Fault estimation based fault-tolerant control method and system for autonomous vehicle

Abstract

The application discloses a kind of automatic driving vehicle fault-tolerant control method and system based on fault estimation.The method can include: establishing two degrees of freedom vehicle dynamics model, determining mass center side angle;Establish vehicle path tracking model;According to vehicle dynamics model and vehicle path tracking model, nominal fault is characterized;According to the nominal fault characterized, establish nominal fault calculation model, calculate nominal fault;Establish fault-tolerant control model, input mass center side angle and nominal fault into fault-tolerant control model, calculate vehicle yaw moment and vehicle front wheel rotation angle;According to the vehicle yaw moment and vehicle front wheel rotation angle calculated by fault-tolerant control model, control automatic driving vehicle is carried out.The present application establishes nominal fault calculation model and fault-tolerant control model, calculates vehicle yaw moment and vehicle front wheel rotation angle, realizes automatic driving vehicle fault-tolerant control.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving control, and more specifically, to a fault-tolerant control method and system for autonomous vehicles based on fault estimation. Background Technology

[0002] In recent years, with the development of artificial intelligence technology, autonomous driving technology has also developed rapidly. In the field of autonomous driving, there is no need for a driver to operate the vehicle; instead, the vehicle automatically collects environmental information and drives itself based on that information.

[0003] Existing research mainly focuses on the fault-tolerant control problem of traditional automobiles and the enrichment and improvement of autonomous driving functions in autonomous vehicles, while research on fault-tolerant control of autonomous vehicles for autonomous driving scenarios is still insufficient.

[0004] Therefore, it is necessary to develop a fault-tolerant control method and system for autonomous vehicles based on fault estimation.

[0005] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention proposes a fault-tolerant control method and system for autonomous vehicles based on fault estimation. It can calculate the vehicle's yaw moment and front wheel steering angle by establishing a nominal fault calculation model and a fault-tolerant control model, thereby achieving fault-tolerant control of autonomous vehicles.

[0007] In a first aspect, embodiments of this disclosure provide a fault-tolerant control method for autonomous vehicles based on fault estimation, including:

[0008] Establish a two-degree-of-freedom vehicle dynamics model and determine the sideslip angle of the center of gravity;

[0009] Establish a vehicle route tracking model;

[0010] The nominal fault is characterized based on the vehicle dynamics model and the vehicle path tracking model;

[0011] Based on the nominal fault described, a nominal fault calculation model is established to calculate the nominal fault;

[0012] Establish a fault-tolerant control model, input the centroid sideslip angle and the nominal fault into the fault-tolerant control model, and calculate the vehicle yaw moment and the vehicle front wheel steering angle;

[0013] The autonomous vehicle is controlled based on the vehicle yaw moment and the front wheel steering angle calculated by the fault-tolerant control model.

[0014] Preferably, the vehicle dynamics model is:

[0015]

[0016]

[0017] Where β is the sideslip angle, γ is the yaw rate, m is the mass of the car, and I z For the moment of inertia, l f and l r ΔM represents the distance from the vehicle's center of gravity to the front and rear axles, respectively. z For the vehicle's yaw moment, δ f C is the steering angle of the vehicle's front wheels. f and C r These are the front and rear wheel lateral stiffness, v x It is the velocity component of the direction the car is facing, v y It is the lateral velocity component of the vehicle. The rate of change of the sideslip angle of the centroid. This is the yaw acceleration.

[0018] Preferably, the vehicle path tracking model uses heading deviation and lateral deviation to characterize the path tracking error, and the differential equation for the heading deviation is:

[0019]

[0020] The differential equation for the lateral deviation is:

[0021]

[0022] Where ρ is the curvature of the reference path, This is the actual vehicle heading angle. e represents the lateral deviation in trajectory tracking, which is the lateral offset of the vehicle's center of gravity from the nearest point P on the reference path. For heading deviation, This is the deviation between the actual heading angle and the reference value.

[0023] Preferably, the nominal fault is characterized as:

[0024]

[0025] Where τ is the nominal fault, τ = τ c +τ f The system state variables are The control input is u = [u1u2]T =[δ f ΔM z ] T The control output is The input matrix is The measurement matrix is ​​C = [0 3*1 I 3*3 The state transition matrix is ​​f(x, t) = [f1 f2] T , τ c =[τ c1 0] T For an input fault in the steering system controller, τ c1 =(δ1-1)δ f +δ2,τ f =[τ f1 0] T This is due to unknown interference with the steering system controller.

[0026] Preferably, based on the nominal fault characterized, a nominal fault calculation model is established, and the calculation of the nominal fault includes:

[0027] The nominal fault, as represented, is extended to the state variables of the system to obtain the augmented system equations for nominal fault estimation.

[0028] An adaptive CFK algorithm is used to iteratively estimate the system state for the augmented system equations, resulting in the discrete transition equations of the augmented system.

[0029] Based on the discrete transfer equations of the augmented system and combined with the adaptive CFK algorithm, the nominal fault calculation model is established, wherein the state variables of the nominal fault calculation model are: The measured value of the nominal fault calculation model is

[0030] The nominal fault is calculated by substituting the yaw rate, heading deviation, and lateral deviation into the nominal fault calculation model.

[0031] Preferably, the augmented system equation is:

[0032]

[0033] Where, x k+1 Let y be the state vector of the discrete system. k Let f(·) be the measurement vector of the discrete system, f(·) be the state transition equation of the discrete system, h(·) be the measurement equation of the discrete system, and w be the measurement vector of the discrete system. k For system noise, v k To measure noise, and w k and vk These are uncorrelated white noises.

[0034] Preferably, the discrete transfer equation of the augmented system is:

[0035]

[0036] The system state variables are represented as x1 = β and x2 = γ, respectively. x4 = e, x5 = τ, k is the sampling point, T is the Kalman filter sampling period, and λ1 and λ2 are sliding mode parameters.

[0037] Preferably, establishing a fault-tolerant control model includes:

[0038] Determine the sliding surface, and establish the fault-tolerant control model based on the fast power-law approaching the sliding surface.

[0039] Preferably, the fault-tolerant control model is:

[0040]

[0041] Where g is the control error, g = xx d x d =[0 γ d 0 0] T γ d For the reference vehicle yaw rate, k1, k2 and ε are sliding mode parameters greater than 0, and s is the sliding surface.

[0042] Secondly, embodiments of this disclosure provide a fault-tolerant control system for autonomous vehicles based on fault estimation, the system comprising:

[0043] Memory, which stores executable instructions;

[0044] A processor that executes the executable instructions in the memory to perform the following steps:

[0045] Establish a two-degree-of-freedom vehicle dynamics model and determine the sideslip angle of the center of gravity;

[0046] Establish a vehicle route tracking model;

[0047] The nominal fault is characterized based on the vehicle dynamics model and the vehicle path tracking model;

[0048] Based on the nominal fault described, a nominal fault calculation model is established to calculate the nominal fault;

[0049] Establish a fault-tolerant control model, input the centroid sideslip angle and the nominal fault into the fault-tolerant control model, and calculate the vehicle yaw moment and the vehicle front wheel steering angle;

[0050] The autonomous vehicle is controlled based on the vehicle yaw moment and the front wheel steering angle calculated by the fault-tolerant control model.

[0051] Preferably, the vehicle dynamics model is:

[0052]

[0053]

[0054] Where β is the sideslip angle, γ is the yaw rate, m is the mass of the car, and I z For the moment of inertia, l f and l r ΔM represents the distance from the vehicle's center of gravity to the front and rear axles, respectively. z For the vehicle's yaw moment, δ f C is the steering angle of the vehicle's front wheels. f and C r These are the front and rear wheel lateral stiffness, v x It is the velocity component of the direction the car is facing, v y It is the lateral velocity component of the vehicle. The rate of change of the sideslip angle of the centroid. This is the yaw acceleration.

[0055] Preferably, the vehicle path tracking model uses heading deviation and lateral deviation to characterize the path tracking error, and the differential equation for the heading deviation is:

[0056]

[0057] The differential equation for the lateral deviation is:

[0058]

[0059] Where ρ is the curvature of the reference path, This is the actual vehicle heading angle. e represents the lateral deviation in trajectory tracking, which is the lateral offset of the vehicle's center of gravity from the nearest point P on the reference path. For heading deviation, This is the deviation between the actual heading angle and the reference value.

[0060] Preferably, the nominal fault is characterized as:

[0061]

[0062] Where τ is the nominal fault, τ = τ c +τ f The system state variables are The control input is u = [u1u2] T =[δ f ΔM z ] T The control output is The input matrix is The measurement matrix is ​​C = [0 3*1 I 3*3 The state transition matrix is ​​f(x, t) = [f1 f2] T , τ c =[τ c1 0] T For an input fault in the steering system controller, τ c1 =(δ1-1)δ f +δ2,τ f =[τ f1 0] T This is due to unknown interference with the steering system controller.

[0063] Preferably, based on the nominal fault characterized, a nominal fault calculation model is established, and the calculation of the nominal fault includes:

[0064] The nominal fault, as represented, is extended to the state variables of the system to obtain the augmented system equations for nominal fault estimation.

[0065] An adaptive CFK algorithm is used to iteratively estimate the system state for the augmented system equations, resulting in the discrete transition equations of the augmented system.

[0066] Based on the discrete transfer equations of the augmented system and combined with the adaptive CFK algorithm, the nominal fault calculation model is established, wherein the state variables of the nominal fault calculation model are: The measured value of the nominal fault calculation model is

[0067] The nominal fault is calculated by substituting the yaw rate, heading deviation, and lateral deviation into the nominal fault calculation model.

[0068] Preferably, the augmented system equation is:

[0069]

[0070] Where, x k+1 Let y be the state vector of the discrete system. kLet f(·) be the measurement vector of the discrete system, f(·) be the state transition equation of the discrete system, h(·) be the measurement equation of the discrete system, and w be the measurement vector of the discrete system. k For system noise, v k To measure noise, and w k and v k These are uncorrelated white noises.

[0071] Preferably, the discrete transfer equation of the augmented system is:

[0072]

[0073] The system state variables are represented as x1 = β and x2 = γ, respectively. x4 = e, x5 = τ, k is the sampling point, T is the Kalman filter sampling period, and λ1 and λ2 are sliding mode parameters.

[0074] Preferably, establishing a fault-tolerant control model includes:

[0075] Determine the sliding surface, and establish the fault-tolerant control model based on the fast power-law approaching the sliding surface.

[0076] Preferably, the fault-tolerant control model is:

[0077]

[0078] Where g is the control error, g = xx d x d =[0 γ d 0 0] T γ d For the reference vehicle yaw rate, k1, k2 and ε are sliding mode parameters greater than 0, and s is the sliding surface.

[0079] The beneficial effects are as follows: Considering the simultaneous impact of steering control system faults and unknown disturbances on the path tracking performance of autonomous vehicles, a fault-tolerant control method for path tracking in autonomous vehicles is designed to improve the reliability of the autonomous vehicle control system. Input faults of the steering control system are analyzed and defined, and the influence of unknown disturbances is considered, with nominal faults characterized and mathematically modeled. An estimation method for the vehicle's center of gravity sideslip angle and predicted faults is designed using adaptive capillary Kalman filtering, and these are used as inputs to the fault-tolerant control model of the autonomous vehicle. A fault-tolerant control model for vehicle path tracking is designed based on the sliding mode control method, enabling the autonomous vehicle to maintain good control performance even when facing steering control system faults and disturbances, thus ensuring the vehicle's stability while achieving path tracking.

[0080] The methods and systems of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0081] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0082] Figure 1 A flowchart illustrating the steps of a fault-tolerant control method for autonomous vehicles based on fault estimation according to an embodiment of the present invention is shown.

[0083] Figure 2 A schematic diagram of a two-degree-of-freedom vehicle dynamics model according to an embodiment of the present invention is shown.

[0084] Figure 3 A schematic diagram of a vehicle path tracking model according to an embodiment of the present invention is shown. Detailed Implementation

[0085] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0086] To facilitate understanding of the solutions and effects of the embodiments of the present invention, two specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0087] Example 1

[0088] Figure 1 A flowchart illustrating the steps of the fault-tolerant control method for autonomous vehicles based on fault estimation according to the present invention is shown.

[0089] like Figure 1As shown, the fault-tolerant control method for autonomous vehicles based on fault estimation includes: Step 101, establishing a two-degree-of-freedom vehicle dynamics model and determining the center-of-gravity sideslip angle; Step 102, establishing a vehicle path tracking model; Step 103, characterizing the nominal fault based on the vehicle dynamics model and the vehicle path tracking model; Step 104, establishing a nominal fault calculation model based on the characterized nominal fault and calculating the nominal fault; Step 105, establishing a fault-tolerant control model, inputting the center-of-gravity sideslip angle and the nominal fault into the fault-tolerant control model, and calculating the vehicle yaw moment and the vehicle front wheel steering angle; Step 106, controlling the autonomous vehicle based on the vehicle yaw moment and the vehicle front wheel steering angle calculated by the fault-tolerant control model.

[0090] In one example, the vehicle dynamics model is:

[0091]

[0092]

[0093] Where β is the sideslip angle, γ is the yaw rate, m is the mass of the car, and I z For the moment of inertia, l f and l r ΔM represents the distance from the vehicle's center of gravity to the front and rear axles, respectively. z For the vehicle's yaw moment, δ f C is the steering angle of the vehicle's front wheels. f and C r These are the front and rear wheel lateral stiffness, v x It is the velocity component of the direction the car is facing, v y It is the lateral velocity component of the vehicle. The rate of change of the sideslip angle of the centroid. Let be the yaw acceleration. A point on the parameter indicates the first derivative with respect to the parameter.

[0094] In one example, the vehicle path tracking model uses heading deviation and lateral deviation to characterize the path tracking error. The differential equation for heading deviation is:

[0095]

[0096] The differential equation for the lateral deviation is:

[0097]

[0098] Where ρ is the curvature of the reference path, This is the actual vehicle heading angle. e represents the lateral deviation in trajectory tracking, which is the lateral offset of the vehicle's center of gravity from the nearest point P on the reference path. For heading deviation, This is the deviation between the actual heading angle and the reference value.

[0099] In one example, the nominal fault is represented as:

[0100]

[0101] Where τ is the nominal fault, τ = τ c +τ f The system state variables are The control input is u = [u1u2] T =[δ f ΔM z ] T The control output is The input matrix is The measurement matrix is ​​C = [0 3*1 I 3*3 The state transition matrix is ​​f(x, t) = [f1 f2] T , τ c =[τ c1 0] T For an input fault in the steering system controller, τ c1 =(δ1-1)δ f +δ2,τ f =[τ f1 0] T This is due to unknown interference with the steering system controller.

[0102] In one example, based on the represented nominal fault, a nominal fault calculation model is established, and the calculation of the nominal fault includes:

[0103] The nominal fault is extended to the system's state variables to obtain the augmented system equations for nominal fault estimation;

[0104] An adaptive CFK algorithm is used to iteratively estimate the system state for the augmented system equations, and the discrete transition equations of the augmented system are obtained.

[0105] Based on the discrete transfer equations of the augmented system and combined with the adaptive CFK algorithm, a nominal fault calculation model is established, wherein the state variables of the nominal fault calculation model are: The measured value of the nominal fault calculation model is

[0106] The yaw rate, heading deviation, and lateral deviation are substituted into the nominal fault calculation model to calculate the nominal fault.

[0107] In one example, the augmented system equation is:

[0108]

[0109] Where, x k+1 Let y be the state vector of the discrete system. k Let f(·) be the measurement vector of the discrete system, f(·) be the state transition equation of the discrete system, h(·) be the measurement equation of the discrete system, and w be the measurement vector of the discrete system. k For system noise, v k To measure noise, and w k and v k These are uncorrelated white noises.

[0110] In one example, the discrete transfer equation of the augmented system is:

[0111]

[0112] The system state variables are represented as x1 = β and x2 = γ, respectively. x4 = e, x5 = τ, k is the sampling point, T is the Kalman filter sampling period, and λ1 and λ2 are sliding mode parameters.

[0113] In one example, establishing a fault-tolerant control model includes:

[0114] Determine the sliding surface, and establish a fault-tolerant control model based on the fast power-law approach of the sliding surface.

[0115] In one example, the fault-tolerant control model is:

[0116]

[0117] Where g is the control error, g = xx d x d =[0 γ d 0 0] T γ d For the reference vehicle yaw rate, k1, k2 and ε are sliding mode parameters greater than 0, and s is the sliding surface.

[0118] Figure 2 A schematic diagram of a two-degree-of-freedom vehicle dynamics model according to an embodiment of the present invention is shown.

[0119] Specifically, to describe the longitudinal and lateral dynamic characteristics during the path tracking process of an autonomous vehicle, a two-degree-of-freedom vehicle dynamics model was established, such as... Figure 2As shown. Ignoring the vehicle's pitch, roll, and vertical motion, disregarding the suspension system, and assuming that the mechanical properties of the four tires are the same, the origin of the dynamic coordinate system xoy is fixed on the vehicle and coincides with the vehicle's center of mass. The x-axis points in the direction of the vehicle's forward movement, and the y-axis is positive from right to left. Assuming that the front wheel steering angle is small, the two-degree-of-freedom vehicle dynamics model can be expressed as formula (1) and formula (2).

[0120] Figure 3 A schematic diagram of a vehicle path tracking model according to an embodiment of the present invention is shown.

[0121] To achieve path tracking control for autonomous vehicles, a system has been established such as... Figure 3 The vehicle path tracking model shown here represents the geodetic coordinate system XOY and ρ is the curvature of the reference path. The actual vehicle heading angle is expressed as... e represents the lateral offset of the vehicle's center of gravity from the nearest point P on the reference path, i.e., the lateral deviation in trajectory tracking. The deviation between the actual heading angle and the reference value is called the heading deviation. In the path tracking model, the heading deviation and the lateral deviation are used to characterize the path tracking error. The differential equation of the heading deviation can be expressed as formula (3). Assuming that the heading deviation is small, the differential equation of the lateral deviation can be expressed as formula (4).

[0122] Nominal faults include steering system controller input faults and unknown system interference faults. Steering system controller input faults are divided into gain-type faults and deviation-type faults. When the vehicle steering system controller malfunctions, the vehicle steering system output changes accordingly. At this time, the vehicle's front wheel steering angle can be expressed as:

[0123] δ ff =δ1δ f +δ2 (9)

[0124] δ ff Let δ1 be the actual front wheel steering angle after the fault occurs, δ2 be the fault gain, and δ1 be the fault deviation. Different combinations of δ1 and δ2 represent different fault combination types. Let... By combining the vehicle dynamics model in equations (1) and (2) with the path tracking model in equations (3) and (4), the equations for the automated vehicle system considering steering system controller failure and disturbances are integrated into:

[0125]

[0126] In the formula, the system state variables are: The control input is u = [u1 u2] T = [δf ΔM z ] T The control output is The input matrix is The measurement matrix is ​​C = [0 3*1 I 3*3 The state transition matrix is ​​f(x, t) = [f1 f2] T and τ c =[τ c1 0] T For the input fault of the steering system controller and τ c1 =(δ1-1)δ f +δ2,τ f =[τ f1 0] T This is an unknown disturbance to the steering system controller. Therefore, formula (10) can be expressed as formula (5).

[0127] The nominal fault is extended to the system's state variables, and the system state variables are represented as x1 = β and x2 = γ, respectively. x4 = e, x5 = τ Thus, the discrete form of the augmented system equations used for nominal fault estimation can be expressed as Equation (6).

[0128] The cubation Kalman filter (CKF) can effectively reduce filter divergence and further improve the reliability of the estimated system. Using the Sage windowing method and multiple suboptimal attenuation factors, an adaptive CFF algorithm is designed for system state estimation, which can significantly improve the estimation effect. According to the discrete state-space equation in formula (6), the iterative steps of the adaptive CKF algorithm can be expressed as:

[0129] ① Initial value selection.

[0130]

[0131] In the formula, Let P be the initial vector, and P0 be the error covariance matrix.

[0132] ② Volume point calculation

[0133]

[0134] ③Time update

[0135]

[0136] Q kLet be the covariance matrix of the system noise.

[0137] ④ Measurement Update

[0138] The prediction equation for measurement updates can be expressed as follows:

[0139]

[0140] The covariance matrix and cross-covariance matrix of the measurement prediction results can be expressed as follows:

[0141]

[0142]

[0143] In the formula, R k For v k The covariance matrix. Prediction error can be obtained by calculating the difference between the actual measured value and the predicted measured value.

[0144]

[0145] In the formula, y k+1 The actual measured value of sampling point k+1 This represents the estimated measurement value for sampling point k+1.

[0146] The fading matrix M k+1 Designed as follows:

[0147]

[0148] In the formula, η is the window width. To improve the adaptability of the fading matrix, an adaptive fading matrix including the correction silver is designed as follows:

[0149]

[0150] In the formula, (M k+1 ) i Let be the main diagonal elements of the fading matrix. Based on the adaptive fading matrix, the adaptive filter gain can be expressed as...

[0151] K k+1 =P xz，k+1|k (P xz，k+1|k +M′ k+1 R k+1 ) -1 (20)

[0152] Based on the adaptive filter gain, the system state estimation results and error covariance matrix can be obtained as follows:

[0153]

[0154]

[0155] Since the system does not contain a differential term for the nominal fault as an unknown input, a differential term for the nominal fault can be reconstructed using the design method of a high-order sliding mode observer. The nominal fault is directly related to the vehicle's yaw rate in the system; therefore, the constructed differential equation for the nominal fault is as follows:

[0156]

[0157] In the formula, λ1 and λ2 are sliding mode parameters, and y1 = x2. Combining the vehicle system model and the constructed nominal fault differential equation, the vehicle state variables and nominal faults are integrated together and then discretized to obtain the augmented system discrete transfer equation for Kalman filter design, which is formula (7). Based on the augmented system discrete transfer equation and combined with the adaptive CFK algorithm, a nominal fault calculation model is established, where the state variables of the nominal fault calculation model are: The measured value of the nominal fault calculation model is The yaw rate, heading deviation, and lateral deviation are substituted into the nominal fault calculation model to calculate the nominal fault.

[0158] To ensure path tracking accuracy and vehicle yaw stability, a fault-tolerant control model is designed to suppress the impact of nominal faults on the overall vehicle control performance. Let g be the control error and express it as g = xx. d , where x d =[0 γ d 0 0] T With the reference vehicle's center of gravity sideslip angle at 0, γ d As a reference for the vehicle's yaw rate, the reference lateral deviation and heading deviation are also set to 0. The trajectory tracking control objective is to track both the vehicle's lateral steady-state value and the trajectory tracking deviation. The sliding surface is designed as...

[0159]

[0160] In the formula k e The sliding mode parameter is greater than 0.

[0161] To effectively reduce chattering in sliding mode controllers, a fast power-law approach is selected for the design of the fault-tolerant control model.

[0162]

[0163] In the formula, k1, k2 and ε are all sliding mode parameters greater than 0.

[0164] The fault-tolerant control model of the sliding surface is calculated based on the fast power-law approach, as shown in formula (8).

[0165] To verify that the output of the fault-tolerant control model has convergence, i.e., system stability, the Lyapunov function was selected as... Differentiation yields This demonstrates that the designed control law can guarantee the convergence of the sliding mode control model, thereby ensuring that the autonomous vehicle can maintain good fault-tolerant control performance under the influence of faults and disturbances. In addition to the fast power-law approaching law, there are also constant-rate approaching laws, exponential approaching laws, and general approaching laws, which can be selected by those skilled in the art according to specific circumstances.

[0166] The center of gravity sideslip angle and the nominal fault are input into the fault-tolerant control model to calculate the vehicle yaw moment and the vehicle front wheel steering angle. The autonomous vehicle is then controlled based on the vehicle yaw moment and the vehicle front wheel steering angle calculated by the fault-tolerant control model.

[0167] Example 2

[0168] A fault-tolerant control system for autonomous vehicles based on fault estimation, comprising:

[0169] Memory, which stores executable instructions;

[0170] The processor executes executable instructions in memory to perform the following steps:

[0171] Establish a two-degree-of-freedom vehicle dynamics model and determine the sideslip angle of the center of gravity;

[0172] Establish a vehicle route tracking model;

[0173] The nominal fault is characterized based on the vehicle dynamics model and the vehicle path tracking model;

[0174] Based on the nominal faults represented, a nominal fault calculation model is established, and the nominal faults are calculated.

[0175] Establish a fault-tolerant control model, input the center of gravity sideslip angle and the nominal fault into the fault-tolerant control model, and calculate the vehicle yaw moment and the vehicle front wheel steering angle;

[0176] The autonomous vehicle is controlled based on the vehicle yaw moment and the front wheel steering angle calculated using the fault-tolerant control model.

[0177] In one example, the vehicle dynamics model is:

[0178]

[0179]

[0180] Where β is the sideslip angle, γ is the yaw rate, m is the mass of the car, and Iz For the moment of inertia, l f and l r ΔM represents the distance from the vehicle's center of gravity to the front and rear axles, respectively. z For the vehicle's yaw moment, δ f C is the steering angle of the vehicle's front wheels. f and C r These are the front and rear wheel lateral stiffness, v x It is the velocity component of the direction the car is facing, v y It is the lateral velocity component of the vehicle. The rate of change of the sideslip angle of the centroid. This is the yaw acceleration.

[0181] In one example, the vehicle path tracking model uses heading deviation and lateral deviation to characterize the path tracking error. The differential equation for heading deviation is:

[0182]

[0183] The differential equation for the lateral deviation is:

[0184]

[0185] Where ρ is the curvature of the reference path, This is the actual vehicle heading angle. e represents the lateral deviation in trajectory tracking, which is the lateral offset of the vehicle's center of gravity from the nearest point P on the reference path. For heading deviation, This is the deviation between the actual heading angle and the reference value.

[0186] In one example, the nominal fault is represented as:

[0187]

[0188] Where τ is the nominal fault, τ = τ c +τ f The system state variables are The control input is u = [u1u2] T =[δ f ΔM z ] T The control output is The input matrix is The measurement matrix is ​​C = [0 3*1 I 3*3 The state transition matrix is ​​f(x, t) = [f1 f2] T , τ c =[τ c1 0]T For an input fault in the steering system controller, τ c1 =(δ1-1)δ f +δ2,τ f =[τ f1 0] T This is due to unknown interference with the steering system controller.

[0189] In one example, based on the represented nominal fault, a nominal fault calculation model is established, and the calculation of the nominal fault includes:

[0190] The nominal fault is extended to the system's state variables to obtain the augmented system equations for nominal fault estimation;

[0191] An adaptive CFK algorithm is used to iteratively estimate the system state for the augmented system equations, and the discrete transition equations of the augmented system are obtained.

[0192] Based on the discrete transfer equations of the augmented system and combined with the adaptive CFK algorithm, a nominal fault calculation model is established, wherein the state variables of the nominal fault calculation model are: The measured value of the nominal fault calculation model is

[0193] The yaw rate, heading deviation, and lateral deviation are substituted into the nominal fault calculation model to calculate the nominal fault.

[0194] In one example, the augmented system equation is:

[0195]

[0196] Where, x k+1 Let y be the state vector of the discrete system. k Let f(·) be the measurement vector of the discrete system, f(·) be the state transition equation of the discrete system, h(·) be the measurement equation of the discrete system, and w be the measurement vector of the discrete system. k For system noise, v k To measure noise, and w k and v k These are uncorrelated white noises.

[0197] In one example, the discrete transfer equation of the augmented system is:

[0198]

[0199] The system state variables are represented as x1 = β and x2 = γ, respectively. x4 = e, x5 = τ, k is the sampling point, T is the Kalman filter sampling period, and λ1 and λ2 are sliding mode parameters.

[0200] In one example, establishing a fault-tolerant control model includes:

[0201] Determine the sliding surface, and establish a fault-tolerant control model based on the fast power-law approach of the sliding surface.

[0202] In one example, the fault-tolerant control model is:

[0203]

[0204] Where g is the control error, g = xx d x d =[0 γ d 0 0] T γ d For the reference vehicle yaw rate, k1, k2 and ε are sliding mode parameters greater than 0, and s is the sliding surface.

[0205] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0206] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A fault-estimation-based automatic driving vehicle fault-tolerant control method, characterized by, The method comprises the following steps: establishing a two-degree-of-freedom vehicle dynamics model to determine a mass center side slip angle; establishing a vehicle path tracking model; characterizing a nominal fault according to the vehicle dynamics model and the vehicle path tracking model; establishing a nominal fault calculation model according to the characterized nominal fault to calculate the nominal fault; establishing a fault-tolerant control model, inputting the mass center side slip angle and the nominal fault into the fault-tolerant control model to calculate a vehicle yaw moment and a vehicle front wheel steering angle; controlling an autonomous vehicle according to the vehicle yaw moment and the vehicle front wheel steering angle calculated by the fault-tolerant control model; wherein the characterized nominal fault is: where τ is the nominal fault, τ = τ c + τ f , the system state variable is The control input is u = [u1u2] T = [δ f ΔM z ] T , the control output is The input matrix is The measurement matrix is C = [0 3* 1I 3*3 ], the state transition matrix is f(x, t) = [f1f2] T , τ c = [τ c1 0] T is the input fault of the steering system controller, τ c1 = (δ1-1) δ f + δ2, τ f = [τ f1 0] T is the unknown disturbance of the steering system controller, δ1 is the fault gain, δ2 is the fault deviation, wherein, according to the characterized nominal fault, establishing a nominal fault calculation model to calculate the nominal fault comprises: augmenting the characterized nominal fault into state quantities of a system to obtain an augmented system equation for nominal fault estimation; performing adaptive CKF algorithm iteration system state estimation on the augmented system equation to obtain an augmented system discrete transition equation; According to the augmented system discrete transfer equation, the nominal fault calculation model is established in combination with an adaptive CKF algorithm, wherein a state quantity of the nominal fault calculation model is A measurement value of the nominal fault calculation model is bringing the yaw rate, the heading deviation and the lateral deviation into the nominal fault calculation model to calculate the nominal fault.

2. The fault-estimation-based automatic driving vehicle fault-tolerant control method according to claim 1, wherein The vehicle dynamics model is: where β is the centroid side slip angle, γ is the yaw rate, m is the vehicle mass, I z is the moment of inertia, l f and l r are the distances from the vehicle centroid to the front and rear axles, ΔM z is the vehicle yaw moment, δ f is the vehicle front wheel steering angle, C f and V r are the front and rear wheel cornering stiffness, v x is the vehicle heading velocity component, v y is the vehicle lateral velocity component, is the rate of change of the centroid side slip angle, is the yaw acceleration.

3. The fault-estimation-based automatic driving vehicle fault-tolerant control method according to claim 1, wherein The vehicle path tracking model uses a heading deviation and a lateral deviation to characterize a path tracking error, and a differential equation of the heading deviation is: a differential equation of the lateral deviation is: where p is the curvature of the reference path, is the actual vehicle heading angle, e is the trajectory tracking lateral deviation, i.e., the lateral offset of the vehicle mass center to the nearest point P on the reference path, is the heading deviation, i.e., the deviation between the actual heading angle and the reference value.

4. The fault-estimation-based automatic driving vehicle fault-tolerant control method according to claim 1, wherein the augmented system equation is: where x k+1 is the discrete system state vector, y k is the discrete system measurement vector, f(·) is the discrete system state transition equation, h(·) is the discrete system measurement equation, w k is the system noise, v k is the measurement noise, and w k and v k are mutually uncorrelated white noises.

5. The fault-estimation-based automatic driving vehicle fault-tolerant control method according to claim 1, wherein the augmented system discrete transition equation is: where the system state quantities are denoted as x1= β, x2= γ, x3= δ, x4= e, and x5= τ, respectively, x4= e, x5= τ, denotes the first-order derivation with respect to τ, k is the sampling point, T is the Kalman filtering sampling period, and λ1 and λ2 are the sliding mode parameters.

6. The fault-estimation-based automatic driving vehicle fault-tolerant control method according to claim 1, wherein establishing a fault-tolerant control model comprises: determining a sliding mode surface, calculating based on a fast power approach law according to the sliding mode surface to establish the fault-tolerant control model.

7. The fault-estimation-based automatic driving vehicle fault-tolerant control method according to claim 6, wherein The fault-tolerant control model is: where g is a control error, g = x - x d , x d = [0 γ d 0 0] T , γ d is a reference vehicle yaw rate, k1, k2 and ε are sliding mode parameters greater than 0, and s is a sliding surface.

8. An automatic driving vehicle fault-tolerant control system based on failure estimation, characterized by, The system comprises: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the following steps: establishing a two-degree-of-freedom vehicle dynamics model to determine a mass center side slip angle; establishing a vehicle path tracking model; characterizing a nominal fault according to the vehicle dynamics model and the vehicle path tracking model; establishing a nominal fault calculation model according to the characterized nominal fault to calculate the nominal fault; establishing a fault-tolerant control model, inputting the mass center side slip angle and the nominal fault into the fault-tolerant control model to calculate a vehicle yaw moment and a vehicle front wheel steering angle; controlling an autonomous vehicle according to the vehicle yaw moment and the vehicle front wheel steering angle calculated by the fault-tolerant control model; wherein the characterized nominal fault is: where τ is the nominal fault, τ = τ c + τ f , the system state variable is The control input is u = [u1u2] T = [δ f ΔM z ] T , the control output is The input matrix is The measurement matrix is C = [0 3* 1I 3*3 ], the state transition matrix is f(x, t) = [f1f2] T , τ c = [τ c1 0] T is the input fault of the steering system controller, τ c1 = (δ1-1)δ f + δ2, τ f = [τ f1 0] T is the unknown disturbance of the steering system controller, δ1 is the fault gain, δ2 is the fault deviation, wherein, according to the characterized nominal fault, establishing a nominal fault calculation model to calculate the nominal fault comprises: augmenting the characterized nominal fault into state quantities of a system to obtain an augmented system equation for nominal fault estimation; performing adaptive CKF algorithm iteration system state estimation on the augmented system equation to obtain an augmented system discrete transition equation; According to the augmented system discrete transfer equation, the nominal fault calculation model is established in combination with an adaptive CKF algorithm, wherein a state quantity of the nominal fault calculation model is A measurement value of the nominal fault calculation model is bringing the yaw rate, the heading deviation and the lateral deviation into the nominal fault calculation model to calculate the nominal fault.

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