CN114721271A - Fuzzy self-adaptive fixed time event trigger control method based on automobile cruise system - Google Patents

Fuzzy self-adaptive fixed time event trigger control method based on automobile cruise system Download PDF

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CN114721271A
CN114721271A CN202210395058.5A CN202210395058A CN114721271A CN 114721271 A CN114721271 A CN 114721271A CN 202210395058 A CN202210395058 A CN 202210395058A CN 114721271 A CN114721271 A CN 114721271A
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vehicle
cruise system
control signal
automobile cruise
fixed time
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CN114721271B (en
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李永明
吴蕊彤
佟绍成
于昆廷
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Liaoning University of Technology
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    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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Abstract

The invention discloses a fuzzy self-adaptive fixed time event trigger control method based on an automobile cruise system, which comprises the steps of obtaining the running information of a controlled vehicle and a front vehicle through the automobile cruise system and establishing an automobile cruise system model; after the automobile cruise system model is converted into a nonlinear strict feedback system model, approximation processing is carried out on a nonlinear function in the model through a fuzzy logic system, and an approximation function is obtained; obtaining a virtual controller according to the running information of the controlled vehicle and the front vehicle, the fixed time designated performance function and the approximation function; calculating and acquiring dynamic change of self-adaptive parameters in the virtual controller, calculating a virtual control signal and designing an event trigger mechanism, and judging whether the control signal meets a trigger condition according to the trigger mechanism; and after being triggered, the control signal of the automobile cruise system is updated, and the updated control signal is transmitted back to the automobile cruise system. The invention can prevent the vehicle from collision accidents and effectively reduce network transmission resources.

Description

Fuzzy self-adaptive fixed time event trigger control method based on automobile cruise system
Technical Field
The invention relates to the field of automobile cruise control, in particular to a fuzzy self-adaptive fixed time event trigger control method based on an automobile cruise system.
Background
With the great increase of the demand of the nation for the automobiles, the rapid popularization of the automobiles brings convenience to the life of people and also causes social problems of traffic accidents, road congestion and the like, however, most of the traffic accidents are shown to be caused by human factors through investigation, including misoperation of drivers, fatigue driving and the like. In recent years, in the automobile industry, in order to improve road traffic safety and reduce traffic accidents caused by human factors, research and development of driving assistance systems have been focused. Nowadays, it is important to establish modern intelligent transportation systems. As one of the most widely used driver assistance systems, an adaptive cruise control system can effectively avoid a vehicle collision on the one hand and can reduce the operation burden on the driver in the case of long-distance driving on the other hand. Therefore, automobile intellectualization will become an important direction for the development of automobile technology in the future. With the gradual maturity of technologies such as sensors, the applicable operating mode and the function of self-adaptation cruise system are constantly perfect. In the field of commercial vehicles and passenger vehicles, the control problem of the adaptive cruise control system has attracted the attention of researchers of various control theories and control engineering, and scholars at home and abroad have obtained a series of research results.
In the field of longitudinal motion control, the self-adaptive cruise system can replace a driver to operate and adjust the opening of an accelerator of a vehicle and brake the vehicle, and the automatic longitudinal control capability of the vehicle is improved, so that traffic accidents caused by fatigue driving caused by long-time operation of the vehicle by the driver are reduced. Currently, there are many control techniques for an adaptive cruise control system of an automobile. However, the prior art still has the following problems:
first, in an adaptive control method for an automobile cruise system, although the conventional techniques can avoid collision of vehicles, the consumption of network transmission resources in online control cannot be saved.
Secondly, most of the existing control methods only consider collision avoidance, and cannot enable the following vehicle and the front vehicle to reach a cruising state within a certain time.
Disclosure of Invention
The invention provides a fuzzy self-adaptive fixed time event trigger control method based on an automobile cruise system, which aims to overcome the technical problem.
A fuzzy self-adaptive fixed time event trigger control method based on an automobile cruise system comprises the following steps,
acquiring running information of a controlled vehicle and a front vehicle through an automobile cruise system, and establishing an automobile cruise system model, wherein the running information comprises a front vehicle speed, a longitudinal speed of the controlled vehicle, an angular speed of wheels, an actual distance between the controlled vehicle and the front vehicle and an expected distance;
converting the automobile cruise system model into a nonlinear strict feedback system model, and carrying out approximation processing on a nonlinear function in the nonlinear strict feedback system model to obtain an approximation function after approximation processing;
step three, obtaining a virtual controller according to the running information of the controlled vehicle and the front vehicle, the fixed time designated performance function and the approximation function;
step four, calculating the dynamic change of the adaptive parameters in the virtual controller, feeding the dynamic change of the adaptive parameters back to the virtual controller, and calculating a virtual control signal according to the virtual simulator;
designing an event trigger mechanism according to the virtual control signal, and judging whether the control signal meets a trigger condition according to the trigger mechanism;
and step six, after the event trigger mechanism is triggered, updating the control signal of the automobile cruise system, and transmitting the updated control signal back to the automobile cruise system.
Preferably, the model of the automobile cruise system is established by formula (1),
Figure BDA0003597051250000021
wherein q isThe difference between the actual distance between the controlled vehicle and the front vehicle and the expected distance between the controlled vehicle and the front vehicle,
Figure BDA0003597051250000022
is the derivative of q, s is the actual distance between the controlled vehicle and the front vehicle,
Figure BDA0003597051250000023
is the derivative of s, s0V is the desired distance of the controlled vehicle from the front vehiclexAs is the longitudinal speed of the vehicle,
Figure BDA0003597051250000024
is v isxDerivative of vsIs the front vehicle speed, and k is the vehicle driving force FwfAnd slip ratio
Figure BDA0003597051250000026
Of a proportionality coefficient, i.e.
Figure BDA0003597051250000027
radIs the effective radius, omega, of the tirefIs the angular velocity of the wheel or wheels,
Figure BDA0003597051250000025
is omegafDerivative of cafIs the air resistance coefficient, f is the rolling resistance coefficient, m is the vehicle mass, g is the gravitational acceleration, J is the tire moment of inertia, TvehIs the drive torque applied to the front wheels.
Preferably, the approximating the nonlinear function in the nonlinear strict feedback system model means that the automobile cruise system model is converted into the nonlinear strict feedback system model through a formula (2), and the nonlinear function in the nonlinear strict feedback system model is approximated through a fuzzy logic system, namely formulas (3) and (4), so as to obtain an approximated function after approximation,
Figure BDA0003597051250000031
wherein q is x1,s=x2,vx=x3,ωf=x4Q is the difference between the actual distance between the controlled vehicle and the preceding vehicle and the expected distance between the controlled vehicle and the preceding vehicle, s is the actual distance between the controlled vehicle and the preceding vehicle, vxAs longitudinal speed of the vehicle, ωfIs the angular velocity of the wheel or wheels,
Figure BDA0003597051250000032
are respectively x1、x2、x3、x4Derivative of, Tveh=u,TvehIs the driving torque applied to the front wheels, and k is the vehicle driving force FwfAnd slip ratio
Figure BDA00035970512500000319
M is the mass of the vehicle, cafIs the coefficient of air resistance, f is the coefficient of rolling resistance, g is the acceleration of gravity, rradIs the effective radius of the tire, J is the moment of inertia of the tire,
Figure BDA0003597051250000033
-fg=n,
Figure BDA0003597051250000034
rrad=r,
Figure BDA0003597051250000035
and
Figure BDA0003597051250000036
is a function of the unknown non-linearity,
Figure BDA0003597051250000037
Figure BDA0003597051250000038
wherein,
Figure BDA0003597051250000039
and
Figure BDA00035970512500000310
is an ideal parameter and is used as a reference,
Figure BDA00035970512500000311
is that
Figure BDA00035970512500000312
The parameter of (2) is estimated by the parameter estimation method,
Figure BDA00035970512500000313
and
Figure BDA00035970512500000320
is the minimum fuzzy approximation error between the unknown nonlinear functions in the ideal automobile cruise system and the actual automobile cruise system, and
Figure BDA00035970512500000314
satisfy the requirement of
Figure BDA00035970512500000315
Figure BDA00035970512500000321
Satisfy the requirement of
Figure BDA00035970512500000322
Figure BDA00035970512500000316
And
Figure BDA00035970512500000323
is a normal number.
Preferably, the step of obtaining the virtual controller according to the running information of the controlled vehicle and the front vehicle, the fixed time specified performance function and the approximation function means that firstly, based on the fixed time specified performance theory, the tracking error is limited according to the formula (5), then, a dynamic virtual error surface is established according to the formulas (6) and (7), and the virtual controller is obtained through the formulas (8), (9) and (10),
Figure BDA00035970512500000317
Figure BDA00035970512500000318
Figure BDA0003597051250000041
where, ζ is the tracking error,
Figure BDA0003597051250000042
h (t) is a designed fixed function of time,
Figure BDA0003597051250000043
for a fixed time, l and phi are design parameters,. etamin> 0 and ηmaxThe values > 0 are all design parameters and,
Figure BDA0003597051250000044
is a converted signal, x1Representing the error integral term q, x2Representing the actual distance s, x between the controlled vehicle and the front vehicle3Representing front vehicle speed vs,x4Representing angular velocity omega of tyref;πiIs a first order filtering output error in the form of a first order filter
Figure BDA0003597051250000045
ξi(0)=αi-1(0) I is 2, 3, 4, wherein TiIs a given constant; xiiIs an intermediate state variable, αi-1Is a virtual controller in which, among other things,
when i is 2, αi-1Is alpha1
Figure BDA0003597051250000046
When i is 3, αi-1Is alpha2
Figure BDA0003597051250000047
When i is 4, αi-1Is alpha3
Figure BDA0003597051250000048
c1,c2,c3In order to be a positive design parameter,
Figure BDA0003597051250000049
is s is0The time derivative of (a) of (b),
Figure BDA00035970512500000410
-fg-n, k being vehicle driving force FwfAnd slip ratio
Figure BDA00035970512500000413
M is the mass of the vehicle, cafIs the air resistance coefficient, f is the rolling resistance coefficient, and g is the gravitational acceleration.
Preferably, the calculation of the dynamic change of the adaptive parameters in the virtual controller means calculation by the adaptive laws (11), (12), (13),
Figure BDA00035970512500000411
Figure BDA00035970512500000412
Figure BDA0003597051250000051
wherein, γ, β and
Figure BDA0003597051250000052
is a positive design constant, z3、z4Is a virtual error surface that is a function of,
Figure BDA0003597051250000053
θhis an adaptive parameter.
Preferably, the judging whether the control signal meets the trigger condition according to the trigger mechanism means obtaining a virtual control signal according to the formula (14)
Figure BDA0003597051250000054
Obtaining a control signal omega (t) according to the formula (15), judging whether a trigger condition is met or not through the formula (16),
Figure BDA0003597051250000055
Figure BDA0003597051250000056
Figure BDA0003597051250000057
wherein,
Figure BDA0003597051250000058
for virtually simulating control signals, z4Is the virtual error plane, c4 is the positive design parameter, tkk、kk∈Z+λ is a transmission rate, which satisfies 0 < λ < 1, e (t) is a measurement error, e (t) ω (t) -u (t), σ is a positive design parameter, u (t) is a control signal, and ω (t) is an updated control signal.
Preferably, the updating process of the control signal of the automobile cruise system refers to the updating process through the formulas (17) and (18),
Figure BDA0003597051250000059
Figure BDA00035970512500000510
where ρ is1,ρ2For additional design parameters, λ is the transmission rate, satisfying 0 < λ < 1, u (t) is the control signal, ω (t) is the updated control signal, i.e., at tkk+1At the moment, the actual input signal u (t) is updated to ω (t)kk+1) Then ω (t)kk+1) Back to the car cruise system.
The invention provides a fuzzy self-adaptive fixed time event trigger control method based on an automobile cruise system, wherein an event trigger controller is added in the existing automobile cruise system control method, and only updated control signals are required to be transmitted to the automobile cruise system, so that network transmission resources can be saved while vehicle collision is avoided; a specified performance theory of fixed time is adopted in a trigger mechanism, so that the vehicle can quickly approach to a cruising state in a short time, a controlled vehicle can keep a specific distance from a front vehicle, and the vehicle is prevented from having a collision accident.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph showing the effect of tracking errors between a controlled vehicle and a leading vehicle within a specified performance range according to the present invention;
FIG. 3 is a graph illustrating the effect of tracking errors within bounds of the present invention;
FIG. 4 is a graph illustrating the effect of the control signal u according to the present invention;
FIG. 5 is a graph illustrating the effect of the control signal ω updated after the event of the present invention;
FIG. 6 is a graph illustrating the comparison between the control signal u and the updated control signal ω according to the present invention;
FIG. 7 is a diagram of adaptive parameters of the present invention
Figure BDA0003597051250000061
A graph;
FIG. 8 is a diagram of adaptive parameters of the present invention
Figure BDA0003597051250000062
A graph;
FIG. 9 is a diagram of the adaptive parameter θ of the present inventionhGraph is shown.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Fig. 1 is a flowchart of the method of the present invention, and as shown in fig. 1, the method of this embodiment may include:
a fuzzy self-adaptive fixed time event trigger control method based on an automobile cruise system comprises the following steps,
step one, acquiring running information of a controlled vehicle and a front vehicle, wherein the running information comprises a front vehicle speed, a longitudinal speed and an angular speed of wheels of the controlled vehicle, an actual distance and an expected distance between the controlled vehicle and the front vehicle, and establishing an automobile cruise system model through a formula (1),
Figure BDA0003597051250000063
whereinQ is the difference between the actual distance between the controlled vehicle and the preceding vehicle and the expected distance between the controlled vehicle and the preceding vehicle,
Figure BDA0003597051250000071
is the derivative of q, s is the actual distance between the controlled vehicle and the front vehicle,
Figure BDA0003597051250000072
is the derivative of s, s0V is the desired distance of the controlled vehicle from the front vehiclexAs is the longitudinal speed of the vehicle,
Figure BDA0003597051250000073
is v isxDerivative of vsIs the front vehicle speed, and k is the vehicle driving force FwfAnd slip ratio
Figure BDA00035970512500000721
Of a proportionality coefficient, i.e.
Figure BDA00035970512500000722
rradIs the effective radius, omega, of the tirefIs the angular velocity of the wheel or wheels,
Figure BDA0003597051250000074
is omegafDerivative of cafIs the air resistance coefficient, f is the rolling resistance coefficient, m is the vehicle mass, g is the gravitational acceleration, J is the tire moment of inertia, TvehIs the drive torque applied to the front wheels.
Step two, converting the automobile cruise system model into a nonlinear strict feedback system model through a formula (2),
Figure BDA0003597051250000075
wherein q is x1,s=x2,vx=x3,ωf=x4Q is the difference between the actual distance between the controlled vehicle and the preceding vehicle and the expected distance between the controlled vehicle and the preceding vehicle, and s is the actual distance between the controlled vehicle and the preceding vehicleDistance between, vxAs the longitudinal speed of the vehicle, ωfIs the angular velocity of the wheel or wheels,
Figure BDA0003597051250000076
are respectively x1、x2、x3、x4Derivative of, Tveh=u,TvehIs the driving torque applied to the front wheels, and k is the vehicle driving force FwfAnd slip ratio
Figure BDA00035970512500000723
M is the mass of the vehicle, cafIs the coefficient of air resistance, f is the coefficient of rolling resistance, g is the acceleration of gravity, rradIs the effective radius of the tire, J is the moment of inertia of the tire,
Figure BDA0003597051250000077
-fg=n,
Figure BDA0003597051250000078
rrad=r,
Figure BDA0003597051250000079
and
Figure BDA00035970512500000710
is a function of the unknown non-linearity,
approximating the nonlinear function in the nonlinear strict feedback system model by a fuzzy logic system, namely formulas (3) and (4), obtaining the approximated function after approximation,
Figure BDA00035970512500000711
Figure BDA00035970512500000712
wherein,
Figure BDA00035970512500000713
and
Figure BDA00035970512500000714
is a function of the number of the parameters,
Figure BDA00035970512500000715
is that
Figure BDA00035970512500000716
The parameter of (2) is estimated by the parameter estimation method,
Figure BDA00035970512500000717
and
Figure BDA00035970512500000724
is the minimum fuzzy approximation error between the unknown nonlinear functions in the ideal automobile cruise system and the actual automobile cruise system, and
Figure BDA00035970512500000718
satisfy the requirement of
Figure BDA00035970512500000719
Figure BDA00035970512500000725
Satisfy the requirement of
Figure BDA00035970512500000726
Figure BDA00035970512500000720
And
Figure BDA00035970512500000727
is a normal number.
Thirdly, obtaining the virtual controller according to the running information of the controlled vehicle and the front vehicle, the fixed time specified performance function and the approximation function means that firstly, based on the fixed time specified performance theory, the tracking error is limited according to the formula (5), then, a dynamic virtual error surface is established according to the formulas (6) and (7), and the virtual controller is obtained through the formulas (8), (9) and (10),
Figure BDA0003597051250000081
Figure BDA0003597051250000082
Figure BDA0003597051250000083
where, ζ is the tracking error,
Figure BDA0003597051250000084
h (i) as a designed fixed function of time,
Figure BDA0003597051250000085
for a fixed time, l and phi are design parameters,. etamin> 0 and ηmaxThe values > 0 are all design parameters and,
Figure BDA0003597051250000086
is a converted signal, x1Representing the error integral term q, x2Representing the actual distance s, x between the controlled vehicle and the front vehicle3Representing front vehicle speed vs,x4Representing angular velocity omega of tyref;πiIs a first order filtering output error in the form of a first order filter
Figure BDA0003597051250000087
ξi(0)=αi-1(0) I is 2, 3, 4, wherein TiIs a given constant; xiiIs an intermediate state variable, αi-1Is a virtual controller in which, among other things,
when i is 2, αi-1Is alpha1
Figure BDA0003597051250000088
When i is 3, αi-1Is alpha2
Figure BDA0003597051250000089
When i is 4, αi-1Is alpha3
Figure BDA00035970512500000810
Wherein, c1,c2,c3In order to be a positive design parameter,
Figure BDA00035970512500000811
is s is0The time derivative of (a) of (b),
Figure BDA00035970512500000812
-fg-n, k being vehicle driving force FwfAnd slip ratio
Figure BDA00035970512500000813
M is the mass of the vehicle, cafIs the air resistance coefficient, f is the rolling resistance coefficient, and g is the gravitational acceleration.
Step four, calculating the dynamic change of the adaptive parameters in the virtual controller through adaptive laws (11), (12) and (13), feeding the dynamic change of the adaptive parameters back to the virtual controller, and calculating the virtual control signal according to the feedback means that the virtual control signal is obtained according to a formula (14)
Figure BDA0003597051250000091
Figure BDA0003597051250000092
Figure BDA0003597051250000093
Figure BDA0003597051250000094
Figure BDA0003597051250000095
Wherein, γ, β and
Figure BDA0003597051250000096
is a positive design constant, z3、z4Is a virtual error surface that is a function of,
Figure BDA0003597051250000097
θhfor adaptive parameters, c4For a positive design parameter, λ is the transmission rate, satisfying 0 < λ < 1.
Converting the virtual control signal into a control signal omega (t) according to a formula (15), designing an event trigger mechanism according to a formula (16), and judging whether the control signal meets a trigger condition;
Figure BDA0003597051250000098
Figure BDA0003597051250000099
wherein z is4Is a virtual error surface, tkk、kk∈Z+λ is a transmission rate, which satisfies 0 < λ < 1, e (t) is a measurement error, e (t) ω (t) -u (t), σ is a positive design parameter, u (t) is a control signal, and ω (t) is an updated control signal.
Step six, after the event trigger mechanism is triggered, updating the control signal of the automobile cruise system through formulas (17) and (18), and transmitting the updated control signal back to the automobile cruise system,
Figure BDA00035970512500000910
Figure BDA00035970512500000911
where u is the control signal and ω is the updated control signal, i.e. at tkk+1At the moment, the actual input signal u (t) is updated to ω (t)kk+1) Then ω (t)kk+1) Back to the car cruise system.
The simulation results of the simulation experiments performed in this example are shown in fig. 2 to 9. FIG. 2 is a graph of the effect of tracking error between a controlled vehicle and a leading vehicle within a specified performance range, wherein the dashed line represents a fixed time, FIG. 3 is a graph of the angular velocity of the wheels of the controlled vehicle, FIG. 4 is a graph of the effect of a control signal u, FIG. 5 is a graph of the effect of an updated control signal ω after an event trigger, FIG. 6 is a graph of the effect of a control signal u compared to an updated control signal ω, and FIG. 7 is a graph of the effect of an adaptive parameter
Figure BDA0003597051250000101
FIG. 8 is a graph of adaptive parameters
Figure BDA0003597051250000102
FIG. 9 is a graph of the adaptive parameter θhAnd the graph shows that the controlled vehicle and the front vehicle can well follow the controlled vehicle and the front vehicle through the simulation result graph.
The invention has the following beneficial effects in whole: in the existing control method of the automobile cruise system, an event trigger controller is added, and only the updated control signal is transmitted to the automobile cruise system, so that the network transmission resource can be saved while the collision of the automobile is avoided; a specified performance theory of fixed time is adopted in a trigger mechanism, so that the vehicle can quickly approach to a cruising state in a short time, a controlled vehicle can keep a specific distance from a front vehicle, and the vehicle is prevented from having a collision accident.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (7)

1. A fuzzy self-adaptive fixed time event trigger control method based on an automobile cruise system is characterized by comprising the following steps,
firstly, acquiring running information of a controlled vehicle and a front vehicle through an automobile cruise system, and establishing an automobile cruise system model, wherein the running information comprises a front vehicle speed, a longitudinal speed of the controlled vehicle, an angular speed of wheels, an actual distance between the controlled vehicle and the front vehicle and an expected distance;
converting the automobile cruise system model into a nonlinear strict feedback system model, and carrying out approximation processing on a nonlinear function in the nonlinear strict feedback system model to obtain an approximation function after approximation processing;
step three, obtaining a virtual controller according to the running information of the controlled vehicle and the front vehicle, the fixed time designated performance function and the approximation function;
calculating the dynamic change of the adaptive parameters in the virtual controller, feeding the dynamic change of the adaptive parameters back to the virtual controller, and calculating a virtual control signal according to the virtual simulator;
designing an event trigger mechanism according to the virtual control signal, and judging whether the control signal meets a trigger condition according to the trigger mechanism;
and step six, after the event trigger mechanism is triggered, updating the control signal of the automobile cruise system, and transmitting the updated control signal back to the automobile cruise system.
2. The fuzzy adaptive fixed time event triggering control method based on the automobile cruise system as claimed in claim 1, wherein said establishing the automobile cruise system model is established by formula (1),
Figure FDA0003597051240000011
wherein q is the difference between the actual distance between the controlled vehicle and the front vehicle and the expected distance between the controlled vehicle and the front vehicle,
Figure FDA0003597051240000012
is the derivative of q, s is the actual distance between the controlled vehicle and the leading vehicle,
Figure FDA0003597051240000013
is the derivative of s, s0V is the desired distance of the controlled vehicle from the front vehiclexAs is the longitudinal speed of the vehicle,
Figure FDA0003597051240000014
is v isxDerivative of vsIs the front vehicle speed, and k is the vehicle driving force FwfAnd slip ratio
Figure FDA0003597051240000016
Of a proportionality coefficient, i.e.
Figure FDA0003597051240000017
rradIs the effective radius, omega, of the tirefIs the angular velocity of the wheel or wheels,
Figure FDA0003597051240000015
is omegafDerivative of cafIs the air resistance coefficient, f is the rolling resistance coefficient, m is the vehicle mass, g is the gravitational acceleration, J is the tire moment of inertia, TvehIs the drive torque applied to the front wheels.
3. The fuzzy adaptive fixed time event trigger control method based on the automobile cruise system according to claim 1, wherein said approximating the non-linear function in the non-linear strict feedback system model means that the automobile cruise system model is converted into the non-linear strict feedback system model by formula (2), and the non-linear function in the non-linear strict feedback system model is approximated by fuzzy logic systems, i.e. formula (3) and formula (4), to obtain an approximated function after the approximation,
Figure FDA0003597051240000021
wherein q is x1,s=x2,vx=x3,ωf=x4Q is the difference between the actual distance between the controlled vehicle and the preceding vehicle and the expected distance between the controlled vehicle and the preceding vehicle, s is the actual distance between the controlled vehicle and the preceding vehicle, and s is the distance between the controlled vehicle and the preceding vehicle0Is the desired distance of the controlled vehicle from the leading vehicle, vxAs the longitudinal speed of the vehicle, ωfIs the angular velocity of the wheel or wheels,
Figure FDA0003597051240000022
Figure FDA0003597051240000023
are respectively x1、x2、x3、x4Derivative of, Tveh=u,TvehIs the driving torque applied to the front wheels, and k is the vehicle driving force FwfAnd slip ratio
Figure FDA00035970512400000222
M is the mass of the vehicle, cafIs the coefficient of air resistance, f is the coefficient of rolling resistance, g is the acceleration of gravity, rradIs the effective radius of the tire, J is the moment of inertia of the tire,
Figure FDA0003597051240000024
-fg=n,
Figure FDA0003597051240000025
rrad=r,
Figure FDA0003597051240000026
and
Figure FDA0003597051240000027
is a function of the unknown non-linearity,
Figure FDA0003597051240000028
Figure FDA0003597051240000029
wherein,
Figure FDA00035970512400000210
and
Figure FDA00035970512400000211
is an ideal parameter and is used as a reference,
Figure FDA00035970512400000212
is that
Figure FDA00035970512400000213
The parameter of (2) is estimated by the parameter estimation method,
Figure FDA00035970512400000214
and
Figure FDA00035970512400000215
is the minimum fuzzy approximation error between the unknown nonlinear functions in the ideal automobile cruise system and the actual automobile cruise system, and
Figure FDA00035970512400000216
satisfy the requirement of
Figure FDA00035970512400000217
Figure FDA00035970512400000218
Satisfy the requirement of
Figure FDA00035970512400000219
Figure FDA00035970512400000220
And
Figure FDA00035970512400000221
is a normal number.
4. The fuzzy adaptive fixed time event triggering control method based on the automobile cruise system as claimed in claim 1, wherein said obtaining the virtual controller according to the driving information of the controlled vehicle and the preceding vehicle, the fixed time specified performance function and the approximation function means that firstly based on the fixed time specified performance theory, the tracking error is limited according to formula (5), then the dynamic virtual error surface is established according to formulas (6) and (7), and the virtual controller is obtained through formulas (8), (9) and (10),
Figure FDA0003597051240000031
Figure FDA0003597051240000032
Figure FDA0003597051240000033
wherein,
Figure FDA0003597051240000034
h (t) is a designed fixed function of time,
Figure FDA0003597051240000035
for a fixed time, l and phi are design parameters,. etamin> 0 and ηmaxThe values > 0 are all design parameters and,
Figure FDA0003597051240000036
is a converted signal, x1Representing the error integral term q, x2Representing the actual distance s, x between the controlled vehicle and the front vehicle3Representing front vehicle speed vs,x4Representative of angular velocity ω of tiref;πiIs a first order filtering output error in the form of a first order filter
Figure FDA0003597051240000037
Wherein T isiIs a given constant; xi shapeiIs an intermediate state variable, αi-1Is a virtual controller in which, among other things,
when i is 2, αi-1Is alpha1
Figure FDA0003597051240000038
When i is 3, αi-1Is alpha2
Figure FDA0003597051240000039
When i is 4, alphai-1Is alpha3
Figure FDA00035970512400000310
c1,c2,c3In order to be a positive design parameter,
Figure FDA00035970512400000311
is s is0The time derivative of (a) of (b),
Figure FDA00035970512400000312
-fg-n, k being vehicle driving force FwfAnd slip ratio
Figure FDA00035970512400000313
M is the mass of the vehicle, cafIs the air resistance coefficient, f is the rolling resistance coefficient, and g is the gravitational acceleration.
5. The fuzzy adaptive fixed time event trigger control method based on the automobile cruise system as claimed in claim 1, wherein said calculating the dynamic change of the adaptive parameter in the virtual controller is calculated by adaptive laws (11), (12), (13),
Figure FDA0003597051240000041
Figure FDA0003597051240000042
Figure FDA0003597051240000043
wherein, γ, β and
Figure FDA0003597051240000044
is a positive design constant, z3、z4Is a virtual error surface that is a function of,
Figure FDA0003597051240000045
θhis an adaptive parameter.
6. The fuzzy adaptive fixed time event trigger control method based on the automobile cruise system as claimed in claim 1, wherein said determining whether the control signal satisfies the trigger condition according to the trigger mechanism is to obtain the virtual control signal according to the formula (14)
Figure FDA0003597051240000046
Obtaining a control signal omega (t) according to a formula (15), judging whether a triggering condition is met or not according to a formula (16),
Figure FDA0003597051240000047
Figure FDA0003597051240000048
Figure FDA0003597051240000049
wherein,
Figure FDA00035970512400000410
for virtually simulating control signals, z4Is a virtual error surface, c4For a positive design parameter, tkk、kk∈Z+λ is a transmission rate, which satisfies 0 < λ < 1, e (t) is a measurement error, e (t) ω (t) -u (t), σ is a positive design parameter, u (t) is a control signal, and ω (t) is an updated control signal.
7. The fuzzy adaptive fixed time event triggering control method based on the automobile cruise system as claimed in claim 1, wherein said updating the control signal of the automobile cruise system is performed by the formula (17), (18),
Figure FDA00035970512400000411
Figure FDA00035970512400000412
where ρ is1,ρ2For additional design parameters, λ is the transmission rate, satisfying 0 < λ < 1, u (t) is the control signal, ω (t) is the updated control signal, i.e., at tkk+1At the moment, the actual input signal u (t) is updated to ω (t)kk+1) Then ω (t)kk+1) Back to the car cruise system.
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