching disturbance by using a non-matching disturbance observer, taking into account the influence of EMB single-wheel failure and non-matching disturbance, thus solving the problem of insufficient control accuracy and robustness of EMB single-wheel failure, and thereby achieving stable control of the vehicle under failure conditions.

[0006] In a first aspect, embodiments of this application provide a vehicle EMB single-wheel failure control method that also considers the suppression of mismatched disturbances, comprising the following steps:

[0007] Step S1: Collect real-time vehicle status parameters and identify braking intention. If braking intention exists, calculate the target braking intensity of the vehicle based on the braking intention and execute step S2. Step S2: Input the real-time vehicle state parameters into the two-degree-of-freedom vehicle dynamics model to obtain the vehicle motion state parameters, which include the ideal yaw rate and the ideal center-of-mass sideslip angle. Step S3: Input the actual braking torque and the desired braking torque into the EMB failure judgment unit to determine whether the EMB has failed. If the EMB fails, proceed to step S4; otherwise, proceed to step S5-1. Step S4: Input the vehicle longitudinal speed, actual centroid sideslip angle and actual yaw rate from the control input and the vehicle real-time state parameters into the unmatched disturbance observer and output the unmatched disturbance estimate, where the control input is the front wheel steering angle and the additional yaw moment and execute S5-2; Step S5-1: Distribute the braking torque of each wheel according to the ideal braking force distribution coefficient based on the vehicle's target braking intensity; Step S5-2: Based on the EMB failure braking force of the failed wheel and the remaining braking force of the braking system, distinguish between the first working condition and the second working condition. In the first working condition, reconstruct the braking torque of each wheel according to the torque reconstruction controller. In the second working condition, input the unmatched disturbance estimate, the center of gravity sideslip angle and the yaw rate into the vehicle stability sliding mode controller to obtain the additional yaw torque. Input the additional yaw torque into the braking torque reconstruction unit to redistribute the braking torque. Step S6: Execute the braking command based on the braking torque.

[0008] Secondly, embodiments of this application provide a vehicle EMB single-wheel failure control device that also considers non-matching disturbance suppression, comprising: The controlled vehicle unit is used to collect real-time status parameters of the vehicle and identify braking intentions. If a braking intention exists, the target braking intensity of the vehicle is calculated based on the braking intention. A two-degree-of-freedom vehicle dynamics model is used to obtain real-time vehicle state parameters and vehicle motion state parameters, including ideal yaw rate and ideal centroid sideslip angle. The EMB failure judgment unit is used to input the actual braking torque and the expected braking torque into the EMB failure judgment unit to determine whether the EMB has failed. The unmatched disturbance observer is used to acquire the vehicle's longitudinal speed, actual center of gravity sideslip angle, and actual yaw rate from the control input and real-time vehicle state parameters. The unmatched disturbance observer is then output as an estimate of the unmatched disturbance. The control input is the front wheel steering angle and the additional yaw moment. The I-curve braking torque distribution unit is used to distribute the braking torque of each wheel according to the ideal braking force distribution coefficient based on the vehicle's target braking intensity when no EMB failure occurs. A vehicle stability sliding mode controller is used to output an additional yaw moment based on the mismatch disturbance estimate, the center of gravity sideslip angle, and the yaw rate when an EMB failure occurs. Braking torque reconfiguration unit, used to reconfigure braking torque based on additional yaw moment; The EMB braking system execution unit is used to execute braking commands on the controlled vehicle unit based on the braking torque.

[0009] Thirdly, embodiments of this application provide an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the vehicle EMB single-wheel failure control method that takes into account the suppression of mismatched disturbances.

[0010] The main contributions and innovations of this invention are as follows: (1) A vehicle EMB single-wheel failure control method based on unmatched disturbance observer is proposed, which takes into account the coordinated suppression of matched and unmatched disturbances. By constructing a sliding mode surface containing the error of centroid sideslip angle and yaw rate, and taking advantage of the strong robustness and fast convergence of sliding mode control, the unmatched disturbance value is output in real time while estimating the critical state of the vehicle. By accurately capturing the unmatched disturbance that cannot be directly canceled under the failure condition, accurate feedforward compensation is provided for subsequent control.

[0011] (2) An adaptive sliding mode control strategy integrating feedforward and feedback is constructed. The disturbance estimate output by the unmatched disturbance observer is embedded into the control law as a feedforward compensation term to quickly cancel the observed disturbance. At the same time, with yaw rate and centroid sideslip angle as tracking targets, a feedback control law containing equivalent control and switching control is designed to enhance the suppression of matched disturbances and unobserved disturbances. This architecture balances the speed of disturbance suppression with system robustness, significantly improving the vehicle's dynamic response and edge stability under failure conditions.

[0012] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is an architectural diagram of a vehicle EMB single-wheel failure control device that combines the coordinated suppression of matched and unmatched disturbances according to an embodiment of this application; Figure 2 This is a flowchart of a vehicle EMB single-wheel failure control method that takes into account both matched and unmatched disturbances in a coordinated manner. Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0014] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0015] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0016] Example 1 For vehicles equipped with electromechanical braking (EMB) systems, abnormal braking force in a single wheel can easily lead to yaw instability under controllable EMB failure conditions. Existing yaw stability control schemes only treat the braking force decay caused by EMB failure as a matched disturbance for suppression, without fully considering the impact of unmatched disturbances such as changes in road surface adhesion, lateral wind disturbance, tire nonlinearity, and unmodeled dynamics on the system control performance. This results in low control accuracy and insufficient robustness, making it difficult to ensure vehicle stability under failure conditions.

[0017] Therefore, such as Figure 2 As shown, this invention proposes a vehicle EMB single-wheel failure control method that takes into account both matched and unmatched disturbances. This method achieves accurate estimation of unmatched disturbances by constructing a dedicated unmatched disturbance observer; comprehensively considering the EMB single-wheel failure characteristics and the effect of unmatched disturbances, the additional yaw moment calculation unit outputs the compensation torque required for vehicle stability; and then the braking torque reconstruction unit optimizes the allocation of the EMB braking torque of the healthy wheel, thereby achieving coordinated control of vehicle yaw stability and braking performance under failure conditions.

[0018] Specifically, the vehicle EMB single-wheel failure control method provided in this solution, which also considers the suppression of mismatched disturbances, includes the following steps: Step S1: Collect real-time vehicle status parameters and identify braking intention. If braking intention exists, calculate the target braking intensity of the vehicle based on the braking intention and execute step S2. Step S2: Input the real-time vehicle state parameters into the two-degree-of-freedom vehicle dynamics model to obtain the vehicle motion state parameters, which include the ideal yaw rate and the ideal center-of-mass sideslip angle. Step S3: Input the vehicle longitudinal speed, actual center of gravity sideslip angle and actual yaw rate from the control input and the vehicle real-time state parameters into the unmatched disturbance observer and output the unmatched disturbance estimate, where the control input is the front wheel steering angle and the additional yaw moment; Step S4: Input the actual braking torque and the desired braking torque into the EMB failure judgment unit to determine whether the EMB has failed. If the EMB fails, proceed to step S5-2; if the EMB fails, proceed to step S5-1. Step S5-1: Distribute the braking torque of each wheel according to the ideal braking force distribution coefficient based on the vehicle's target braking intensity; Step S5-2: Based on the EMB failure braking force of the failed wheel and the remaining braking force of the braking system, distinguish between the first working condition and the second working condition. In the first working condition, reconstruct the braking torque of each wheel according to the torque reconstruction controller. In the second working condition, input the unmatched disturbance estimate, the center of gravity sideslip angle and the yaw rate into the vehicle stability sliding mode controller to obtain the additional yaw torque. Input the additional yaw torque into the braking torque reconstruction unit to redistribute the braking torque. Step S6: Execute the braking command based on the braking torque.

[0019] Specifically, this solution targets vehicles controlled by EMB. Real-time vehicle state parameters are collected and input into a two-degree-of-freedom vehicle dynamics model to obtain reference yaw rate and sideslip angle parameters. Unknown mismatched disturbances in the controlled vehicle, along with ideal yaw rate and sideslip angle parameters, are input to the mismatched disturbance observer unit to observe estimated values ​​of the mismatched disturbances. The actual braking torque and desired braking torque are input to the EMB failure judgment unit to determine if the EMB has failed. If not, the I-curve braking torque allocation unit allocates the EMB braking force to each wheel according to the ideal braking force allocation coefficient. If a single wheel of the EMB fails, the estimated value of the mismatched disturbance observed by the mismatched disturbance observer unit, along with the sideslip angle error and yaw rate tracking error, are input to the adaptive sliding mode controller's additional yaw torque calculation unit to obtain the additional yaw torque to maintain the vehicle. This additional yaw torque is then input to the braking torque reconstruction unit to redistribute the EMB's braking torque. Finally, the allocated braking torque signal is input to the EMB braking system execution unit to output the actual executed braking torque.

[0020] Specifically, in step S1, real-time vehicle status parameters are collected by sensors mounted on the vehicle, and braking intention is identified based on these parameters. If braking intention exists, the total braking demand is calculated and step S2 is executed. That is, this method only activates the subsequent vehicle EMB single-wheel failure control method when the driver has a braking intention; when there is no braking intention, there is no braking torque demand, and the braking system does not perform any action.

[0021] In some embodiments, the vehicle's real-time status parameters include the vehicle's longitudinal speed. Lateral velocity yaw rate , centroid side slip angle Steering wheel angle Wheel speed of the four wheels Actual braking torque and brake pedal travel .

[0022] Furthermore, the driver's braking intention is identified based on real-time vehicle status parameters. Specifically, if the brake pedal travel is greater than 0, there is a braking intention; if the brake pedal travel is 0, there is no braking intention.

[0023] Furthermore, not only can the presence of braking intent be identified based on the brake pedal travel, but also the driver's braking intensity can be calculated based on the brake pedal travel once braking intent is present. In some embodiments, the ratio of the current brake pedal travel to the maximum pedal travel is taken as the driver's braking intensity, calculated using the following formula: ; Where z is the driver's braking intensity, This refers to the brake pedal travel. This represents the maximum pedal travel.

[0024] Assuming the braking system is operating under normal conditions and the road surface has sufficient grip, the driver's required braking intensity can be equivalent to the vehicle's target braking intensity. That is, the vehicle's target braking intensity is the product of the driver's braking intensity and the vehicle's weight, calculated using the following formula: ; Where F total Let z be the target braking intensity of the vehicle, m be the vehicle weight, and g be the acceleration due to gravity.

[0025] If we ignore the lateral load transfer of the vehicle, the braking torque of each wheel can be initially distributed as follows: ; Where T b1 T b2 T b3 T b4 The braking torque for all four wheels. For vehicle quality, acceleration due to gravity The effective rolling radius of the wheel, This is the ideal braking force distribution coefficient.

[0026] Regarding step S2: Step S2: Input the real-time vehicle state parameters into the two-degree-of-freedom vehicle dynamics model to obtain the vehicle motion state parameters, which include the ideal yaw rate parameters and the ideal center-of-mass sideslip angle parameters.

[0027] In some embodiments, under the premise of adopting a linear model for tire lateral force, the steady-state response formula is derived based on a two-degree-of-freedom vehicle dynamics model. The vehicle longitudinal velocity and front wheel steering angle in the real-time vehicle state parameters are substituted into the steady-state response formula to solve for the ideal yaw rate parameter, and the ideal center of mass sideslip angle is preset to 0.

[0028] Specifically, the steady-state response formula is as follows: ; Where γ des These are the ideal yaw rate parameters. It is the vehicle's longitudinal speed. This is the distance from the center of gravity to the front axle. Where δf is the distance from the center of gravity to the rear axle, K is the vehicle stability factor, and δf is the front wheel steering angle.

[0029] That is, adopting the linear tire model assumption, and focusing on the vehicle's lateral and yaw motions, a two-degree-of-freedom vehicle dynamics model is established: Lateral force balance equation: describes the balance relationship between the lateral acceleration of the vehicle's center of gravity and the lateral forces of the front and rear axle tires; Yaw moment balance equation: describes the balance relationship between the vehicle's yaw acceleration and the yaw moment generated by the lateral forces of the front and rear axle tires.

[0030] And assuming the tire lateral force adopts a linear model:

[0031] Furthermore, under the assumption of a linear tire model, the classic linear two-degree-of-freedom bicycle model is as follows:

[0032] The real-time vehicle state parameters are substituted into the two-degree-of-freedom vehicle dynamics model to obtain the steady-state solution in order to solve for the ideal yaw rate parameters and the ideal center-of-mass sideslip angle parameters.

[0033] To describe the actual dynamic response of the vehicle during EMB single-wheel failure and braking torque reconstruction, a three-degree-of-freedom vehicle planar dynamics model equation, including longitudinal motion, lateral motion, and yaw motion, is established as follows:

[0034]

[0035] ; The corresponding wheel dynamics model is:

[0036] In some embodiments, based on the vehicle's three-degree-of-freedom dynamic equations (longitudinal, lateral, and yaw), and considering the vehicle's longitudinal velocity... The centroid sideslip angle is selected as the time-varying parameter for real-time measurement. and yaw rate As system state variables, consider the unmatched disturbances within the system to establish a vehicle lateral stability state-space model:

[0037] Select the vehicle's sideslip angle and yaw rate as the system state variables:

[0038] Front wheel steering angle and additional yaw moment are selected as control inputs:

[0039] Integrated unmatched disturbances It is used to characterize disturbances that cannot be directly canceled by control inputs, such as changes in road surface adhesion coefficient, lateral wind disturbance, tire nonlinear characteristic deviation, unmodeled lateral force, and parameter perturbations.

[0040] Under conditions where the front wheel steering angle of the vehicle is small , The system state matrix, To control the input matrix, Assign matrices to unmatched perturbations. This is the output matrix. It can be represented as:

[0041]

[0042]

[0043]

[0044] in Lateral velocity; The longitudinal speed of the vehicle; This refers to the yaw rate; It is the centroid sideslip angle; and These are the lateral forces on the left and right front wheels, respectively. and These are the lateral forces on the left and right rear wheels, respectively. The steering angle of the front wheels; and These are the longitudinal forces on the left and right front wheels, respectively. and These are the longitudinal forces on the left and right rear wheels, respectively. Let yaw moment of inertia be the moment of inertia of the vehicle about its center of mass. This is the distance from the center of mass to the front axle; This is the distance from the center of mass to the rear axle; Wheelbase; To add yaw moment; and These are the lateral stiffness of the tires on the front and rear axles, respectively. and These are the slip angles of the front and rear wheels, respectively. No. Wheel EMB Additional Braking Torque , This represents the wheel speed of each wheel.

[0045] Regarding step S3: In some embodiments, a unified description model of EMB faults is constructed based on the actual braking torque and the expected braking torque. The unified description model of EMB faults is then transformed into a linear parameterized model. The recursive least squares method is used to perform online parameter estimation on the linear parameterized model to obtain estimated parameters, which include the failure degree factor and the bias fault torque. The failure of EMB is determined based on the estimated parameters.

[0046] Furthermore, the actual braking torque and the controller's expected braking torque from the vehicle's real-time state parameters are obtained, and a unified description model for EMB faults is established: ; in This refers to the actual braking torque output by the EMB. The desired braking torque issued by the controller, Failure factor (0 indicates complete failure, 1 indicates normal operation); This is the offset fault torque.

[0047] Assuming in a short period of time, and Since the faults are constant or slowly changing, the unified description model of EMB faults can be transformed into a linear parameterized model: ; Suppose that, in discrete time, the relationship at the k-th sampling time is as follows: ; in For regression vectors; Let be the desired braking torque at time k; Let k be the failure factor and the offset fault torque at time k.

[0048] Furthermore, for each sampling time k, the recursive least squares method is used to perform online parameter estimation of the linear parameterized model to obtain the estimated parameters. Specifically, at each sampling time, the gain is calculated using the current covariance matrix and regression vector, and then the estimated parameters and the covariance matrix for the next time step are updated using the gain, the actual braking torque, and the expected braking torque.

[0049] The process of online parameter estimation is as follows: First, the gain at each sampling time k is calculated as follows: ; Secondly, perform online parameter estimation: ; Finally, update the covariance matrix: .

[0050] in To estimate the gain using recursive least squares; Let be the covariance matrix at the k-th sampling time. Let be the covariance matrix of the previous sampling time. This represents the estimated EMB fault parameters of the i-th wheel at the previous sampling time. The actual braking torque output by the EMB at the k-th sampling time; It is the forgetting factor and 0 < ≤1, It is an identity matrix.

[0051] After obtaining the failure severity factor and the offset fault torque, this solution can calculate and determine whether the EMB has failed and the type of failure based on the failure severity factor and the offset fault torque. The specific rules are as follows: When the failure factor is 1 and the offset fault torque is 0, no EMB failure occurs. EMB failure occurs when the failure factor is not 1 or the offset fault torque is not 0.

[0052] Furthermore, this solution can also determine the EMB fault type based on the failure degree factor and the offset fault torque, with the specific rules as follows: When the failure factor is less than 1 and greater than 0, and the bias fault torque is 0, it is determined that the system has experienced a partial failure. At this time, the braking torque output is reduced proportionally. When the failure factor is 1 and the bias fault torque is not 0, it is determined that the system has a jamming fault. At this time, the braking torque will output an additional constant value. When the failure factor is less than 1 and greater than 0, and the offset fault torque is not 0, it is determined that the system has a compound fault. At this time, the braking torque offset and partial failure exist simultaneously.

[0053] Regarding step S4: This scheme inputs the real-time vehicle state parameters, including the impact of mismatched disturbances, and the control inputs into the mismatched disturbance observer. The sliding mode disturbance observation algorithm is used to synchronously estimate the system state vector and the disturbance, and finally outputs the estimated value of the mismatched disturbance.

[0054] In some embodiments, this solution acquires real-time vehicle state parameters and constructs a system state vector. Specifically, it acquires the vehicle's longitudinal velocity. Actual centroid sideslip angle Actual yaw rate And construct the system state vector as .

[0055] Furthermore, the system state vector and control input are substituted into the sliding mode disturbance observer state estimation equation in the unmatched disturbance observer to obtain the state estimation error. The time derivative is calculated based on the state estimation error to construct the sliding surface differential equation. The unmatched disturbance estimate is updated according to the sliding mode reachability condition and the sliding surface differential equation. The state estimation error includes the centroid sideslip angle estimation error and the yaw rate estimation error.

[0056] The specific calculation process is as follows: The calculation process for obtaining the state estimation error is as follows: To achieve the system state vector Total mismatched disturbance To achieve synchronous estimation, this scheme introduces a state estimation vector. and disturbance estimation term Based on the original system state equations, the sliding mode disturbance observer state estimation equations for the unmatched disturbance observer are constructed: In some embodiments, the constructed sliding mode perturbation observer state estimation equation is as follows: ; in For the state estimation vector, It is a control input. It is the disturbance estimation term. The system state matrix, To control the input matrix, Assign matrices to unmatched perturbations. This is the output matrix. The output estimation error is equivalent to the state estimation error. , denoted as The state estimation error at the current moment is calculated by substituting the system state vector and control input into the sliding mode disturbance observer state estimation equation, as shown below: ; in The centroid sideslip angle estimated by the observer; This represents the error in estimating the centroid sideslip angle. The yaw rate estimated by the observer; This represents the estimation error of the yaw rate.

[0057] In some embodiments, the state estimation error differential equation is constructed by taking the time derivative of the state estimation error, a sliding surface is constructed based on the state estimation error, and the sliding surface differential equation is constructed by taking the time derivative of the sliding surface and combining it with the state estimation error differential equation.

[0058] Furthermore, the constructed state estimation error differential equation is as follows: ; ; in This is the difference between the actual disturbance and the estimated disturbance value. Let the state estimation error be denoted as . ; For the state estimation vector, It is a control input. The system state matrix, To control the input matrix, Assign matrices to unmatched perturbations. For the output matrix, is the observer gain matrix.

[0059] The sliding surface must be able to reflect the coupling relationship between the state estimation error and the disturbance estimation error, ensuring that the system state can converge to the sliding surface in a finite time, and that the disturbance can be accurately estimated through the sliding reachability conditions during the sliding motion phase.

[0060] Therefore, in some embodiments, a sliding surface is selected. State estimation error Linear combination: ; in Here is the sliding surface coefficient matrix; The sliding surface coefficient must satisfy the following conditions: To ensure the accuracy of disturbance estimation It can be fed back to the perturbation estimation update law through the sliding mode surface. It is the state estimation error.

[0061] In some embodiments, differentiating the sliding surface yields the following differential equation: ; ; It is the disturbance estimation term. The system state matrix, The observer gain matrix is... For the output matrix, Assign matrices to unmatched perturbations. This is the sliding surface coefficient matrix.

[0062] In some embodiments, the mismatched disturbance estimate is updated based on the sliding mode reachability condition and the sliding surface differential equation. Further, the changes in the sliding surface value are monitored in real time to determine whether the system state has converged. When the system state converges, the state estimation error and the sliding mode value are substituted into the estimation update law to update the mismatched disturbance estimate. Even further, if convergence is slow or chattering is excessive, the sliding mode gain and chattering suppression coefficient are dynamically adjusted.

[0063] That is, real-time monitoring of the sliding surface The numerical changes in the value are used to determine whether the system state has converged to the sliding surface; if slow convergence or excessive chattering occurs, the sliding mode gain k and chattering suppression coefficient are dynamically adjusted. To ensure the real-time performance and estimation accuracy of the observer; repeatedly apply the state estimation error. Sliding surface Substituting the perturbation estimation update law, we can calculate the current time-time mismatch perturbation estimate. This completes one disturbance estimation.

[0064] In some embodiments, to make the state estimation error converge to the sliding surface and suppress the effects of mismatched perturbations, the update law for the mismatched perturbation estimation is designed based on the sliding reachability condition as follows: ; in Here, k represents the estimate of the unmatched perturbation, and k is the sliding mode gain. Let ε be the saturation function, and ε be the chattering suppression coefficient. This is the estimated value of the equivalent disturbance.

[0065] It should be noted that when the sliding surface If the value approaches 0 and remains within the minimum neighborhood of 0 without significantly increasing, then the system state is considered to have converged to the sliding surface.

[0066] The derivation of this estimation update law is as follows: When the system enters the sliding mode phase, the sliding condition is satisfied. The disturbance estimation error is forced to converge to zero, at which point the equivalent disturbance estimate can be solved using the sliding mode equivalent control method:

[0067] in This is the equivalent disturbance estimate. The system state matrix, For the output matrix, Assign matrices to unmatched perturbations. Here is the sliding surface coefficient matrix. is the observer gain matrix.

[0068] Because chattering occurs during sliding mode motion, which severely affects the accuracy of disturbance estimation, a saturation function is introduced. Instead of the traditional sign function, a sliding mode chattering suppression coefficient is introduced, where ε is the chattering suppression coefficient. A sliding mode gain k is also introduced to enhance the robustness of the observer and ensure that the sliding mode reachability condition is met, where k must satisfy... , To define the upper limit of the rate of change of the total mismatched disturbance d, and considering the equivalent disturbance estimate, saturation function, and sliding mode gain, the update law for the total mismatched disturbance estimate is designed as follows: .

[0069] Regarding step S5-1: When no EMB failure occurs, the braking torque of each wheel is distributed according to the ideal braking force distribution coefficient based on the vehicle's target braking intensity. The distribution formula is as follows: ; Where T bi Let the braking torque be that of the i-th wheel. The effective rolling radius of the wheel, Let F be the ideal braking force distribution coefficient for the i-th wheel. total It is the target braking intensity of the vehicle.

[0070] Regarding step S5-2: When an EMB failure occurs, in order to ensure that the vehicle's yaw moment is zero, the remaining braking force of the entire braking system needs to be adjusted. The wheel with the EMB failure means that the wheel has no braking force at this time. At this time, the braking system control objectives are twofold: ① the total longitudinal braking force is equal to that before the EMB failure; ② the yaw moment is zero.

[0071] In addition, since the compensation requirements for different EMB failure conditions are different, the EMB failure conditions are classified according to the EMB failure braking force of the failed wheel and the remaining braking force of the braking system.

[0072] Specifically, the first operating condition is defined as when the remaining braking force of the braking system on the same side of the wheel is greater than or equal to the braking force lost by the EMB of the failed wheel; and the second operating condition is defined as when the remaining braking force of the braking system on the same side of the wheel is less than the braking force lost by the EMB of the failed wheel and less than the remaining braking force of the entire vehicle's braking system. This can be simply expressed as: First operating condition: The remaining braking force of the braking system of the same side wheel is greater than or equal to the braking force lost by the EMB of the failed wheel; Second operating condition: Remaining braking force of the same-side wheel braking system < Braking force lost by the failed wheel EMB < Remaining braking force of the whole vehicle.

[0073] In the first operating condition, the fault-free wheel on the side of the failed wheel can compensate for the lost braking force, which is used to ensure braking strength and also to ensure that the braking force on both sides is equal. When the EMB on the same side compensates for the lost braking force, the faster the EMB execution system of the wheel being compensated responds, the smaller the yaw rate caused by the loss of braking force during this period, and the sooner the vehicle can reach a stable state.

[0074] Under the first operating condition, the braking torque of each wheel is reconstructed according to the torque reconstruction controller, as expressed as: ; And the additional yaw moment generated by the vehicle's braking force distribution is satisfied as follows: ; in Let be the expected braking force of the EMB of the i-th wheel after the failure of a single wheel EMB (i=1, 2, 3, 4). Let EMB be the expected braking force of the i-th wheel when it is not in failure. The failed wheel lost braking force via EMB. To add yaw moment.

[0075] In the second operating condition, the braking force of the normal wheels on the same side cannot fully compensate for the braking force loss of the failed wheel. Therefore, it is necessary to adjust the braking force of the front and rear axles on the opposite side to ensure that the total longitudinal braking force of the vehicle remains consistent with that before the EMB failure. Simultaneously, to maintain the vehicle's driving stability after a single wheel EMB failure, an additional yaw moment needs to be applied to counteract the instability yaw moment caused by the single wheel EMB failure, making the total yaw moment of the vehicle zero and ensuring stable vehicle operation. Accordingly, this solution designs a vehicle stability sliding mode controller, using yaw rate and center-of-gravity sideslip angle as core state variables, combined with unmatched disturbance estimates, and constructing the structure of the vehicle stability sliding mode controller based on sliding mode control theory. This vehicle stability sliding mode controller integrates equivalent control with disturbance feedforward compensation and robust switching control modules. By designing sliding surfaces corresponding to the state variables, it completes feedforward compensation control based on unmatched disturbance estimates, and finally outputs the required additional yaw moment, achieving precise control of vehicle driving stability.

[0076] At this point, the estimated value of the mismatched disturbance, the centroid sideslip angle and the yaw rate are input into the vehicle stability sliding mode controller to calculate the additional yaw moment, and the additional yaw moment is input into the braking torque reconfiguration unit to redistribute the braking torque. In some embodiments, a sliding surface is designed with the sideslip angle and yaw rate as state variables, and feedforward compensation is performed in combination with the estimated value of the mismatched disturbance. An additional yaw moment is obtained by solving a sliding mode control law that combines equivalent control and switching control. The additional yaw moment is input into the torque reconstruction controller. With the tire load rate as the optimization objective, the braking torque of each wheel is obtained by solving the torque reconstruction controller by integrating the vehicle longitudinal force constraint, the additional yaw moment constraint, the EMB torque constraint, and the road adhesion constraint.

[0077] Specifically, in the step of "designing the sliding surface using the sideslip angle and yaw rate as state variables, and performing feedforward compensation based on the estimated values ​​of the mismatched disturbance", The sideslip angle error is calculated based on the difference between the actual sideslip angle and the ideal sideslip angle. The yaw rate error is calculated based on the difference between the yaw rate and the ideal yaw rate. The calculation formulas are as follows: ; Where e1 is the error of the center of mass sideslip angle, and e2 is the error of the yaw rate. The sideslip angle is the angle of the centroid. For the ideal centroid sideslip angle, The yaw rate is angular velocity. The ideal yaw rate.

[0078] To ensure that the vehicle's actual yaw rate follows the ideal yaw rate, i.e., the yaw rate error... To simultaneously minimize the rate of change of the system response, this scheme employs a sliding mode control method to design the sliding surface for the yaw rate, which is represented as follows: ; Where e1 is the error of the center of mass sideslip angle, and e2 is the error of the yaw rate. This is the sliding surface coefficient.

[0079] Regarding the sliding surface, the derivative yields the following formula: ; Substitution and The formula following the expression is as follows:

[0080] in These are the combination coefficients in the derivative of the sliding surface. , System state matrix elements, To control the input matrix The elements in.

[0081] To ensure the system state converges to the sliding surface, a convergence law is designed:

[0082] in, To achieve sliding mode gain, This is the boundary layer thickness, used to reduce chattering.

[0083] The saturation function is defined as:

[0084] Let the derivative of the sliding surface satisfy the reaching law:

[0085] Due to actual disturbances Unknown, estimated value using the output of an unmatched perturbation observer. Perform feedforward compensation when taking This yields a simplified additional yaw moment:

[0086] In some embodiments, the sliding mode control law is expressed as: ; ; ; in For equivalent control, To switch control, It is the additional yaw moment, and K is the switching gain. This is a boundary layer thickness parameter used to achieve smooth switching and reduce chattering. These are the combination coefficients in the derivative of the sliding surface. Let the moment of inertia of the vehicle's yaw motion about its center of mass be... These are estimates of the mismatched perturbation. The sideslip angle is the angle of the centroid. The yaw rate is angular velocity. For the front wheel steering angle, Let be the desired yaw rate.

[0087] After obtaining the additional yaw moment, this scheme designs a reasonable torque distribution strategy, ensuring that the required torque output by the upper-level stability controller can be accurately tracked and executed, thus effectively improving the vehicle's stability control performance. Specifically, this scheme selects an appropriate optimization objective function for the braking torque reconstruction unit. Considering the nonlinearity of tire mechanical characteristics, the cooperative force of the tires during vehicle operation is mainly affected by the coupling effect of the road adhesion coefficient and the vertical load of the tires. The tire load rate can intuitively represent the utilization degree of the adhesion potential between the tire and the ground, and is an important indicator reflecting the vehicle's driving stability. Therefore, the tire load rate is selected as the optimization objective of the torque reconstruction controller. That is, the braking torque reconstruction unit uses the tire load rate as the optimization objective. The tire load rate is specifically defined as: ; in For tire load rate, For the longitudinal force of each wheel, The lateral forces of each wheel, The load borne by each wheel.

[0088] According to the definition of tire load factor, a lower load factor means the tire is further from its adhesion limit, has a larger adhesion margin, and helps improve the vehicle's stability margin. Therefore, the optimization objective function can be defined as the square of the tire load factor for each tire, and the stability margin is maximized by minimizing this objective function. Its expression is as follows: .

[0089] In practical engineering applications, the complex nonlinear coupling between tire lateral and longitudinal forces makes it difficult to obtain the tire lateral force. Accurate calculation of this force requires a complex decoupling process, significantly increasing the design complexity of the controller. Furthermore, the yaw moment is primarily influenced by the tire's longitudinal force.

[0090] Based on the above reasons, this scheme simplifies the objective function to a certain extent, ignoring the effect of tire lateral force. Assuming that all tires have the same road adhesion coefficient, the problem of controlling the tire load rate is simplified to controlling the tire longitudinal force, resulting in the following simplified objective function: ; Converting it to a force rectangle is: .

[0091] That is, in some embodiments, the square of the tire load rate of each tire is used as the objective function, and the optimization objective is set to minimize the objective function.

[0092] In addition, when distributing torque, in addition to satisfying the constraints of yaw moment and longitudinal force, it is also necessary to consider the torque saturation limit of the drive motor and the road surface adhesion limit. The various constraint terms are expressed as follows: The longitudinal force constraint of the vehicle is: ; The additional yaw moment constraint is: ; The EMB torque constraint and road surface adhesion constraint are as follows: ; The road surface adhesion constraint is: .

[0093] in These are the EMB braking torques for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. For the first Braking torque of each wheel; For the first The maximum braking torque that an EMB actuator is allowed to output; The effective rolling radius of the wheel; The steering angle of the front wheels; The wheelbase of the vehicle; The total longitudinal braking force of the vehicle; This is the additional yaw moment generated by the difference in braking force between the left and right wheels; For the first The coefficient of adhesion of the road surface where each wheel is located; For the first Vertical load on each wheel.

[0094] Regarding step S6: This solution generates precise control commands for the braking torque of each wheel, which are sent to the EMB brake actuator unit via the CAN bus. The actuator unit converts the ECU's digital commands into physical braking torque, thereby achieving dynamic, closed-loop control of the vehicle's braking torque.

[0095] Example 2 Based on the same concept, referencing Figure 1 This application also proposes a vehicle EMB single-wheel failure control device that takes into account both mismatch disturbance suppression and other factors, comprising: The controlled vehicle unit is used to collect real-time status parameters of the vehicle and identify braking intentions. If a braking intention exists, the target braking intensity of the vehicle is calculated based on the braking intention. A two-degree-of-freedom vehicle dynamics model is used to obtain real-time vehicle state parameters and vehicle motion state parameters, including ideal yaw rate and ideal centroid sideslip angle. The EMB failure judgment unit is used to input the actual braking torque and the expected braking torque into the EMB failure judgment unit to determine whether the EMB has failed. The unmatched disturbance observer is used to acquire the vehicle's longitudinal speed, actual center of gravity sideslip angle, and actual yaw rate from the control input and real-time vehicle state parameters. The unmatched disturbance observer is then output as an estimate of the unmatched disturbance. The control input is the front wheel steering angle and the additional yaw moment. The I-curve braking torque distribution unit is used to distribute the braking torque of each wheel according to the ideal braking force distribution coefficient based on the vehicle's target braking intensity when no EMB failure occurs. A vehicle stability sliding mode controller is used to output an additional yaw moment based on the mismatch disturbance estimate, the center of gravity sideslip angle, and the yaw rate when an EMB failure occurs. Braking torque reconfiguration unit, used to reconfigure braking torque based on additional yaw moment; The EMB braking system execution unit is used to execute braking commands on the controlled vehicle unit based on the braking torque.

[0096] The specific implementation of the vehicle EMB single-wheel failure control device that also takes into account the suppression of mismatched disturbances is described in Example 1, and will not be repeated here.

[0097] Example 3 This embodiment also provides an electronic device, see reference. Figure 3 It includes a memory 404 and a processor 402, the memory 404 storing a computer program and the processor 402 being configured to run the computer program to perform the steps in any of the embodiments of the vehicle EMB single-wheel failure control method that takes into account mismatch disturbance suppression.

[0098] Specifically, the processor 402 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0099] The memory 404 may include a large-capacity memory 404 for data or instructions. The memory 404 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 402.

[0100] The processor 402 reads and executes computer program instructions stored in the memory 404. Optionally, the electronic device may further include a transmission device 406 and an input / output device 408, wherein the transmission device 406 is connected to the processor 402, and the input / output device 408 is connected to the processor 402.

[0101] The transmission device 406 can be used to receive or send data via a network. Specific examples of the network described above may include wired or wireless networks provided by the communication provider of the electronic device. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 406 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0102] The input / output device 408 is used to input or output information. In this embodiment, the input information may be real-time vehicle status parameters, and the output information may be braking commands, etc.

[0103] Optionally, in this embodiment, the processor 402 can be configured to perform the following steps via a computer program: Step S1: Collect real-time vehicle status parameters and identify braking intention. If braking intention exists, calculate the target braking intensity of the vehicle based on the braking intention and execute step S2. Step S2: Input the real-time vehicle state parameters into the two-degree-of-freedom vehicle dynamics model to obtain the vehicle motion state parameters, which include the ideal yaw rate and the ideal center-of-mass sideslip angle. Step S3: Input the actual braking torque and the desired braking torque into the EMB failure judgment unit to determine whether the EMB has failed. If the EMB fails, proceed to step S4; otherwise, proceed to step S5-1. Step S4: Input the vehicle longitudinal speed, actual centroid sideslip angle and actual yaw rate from the control input and the vehicle real-time state parameters into the unmatched disturbance observer and output the unmatched disturbance estimate, where the control input is the front wheel steering angle and the additional yaw moment and execute S5-2; Step S5-1: Distribute the braking torque of each wheel according to the ideal braking force distribution coefficient based on the vehicle's target braking intensity; Step S5-2: Based on the EMB failure braking force of the failed wheel and the remaining braking force of the braking system, distinguish between the first working condition and the second working condition. In the first working condition, reconstruct the braking torque of each wheel according to the torque reconstruction controller. In the second working condition, input the unmatched disturbance estimate, the center of gravity sideslip angle and the yaw rate into the vehicle stability sliding mode controller to obtain the additional yaw torque. Input the additional yaw torque into the braking torque reconstruction unit to redistribute the braking torque. Step S6: Execute the braking command based on the braking torque.

[0104] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0105] Generally, various embodiments can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention can be implemented in hardware, while others can be implemented by firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, these blocks, apparatuses, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0106] Embodiments of the present invention can be implemented by computer software, which may be executable by a data processor of a mobile device, such as a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products), including software routines, applets, and / or macros, can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer-executable components configured to perform embodiments when the program is run. One or more computer-executable components may be at least one piece of software code or a portion thereof. Additionally, it should be noted that any block in the logical flow of the figures may represent a program step, or interconnected logical circuitry, blocks and functions, or a combination of program steps and logical circuitry, blocks and functions. The software may be stored on physical media such as memory chips or blocks of storage implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. The physical medium is a non-transient medium.

[0107] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A vehicle EMB single-wheel failure control method that also considers the suppression of mismatched disturbances, characterized in that, Includes the following steps: Step S1: Collect real-time vehicle status parameters and identify braking intention. If braking intention exists, calculate the target braking intensity of the vehicle based on the braking intention and execute step S2. Step S2: Input the real-time vehicle state parameters into the two-degree-of-freedom vehicle dynamics model to obtain the vehicle motion state parameters, which include the ideal yaw rate and the ideal center-of-mass sideslip angle. Step S3: Input the actual braking torque and the desired braking torque into the EMB failure judgment unit to determine whether the EMB has failed. If the EMB fails, proceed to step S4; otherwise, proceed to step S5-1. Step S4: Input the vehicle longitudinal speed, actual centroid sideslip angle and actual yaw rate from the control input and the vehicle real-time state parameters into the unmatched disturbance observer and output the unmatched disturbance estimate, where the control input is the front wheel steering angle and the additional yaw moment and execute S5-2; Step S5-1: Distribute the braking torque of each wheel according to the ideal braking force distribution coefficient based on the vehicle's target braking intensity; Step S5-2: Based on the EMB failure braking force of the failed wheel and the remaining braking force of the braking system, distinguish between the first working condition and the second working condition. In the first working condition, reconstruct the braking torque of each wheel according to the torque reconstruction controller. In the second working condition, input the unmatched disturbance estimate, the center of gravity sideslip angle and the yaw rate into the vehicle stability sliding mode controller to obtain the additional yaw torque. Input the additional yaw torque into the braking torque reconstruction unit to redistribute the braking torque. Step S6: Execute braking command based on braking torque.

2. The vehicle EMB single-wheel failure control method that takes into account both mismatch disturbance suppression as described in claim 1, characterized in that, The vehicle's real-time status parameters include longitudinal speed, lateral speed, yaw rate, center of gravity sideslip angle, steering wheel angle, wheel speed of the four wheels, actual braking torque, and brake pedal travel. When the brake pedal travel is greater than 0, there is an intention to brake; when the brake pedal travel is 0, there is no intention to brake.

3. The vehicle EMB single-wheel failure control method that takes into account both mismatch disturbance suppression as described in claim 1, characterized in that, Under the premise of adopting a linear model for tire lateral force, the steady-state response formula is derived based on the two-degree-of-freedom vehicle dynamics model. The vehicle longitudinal velocity and front wheel steering angle in the real-time vehicle state parameters are substituted into the steady-state response formula to solve for the ideal yaw rate parameter, and the ideal center of mass sideslip angle is preset to 0.

4. The vehicle EMB single-wheel failure control method that takes into account both mismatch disturbance suppression as described in claim 1, characterized in that, A unified description model of EMB faults is constructed based on the actual braking torque and the expected braking torque. The unified description model of EMB faults is then transformed into a linear parameterized model. The recursive least squares method is used to perform online parameter estimation on the linear parameterized model to obtain the estimated parameters, which include the failure degree factor and the bias fault torque. The failure of EMB is determined based on the estimated parameters.

5. The vehicle EMB single-wheel failure control method that takes into account both mismatch disturbance suppression as described in claim 1, characterized in that, The system obtains real-time vehicle state parameters and constructs a system state vector. The system state vector and control input are substituted into the sliding mode disturbance observer state estimation equation in the unmatched disturbance observer to obtain the state estimation error. The sliding surface differential equation is constructed based on the time derivative of the state estimation error. The unmatched disturbance estimate is updated according to the sliding mode reachability condition and the sliding surface differential equation. The state estimation error includes the centroid sideslip angle estimation error and the yaw rate estimation error.

6. The vehicle EMB single-wheel failure control method that takes into account both mismatch disturbance suppression as described in claim 5, characterized in that, The state estimation equations for the sliding mode perturbation observer are as follows: ； in For the state estimation vector, It is a control input. It is the disturbance estimation term. The system state matrix, To control the input matrix, Assign matrices to unmatched perturbations. For the output matrix, The observer gain matrix is... The output estimation error is equivalent to the state estimation error. , denoted as ; The differential equation of the sliding surface is: ； ； in Here is the sliding surface coefficient matrix; The sliding surface coefficient, It is the disturbance estimation term. The system state matrix, The observer gain matrix is... For the output matrix, Assign matrices to unmatched perturbations.

7. The vehicle EMB single-wheel failure control method that takes into account both mismatch disturbance suppression as described in claim 5, characterized in that, Real-time monitoring of sliding surface numerical changes is used to determine whether the system state has converged. When the system state converges, the state estimation error and sliding surface value are substituted into the estimation update law to update the unmatched disturbance estimate. If convergence is slow or chattering is too large, the sliding surface gain and chattering suppression coefficient are dynamically adjusted. The estimation update law is as follows: ； ； in This is the equivalent disturbance estimate. The system state matrix, For the output matrix, Assign matrices to unmatched perturbations. Here is the sliding surface coefficient matrix. Let k be the observer gain matrix, and k be the sliding mode gain. ε is a saturation function, and ε is the chattering suppression coefficient.

8. The vehicle EMB single-wheel failure control method that takes into account both mismatch disturbance suppression as described in claim 1, characterized in that, A sliding mode surface is designed with the sideslip angle and yaw rate as state variables. Feedforward compensation is performed using the estimated value of the mismatched disturbance. An additional yaw moment is obtained by solving a sliding mode control law that combines equivalent control and switching control. The additional yaw moment is input into the torque reconstruction controller. With the tire load rate as the optimization objective, the braking torque of each wheel is obtained by solving the torque reconstruction controller, which integrates the vehicle longitudinal force constraint, the additional yaw moment constraint, the EMB torque constraint, and the road adhesion constraint.

9. A vehicle EMB single-wheel failure control device that also considers mismatch disturbance suppression, characterized in that, include: The controlled vehicle unit is used to collect real-time status parameters of the vehicle and identify braking intentions. If a braking intention exists, the target braking intensity of the vehicle is calculated based on the braking intention. A two-degree-of-freedom vehicle dynamics model is used to obtain real-time vehicle state parameters and vehicle motion state parameters, including ideal yaw rate and ideal centroid sideslip angle. The EMB failure judgment unit is used to input the actual braking torque and the expected braking torque into the EMB failure judgment unit to determine whether the EMB has failed. The unmatched disturbance observer is used to acquire the vehicle's longitudinal speed, actual center of gravity sideslip angle, and actual yaw rate from the control input and real-time vehicle state parameters. The unmatched disturbance observer is then output as an estimate of the unmatched disturbance. The control input is the front wheel steering angle and the additional yaw moment. The I-curve braking torque distribution unit is used to distribute the braking torque of each wheel according to the ideal braking force distribution coefficient based on the vehicle's target braking intensity when no EMB failure occurs. A vehicle stability sliding mode controller is used to output an additional yaw moment based on the mismatch disturbance estimate, the center of gravity sideslip angle, and the yaw rate when an EMB failure occurs. Braking torque reconfiguration unit, used to reconfigure braking torque based on additional yaw moment; The EMB braking system execution unit is used to execute braking commands on the controlled vehicle unit based on the braking torque.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the vehicle EMB single-wheel failure control method that takes into account the suppression of mismatched disturbances as described in any one of claims 1 to 8.

Images