CN112289020A - Vehicle path tracking safety control method based on self-adaptive triggering mechanism under hybrid network attack - Google Patents

Vehicle path tracking safety control method based on self-adaptive triggering mechanism under hybrid network attack Download PDF

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CN112289020A
CN112289020A CN202010991263.9A CN202010991263A CN112289020A CN 112289020 A CN112289020 A CN 112289020A CN 202010991263 A CN202010991263 A CN 202010991263A CN 112289020 A CN112289020 A CN 112289020A
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vehicle
path tracking
attack
network attack
vehicle path
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CN112289020B (en
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缪巍巍
曾锃
张明轩
张厦千
张震
王传君
李世豪
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Information and Telecommunication Branch of State Grid Jiangsu Electric Power Co Ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
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    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
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    • G08—SIGNALLING
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    • G08G1/00—Traffic control systems for road vehicles
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Abstract

The invention discloses a vehicle path tracking security control method based on a self-adaptive trigger mechanism under hybrid network attack, which introduces the self-adaptive event trigger mechanism to reduce network load, ensures the security and stability of a vehicle path tracking system and reduces the bandwidth load of a data transmission network while considering the influence of replay attack and deception attack on network security. According to the vehicle path tracking safety control method based on the self-adaptive trigger mechanism under the hybrid network attack, the controller gain is obtained by utilizing the linear matrix inequality and the Lyapunov stability theory, the system stability is ensured, and the requirement of network bandwidth is reduced.

Description

Vehicle path tracking safety control method based on self-adaptive triggering mechanism under hybrid network attack
Technical Field
The invention belongs to the field of network control, and particularly relates to a vehicle path tracking security control method based on a self-adaptive event trigger mechanism under hybrid network attack.
Background
With the development of the latest scientific technologies, many types of autonomous cars have been developed and have begun to be deployed in complex real-world environments. Automated unmanned vehicles are one of the most interesting vehicle types, consisting of an electronic platform with many sensory inputs and complex processing elements. Today, autopilot platforms contain tens of processor cores and millions of lines of code. It has been found in practice that even in urban areas, autonomous vehicles can maneuver while complying with the actual traffic regulations. At present, cooperative control of a variety of autonomous ground vehicles is widely studied due to its potential applications in task allocation, data fusion, formation, and the like. Accordingly, many scholars have paid great attention to the study of autonomous ground vehicles.
In a network system of an autonomous vehicle, the limitation of network resources has a great influence on system performance, and many scholars have been devoted to solving these problems. The time-triggered scheme has a simple task model, so that the time-triggered scheme is widely applied to network systems. However, when the system is stable, periodic sampling can produce large amounts of redundant data, resulting in network congestion. In order to conserve network resources, researchers have begun looking for more efficient data transmission schemes. The self-adaptive event triggering mechanism provided by the invention adopts a variable threshold value on the basis of the original event triggering mechanism and self-adaptively adjusts the sampling interval according to the system state.
The network system of the automatic driving vehicle is continuously enlarged in scale and the structure is gradually complicated, and the introduction of the network effectively relieves the complexity of control, but also needs to overcome many challenges brought by the network. Current control system security issues include replay and spoofing attacks, which aim to degrade system performance by replacing transmitted data or preventing data communication. At present, limited network bandwidth cannot meet the data transmission requirement of a system, the system security control faces a severe security situation, and the development of a network control system is severely restricted by the problems. Therefore, research on a vehicle path tracking security control method based on a self-adaptive trigger mechanism under hybrid network attack is a problem to be solved urgently at present.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a method for designing a vehicle path tracking system safety controller based on a self-adaptive event trigger mechanism under the background of various network attacks aiming at the problems of the current automatic driving vehicle on the basis of the prior art, reconstructs a power model of the vehicle when considering various factors influencing the vehicle driving, introduces the self-adaptive event trigger mechanism to reduce the requirements on network bandwidth while reducing the influence of replay attack and deception attack on the vehicle data transmission safety, ensures the safety and stability of the automatic driving vehicle, reduces the occupation of the transmission data on the network bandwidth, and improves the system data transmission efficiency.
The technical scheme is as follows:
a vehicle path tracking security control method based on a self-adaptive event trigger mechanism under hybrid network attack comprises the following steps:
step one, establishing a vehicle path tracking model according to a vehicle running track;
secondly, reconstructing an automobile power model according to various influence factors such as the mass, the inertia, the direction, the offset angle and the like of the automobile;
step three, constructing a vehicle system model with state-related uncertainty according to the change of the longitudinal speed of the vehicle along with time;
step four, considering the limitation of communication resources of a communication transmission network, introducing a self-adaptive event triggering mechanism to reduce the network communication burden;
step five, considering the influence of deception attack and replay attack, establishing a network attack model under the mixed network attack
Step six, comprehensively considering the network attack influence and keeping the stability of the vehicle, and designing a vehicle path tracking control system model under the hybrid network attack by combining the steps one to five;
seventhly, obtaining a sufficiency condition for ensuring the stability of the mean square index of the system by utilizing the Lyapunov stability theory;
step eight, connecting columns and solving linear matrix inequality to obtain controller gain
Further, in step one, a model of the vehicle path tracking system variation can be obtained by deriving the lateral offset and the direction error, and the curve coordinate of the origin of the vehicle along the path δ can be expressed as:
Figure BDA0002691042650000021
e denotes the lateral offset, ψ, from the center of gravity of the vehicle to the nearest point O on the target pathhIs the actual forward angle of the vehicle, psidIs the required forward angle, psi, of the vehicleeIndicating heading error, ψe＝ψh-ψd，
Figure BDA0002691042650000022
The automatic driving vehicle path tracking model is as follows:
Figure BDA0002691042650000023
assuming that the forward error psi is very small, then the error
Figure BDA0002691042650000024
Can be expressed as:
Figure BDA0002691042650000025
beta is the sideslip angle of the car, LdIs the expected distance.
Further, in step 2, the vehicle dynamics equation is:
Figure BDA0002691042650000026
wherein ,
Figure BDA0002691042650000027
representing vehicle yaw rate, m representing vehicle mass, IZRepresenting vehicle inertia,/f and lrRespectively representing the distance from the center of the vehicle to the front and rear axles, MZIs an external bias moment, Fyf and FyrRespectively, representing generalized front-to-back lateral forces.
Figure BDA0002691042650000028
Cf and CrRespectively representing the turning angles, beta, of the front and rear wheelsf and βrIs the car slip angle, ρfIs the front wheel steering angle, defined as follows:
Figure BDA0002691042650000031
the power model of the trolley can be obtained
Figure BDA0002691042650000032
Further, in step 3, a system model is established to perform safety control on the vehicle path tracking based on the uncertainty system, and the system model is as follows:
Figure BDA0002691042650000033
wherein ：αi＝αi(xi(t),ηi(t))，αj＝αj(yj(t-λ(t)),ηj(t- λ (t))), η (t) is the time-varying parameter of the uncertainty system, α (x (t), η (t)) is the uncertainty parameter function, Ai、Bi、Ci and DiIs a coefficient matrix of the system; x (t) is a system state vector; u (t) is the system control input, and u (t) Kyr(t), K is the controller gain, yr(t) is the system true input; ω (t) is the system disturbance.
Further, in step four, the adaptive time-triggered mechanism model:
Figure BDA0002691042650000034
tk+1h is the next time instant to transmit data, tkh is the last time data was transmitted, Ω > 0 is a weight matrix,
Figure BDA0002691042650000035
Figure BDA0002691042650000036
is the maximum allowed packet loss number; ε (t) is a threshold and ε (t) e (0, 1)]；y(tkh) Is the data transmitted at the last instant, y (t)kh + vh) is the current time instant sample data.
Further, step five, considering the influence of the deception attack and the replay attack, establishing a network attack model under the hybrid network attack:
the system transmission under replay attack is represented as:
y1(t)＝h(t)yr(t)+(1-h(t))ye(t)
wherein h (t) epsilon [0,1]Is a variable of the number of bernoulli variables,
Figure BDA0002691042650000037
is the mathematical expectation of h (t), yr(t)＝ye(tr)，ye(tr) Is shown at trDuplicate data transmitted at a time, ye(t) represents data obtained by an adaptive event triggering mechanism.
It is assumed that the system is subject to a spoofing attack after being subject to a replay attack. f (y (t- χ (t))) is a spoofing attack expression suffered by the system, the data transmitted by the system is as follows:
y2(t)＝θ(t)f(y(t-χ(t)))+(1-θ(t))y1(t)
θ (t) is a Bernoulli variable and θ (t) is ∈ [0,1 ]]，
Figure BDA00026910426500000410
Is the mathematical expectation of θ (t).
Further, step six, comprehensively considering the network attack influence and keeping the stability of the vehicle, and combining the steps (1) to (5) to design a vehicle path tracking control system model under the hybrid network attack:
Figure BDA0002691042650000041
and (3) bringing in, obtaining the relevant uncertain system of the state of the automatic driving vehicle:
Figure BDA0002691042650000042
αi＝αi(xi(t),ηi(t))，αj＝αj(yj(t-λ(t)),ηj(t-λ(t)))
further, in the seventh step, the step of obtaining the sufficiency condition for the system mean square index stability is as follows:
s3-1, stably constructing the Lyapunov function as follows:
V(t)＝V1(t)+V2(t)+V3(t)
V1(t)＝x(t)TP(t)x(t)
Figure BDA0002691042650000043
Figure BDA0002691042650000044
s3-2, setting parameters: positive number rho1,ρ2Bernoulli variable expectation
Figure BDA0002691042650000045
Upper bound of delay lambdam,χmAdaptive event trigger parameter epsilone；
S3-3, judging whether a matrix P is more than 0 and Q is present1k＞0，Q2k＞0，R1k＞0，R2k＞0，Ω＞0，Ui＞0，S1k、S2k、W1k、W2kFor any i, j, k is 1, 2, 3, 4, such that the following inequality is satisfied
Figure BDA0002691042650000046
Γijk-Ui＜0
Figure BDA0002691042650000047
Figure BDA0002691042650000048
Figure BDA0002691042650000049
S3-4, if the data exists, determining parameters and ending; if not, returning to S3-2 to adjust the parameters, and repeating S3-2-S3-4.
Further, in step eight, for a given parameter: positive number rho1,ρ2Bernoulli variable expectation
Figure BDA0002691042650000051
Upper bound of delay lambdam,χmAdaptive event trigger parameter epsiloneThe existence matrix P > 0, Q1k＞0，Q2k＞0，R1k＞0，R2k＞0，Ω＞0，Ui＞0，S1k、S2k、W1k、W2kFor any i, j, k is 1, 2, 3, 4, such that the following inequality is satisfied
Figure BDA0002691042650000052
Figure BDA0002691042650000053
Figure BDA0002691042650000054
Figure BDA0002691042650000055
Figure BDA0002691042650000056
The path tracking output feedback controller gain is designed as follows:
Figure BDA0002691042650000057
has the advantages that:
1. the invention sequentially considers the influence of random replay attack and deception attack and establishes a vehicle path tracking control system model under various network attacks;
2. in order to optimize the bandwidth, improve the data transmission efficiency and reduce the bandwidth load, the invention introduces a self-adaptive event triggering mechanism to improve the data transmission efficiency;
3. considering the influence of various factors such as vehicle speed, direction, offset angle, time and the like, introducing a polyhedron with limited vertexes into a vehicle system to construct a vehicle mathematical model;
4. based on the newly established system model, the controller gain is obtained by utilizing the linear matrix inequality and the Lyapunov stability theory, the system stability is ensured, and the requirement of network bandwidth is reduced.
Drawings
FIG. 1 is a flow chart of a system safety control method design provided by the present invention;
FIG. 2 is a schematic view of a vehicle dynamics model;
FIG. 3 is a state response of the system in a simulation case;
FIG. 4 is the occurrence of a spoofing attack in a simulation case;
FIG. 5 is the occurrence of a replay attack in a simulation case;
FIG. 6 is the adaptive event trigger mechanism release and interval times in the simulation case;
fig. 7 is a relationship between a replay attack signal and a normal transmission signal in the simulation case.
Detailed Description
The following examples are merely illustrative, and are intended to clearly illustrate the technical solutions of the present invention, and therefore, the application scope of the present invention is not limited thereto. Unless otherwise defined, all terms or expressions which have been employed herein are used as terms of their ordinary meaning in the art to which this invention pertains.
Fig. 1 is a flow chart of the design of a safety controller of a vehicle path tracking control system, which is mainly used for showing the design steps of a vehicle path tracking control method, and comprises the following steps:
the method comprises the following steps: considering a vehicle running track, establishing a vehicle path tracking model;
step two: considering various influence factors such as mass, inertia, direction, offset angle and the like of the vehicle, constructing an automobile power model;
step three: considering the change of the longitudinal speed of the vehicle along with time, constructing a vehicle system model with state-related uncertainty;
step four: considering the limitation of communication resources of a communication transmission network, and introducing a self-adaptive event triggering mechanism for reducing network communication burden;
step five: the influence of deception attack and replay attack is considered, and a network attack model under hybrid network attack is established;
step six: comprehensively considering the influence of network attack and keeping the stability of the vehicle, and designing a vehicle path tracking control system model under the hybrid network attack by combining the steps (1) to (5);
step seven: obtaining a sufficient condition for ensuring the stability of the mean square index of the system by utilizing the Lyapunov stability theory;
step eight: and connecting columns and solving a linear matrix inequality to obtain the gain of the controller.
Note:
Figure BDA0002691042650000061
is a set of natural numbers, and the natural numbers,
Figure BDA0002691042650000062
representing an n-dimensional euclidean space; a. theTIs the transpose of matrix A, I is the identity matrix, A > 0 means A is a true symmetric positive definite matrix; symmetric matrix with matrix B and symmetric matrix A, C for j
Figure BDA0002691042650000063
Denotes the symmetry term of the matrix weight.
The method comprises the following steps: considering a vehicle running track, establishing a vehicle path tracking model:
as shown in fig. 2, a model of the vehicle path tracking system variation can be derived by deriving the lateral offset and the directional error, and the curvilinear coordinate of the origin of the vehicle along the path δ can be expressed as:
Figure BDA0002691042650000064
e denotes the lateral offset, ψ, from the center of gravity of the vehicle to the nearest point O on the target pathhIs the actual forward angle of the vehicle, psidIs the required forward angle, psi, of the vehicleeIndicating heading error, ψe＝ψh-ψd，
Figure BDA0002691042650000065
The automatic driving vehicle path tracking model is as follows:
Figure BDA0002691042650000066
assuming that the forward error psi is very small, then the error
Figure BDA0002691042650000067
Can be expressed as:
Figure BDA0002691042650000071
beta is the sideslip angle of the car, LdIs the expected distance.
Step two: considering various influence factors such as the mass, inertia, direction, offset angle and the like of the vehicle, constructing an automobile power model:
the vehicle dynamics equation is:
Figure BDA0002691042650000072
wherein ,
Figure BDA0002691042650000073
representing vehicle yaw rate, m representing vehicle mass, IZRepresenting vehicle inertia,/f and lrRespectively representing the distance from the center of the vehicle to the front and rear axles, MZIs an external bias moment, Fyf and FyrRespectively, representing generalized front-to-back lateral forces.
Figure BDA0002691042650000074
Cf and CrRespectively representing the turning angles, beta, of the front and rear wheelsf and βrIs the car slip angle, ρfIs the front wheel steering angle, defined as follows:
Figure BDA0002691042650000075
the power model of the trolley can be obtained
Figure BDA0002691042650000076
Let x (t) be [. psie β Υ]TController input
Figure BDA00026910426500000711
System disturbance ω (t) ═ P (δ)]TThen the state space equation of the car can be expressed as:
Figure BDA0002691042650000077
wherein
Figure BDA0002691042650000078
Figure BDA0002691042650000079
Figure BDA00026910426500000710
In the invention, the output feedback controller is designed as follows: u (t) ═ Kyr(t), K is the controller gain, yr(t) is the system real input.
Step three: considering the change of the longitudinal speed of the vehicle along with the time, a vehicle system model with state-dependent uncertainty is constructed:
the longitudinal speed of the autonomous vehicle of the path following system varies with time, and can be modeled in the uncertainty system as follows:
Figure BDA0002691042650000081
x (t) is a system state vector; u (t) is the system control input; y (t) is a measured value; α (x (t), η (t)) is an uncertain parameter vector function comprising a time-varying parameter η (t) and a state-dependent parameter perturbation αi(xi(t),ηi(t)), further α (x (t), η (t)) satisfies the following condition:
Figure BDA0002691042650000082
xi(t) and ηi(t) is an element of x (t) and η (t), respectively. A (α (x (t), η (t))), B (α (x (t), η (t))), C (α (x (t), η (t))), D (α (x (t), η (t))) are system matrices belonging to the following convex polygon sets:
Figure BDA0002691042650000083
wherein
Figure BDA0002691042650000084
Ai,Bi,Ci,DiAre the vertices of the respective uncertainty polygon, which are a constant real matrix of the respective dimension.
Let the longitudinal velocity vxBounded, then the system matrix can be represented as:
Figure BDA0002691042650000085
wherein
Figure BDA0002691042650000086
Figure BDA0002691042650000087
Step four: considering the limitation of communication resources of a communication transmission network, in order to reduce the network communication burden, a self-adaptive event triggering mechanism is introduced:
for the adaptive event triggering mechanism, the next transmission time is:
Figure BDA0002691042650000091
tk+1h is the next time instant to transmit data, tkh is the last time data was transmitted, Ω > 0 is a weight matrix,
Figure BDA0002691042650000092
Figure BDA0002691042650000093
is the maximum allowed packet loss number; ε (t) is a threshold and ε (t) e (0, 1)]；y(tkh) Is the data transmitted at the last instant, y (t)kh + vh) is the current time instant sample data.
The threshold value ε (t) is defined as follows:
Figure BDA0002691042650000094
εe> 0, by adjusting epsiloneThe convergence rate of epsilon (t) can be obtained; e.g. of the typek(tkh)＝y(tkh)-y(tkh+vh)。
Adaptive event trigger mechanism state vector y (t)kh) Is represented as follows:
ye(t)＝y(tkh)＝ek(t)+y(t-λ(t))
step five: considering the influence of the deception attack and the replay attack, establishing a network attack model under the hybrid network attack:
the system transmission under replay attack is represented as:
y1(t)＝h(t)yr(t)+(1-h(t))ye(t) (12)
wherein h (t) epsilon [0,1]Is a variable of the number of bernoulli variables,
Figure BDA0002691042650000095
is the mathematical expectation of h (t), yr(t)＝ye(tr)，ye(tr) Is shown at trDuplicate data transmitted at a time, ye(t) represents data obtained by an adaptive event triggering mechanism.
It is assumed that the system is subject to a spoofing attack after being subject to a replay attack. f (y (t- χ (t))) is a spoofing attack expression suffered by the system, the data transmitted by the system is as follows:
y2(t)＝θ(t)f(y(t-χ(t)))+(1-θ(t))y1(t) (13)
θ (t) is a Bernoulli variable and θ (t) is ∈ [0,1 ]]，
Figure BDA0002691042650000096
Is the mathematical expectation of θ (t).
Step six: comprehensively considering the influence of network attack and keeping the stability of the vehicle, and combining the steps (1) to (5) to design a vehicle path tracking control system model under the hybrid network attack:
Figure BDA0002691042650000097
and (3) bringing in, obtaining the relevant uncertain system of the state of the automatic driving vehicle:
Figure BDA0002691042650000101
αi＝αi(xi(t),ηi(t))，αj＝αj(yj(t-λ(t)),ηj(t-λ(t)))
step seven: utilizing the Lyapunov stability theory to obtain the sufficiency condition for ensuring the stability of the system mean square index:
Figure BDA0002691042650000102
for a given positive number p1,ρ2Bernoulli variable expectation
Figure BDA0002691042650000103
Upper bound of delay lambdam,χmAdaptive event trigger parameter epsilone(ii) a Judging whether a matrix P is more than 0 and Q is present1k＞0，Q2k＞0，R1k＞0，R2k＞0，Ω＞0，Ui＞0，S1k、S2k、W1k、W2kFor any i, j, k is 1, 2, 3, 4, such that the following inequality is satisfied
Figure BDA0002691042650000104
Γijk-Ui＜0 (17)
Figure BDA0002691042650000105
Figure BDA0002691042650000106
Figure BDA0002691042650000107
Figure BDA0002691042650000108
Figure BDA0002691042650000109
Figure BDA0002691042650000111
Figure BDA0002691042650000112
Θ33k＝-Q1k-S1k
Figure BDA0002691042650000113
Figure BDA0002691042650000114
Figure BDA0002691042650000115
Π31＝[Π311 Π312 Π313]
Figure BDA0002691042650000116
Figure BDA0002691042650000117
Π41＝[Π411 Π412 0],Π51＝[Π511 Π512 0]，Π61＝[Π611 Π612 Π613 0]
Figure BDA0002691042650000118
Figure BDA0002691042650000119
Figure BDA00026910426500001110
Figure BDA00026910426500001111
Figure BDA00026910426500001112
Figure BDA00026910426500001113
Figure BDA00026910426500001114
Figure BDA0002691042650000121
Step eight: and (3) connecting columns and solving a linear matrix inequality to obtain the gain of the controller:
for a given positive number p1,ρ2Bernoulli variable expectation
Figure BDA0002691042650000122
Upper bound of delay lambdam,χmAdaptive event trigger parameter epsiloneThe existence matrix P > 0, Q1k＞0，Q2k＞0，R1k＞0，R2k＞0，Ω＞0，Ui＞0，S1k、S2k、W1k、W2kFor any i, j, k is 1, 2, 3, 4, such that the following inequality is satisfied
Figure BDA0002691042650000123
Figure BDA0002691042650000124
Figure BDA0002691042650000125
Figure BDA0002691042650000126
Figure BDA0002691042650000127
The path tracking output feedback controller gain is designed as follows:
Figure BDA0002691042650000128
Figure BDA0002691042650000129
Figure BDA00026910426500001210
Figure BDA00026910426500001211
Figure BDA00026910426500001212
Θ33k＝-Q1k-S1k
Figure BDA0002691042650000131
Figure BDA0002691042650000132
Figure BDA0002691042650000133
Figure BDA0002691042650000134
Figure BDA0002691042650000135
Figure BDA0002691042650000136
Figure BDA0002691042650000137
Figure BDA0002691042650000138
Figure BDA0002691042650000139
Figure BDA00026910426500001310
Figure BDA00026910426500001311
Figure BDA00026910426500001312
Figure BDA00026910426500001313
Figure BDA00026910426500001314
Figure BDA00026910426500001315
simulation analysis
The validity of the proposed method is verified by a simulation case. The simulation was carried out on a MatlabSimulink platform with a complete vehicle model, with vehicle and path specific parameters as in table-1.
TABLE-1 vehicle and Path specific parameters
Figure BDA0002691042650000141
Consider the system matrix in equation (15) as
Figure BDA0002691042650000142
Figure BDA0002691042650000143
Figure BDA0002691042650000144
The non-linear function of the spoof attack signal is
Figure BDA0002691042650000145
Giving the value of the following parameter rho1＝0.13，ρ2＝1.01,εe＝0.02，e1＝e2＝4，α1＝0.75,α2＝0.95，λM＝0.4，χM0.3. Solving line by utilizing Matlab simulation based on the parametersThe inequalities (21) - (25) of the property matrix are obtained to be feasible solutions
Y＝[-0.00004 0.00001 -0.0113 -0.1891]；
Figure BDA0002691042650000151
K＝[0.0004 -0.00001 0.0001 0.0012]
Under the condition of setting the initial conditions of the system, the following simulation result graph is obtained: figure 3 shows the status response of the system. Fig. 4 and 5 show the occurrence timings of a spoofing attack and a replay attack, respectively. The release times and intervals for the adaptive event triggering scheme are shown in fig. 6. Fig. 7 is a relationship between a replay attack signal and a normal transmission signal. From these data, we can conclude that systems with hybrid attack and adaptive event triggering schemes are asymptotically stable and it is feasible to design methods for automatic ground vehicle path tracking.
The above examples are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and modifications, improvements and equivalents which are within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A vehicle path tracking security control method based on a self-adaptive event trigger mechanism under hybrid network attack is characterized by comprising the following steps:
step one, establishing a vehicle path tracking model according to a vehicle running track;
secondly, reconstructing an automobile power model according to various influence factors such as the mass, the inertia, the direction, the offset angle and the like of the automobile;
step three, constructing a vehicle system model with state-related uncertainty according to the change of the longitudinal speed of the vehicle along with time;
introducing a self-adaptive event triggering mechanism to reduce the network communication burden;
step five, considering the influence of deception attack and replay attack, establishing a network attack model under the mixed network attack
Step six, comprehensively considering the network attack influence and keeping the stability of the vehicle, and designing a vehicle path tracking control system model under the hybrid network attack by combining the steps one to five;
seventhly, obtaining a sufficiency condition for ensuring the stability of the mean square index of the system by utilizing the Lyapunov stability theory;
and step eight, connecting columns and solving a linear matrix inequality to obtain the gain of the controller.
2. The vehicle path tracking security control method based on the adaptive event trigger mechanism under the hybrid network attack as claimed in claim 1, wherein in the step one, a model of the vehicle path tracking system change can be obtained by deriving the lateral offset and the direction error, and the curve coordinate of the origin of the trolley along the path δ can be represented as:
Figure FDA0002691042640000011
e denotes the lateral offset, ψ, from the center of gravity of the vehicle to the nearest point O on the target pathhIs the actual forward angle of the vehicle, psidIs the required forward angle, psi, of the vehicleeIndicating heading error, ψe＝ψh-ψd，
Figure FDA0002691042640000012
The automatic driving vehicle path tracking model is as follows:
Figure FDA0002691042640000013
assuming that the forward error psi is very small, then the error
Figure FDA0002691042640000014
Can be expressed as:
Figure FDA0002691042640000015
beta is the sideslip angle of the car, LdIs the expected distance.
3. The vehicle path tracking security control method based on the adaptive event triggering mechanism under the hybrid network attack as claimed in claim 1, wherein in step 2, the vehicle dynamics equation is:
Figure FDA0002691042640000016
wherein ,
Figure FDA0002691042640000017
representing vehicle yaw rate, m representing vehicle mass, IZRepresenting vehicle inertia,/f and lrRespectively representing the distance from the center of the vehicle to the front and rear axles, MZIs an external bias moment, Fyf and FyrRespectively, representing generalized front-to-back lateral forces.
Figure FDA0002691042640000021
Cf and CrRespectively representing the turning angles, beta, of the front and rear wheelsf and βrIs the car slip angle, ρfIs the front wheel steering angle, defined as follows:
Figure FDA0002691042640000022
the power model of the trolley can be obtained
Figure FDA0002691042640000023
4. The vehicle path tracking security control method based on the adaptive event triggering mechanism under the hybrid network attack as claimed in claim 1, wherein in step 3, a system model is established for security control of vehicle path tracking based on an uncertainty system, and the system model is:
Figure FDA0002691042640000024
wherein ：αi＝αi(xi(t),ηi(t))，αj＝αj(yj(t-λ(t)),ηj(t- λ (t))), η (t) is the time-varying parameter of the uncertainty system, α (x (t), η (t)) is the uncertainty parameter function, Ai、Bi、Ci and DiIs a coefficient matrix of the system; x (t) is a system state vector; u (t) is the system control input, and u (t) Kyr(t), K is the controller gain, yr(t) is the system true input; ω (t) is the system disturbance.
5. The vehicle path tracking security control method based on the adaptive event triggering mechanism under the hybrid network attack as claimed in claim 1, wherein in step four, the adaptive time triggering mechanism model:
Figure FDA0002691042640000025
tk+1h is the next time instant to transmit data, tkh is the last time data was transmitted, Ω > 0 is a weight matrix,
Figure FDA0002691042640000026
Figure FDA0002691042640000027
is the maximum allowed packet loss number; ε (t) is a threshold and ε (t) e (0, 1)]；y(tkh) Is the data transmitted at the last instant, y (t)kh + vh) is the current time instant sample data.
6. The vehicle path tracking security control method based on the adaptive event trigger mechanism under the hybrid network attack as recited in claim 1, wherein in step five, the influence of the spoofing attack and the replay attack is considered, and a network attack model under the hybrid network attack is established:
the system transmission under replay attack is represented as:
y1(t)＝h(t)yr(t)+(1-h(t))ye(t)
wherein h (t) epsilon [0,1]Is a variable of the number of bernoulli variables,
Figure FDA0002691042640000031
is the mathematical expectation of h (t), yr(t)＝ye(tr)，ye(tr) Is shown at trDuplicate data transmitted at a time, ye(t) represents data obtained by an adaptive event triggering mechanism.
It is assumed that the system is subject to a spoofing attack after being subject to a replay attack. f (y (t- χ (t))) is a spoofing attack expression suffered by the system, the data transmitted by the system is as follows:
y2(t)＝θ(t)f(y(t-χ(t)))+(1-θ(t))y1(t)
θ (t) is a Bernoulli variable and θ (t) is ∈ [0,1 ]]，
Figure FDA0002691042640000032
Is the mathematical expectation of θ (t).
7. The vehicle path tracking security control method based on the adaptive event triggering mechanism under the hybrid network attack according to claim 1, characterized in that, step six, comprehensively considering the network attack effect and keeping the stability of the vehicle, and combining steps (1) to (5) to design a vehicle path tracking control system model under the hybrid network attack:
Figure FDA0002691042640000033
and (3) bringing in, obtaining the relevant uncertain system of the state of the automatic driving vehicle:
Figure FDA0002691042640000034
αi＝αi(xi(t),ηi(t))，αj＝αj(yj(t-λ(t)),ηj(t-λ(t)))
8. the vehicle path tracking security control method based on the adaptive event trigger mechanism under the hybrid network attack according to claim 1, wherein in the seventh step, the step of obtaining the sufficiency condition of the system mean square index stability is as follows:
s3-1, stably constructing the Lyapunov function as follows:
V(t)＝V1(t)+V2(t)+V3(t)
V1(t)＝x(t)TP(t)x(t)
Figure FDA0002691042640000035
Figure FDA0002691042640000036
s3-2, setting parameters: positive number rho1,ρ2Bernoulli variable expectation
Figure FDA0002691042640000041
Upper bound of delay lambdam,χmAdaptive event trigger parameter epsilone；
S3-3, judging whether a matrix P is more than 0 and Q is present1k＞0，Q2k＞0，R1k＞0，R2k＞0，Ω＞0，Ui＞0，S1k、S2k、W1k、W2kFor any i, j, k is 1, 2, 3, 4, such that the following inequality is satisfied
Figure FDA0002691042640000042
Γijk-Ui＜0
Figure FDA0002691042640000043
Figure FDA0002691042640000044
Figure FDA0002691042640000045
S3-4, if the data exists, determining parameters and ending; if not, returning to S3-2 to adjust the parameters, and repeating S3-2-S3-4.
9. The vehicle path tracking security control method based on the adaptive event trigger mechanism under the hybrid network attack as claimed in claim 1, wherein in step eight, for given parameters: positive number rho1,ρ2Bernoulli variable expectation
Figure FDA0002691042640000046
Upper bound of delay lambdam,χmAdaptive event trigger parameter epsiloneThe existence matrix P > 0, Q1k＞0，Q2k＞0，R1k＞0，R2k＞0，Ω＞0，Ui＞0，S1k、S2k、W1k、W2kFor any i, j, k is 1, 2, 3, 4, such that the following inequality is satisfied
Figure FDA0002691042640000047
Figure FDA00026910426400000412
Figure FDA0002691042640000048
Figure FDA00026910426400000411
Figure FDA0002691042640000049
The path tracking output feedback controller gain is designed as follows:
Figure FDA00026910426400000410
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