CN114179818A - Intelligent automobile transverse control method based on adaptive preview time and sliding mode control - Google Patents

Intelligent automobile transverse control method based on adaptive preview time and sliding mode control Download PDF

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CN114179818A
CN114179818A CN202111667624.5A CN202111667624A CN114179818A CN 114179818 A CN114179818 A CN 114179818A CN 202111667624 A CN202111667624 A CN 202111667624A CN 114179818 A CN114179818 A CN 114179818A
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vehicle
sliding mode
angle
mode control
automobile
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Inventor
贝绍轶
胡宏振
周丹
周鑫烨
高陈诚
朱芸海
倪曦
吴凡
李波
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Jiangsu University of Technology
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Jiangsu University of Technology
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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
    • 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
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • 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

Abstract

The invention discloses an intelligent automobile transverse control method based on self-adaptive preview time and sliding mode control. And according to the assumption of the steady-state yaw rate, obtaining the ideal yaw rate by using the expectation method according to the expectation time, using the ideal yaw rate as the input of the sliding mode controller, establishing the sliding mode controller based on the difference between the actual yaw rate and the ideal yaw rate, and selecting a proper switching function to optimize the ideal steering wheel angle. The control method provided by the invention can overcome the error caused by the traditional fixed preview time and further improve the tracking performance of the intelligent automobile.

Description

Intelligent automobile transverse control method based on adaptive preview time and sliding mode control
Technical Field
The invention relates to the technical field of automobile control, in particular to an intelligent automobile transverse control method based on self-adaptive preview time and sliding mode control.
Background
Unmanned vehicles are a new future concept that satisfies people's prospect for the future world. The system is a comprehensive system integrating links of environment perception, planning decision, control execution and the like, integrates the technologies of sensors, information interaction, artificial intelligence, automatic control, traditional automobiles and the like, and is innovative embodiment of interdisciplinary fusion. The trajectory tracking is an indispensable part in the control execution technology, depends on the vehicle bottom layer control technology, simultaneously requires good tracking on a planned trajectory, has important significance on the safety and control of the intelligent automobile, can reduce the probability of traffic accidents caused by negligence of drivers, and can reduce the dependence of the conventional vehicles on the operation of the drivers. However, there are many difficulties in the tracking technology of the intelligent vehicle. Firstly, the accuracy of the nonlinear model of the vehicle has a great influence on the control precision, and the nonlinear model is generally complex, which leads to the need of more complex controllers; secondly, external disturbance caused by environmental road factors and system unmodeled disturbance caused by vehicle transverse and longitudinal coupling also influence the control precision; secondly, the improvement of the accuracy performed by the controller and the reduction of the deviation amount of the ideal control parameter are also difficult.
The preview theory can accurately reflect the control behavior of the driver, and has simple structure and strong adaptability, thereby having wide application in the field of track tracking. In the prior art, the invention patent with the application number of 201710378710.1 discloses a pre-aiming distance self-adaptive intelligent vehicle transverse control method, the pre-aiming distance is calculated by adopting a method of fixing pre-aiming time in the pre-aiming theory, and an ideal working condition is taken as a calculation basis during calculation, but the fixed pre-aiming distance cannot adapt to various speeds and road conditions, so that the tracking accuracy is greatly influenced. The sliding mode controller is insensitive to disturbance of parameter change and has strong anti-interference capability and robustness, so the sliding mode controller is selected for control.
Disclosure of Invention
The invention provides an intelligent automobile transverse control method based on self-adaptive preview time and sliding mode control, which can determine the preview time on the basis of tracking precision, road boundary and whole automobile response characteristics in a self-adaptive manner, and calculate an ideal yaw velocity by utilizing the parameters to input the ideal yaw velocity into a sliding mode controller.
The invention adopts the following specific technical scheme:
an intelligent automobile transverse control method based on adaptive preview time and sliding mode control comprises the following steps:
the method comprises the following steps: establishing a two-degree-of-freedom dynamic model considering the transverse and longitudinal movement of the intelligent automobile and the cornering characteristic of the tire;
step two: designing a self-adaptive preview model with target optimization;
step three: designing a vehicle transverse track tracking controller to obtain a complete system control law;
step four: and inputting the steering wheel angle for control.
The specific method of the first step comprises the following steps:
making a bicycle model assumption, and assuming that the motion of the vehicle is a motion on a two-dimensional plane; assuming that the left wheel and the right wheel are stressed symmetrically, establishing a simplified two-degree-of-freedom vehicle dynamics model:
Figure BDA0003448652690000021
Figure BDA0003448652690000022
wherein m is the vehicle mass,
Figure BDA0003448652690000023
is the lateral acceleration of the vehicle, vx is the longitudinal velocity of the vehicle, ω is the yaw rate of the vehicle, IzIs the moment of inertia of the vehicle at the center of mass,
Figure BDA0003448652690000024
is the yaw angular acceleration of the vehicle, a is the distance from the front circumference to the center of mass, b is the distance from the rear axle to the center of mass, FyfIs the lateral force of the front axle, FyrIs the lateral force of the rear axle.
Under the assumption of a small rotation angle, the tire is out of a linear region, and the relationship among cornering force, cornering angle and cornering stiffness is as follows:
Fyf＝Cfαf
Fyr＝Crαr
in the formula, Cf、CrRespectively front wheel cornering stiffness and rear wheel cornering stiffness, alphaf、αrRespectively, a front wheel side slip angle and a rear wheel side slip angle. The slip angle of the front wheel and the slip angle of the rear wheel of the automobile are related to the motion parameters thereof, and the slip angles of the mass center can be approximated to be vx and vy on the assumption that the speeds of the front shaft and the rear shaft of the automobile are respectively
Figure BDA0003448652690000025
The angle between the front wheel speed direction and the x-axis can be expressed as θ:
Figure BDA0003448652690000026
the slip angle of the front and rear wheels of the vehicle can be expressed as:
Figure BDA0003448652690000027
Figure BDA0003448652690000028
the state space equation for the two degrees of freedom of the vehicle with respect to the yaw angle ω and the centroid slip angle β can be derived:
Figure BDA0003448652690000031
the specific method of the second step is as follows:
ideal yaw rate omega can be obtained based on steady state single point preview modeldComprises the following steps:
Figure BDA0003448652690000032
the steady state response of the automobile entering under the step input of the front wheel angle is substituted into the state equation of the automobile to obtain the steady state yaw velocity gain GωAnd the automobile stability factor K:
Figure BDA0003448652690000033
Figure BDA0003448652690000034
in the formula iswIs the angular transmission ratio of the steering wheel angle and the front wheel angle.
Designing an objective function J1 based on the lateral offset of the current centroid:
Figure BDA0003448652690000035
where L _ Drv _2 is the centroid lateral offset and t is the model prediction time.
Designing an objective function J2 based on the distance between the current centroid and the boundary:
Figure BDA0003448652690000036
Figure BDA0003448652690000037
where g is a safety function, and Δ is the centerline-to-boundary distance as the vehicle position approaches the boundary.
Designing an objective function J3 based on the response characteristic of the whole vehicle:
J3＝(tp-T)2
where T is the time relating to the vehicle steering response characteristic, it may be 1s or less when the speed is high, and it may be increased as appropriate when the speed is low.
Get the total objective function Jmin:
Jmin＝min(ω1J1+ω2J2+ω3J3)
in the formula, ω1、ω2、ω3For the weight coefficients, the setting of the weights is dependent on the purpose to be achieved, ω1Related to the accuracy of the trajectory tracking; omega2Related to the distance of the vehicle from the road boundary; omega3And the response characteristic of the whole vehicle.
The concrete method of the third step is as follows:
for a system that is uncertain and needs to consider perturbation of internal parameters and external disturbances, the system state equation can be expressed as:
Figure BDA0003448652690000041
the state space equation according to the two degrees of freedom can be rewritten as:
Figure BDA0003448652690000042
Figure BDA0003448652690000043
Figure BDA0003448652690000044
the equations for the controlled system can be expressed as the following equations of state:
Figure BDA0003448652690000045
wherein f (ω)r) The yaw rate omega can be writtenrAnd centroid slip angle β:
Figure BDA0003448652690000046
determining a gain value K (t) of a proper sliding mode control law:
K(t)＝max(|e(τ)|)+ρ(ρ>0)
and selecting the difference between the actual yaw velocity and the ideal yaw velocity d as the tracking error of the system:
e＝ωr-ωd
the switching function is set as:
Figure BDA0003448652690000047
where λ is a positive weighting factor.
The switching function s is derived as:
Figure BDA0003448652690000051
order to
Figure BDA0003448652690000052
Solving a sliding mode control law as follows:
Figure BDA0003448652690000053
it can be known that
Figure BDA0003448652690000054
Comprises the following steps:
Figure BDA0003448652690000055
order to
Figure BDA0003448652690000056
Yaw angular acceleration at this time
Figure BDA0003448652690000057
The front wheel steering angle input can be found as:
Figure BDA0003448652690000058
the available sliding mode control law is as follows:
Figure BDA0003448652690000059
the switching gain K (t) represents the approaching speed of the moving point of the system to the switching surface, and K (t) is a sliding mode surface reachable condition required to be met:
Figure BDA00034486526900000510
the sign function sgn (x) is replaced by a saturation function sat (x), so that the buffeting suppression effect is achieved:
Figure BDA00034486526900000511
obtaining a final sliding mode control law:
Figure BDA00034486526900000512
wherein ε represents the boundary layer thickness.
The concrete method of the fourth step is as follows:
steering wheel angle delta to obtain final control inputst：
δst＝δh*isw。
The invention has the beneficial effects that: the pre-aiming time can be determined adaptively on the basis of tracking accuracy, road boundaries and the response characteristics of the whole vehicle, the ideal yaw velocity is calculated by utilizing the parameters and is input into the sliding mode controller, and the control method can improve the anti-interference capability and robustness of the model and improve the accuracy of track tracking.
Drawings
FIG. 1 is a two degree of freedom kinematic model of a vehicle of the present invention.
FIG. 2 is a steady state single point preview model of the present invention.
Fig. 3 is an overall configuration diagram of the controller in the present invention.
Fig. 4 is a comparison graph of the respective speed trajectories at the adaptive preview time.
Fig. 5 is a comparison diagram of yaw angles of respective velocities at the adaptive preview time.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings as follows:
as shown in fig. 1, the invention discloses an intelligent vehicle lateral control method based on adaptive preview time and sliding mode control, which specifically comprises the following steps:
the method comprises the following steps: establishing a two-degree-of-freedom dynamic model considering the transverse and longitudinal movement of the intelligent automobile and the cornering characteristic of the tire;
step two: designing a self-adaptive preview model with target optimization;
step three: designing a vehicle transverse track tracking controller to obtain a complete system control law;
step four: and inputting the steering wheel angle for control.
The specific method of the first step comprises the following steps:
making a bicycle model assumption, and assuming that the motion of the vehicle is a motion on a two-dimensional plane; assuming that the left wheel and the right wheel are stressed symmetrically, establishing a simplified two-degree-of-freedom vehicle dynamics model:
Figure BDA0003448652690000061
Figure BDA0003448652690000062
wherein m is the vehicle mass,
Figure BDA0003448652690000063
is the lateral acceleration of the vehicle, vx is the longitudinal velocity of the vehicle, ω is the yaw rate of the vehicle, IzIs the moment of inertia of the vehicle about the z-axis at the center of mass,
Figure BDA0003448652690000064
is the yaw angular acceleration of the vehicle, a is the distance from the front circumference to the center of mass, b is the distance from the rear axle to the center of mass, FyfIs the lateral force of the front axle, FyrIs the lateral force of the rear axle.
Under the assumption of a small rotation angle, the tire is out of a linear region, and the relationship among cornering force, cornering angle and cornering stiffness is as follows:
Fyf＝Cfαf
Fyr＝Crαr
in the formula, Cf、CrRespectively front wheel cornering stiffness and rear wheel cornering stiffness, alphaf、αrRespectively front wheel side slip angle and rear wheel side slip angleAnd (4) an angle. The slip angle of the front wheel and the slip angle of the rear wheel of the automobile are related to the motion parameters thereof, and the slip angles of the mass center can be approximated to be vx and vy on the assumption that the speeds of the front shaft and the rear shaft of the automobile are respectively
Figure BDA0003448652690000065
The angle between the front wheel speed direction and the x-axis can be expressed as θ:
Figure BDA0003448652690000071
the slip angle of the front and rear wheels of the vehicle can be expressed as:
Figure BDA0003448652690000072
Figure BDA0003448652690000073
the state space equation for the two degrees of freedom of the vehicle with respect to the yaw angle ω and the centroid slip angle β can be derived:
Figure BDA0003448652690000074
the specific method of the second step is as follows:
as shown in FIG. 2, the ideal yaw rate ω can be obtained based on the steady-state single-point preview modeldComprises the following steps:
Figure BDA0003448652690000075
the steady state response of the automobile entering under the step input of the front wheel angle is substituted into the state equation of the automobile to obtain the steady state yaw velocity gain GωAnd the automobile stability factor K:
Figure BDA0003448652690000076
Figure BDA0003448652690000077
in the formula iswIs the angular transmission ratio of the steering wheel angle and the front wheel angle.
Designing an objective function J1 based on the lateral offset of the current centroid:
Figure BDA0003448652690000078
where L _ Drv _2 is the centroid lateral offset and t is the model prediction time.
Designing an objective function J2 based on the distance between the current centroid and the boundary:
Figure BDA0003448652690000079
Figure BDA0003448652690000081
where g is a safety function, and Δ is the centerline-to-boundary distance as the vehicle position approaches the boundary.
Designing an objective function J3 based on the response characteristic of the whole vehicle:
J3＝(tp-T)2
where T is the time relating to the vehicle steering response characteristic, it may be 1s or less when the speed is high, and it may be increased as appropriate when the speed is low.
Get the total objective function Jmin:
Jmin＝min(ω1J1+ω2J2+ω3J3)
in the formula, ω1、ω2、ω3Setting of weights for the weight coefficientsThe object achieved is related to1Related to the accuracy of the trajectory tracking; omega2Related to the distance of the vehicle from the road boundary; omega3And the response characteristic of the whole vehicle.
As shown in fig. 3, the specific method of step three is as follows:
for a system that is uncertain and needs to consider perturbation of internal parameters and external disturbances, the system state equation can be expressed as:
Figure BDA0003448652690000082
the state space equation according to the two degrees of freedom can be rewritten as:
Figure BDA0003448652690000083
Figure BDA0003448652690000084
Figure BDA0003448652690000085
the equations for the controlled system can be expressed as the following equations of state:
Figure BDA0003448652690000086
where f (ω r) can be written as an expression of the yaw angular velocity ω r and the centroid slip angle β:
Figure BDA0003448652690000087
determining a gain value K (t) of a proper sliding mode control law:
K(t)＝max(|e(τ)|)+ρ(ρ>0)
and selecting the difference between the actual yaw velocity omega r and the ideal yaw velocity omega d as the tracking error of the system:
e＝ωr-ωd
the switching function is set as:
Figure BDA0003448652690000091
where λ is a positive weighting factor.
The switching function s is derived as:
Figure BDA0003448652690000092
order to
Figure BDA0003448652690000093
Solving a sliding mode control law as follows:
Figure BDA0003448652690000094
it can be known that
Figure BDA0003448652690000095
Comprises the following steps:
Figure BDA0003448652690000096
order to
Figure BDA0003448652690000097
Yaw angular acceleration at this time
Figure BDA0003448652690000098
The front wheel steering angle input can be found as:
Figure BDA0003448652690000099
the available sliding mode control law is as follows:
Figure BDA00034486526900000910
the switching gain K (t) represents the approaching speed of the moving point of the system to the switching surface, and K (t) is a sliding mode surface reachable condition required to be met:
Figure BDA00034486526900000911
the sign function sgn (x) is replaced by a saturation function sat (x), so that the buffeting suppression effect is achieved:
Figure BDA00034486526900000912
obtaining a final sliding mode control law:
Figure BDA00034486526900000913
wherein ε represents the boundary layer thickness.
The concrete method of the fourth step is as follows:
steering wheel angle delta to obtain final control inputst：
δst＝δh*isw。
Example (b): a joint simulation platform is set up based on MATLAB/Simulink and vehicle dynamics software Carsim and is used for testing the trajectory tracking controller designed by the application. The target vehicle was subjected to a double lane test at a constant longitudinal movement speed on a road surface having a road surface adhesion coefficient of 0.5. The target vehicle speed is between 5m/s and 20m/s, and the target vehicle speed respectively runs at a constant speed, and the figure 4 is a track comparison graph of different speeds under the adaptive preview. According to an experimental curve, the intelligent automobile transverse control method based on the sliding mode control of the self-adaptive preview time can ensure good vehicle transverse control and good tracking condition, the tracking error of a straight road section is below 0.015m, and the maximum tracking error of a curve road section is not more than 0.6 m. And it can be seen from fig. 5 that the smoothness of the yaw angle is better and the control is smooth. Therefore, the designed track tracking controller can realize accurate and stable vehicle transverse track tracking control.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. An intelligent automobile transverse control method based on adaptive preview time and sliding mode control is characterized by comprising the following steps:
the method comprises the following steps: establishing a two-degree-of-freedom dynamic model considering the transverse and longitudinal movement of the intelligent automobile and the cornering characteristic of the tire;
step two: designing a self-adaptive preview model with target optimization;
step three: designing a vehicle transverse track tracking controller to obtain a complete system control law;
step four: and inputting the steering wheel angle for control.
2. The intelligent automobile transverse control method based on the adaptive preview time and the sliding mode control according to claim 1, characterized in that the specific method of the first step is as follows:
(1) making a bicycle model assumption: assuming that the motion of the vehicle is a motion on a two-dimensional plane, assuming that the stress of the left wheel and the right wheel is symmetrical, and establishing a simplified two-degree-of-freedom vehicle dynamic model:
Figure FDA0003448652680000011
Figure FDA0003448652680000012
wherein m is the vehicle mass,
Figure FDA0003448652680000013
is the lateral acceleration of the vehicle, vx is the longitudinal velocity of the vehicle, ω is the yaw rate of the vehicle, IzIs the moment of inertia of the vehicle at the center of mass,
Figure FDA0003448652680000014
is the yaw angular acceleration of the vehicle, a is the distance from the front circumference to the center of mass, b is the distance from the rear axle to the center of mass, FyfIs the lateral force of the front axle, FyrIs the lateral force of the rear axle;
(2) under the assumption of a small rotation angle, the tire is out of a linear region, and the relationship among cornering force, cornering angle and cornering stiffness is as follows:
Fyf＝Cfαf
Fyr＝Crαr
in the formula, Cf、CrRespectively front wheel cornering stiffness and rear wheel cornering stiffness, alphaf、αrThe front wheel side slip angle and the rear wheel side slip angle of the automobile are respectively related to motion parameters of the front wheel side slip angle and the rear wheel side slip angle of the automobile, the speeds of the front shaft and the rear shaft of the automobile are respectively vx and vy, and the centroid slip angle can be approximate to
Figure FDA0003448652680000015
(3) The angle θ between the front wheel speed direction and the x-axis is expressed as:
Figure FDA0003448652680000016
(4) the slip angle of the front and rear wheels of the vehicle is expressed as:
Figure FDA0003448652680000017
Figure FDA0003448652680000021
(5) obtaining a state space equation of the two degrees of freedom of the vehicle about the yaw angle omega and the centroid slip angle beta:
Figure FDA0003448652680000022
3. the intelligent automobile transverse control method based on the adaptive preview time and sliding mode control according to claim 1, characterized in that the specific method of the second step is as follows:
(1) obtaining ideal yaw angular velocity omega based on steady-state single-point preview modeldComprises the following steps:
Figure FDA0003448652680000023
(2) the steady state response of the entering automobile under the step input of the front wheel angle is substituted into the automobile state equation to obtain the steady state yaw velocity gain GωAnd the automobile stability factor K:
Figure FDA0003448652680000024
Figure FDA0003448652680000025
in the formula iswThe angular transmission ratio of the steering wheel angle and the front wheel angle;
(3) designing an objective function J1 based on the lateral offset of the current centroid:
Figure FDA0003448652680000026
where L _ Drv _2 is the centroid lateral offset and t is the model prediction time;
(4) designing an objective function J2 based on the distance between the current centroid and the boundary:
Figure FDA0003448652680000027
Figure FDA0003448652680000028
wherein g is a safety function, and Δ is the distance from the center line to the boundary as the vehicle position is closer to the boundary;
(5) designing an objective function J3 based on the response characteristic of the whole vehicle:
J3＝(tp-T)2
where T is a time related to a steering response characteristic of the vehicle, and may be 1s or less when the speed is high, and may be increased appropriately when the speed is low;
(6) get the total objective function Jmin:
Jmin＝min(ω1J1+ω2J2+ω3J3)
in the formula, ω1、ω2、ω3For the weight coefficients, the setting of the weights is dependent on the purpose to be achieved, ω1Related to the accuracy of the trajectory tracking; omega2Related to the distance of the vehicle from the road boundary; omega3And the response characteristic of the whole vehicle.
4. The intelligent automobile transverse control method based on the adaptive preview time and sliding mode control according to claim 1, characterized in that the specific method of the third step is as follows:
(1) for a system which is uncertain and needs to consider perturbation of internal parameters and external interference, the system state equation is expressed as:
Figure FDA0003448652680000031
(2) the state space equation according to the two degrees of freedom can be rewritten as:
Figure FDA0003448652680000032
Figure FDA0003448652680000033
Figure FDA0003448652680000034
(3) the equations for the controlled system can be expressed as the following equations of state:
Figure FDA0003448652680000035
(4) wherein f (ω)r) The yaw rate omega can be writtenrAnd centroid slip angle β:
Figure FDA0003448652680000036
(5) determining a gain value K (t) of a proper sliding mode control law:
K(t)＝max(|e(τ)|)+ρ(ρ>0)
(6)selecting an actual yaw rate omegarWith ideal yaw rate omegadThe difference is taken as the tracking error of the system:
e＝ωr-ωd
(7) the switching function is set as:
Figure FDA0003448652680000037
wherein λ is a positive weighting coefficient;
(8) the switching function s is derived as:
Figure FDA0003448652680000041
(9) order to
Figure FDA0003448652680000042
Solving a sliding mode control law as follows:
Figure FDA0003448652680000043
(10) it can be known that
Figure FDA0003448652680000044
Comprises the following steps:
Figure FDA0003448652680000045
(11) order to
Figure FDA0003448652680000046
Yaw angular acceleration at this time
Figure FDA0003448652680000047
The front wheel steering angle input can be found as:
Figure FDA0003448652680000048
(12) the available sliding mode control law is as follows:
Figure FDA0003448652680000049
(13) the switching gain K (t) represents the approaching speed of the moving point of the system to the switching surface, and K (t) is a sliding mode surface reachable condition required to be met:
Figure FDA00034486526800000410
(14) the sign function sgn (x) is replaced by a saturation function sat (x), so that the buffeting suppression effect is achieved:
Figure FDA00034486526800000411
(15) obtaining a final sliding mode control law:
Figure FDA00034486526800000412
wherein ε represents the boundary layer thickness.
5. The intelligent automobile transverse control method based on the adaptive preview time and sliding mode control according to claim 1, wherein the concrete method of the fourth step is as follows: obtaining the steering wheel angle of the final control input:
δst＝δh*isw。
CN202111667624.5A 2021-12-31 2021-12-31 Intelligent automobile transverse control method based on adaptive preview time and sliding mode control Pending CN114179818A (en)

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