CN115071736A - Fault-tolerant control method and system for automatic driving vehicle based on fault estimation - Google Patents

Fault-tolerant control method and system for automatic driving vehicle based on fault estimation Download PDF

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CN115071736A
CN115071736A CN202210654905.5A CN202210654905A CN115071736A CN 115071736 A CN115071736 A CN 115071736A CN 202210654905 A CN202210654905 A CN 202210654905A CN 115071736 A CN115071736 A CN 115071736A
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fault
vehicle
nominal
model
tolerant control
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邓希兰
原诚寅
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Beijing National New Energy Vehicle Technology Innovation Center Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0225—Failure correction strategy
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Steering Control In Accordance With Driving Conditions (AREA)

Abstract

The application discloses a fault-tolerant control method and system for an automatic driving vehicle based on fault estimation. The method can comprise the following steps: establishing a two-degree-of-freedom vehicle dynamic model, and determining a centroid slip angle; establishing a vehicle path tracking model; according to the vehicle dynamic model and the vehicle path tracking model, representing a nominal fault; establishing a nominal fault calculation model according to the represented nominal fault, and calculating the nominal fault; establishing a fault-tolerant control model, inputting a centroid side deflection angle and a nominal fault into the fault-tolerant control model, and calculating a vehicle yaw moment and a vehicle front wheel corner; and controlling the automatic driving vehicle according to the vehicle yaw moment and the vehicle front wheel steering angle calculated by the fault-tolerant control model. The invention realizes the fault-tolerant control of the automatic driving vehicle by establishing a nominal fault calculation model and a fault-tolerant control model and calculating the yaw moment and the front wheel rotation angle of the vehicle.

Description

Fault-tolerant control method and system for automatic driving vehicle based on fault estimation
Technical Field
The invention relates to the field of automatic driving control, in particular to a fault-tolerant control method and system of an automatic driving vehicle based on fault estimation.
Background
In recent years, with the development of artificial intelligence technology, automatic driving technology has also been rapidly developed. In the field of automatic driving, a driver does not need to operate a vehicle, but the vehicle automatically acquires environmental information and automatically drives according to the environmental information.
The existing related research mainly focuses on the fault-tolerant control problem of the traditional automobile and the enrichment and improvement of the automatic driving function of the automatic driving vehicle, and the fault-tolerant control research of the automatic driving vehicle facing to the automatic driving scene is not sufficient.
Therefore, it is necessary to develop a fault-tolerant control method and system for an autonomous vehicle based on fault estimation.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art that is already known to a person skilled in the art.
Disclosure of Invention
The invention provides a fault-tolerant control method and a fault-tolerant control system for an automatic driving vehicle based on fault estimation, which can calculate a yaw moment and a front wheel rotation angle of the vehicle by establishing a nominal fault calculation model and a fault-tolerant control model so as to realize the fault-tolerant control of the automatic driving vehicle.
In a first aspect, an embodiment of the present disclosure provides an error-tolerant control method for an autonomous vehicle based on fault estimation, including:
establishing a two-degree-of-freedom vehicle dynamic model, and determining a centroid slip angle;
establishing a vehicle path tracking model;
according to the vehicle dynamic model and the vehicle path tracking model, nominal faults are represented;
establishing a nominal fault calculation model according to the represented nominal faults, and calculating the nominal faults;
establishing a fault-tolerant control model, inputting the centroid slip angle and the nominal fault into the fault-tolerant control model, and calculating a vehicle yaw moment and a vehicle front wheel corner;
and controlling the automatically-driven vehicle according to the vehicle yaw moment and the vehicle front wheel steering angle calculated by the fault-tolerant control model.
Preferably, the vehicle dynamics model is:
Figure BDA0003689025890000021
Figure BDA0003689025890000022
wherein beta is the centroid slip angle, gamma is the yaw angular velocity, m is the vehicle mass, I z For rotational inertia, /) f And l r Respectively the distance of the centre of mass of the vehicle from the front axle and the rear axle, Δ M z For yaw moment, delta, of the vehicle f For the front wheel angle of the vehicle, C f And C r Front and rear wheel side deflection stiffness, v, respectively x Is the velocity component of the heading, v y Is the component of the velocity in the lateral direction of the vehicle,
Figure BDA0003689025890000023
is the rate of change of the centroid slip angle,
Figure BDA0003689025890000024
is yaw angular acceleration.
Preferably, the vehicle path tracking model adopts a heading deviation and a lateral deviation to characterize a path tracking error, and a differential equation of the heading deviation is as follows:
Figure BDA0003689025890000025
the differential equation of the lateral deviation is:
Figure BDA0003689025890000026
where p is the curvature of the reference path,
Figure BDA0003689025890000027
for the actual vehicle heading angle,
Figure BDA0003689025890000028
e is the track tracking lateral deviation, i.e. the lateral offset of the vehicle centroid to the closest point P on the reference path,
Figure BDA0003689025890000029
is the deviation of the course of the vehicle,
Figure BDA00036890258900000210
i.e. the deviation between the actual heading angle and the reference value.
Preferably, the nominal fault characterized is:
Figure BDA0003689025890000031
where τ is the nominal fault, τ ═ τ c +τ f The system state quantity is
Figure BDA0003689025890000032
Control input is u ═ u 1 u 2 ] T ＝[δ f ΔM z ] T Control the output to
Figure BDA0003689025890000033
The input matrix is
Figure BDA0003689025890000034
The measurement matrix is C ═ 0 3*1 I 3*3 ]The state transition matrix is f (x, t) ═ f 1 f 2 ] T ，
Figure BDA0003689025890000035
τ c ＝[τ c1 0] T For input faults in the steering system controller, τ c1 ＝(δ 1 -1)δ f +δ 2 ，τ f ＝[τ f1 0] T Is an unknown disturbance of the steering system controller.
Preferably, a nominal fault calculation model is established based on the nominal faults characterized, calculating the nominal faults comprising:
the characterized nominal faults are expanded into state quantities of the system, and an expanded system equation used for nominal fault estimation is obtained;
performing adaptive CFK algorithm for the augmented system equation to perform iterative system state estimation to obtain an augmented system discrete transfer equation;
establishing the nominal fault calculation model according to the discrete transfer equation of the augmentation system and by combining an adaptive CFK algorithm, wherein the state quantity of the nominal fault calculation model is
Figure BDA0003689025890000036
Figure BDA0003689025890000037
The nominal fault calculation model has the measured value of
Figure BDA0003689025890000038
And substituting the yaw rate, the course deviation and the lateral deviation into the nominal fault calculation model to calculate the nominal fault.
Preferably, the augmented system equation is:
Figure BDA0003689025890000039
wherein x is k+1 As a discrete system state vector, y k For discrete system measurement vectors, f (-) is a discrete system state transition equation, h (-) is a discrete system measurement equation, w k Is system noise, v k For measuring noise, and w k And v k Are uncorrelated white noise.
Preferably, the discrete transfer equation of the augmentation system is:
Figure BDA0003689025890000041
wherein the system state quantities are respectively expressed as x 1 ＝β，x 2 ＝γ，
Figure BDA0003689025890000042
x 4 ＝e，x 5 ＝ τ，
Figure BDA0003689025890000043
k is the sampling point, T is the Kalman filter sampling period, lambda 1 And λ 2 Are sliding mode parameters.
Preferably, the establishing of the fault tolerance control model comprises:
determining a sliding mode surface, and calculating based on a fast power approach law according to the sliding mode surface to establish the fault-tolerant control model.
Preferably, the fault-tolerant control model is:
Figure BDA0003689025890000044
wherein g is a control error, and g is x-x d ，x d ＝[0 γ d 0 0] T ，γ d For reference to vehicle yaw rate, k 1 、k 2 And epsilon is a sliding mode parameter larger than 0, and s is a sliding mode surface.
In a second aspect, the disclosed embodiments provide an automated vehicle fault tolerance control system based on fault estimation, the system comprising:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the steps of:
establishing a two-degree-of-freedom vehicle dynamic model, and determining a centroid slip angle;
establishing a vehicle path tracking model;
according to the vehicle dynamic model and the vehicle path tracking model, nominal faults are represented;
establishing a nominal fault calculation model according to the represented nominal faults, and calculating the nominal faults;
establishing a fault-tolerant control model, inputting the centroid slip angle and the nominal fault into the fault-tolerant control model, and calculating a vehicle yaw moment and a vehicle front wheel corner;
and controlling the automatically-driven vehicle according to the vehicle yaw moment and the vehicle front wheel steering angle calculated by the fault-tolerant control model.
Preferably, the vehicle dynamics model is:
Figure BDA0003689025890000051
Figure BDA0003689025890000052
wherein beta is the centroid slip angle, gamma is the yaw angular velocity, m is the vehicle mass, I z For rotational inertia, /) f And l r Respectively the distance of the centre of mass of the vehicle from the front axle and the rear axle, Δ M z For vehicle yaw moment, δ f For the front wheel angle of the vehicle, C f And C r Respectively front and rear wheel side deflection stiffness, v x Is the velocity component of the heading, v y Is the component of the velocity in the lateral direction of the vehicle,
Figure BDA0003689025890000053
is the rate of change of the centroid slip angle,
Figure BDA0003689025890000054
is yaw angular acceleration.
Preferably, the vehicle path tracking model adopts a heading deviation and a lateral deviation to characterize a path tracking error, and a differential equation of the heading deviation is as follows:
Figure BDA0003689025890000055
the differential equation of the lateral deviation is:
Figure BDA0003689025890000056
where p is the curvature of the reference path,
Figure BDA0003689025890000057
for the actual vehicle heading angle,
Figure BDA0003689025890000058
e is the track tracking lateral deviation, i.e. the lateral offset of the vehicle centroid to the closest point P on the reference path,
Figure BDA0003689025890000059
in order to be the course deviation,
Figure BDA00036890258900000510
i.e. the deviation between the actual heading angle and the reference value.
Preferably, the nominal fault characterized is:
Figure BDA00036890258900000511
where τ is the nominal fault, τ ═ τ c +τ f The system state quantity is
Figure BDA00036890258900000512
Control input is u ═ u 1 u 2 ] T ＝[δ f ΔM z ] T Control the output to
Figure BDA0003689025890000061
The input matrix is
Figure BDA0003689025890000062
The measurement matrix is C ═ 0 3*1 I 3*3 ]Moment of state transitionThe matrix is f (x, t) ═ f 1 f 2 ] T ，
Figure BDA0003689025890000063
τ c ＝[τ c1 0] T For input faults in the steering system controller, τ c1 ＝(δ 1 -1)δ f +δ 2 ，τ f ＝[τ f1 0] T Is an unknown disturbance of the steering system controller.
Preferably, a nominal fault calculation model is established based on the nominal faults characterized, calculating the nominal faults comprising:
the characterized nominal faults are expanded into state quantities of the system, and an expanded system equation used for nominal fault estimation is obtained;
performing adaptive CFK algorithm for the augmented system equation to perform iterative system state estimation to obtain an augmented system discrete transfer equation;
establishing the nominal fault calculation model according to the discrete transfer equation of the augmentation system and by combining an adaptive CFK algorithm, wherein the state quantity of the nominal fault calculation model is
Figure BDA0003689025890000064
Figure BDA0003689025890000065
The nominal fault calculation model has the measured value of
Figure BDA0003689025890000066
And substituting the yaw rate, the course deviation and the lateral deviation into the nominal fault calculation model to calculate the nominal fault.
Preferably, the augmented system equation is:
Figure BDA0003689025890000067
wherein x is k+1 As a discrete systemSystem state vector, y k For discrete system measurement vectors, f (-) is a discrete system state transition equation, h (-) is a discrete system measurement equation, w k Is system noise, v k For measuring noise, and w k And v k Are white noises that are uncorrelated with each other.
Preferably, the discrete transfer equation of the augmentation system is:
Figure BDA0003689025890000071
wherein the system state quantities are respectively expressed as x 1 ＝β，x 2 ＝γ，
Figure BDA0003689025890000072
x 4 ＝e，x 5 ＝ τ，
Figure BDA0003689025890000073
k is the sampling point, T is the Kalman filter sampling period, lambda 1 And λ 2 Are sliding mode parameters.
Preferably, the establishing of the fault tolerance control model comprises:
determining a sliding mode surface, and calculating based on a fast power approach law according to the sliding mode surface to establish the fault-tolerant control model.
Preferably, the fault-tolerant control model is:
Figure BDA0003689025890000074
wherein g is a control error, and g is x-x d ，x d ＝[0 γ d 0 0] T ，γ d For reference to vehicle yaw rate, k 1 、k 2 And epsilon is a sliding mode parameter larger than 0, and s is a sliding mode surface.
The beneficial effects are that: considering the influence of the simultaneous action of the steering control system fault and the unknown interference on the path tracking effect of the automatic driving vehicle, the path tracking fault-tolerant control method for the automatic driving vehicle is designed for improving the reliability of the control system of the automatic driving vehicle. The input faults of the steering control system are analyzed and defined, meanwhile, the influence of unknown interference of the steering control system is considered, and the nominal faults are characterized and mathematically modeled. An estimation method for the vehicle mass center side slip angle and the estimated fault is designed by utilizing the adaptive volume Kalman filtering, and the estimation method is used as the input quantity of the fault-tolerant control model of the automatic driving vehicle. A vehicle path tracking fault-tolerant control model is designed based on a sliding mode control method, so that the automatic driving vehicle can still keep good control performance under the condition of facing to the fault and the interference of a steering control system, and the stability of the vehicle can be ensured while the automatic driving vehicle realizes path tracking.
The method and system of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
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The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 shows a flow chart of the steps of a fault estimation based fault tolerance control method for an autonomous vehicle according to one embodiment of the present invention.
FIG. 2 shows a schematic diagram of a two degree of freedom vehicle dynamics model according to one embodiment of the present invention.
FIG. 3 shows a schematic diagram of a vehicle path tracking model according to one embodiment of the invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the following describes preferred embodiments of the invention, it should be understood that the invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein.
To facilitate understanding of the scheme of the embodiment of the present invention and the effect thereof, two specific application examples are given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
Example 1
Fig. 1 shows a flow chart of the steps of a fault estimation based fault tolerant control method of an autonomous vehicle according to the invention.
As shown in fig. 1, the fault-tolerant control method for an autonomous vehicle based on fault estimation includes: step 101, establishing a two-degree-of-freedom vehicle dynamics model, and determining a centroid slip angle; step 102, establishing a vehicle path tracking model; 103, representing a nominal fault according to the vehicle dynamics model and the vehicle path tracking model; 104, establishing a nominal fault calculation model according to the represented nominal fault, and calculating the nominal fault; step 105, establishing a fault-tolerant control model, inputting the centroid slip angle and the nominal fault into the fault-tolerant control model, and calculating the yaw moment and the front wheel corner of the vehicle; and 106, controlling the automatic driving vehicle according to the vehicle yaw moment and the vehicle front wheel rotation angle calculated by the fault-tolerant control model.
In one example, the vehicle dynamics model is:
Figure BDA0003689025890000091
Figure BDA0003689025890000092
wherein beta is the centroid slip angle, gamma is the yaw angular velocity, m is the vehicle mass, I z For rotational inertia, /) f And l r Respectively the distance of the centre of mass of the vehicle from the front axle and the rear axle, Δ M z For yaw moment, delta, of the vehicle f For vehicle front wheel steering，C f And C r Respectively front and rear wheel side deflection stiffness, v x Is the velocity component of the heading, v y Is the component of the velocity in the lateral direction of the vehicle,
Figure BDA0003689025890000093
is the rate of change of the centroid slip angle,
Figure BDA0003689025890000094
for yaw acceleration, a point on the parameter represents a first derivative of the parameter.
In one example, the vehicle path tracking model uses a heading bias and a lateral bias to characterize a path tracking error, the differential equation for the heading bias being:
Figure BDA0003689025890000095
the differential equation for the lateral deviation is:
Figure BDA0003689025890000096
where p is the curvature of the reference path,
Figure BDA0003689025890000097
for the actual vehicle heading angle,
Figure BDA0003689025890000098
e is the track tracking lateral deviation, i.e. the lateral offset of the vehicle centroid to the closest point P on the reference path,
Figure BDA0003689025890000099
in order to be the course deviation,
Figure BDA00036890258900000910
i.e. the deviation between the actual heading angle and the reference value.
In one example, the nominal fault characterized is:
Figure BDA0003689025890000101
where τ is the nominal fault, τ ═ τ c +τ f The system state quantity is
Figure BDA0003689025890000102
Control input is u ═ u 1 u 2 ] T ＝[δ f ΔM z ] T Control the output to
Figure BDA0003689025890000103
The input matrix is
Figure BDA0003689025890000104
The measurement matrix is C ═ 0 3*1 I 3*3 ]The state transition matrix is f (x, t) ═ f 1 f 2 ] T ，
Figure BDA0003689025890000105
τ c ＝[τ c1 0] T For input faults in the steering system controller, τ c1 ＝(δ 1 -1)δ f +δ 2 ，τ f ＝[τ f1 0] T Is an unknown disturbance of the steering system controller.
In one example, a nominal fault calculation model is built from the characterized nominal faults, the calculating the nominal faults comprising:
the method comprises the steps of (1) augmenting a nominal fault represented into a state quantity of a system to obtain an augmented system equation for nominal fault estimation;
performing iterative system state estimation by using a self-adaptive CFK algorithm aiming at an augmentation system equation to obtain an augmentation system discrete transfer equation;
establishing a nominal fault calculation model according to an augmented system discrete transfer equation and combining an adaptive CFK algorithm, wherein the state quantity of the nominal fault calculation model is
Figure BDA0003689025890000106
The measured value of the nominal fault calculation model is
Figure BDA0003689025890000107
And (4) bringing the yaw angular speed, the course deviation and the transverse deviation into a nominal fault calculation model to calculate the nominal fault.
In one example, the augmented system equation is:
Figure BDA0003689025890000108
wherein x is k+1 As a discrete system state vector, y k For discrete system measurement vectors, f (-) is a discrete system state transition equation, h (-) is a discrete system measurement equation, w k Is system noise, v k For measuring noise, and w k And v k Are white noises that are uncorrelated with each other.
In one example, the augmented system discrete transfer equation is:
Figure BDA0003689025890000111
wherein the system state quantities are respectively expressed as x 1 ＝β，x 2 ＝γ，
Figure BDA0003689025890000112
x 4 ＝e，x 5 ＝ τ，
Figure BDA0003689025890000113
k is the sampling point, T is the Kalman filter sampling period, lambda 1 And λ 2 Are sliding mode parameters.
In one example, establishing the fault tolerance control model includes:
determining a sliding mode surface, calculating based on a fast power approach law according to the sliding mode surface, and establishing a fault-tolerant control model.
In one example, the fault tolerance control model is:
Figure BDA0003689025890000114
wherein g is a control error, and g is x-x d ，x d ＝[0 γ d 0 0] T ，γ d For reference to vehicle yaw rate, k 1 、k 2 And epsilon is a sliding mode parameter larger than 0, and s is a sliding mode surface.
FIG. 2 shows a schematic diagram of a two degree of freedom vehicle dynamics model according to one embodiment of the present invention.
Specifically, in order to describe longitudinal and transverse dynamics characteristics in the unmanned vehicle path tracking process, a two-degree-of-freedom vehicle dynamics model is established, as shown in fig. 2. Ignoring the pitch, roll and vertical motions of the vehicle, and considering the mechanical properties of the four tires to be the same, regardless of the suspension system, the origin of the dynamic coordinate system xoy is fixed on the vehicle and coincides with the center of mass of the vehicle, the x-axis points in the forward direction of the vehicle, 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).
FIG. 3 shows a schematic diagram of a vehicle path tracking model according to one embodiment of the invention.
In order to realize the path tracking control of the autonomous vehicle, a vehicle path tracking model as shown in fig. 3 is established, in which XOY represents a geodetic coordinate system, ρ is the curvature of a reference path,
Figure BDA0003689025890000121
is the actual vehicle heading angle and is expressed as
Figure BDA0003689025890000122
e is the lateral offset of the vehicle centroid to the closest point P on the reference path i.e. the trajectory tracking lateral deviation,
Figure BDA0003689025890000123
is the deviation between the actual course angle and the reference value, namely the course deviation. The path tracking error is characterized by using the heading deviation and the transverse deviation in the path tracking model, wherein the heading deviation differential equation can be expressed as formula (3), and the transverse deviation differential equation can be expressed as formula (4) assuming that the heading deviation is small.
Nominal faults include steering system controller input faults and system unknown disturbance faults. The input faults of the steering system controller are divided into gain faults and deviation faults, when the vehicle steering system controller is in fault, the output quantity of the vehicle steering system is correspondingly changed, and the turning angle of the front wheel of the vehicle can be represented as follows:
δ ff ＝δ 1 δ f +δ 2 (9)
δ ff for the actual front wheel angle after failure, delta 1 To a fault gain, δ 2 Is a fault deviation. Different delta 1 And delta 2 The value collocation represents different fault combination types. Order to
Figure BDA0003689025890000124
Figure BDA0003689025890000125
Figure BDA0003689025890000126
Combining the vehicle dynamics model in equations (1) (2) and the path tracking model in equations (3) (4), the autonomous vehicle system equations that account for steering system controller faults and disturbances are integrated as:
Figure BDA0003689025890000127
wherein the system state quantity is
Figure BDA0003689025890000128
Control input is u ═ u 1 u 2 ] T ＝ [δ f ΔM z ] T Control the output to
Figure BDA0003689025890000129
The input matrix is
Figure BDA00036890258900001210
The measurement matrix is C ═ 0 3*1 I 3*3 ]The state transition matrix is f (x, t) ═ f 1 f 2 ] T And is
Figure BDA0003689025890000131
Figure BDA0003689025890000132
τ c ＝[τ c1 0] T Is an input fault of the steering system controller c1 ＝(δ 1 -1)δ f +δ 2 ，τ f ＝[τ f1 0] T Is an unknown disturbance of the steering system controller. The formula (10) can be expressed as the formula (5).
The nominal fault is expanded into the state quantity of the system, and the state quantity of the system is respectively expressed as x 1 ＝β，x 2 ＝γ，
Figure BDA0003689025890000133
x 4 ＝e，x 5 ＝τ，
Figure BDA0003689025890000134
The discrete form of the augmented system equation for nominal fault estimation can thus be expressed as equation (6).
The Cubature Kalman Filter (CKF) can effectively reduce the filter divergence, and can further improve the reliability of the estimation system. A Sage windowing method and multiple suboptimal attenuation factors are utilized, and a self-adaptive CFK algorithm is designed to estimate the system state, so that the estimation effect can be obviously improved. According to the discrete state space equation in equation (6), the iterative step of the adaptive CKF algorithm can be expressed as:
selecting an initial value.
Figure BDA0003689025890000135
Figure BDA0003689025890000136
In the formula (I), the compound is shown in the specification,
Figure BDA0003689025890000137
as an initial vector, P 0 Is an error covariance matrix.
② volumetric point calculation
Figure BDA0003689025890000138
Time updating
Figure BDA0003689025890000139
Wherein Q k Is the covariance matrix of the system noise.
Measurement update
The prediction equation for the measurement update can be expressed as
Figure BDA00036890258900001310
The covariance matrix and cross-covariance matrix of the measurement predictions may be expressed as:
Figure BDA0003689025890000141
Figure BDA0003689025890000142
in the formula, R k Is v is k The covariance matrix of (2). The prediction error can be obtained by calculating the actual measurement and predictionDifference between the measured values is obtained
Figure BDA0003689025890000143
In the formula, y k+1 Is the actual measurement of the k +1 sample point,
Figure BDA0003689025890000144
is the estimated measurement of the k +1 sample point.
Will fade out matrix M k+1 The design is as follows:
Figure RE-GDA0003780794680000145
where η is the window width. To improve the adaptivity of the fading matrix, the adaptive fading matrix containing the correction silver is designed as
Figure BDA0003689025890000146
Wherein (M) k+1 ) i Is the main diagonal element of the fading matrix. The adaptive filter gain may be expressed as an adaptive fading matrix
K k+1 ＝P xz，k+1|k (P xz，k+1|k +M′ k+1 R k+1 ) -1 (20)
According to the adaptive filter gain, the system state estimation result and the error covariance matrix can be obtained as
Figure BDA0003689025890000147
Figure BDA0003689025890000148
As the unknown input quantity of the system, the system does not contain the differential term of the nominal fault, so the differential term of the nominal fault can be reconstructed by utilizing the design mode of a high-order sliding mode observer. The nominal fault is directly related to the yaw rate of the vehicle in the system, and the nominal fault differential equation is constructed as follows:
Figure BDA0003689025890000151
in the formula, λ 1 And λ 2 Is a sliding mode parameter, and y 1 ＝x 2 . And integrating the vehicle state quantity and the nominal fault together by combining a vehicle system model and a constructed nominal fault differential equation, and then carrying out discretization treatment to obtain an augmented system discrete transfer equation used for Kalman filtering design as a formula (7). Establishing a nominal fault calculation model according to an augmented system discrete transfer equation and by combining an adaptive CFK algorithm, wherein the state quantity of the nominal fault calculation model is
Figure BDA0003689025890000152
The nominal failure calculation model has the measured value of
Figure BDA0003689025890000153
And substituting the yaw angular speed, the course deviation and the transverse deviation into a nominal fault calculation model to calculate a nominal fault.
In order to ensure the path tracking precision and the vehicle yaw stability, a fault-tolerant control model is designed to inhibit the influence of a nominal fault on the control performance of the whole vehicle. Let g be the control error and represent it as g ═ x-x d Wherein x is d ＝[0 γ d 0 0] T The reference vehicle mass center slip angle is 0, gamma d To reference the vehicle yaw rate, the reference lateral deviation and heading deviation are also 0. The track tracking control target is to realize the tracking of the vehicle transverse steady state value and the track tracking deviation. The slip form surface is designed into
Figure BDA0003689025890000154
In the formula k e A sliding mode parameter greater than 0.
In order to effectively weaken the buffeting phenomenon of the sliding mode controller, a fast power approximation law is selected for designing a fault-tolerant control model:
Figure BDA0003689025890000155
in the formula, k 1 、k 2 And ε are both sliding mode parameters greater than 0.
And (3) calculating the sliding mode surface based on a fast power approach law to obtain a fault-tolerant control model as a formula (8).
In order to verify that the output of the fault-tolerant control model has convergence, namely system stability, the Lyapunov function is selected as
Figure BDA0003689025890000156
Derived by derivation
Figure BDA0003689025890000157
Figure BDA0003689025890000158
Therefore, the designed control law can ensure the convergence of the sliding mode control model, so that the automatic driving vehicle can still maintain a good fault-tolerant control effect under the action of faults and interference. In addition to the fast power approach law, there are also constant velocity approach law, exponential approach law, general approach law, etc., and those skilled in the art can select the law according to the specific situation.
And inputting the centroid side slip angle and the nominal fault into a fault-tolerant control model, calculating a vehicle yaw moment and a vehicle front wheel corner, and controlling the automatic driving vehicle according to the vehicle yaw moment and the vehicle front wheel corner which are calculated by the fault-tolerant control model.
Example 2
A fault-tolerant control system for an autonomous vehicle based on fault estimation, comprising:
a memory storing executable instructions;
a processor executing executable instructions in the memory to implement the steps of:
establishing a two-degree-of-freedom vehicle dynamic model, and determining a centroid slip angle;
establishing a vehicle path tracking model;
according to the vehicle dynamic model and the vehicle path tracking model, representing a nominal fault;
establishing a nominal fault calculation model according to the represented nominal fault, and calculating the nominal fault;
establishing a fault-tolerant control model, inputting a mass center slip angle and a nominal fault into the fault-tolerant control model, and calculating a vehicle yaw moment and a vehicle front wheel corner;
and controlling the automatic driving vehicle according to the vehicle yaw moment obtained by calculation of the fault-tolerant control model and the vehicle front wheel corner.
In one example, the vehicle dynamics model is:
Figure BDA0003689025890000161
Figure BDA0003689025890000162
wherein beta is the centroid slip angle, gamma is the yaw angular velocity, m is the vehicle mass, I z For rotational inertia, /) f And l r Respectively the distance of the centre of mass of the vehicle from the front axle and the rear axle, Δ M z For yaw moment, delta, of the vehicle f For the front wheel angle, C, of the vehicle f And C r Respectively front and rear wheel side deflection stiffness, v x Is the velocity component of the heading, v y Is the component of the velocity in the lateral direction of the vehicle,
Figure BDA0003689025890000163
is the rate of change of the centroid slip angle,
Figure BDA0003689025890000164
is yaw angular acceleration.
In one example, the vehicle path tracking model uses a heading bias and a lateral bias to characterize a path tracking error, the differential equation for the heading bias being:
Figure BDA0003689025890000171
the differential equation for the lateral deviation is:
Figure BDA0003689025890000172
where p is the curvature of the reference path,
Figure BDA0003689025890000173
for the actual vehicle heading angle,
Figure BDA0003689025890000174
e is the track tracking lateral deviation, i.e. the lateral offset of the vehicle centroid to the closest point P on the reference path,
Figure BDA0003689025890000175
in order to be the course deviation,
Figure BDA0003689025890000176
i.e. the deviation between the actual heading angle and the reference value.
In one example, the nominal fault characterized is:
Figure BDA0003689025890000177
where τ is the nominal fault, τ ═ τ c +τ f The system state quantity is
Figure BDA0003689025890000178
Control input is u ═ u 1 u 2 ] T ＝[δ f ΔM z ] T Control the output to
Figure BDA0003689025890000179
The input matrix is
Figure BDA00036890258900001710
The measurement matrix is C ═ 0 3*1 I 3*3 ]The state transition matrix is f (x, t) ═ f 1 f 2 ] T ，
Figure BDA00036890258900001711
τ c ＝[τ c1 0] T For input faults in the steering system controller, τ c1 ＝(δ 1 -1)δ f +δ 2 ，τ f ＝[τ f1 0] T Is an unknown disturbance of the steering system controller.
In one example, a nominal fault calculation model is built from the characterized nominal faults, the calculating the nominal faults comprising:
the method comprises the steps of (1) augmenting a nominal fault represented into a state quantity of a system to obtain an augmented system equation for nominal fault estimation;
performing iterative system state estimation by using a self-adaptive CFK algorithm aiming at an augmentation system equation to obtain an augmentation system discrete transfer equation;
establishing a nominal fault calculation model according to an augmented system discrete transfer equation and combining an adaptive CFK algorithm, wherein the state quantity of the nominal fault calculation model is
Figure BDA0003689025890000181
The measured value of the nominal fault calculation model is
Figure BDA0003689025890000182
And (4) bringing the yaw angular speed, the course deviation and the transverse deviation into a nominal fault calculation model to calculate the nominal fault.
In one example, the augmented system equation is:
Figure BDA0003689025890000183
wherein x is k+1 As a discrete system state vector, y k For discrete system measurement vectors, f (-) is a discrete system state transition equation, h (-) is a discrete system measurement equation, w k Is system noise, v k For measuring noise, and w k And v k Are white noises that are uncorrelated with each other.
In one example, the augmented system discrete transfer equation is:
Figure BDA0003689025890000184
wherein the system state quantities are respectively expressed as x 1 ＝β，x 2 ＝γ，
Figure BDA0003689025890000185
x 4 ＝e，x 5 ＝ τ，
Figure BDA0003689025890000186
k is the sampling point, T is the Kalman filter sampling period, lambda 1 And λ 2 Are sliding mode parameters.
In one example, establishing the fault tolerance control model includes:
determining a sliding mode surface, calculating based on a fast power approach law according to the sliding mode surface, and establishing a fault-tolerant control model.
In one example, the fault tolerance control model is:
Figure BDA0003689025890000187
wherein g is a control error, and g is x-x d ，x d ＝[0 γ d 0 0] T ，γ d For reference to vehicle yaw rate, k 1 、k 2 And epsilon is a sliding mode parameter larger than 0, and s is a sliding mode surface.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims (10)

1. A fault-tolerant control method for an autonomous vehicle based on fault estimation is characterized by comprising the following steps:
establishing a two-degree-of-freedom vehicle dynamic model, and determining a centroid slip angle;
establishing a vehicle path tracking model;
according to the vehicle dynamic model and the vehicle path tracking model, nominal faults are represented;
establishing a nominal fault calculation model according to the represented nominal fault, and calculating the nominal fault;
establishing a fault-tolerant control model, inputting the centroid slip angle and the nominal fault into the fault-tolerant control model, and calculating a vehicle yaw moment and a vehicle front wheel corner;
and controlling the automatic driving vehicle according to the vehicle yaw moment and the vehicle front wheel steering angle which are calculated by the fault-tolerant control model.
2. The fault-tolerant control method for a fault-estimation-based autonomous vehicle of claim 1, wherein the vehicle dynamics model is:
Figure FDA0003689025880000011
Figure FDA0003689025880000012
wherein beta is the centroid slip angle, gamma is the yaw angular velocity, m is the vehicle mass, I z Is moment of inertia,/ f And l r Respectively the distance of the centre of mass of the vehicle from the front axle and the rear axle, Δ M z For yaw moment, delta, of the vehicle f For the front wheel angle of the vehicle, C f And C r Respectively front and rear wheel side deflection stiffness, v x Is the velocity component of the heading, v y Is the component of the velocity in the lateral direction of the vehicle,
Figure FDA0003689025880000013
is the rate of change of the centroid slip angle,
Figure FDA0003689025880000014
is yaw angular acceleration.
3. The fault-estimation based fault-tolerant control method for an autonomous vehicle as claimed in claim 1, wherein the vehicle path tracking model characterizes the path tracking error using a heading bias and a lateral bias, the differential equation of the heading bias being:
Figure FDA0003689025880000021
the differential equation of the lateral deviation is:
Figure FDA0003689025880000022
where p is the curvature of the reference path,
Figure FDA0003689025880000023
for the actual vehicle heading angle,
Figure FDA0003689025880000024
e is the track tracking lateral deviation, namely the lateral deviation of the centroid of the vehicle to the nearest point P on the reference path,
Figure FDA0003689025880000025
in order to be the course deviation,
Figure FDA0003689025880000026
i.e. the deviation between the actual heading angle and the reference value.
4. A fault-estimation based fault-tolerant control method for an autonomous vehicle as claimed in claim 1, wherein the nominal fault characterized is:
Figure FDA0003689025880000027
where τ is the nominal fault, τ ═ τ c +τ f The system state quantity is
Figure FDA0003689025880000028
Control input is u ═ u 1 u 2 ] T ＝[δ f ΔM z ] T Control the output to
Figure FDA0003689025880000029
The input matrix is
Figure FDA00036890258800000210
The measurement matrix is C ═ 0 3*1 I 3*3 ]The state transition matrix is f (x, t) ═ f 1 f 2 ] T ，
Figure FDA00036890258800000211
τ c ＝[τ c1 0] T For steering systemInput faults, τ, of the system controller c1 ＝(δ 1 -1)δ f +δ 2 ，τ f ＝[τ f1 0] T Is an unknown disturbance of the steering system controller.
5. A fault-estimation based fault-tolerant control method for an autonomous vehicle as claimed in claim 1, wherein a nominal fault calculation model is built from the characterized nominal faults, calculating the nominal faults comprising:
augmenting the characterized nominal fault into a state quantity of a system to obtain an augmented system equation for nominal fault estimation;
performing adaptive CFK algorithm for the augmented system equation to perform iterative system state estimation to obtain an augmented system discrete transfer equation;
establishing the nominal fault calculation model according to the discrete transfer equation of the augmentation system and by combining an adaptive CFK algorithm, wherein the state quantity of the nominal fault calculation model is
Figure FDA0003689025880000031
Figure FDA0003689025880000032
The nominal fault calculation model has the measured value of
Figure FDA0003689025880000033
And substituting the yaw angular speed, the heading deviation and the lateral deviation into the nominal fault calculation model to calculate the nominal fault.
6. The fault-tolerant control method for the autonomous vehicle based on the estimation of the fault according to claim 5, wherein the augmented system equation is:
Figure RE-FDA0003780794670000034
wherein x is k+1 As a discrete system state vector, y k For discrete system measurement vectors, f (-) is a discrete system state transition equation, h (-) is a discrete system measurement equation, w k Is system noise, v k To measure noise, and w k And v k Are white noises that are uncorrelated with each other.
7. The fault-tolerant control method for autonomous vehicles based on estimation of faults as claimed in claim 5, wherein the augmented system discrete transfer equation is:
Figure FDA0003689025880000035
wherein the system state quantities are respectively expressed as x 1 ＝β，x 2 ＝γ，
Figure FDA0003689025880000036
x 4 ＝e，x 5 ＝τ，
Figure FDA0003689025880000037
k is the sampling point, T is the Kalman filter sampling period, lambda 1 And λ 2 Are sliding mode parameters.
8. The fault-tolerant control method for the fault-estimation-based autonomous vehicle of claim 1, wherein establishing a fault-tolerant control model comprises:
determining a sliding mode surface, and calculating based on a fast power approach law according to the sliding mode surface to establish the fault-tolerant control model.
9. The fault-tolerant control method for a fault-estimation-based autonomous vehicle of claim 8, wherein the fault-tolerant control model is:
Figure FDA0003689025880000041
wherein g is a control error, and g is x-x d ，x d ＝[0 γ d 0 0] T ，γ d For reference to yaw rate of vehicle, k 1 、k 2 And epsilon is a sliding mode parameter larger than 0, and s is a sliding mode surface.
10. A fault-tolerant control system for autonomous vehicles based on fault estimation, the system comprising:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the steps of:
establishing a two-degree-of-freedom vehicle dynamic model, and determining a centroid slip angle;
establishing a vehicle path tracking model;
according to the vehicle dynamic model and the vehicle path tracking model, nominal faults are represented;
establishing a nominal fault calculation model according to the characterized nominal faults, and calculating the nominal faults;
establishing a fault-tolerant control model, inputting the centroid slip angle and the nominal fault into the fault-tolerant control model, and calculating a vehicle yaw moment and a vehicle front wheel corner;
and controlling the automatic driving vehicle according to the vehicle yaw moment and the vehicle front wheel steering angle calculated by the fault-tolerant control model.
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