CN117022327A

CN117022327A - Sliding mode track tracking control method, device, equipment and storage medium

Info

Publication number: CN117022327A
Application number: CN202311138483.7A
Authority: CN
Inventors: 王欣志; 刘金波; 王宇; 张建; 高原; 李博
Original assignee: FAW Group Corp
Current assignee: FAW Group Corp
Priority date: 2023-09-05
Filing date: 2023-09-05
Publication date: 2023-11-10

Abstract

The application relates to the technical field of automatic driving, in particular to a sliding mode track tracking control method, a device, equipment and a storage medium, wherein the method comprises the following steps: establishing a vehicle kinematic model and a vehicle kinetic model; acquiring an equivalent rear wheel steering angle, and establishing a discrete track control model based on the vehicle kinematics model, the vehicle dynamics model and the equivalent rear wheel steering angle; acquiring front axle midpoint coordinates of a vehicle expected track and a front axle midpoint of the vehicle, generating an expected lateral movement distance sequence based on the vehicle expected track, and calculating a track tracking error based on the front axle midpoint coordinates and the expected lateral movement distance sequence; and establishing a sliding mode track tracking control logic based on the expected lateral movement distance sequence, the track tracking error, the discrete track control model and preset design parameters, and performing sliding mode control on the vehicle according to the sliding mode track tracking control logic. The application is convenient for preventing the buffeting phenomenon of the automatic driving vehicle.

Description

Sliding mode track tracking control method, device, equipment and storage medium

Technical Field

The present application relates to the field of autopilot technology, and in particular, to a sliding mode track tracking control method, apparatus, device, and storage medium.

Background

With the continuous rapid updating and iteration of our science and technology, the automatic driving technology has become a research hotspot in the current automobile industry, and the track tracking control is an extremely important ring in the automatic driving technology. Currently, the mainstream trajectory tracking control algorithms include PID control, LQR control, sliding mode control, model Predictive Control (MPC), intelligent control, and the like.

Compared with other control algorithms, the sliding mode control has two advantages, namely the system uncertainty can be overcome, the perturbation response to the system parameters is not obvious, and the robustness is extremely strong; and secondly, the algorithm is simple in structure and has a good control effect on the control of a nonlinear system. Based on the advantages of the two points, the sliding mode control is widely applied and researched in automatic driving track tracking control.

However, when the track tracking is performed by adopting the conventional sliding mode control, after the system state track reaches the sliding mode surface, the system state track is difficult to slide along the sliding mode surface to the balance point strictly, but the balance point is approached back and forth in a traversing manner at two sides of the sliding mode surface, so that a buffeting phenomenon is generated, and the buffeting phenomenon is unacceptable for a track tracking control system with extremely high precision requirements.

Disclosure of Invention

The embodiment of the application provides a sliding mode track tracking control method, a device, equipment and a storage medium, and in a first aspect, the sliding mode track tracking control method provided by the application adopts the following technical scheme that:

establishing a vehicle kinematic model based on the vehicle kinematic parameters, and establishing a vehicle kinematic model based on the vehicle kinematic parameters and the vehicle kinetic parameters;

acquiring an equivalent rear wheel steering angle, and establishing a discrete track control model based on the vehicle kinematic model, the vehicle dynamic model and the equivalent rear wheel steering angle;

acquiring front axle midpoint coordinates of a vehicle expected track and a front axle midpoint of the vehicle, generating an expected lateral movement distance sequence based on the vehicle expected track, and calculating a track tracking error based on the front axle midpoint coordinates and the expected lateral movement distance sequence;

and establishing a sliding mode track tracking control logic based on the expected lateral movement distance sequence, the track tracking error, the discrete track control model and preset design parameters, and performing sliding mode control on the vehicle according to the sliding mode track tracking control logic.

In a specific embodiment, the building the vehicle kinematic model based on the vehicle kinematic parameters includes:

establishing a geodetic coordinate system, and establishing a vehicle body fixed coordinate system based on the midpoint of the front axle of the vehicle in the geodetic coordinate system;

based on the geodetic coordinate system, acquiring a vehicle centroid ordinate and a vehicle yaw angle in the vehicle kinematic parameters and a distance from the vehicle centroid to a front axle, and calculating a vehicle yaw angular speed based on the vehicle yaw angle;

acquiring a vehicle longitudinal speed and a vehicle lateral speed in the vehicle kinematic parameters based on the vehicle body fixed coordinate system;

and taking the ordinate of the center of mass and the yaw angle of the vehicle as state quantities of the vehicle kinematic model, taking the yaw rate of the vehicle as input quantity of the vehicle kinematic model, taking the ordinate of the midpoint of the front axle of the vehicle as output quantity of the vehicle kinematic model, and building the vehicle kinematic model based on the longitudinal speed of the vehicle, the lateral speed of the vehicle and the distance from the center of mass of the vehicle to the front axle.

In a specific embodiment, the establishing a discrete trajectory control model based on the vehicle kinematics model, the vehicle dynamics model, and the equivalent rear wheel steering angle includes:

generating a system continuous state quantity based on the state quantity of the vehicle dynamics model;

establishing a continuous track control model based on the acquired vehicle front wheel steering angle, the system continuous state quantity and the equivalent rear wheel steering angle;

discretizing the continuous track control model to generate the discrete track control model.

In a specific embodiment, the state quantity of the vehicle kinematic model on which the system continuous state quantity is generated includes at least one of a vehicle centroid ordinate and a vehicle yaw angle; the state quantity of the vehicle dynamics model on which the system continuous state quantity is generated includes at least one of a vehicle lateral speed and a yaw rate.

In a specific embodiment, the generating a sequence of desired lateral movement distances at the same time as the front axle midpoint coordinates based on the desired trajectory of the vehicle comprises:

discretizing the system continuous state quantity to generate a system discrete state quantity;

generating an expected track point coordinate sequence based on the expected track of the vehicle and a preset sampling time;

generating a pretightening distance based on the front axle midpoint coordinates of the vehicle front axle midpoint and the expected track point coordinate sequence;

processing the system discrete state quantity, the front axis midpoint coordinates, and the pretighting distance to generate the desired lateral movement distance sequence.

In a specific embodiment, the generating the pretightening distance based on the front axle midpoint coordinates of the vehicle front axle midpoint and the desired track point coordinate sequence includes:

acquiring a first element in the expected track point coordinate sequence corresponding to the front axle midpoint coordinate of the front axle midpoint of the vehicle, and recording the first element as a first expected track point coordinate;

and calculating the pretightening distance according to the first expected track point coordinate and the corresponding front axle midpoint coordinate of the front axle midpoint of the vehicle.

In a specific embodiment, said calculating a trajectory tracking error based on said front axis midpoint coordinates and said sequence of desired lateral movement distances comprises:

acquiring the ordinate of the midpoint coordinates of the front axle of the vehicle, and recording the ordinate as the ordinate of the midpoint of the front axle;

acquiring a first element in the expected lateral movement distance sequence, and recording the first element as a first expected lateral movement distance;

the trajectory tracking error is generated based on the front axis midpoint ordinate and the first desired lateral movement distance.

In a second aspect, the present application provides a sliding mode trajectory tracking control device, including:

the first model building module is used for building a vehicle kinematic model based on the vehicle kinematic parameters and building a vehicle kinetic model based on the vehicle kinematic parameters and the vehicle kinetic parameters;

the second model building module is used for obtaining an equivalent rear wheel steering angle and building a discrete track control model based on the vehicle kinematics model, the vehicle dynamics model and the equivalent rear wheel steering angle;

the error calculation module is used for acquiring front axle midpoint coordinates of a vehicle expected track and a vehicle front axle midpoint, generating an expected lateral movement distance sequence based on the vehicle expected track, and calculating track tracking error based on the front axle midpoint coordinates and the expected lateral movement distance sequence;

and the sliding mode control module is used for establishing sliding mode track tracking control logic based on the expected lateral movement distance sequence, the track tracking error, the discrete track control model and preset design parameters, and carrying out sliding mode control on the vehicle according to the sliding mode track tracking control logic.

In a third aspect, the present application provides a computer device, which adopts the following technical scheme: the sliding mode track tracking control system comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and execute any sliding mode track tracking control method.

In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical solutions: a computer program capable of being loaded by a processor and executing any one of the sliding mode trajectory tracking control methods described above is stored.

Drawings

FIG. 1 is a flowchart of a sliding mode track tracking control method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of coordinates used to embody information on the state of motion of an autonomous vehicle in accordance with the present application;

FIG. 3 is a schematic diagram of coordinates used to embody the information of the trajectory of an autonomous vehicle according to the present application;

fig. 4 is a schematic structural diagram of a sliding-mode track tracking control device according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.

Fig. 1 is a flowchart of a sliding mode track tracking control method according to an embodiment of the present application, and referring to fig. 1, the method may be performed by an apparatus for performing the method, where the apparatus may be implemented by software and/or hardware, and the method includes:

s100, a vehicle kinematic model is built based on vehicle kinematic parameters, and the vehicle kinematic model is built based on the vehicle kinematic parameters and the vehicle kinematic parameters.

Specifically, S100 includes the following steps:

s101, establishing a geodetic coordinate system, and establishing a vehicle body fixed coordinate system based on the midpoint of the front axle of the vehicle in the geodetic coordinate system.

In implementation, referring to FIG. 2, to facilitate description of vehicle location of an autonomous vehicle, a description is established for use in describingA geodetic coordinate system OXY of the vehicle position information; further, in order to describe the speed direction and the rotation direction of the autonomous vehicle, a midpoint of a front axle of the autonomous vehicle model is determined in the geodetic coordinate system, and the midpoint of the front axle of the autonomous vehicle model is taken as an origin of coordinates O _b Establishing a vehicle body fixed coordinate system O _b X _b Y _b The method comprises the steps of carrying out a first treatment on the surface of the Further, the origin O of the geodetic coordinate system and the midpoint of the front axle of the vehicle are straight lines, and the abscissa axis O of the vehicle body fixed coordinate system _b X _b Arranged along the straight line and the axis of abscissa O of the vehicle body fixed coordinate system _b X _b Is arranged along an origin O away from the geodetic coordinate system; ordinate axis O of vehicle body fixed coordinate system _b Y _b Is arranged along a longitudinal axis OY towards the geodetic coordinate system.

S102, acquiring a vehicle centroid ordinate and a vehicle yaw angle in vehicle kinematic parameters and a distance from the vehicle centroid to a front axle based on a geodetic coordinate system, and calculating a vehicle yaw angle speed based on the vehicle yaw angle.

The autonomous vehicle model is arranged in the geodetic coordinate system, and with continued reference to fig. 2, the vehicle centroid ordinate y is obtained from the geodetic coordinate system _c Vehicle yaw angle ψ, vehicle centroid to front axle distance L _f The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the ordinate y of the mass center of the vehicle _c Vehicle yaw angle ψ, vehicle centroid to front axle distance L _f All belong to the kinematic parameters of the vehicle.

Further, after the vehicle yaw angle ψ is obtained, the first derivative of the vehicle yaw angle ψ with respect to time, that is, the vehicle yaw rate γ is also calculated.

S103, based on a vehicle body fixed coordinate system, acquiring the longitudinal speed and the lateral speed of the vehicle in the vehicle kinematic parameters.

The vehicle kinematics parameters not only comprise the vehicle centroid ordinate y acquired based on the geodetic system _c Vehicle yaw angle ψ, vehicle centroid to front axle distance L _f Also comprises a vehicle longitudinal speed v based on a vehicle body fixed coordinate system _x With lateral velocity v of vehicle _y 。

In practice, in terms of geodetic coordinatesIs obtained by the vehicle mass center ordinate y _c Vehicle yaw angle ψ, vehicle centroid to front axle distance L _f At the same time, the longitudinal speed v of the vehicle is obtained according to the fixed coordinate system of the vehicle body _x With lateral velocity v of vehicle _y 。

S104, taking the ordinate of the center of mass and the yaw angle of the vehicle as state quantities of the vehicle kinematic model, taking the yaw rate of the vehicle as input quantity of the vehicle kinematic model, taking the ordinate of the midpoint of the front axle of the vehicle as output quantity of the vehicle kinematic model, and building the vehicle kinematic model based on the longitudinal speed of the vehicle, the lateral speed of the vehicle and the distance from the center of mass of the vehicle to the front axle.

Acquiring the ordinate y of the mass center of the vehicle through the steps S102 and S103 _c The vehicle yaw angle psi, the vehicle centroid to front axle distance L _f Yaw rate of vehicle gamma, longitudinal speed of vehicle v _x Lateral speed v of vehicle _y Afterwards; further, the vehicle center of mass ordinate y _c The vehicle yaw angle psi is used as a state quantity, the vehicle yaw angular velocity gamma is used as an input quantity, and the vehicle front axle midpoint ordinate y is used _b As output of the kinematic model of the vehicle, in dependence on the obtained longitudinal speed v of the vehicle _x Lateral speed v of vehicle _y Vehicle centroid to front axle distance L _f Jointly constructing a vehicle kinematic model; specifically, the expression of the vehicle kinematic model is as follows:

wherein y is _c Upper-side adding point of (1) represents vehicle centroid ordinate y _c First derivative is calculated for time; the upper dotted point of ψ represents the vehicle yaw angle ψ as a first derivative over time.

S105, establishing a vehicle dynamics model based on the vehicle kinematics parameters and the vehicle dynamics parameters.

After finishing the establishment of the vehicle dynamics model through the step S104, the vehicle dynamics model is further required to be established; for this purpose, it is necessary to obtain a first acquisition corresponding to an autonomous vehicleThe vehicle dynamics parameters to be acquired include the mass M of the whole vehicle and the moment of inertia I of the vehicle around the mass center of the vehicle _z Vehicle centroid to rear axle distance L _r Equivalent cornering stiffness C of front axle of vehicle _f Equivalent cornering stiffness C of rear axle of vehicle _r 。

Further, based on the vehicle dynamics parameters, and at the vehicle lateral speed v in the vehicle dynamics parameters _y Constructing a vehicle dynamics model by taking the yaw rate gamma as a shape body quantity; specifically, the expression of the vehicle dynamics model is as follows:

s200, acquiring an equivalent rear wheel steering angle, and establishing a discrete track control model based on the vehicle kinematics model, the vehicle dynamics model and the equivalent rear wheel steering angle.

Specifically, S200 includes the following steps:

s201, generating a system continuous state quantity based on the state quantity of the vehicle dynamics model.

The state quantity of the vehicle kinematic model obtained through the S104 step is the ordinate y of the mass center of the vehicle _c The state quantity of the vehicle dynamics model obtained through the step S105 is the vehicle lateral velocity v, as compared with the vehicle yaw angle ψ _y Yaw rate γ; a system continuous state quantity is then generated based on the state quantity of the vehicle dynamics model and the state quantity of the vehicle dynamics model.

The state quantity of the vehicle kinematic model based on the system continuous state quantity at least comprises the ordinate y of the center of mass of the vehicle _c One of the yaw angles ψ from the vehicle; the state quantity of the vehicle dynamics model on which the continuous state quantity of the system is generated comprises at least the vehicle lateral speed v _y And one of the yaw rate γ.

In this embodiment, it is preferably based on the ordinate y of the centroid of the vehicle _c Vehicle yaw angle ψ, vehicle lateral speed v _y YawContinuous state quantity χ= [ y ] of angular velocity gamma generation system _c ψ v _y γ] ^T 。

S202, establishing a continuous track control model based on the acquired front wheel steering angle, the system continuous state quantity and the equivalent rear wheel steering angle.

While the autonomous vehicle is running, the steering angle of the front wheels thereof, i.e., delta shown in fig. 2, is acquired in real time _f The method comprises the steps of carrying out a first treatment on the surface of the Further, a system continuous state quantity χ= [ y ] is generated by S202 _c ψ v _y γ] ^T Then, the system continuous state quantity χ= [ y ] is also obtained _c ψ v _y γ] ^T 。

It should be noted that, in the steering process of the vehicle, the suspension system of the autonomous vehicle may cause an equivalent rear wheel steering angle d of the rear wheel of the vehicle, where the equivalent rear wheel steering angle d is a system additive noise.

In order to facilitate the trajectory control of an autonomous vehicle, a corresponding continuous trajectory control model needs to be established in which the front wheel steering angle δ _f The input quantity u of the continuous track control model; and the output quantity eta of the continuous track control model is the ordinate y of the midpoint of the front axle of the vehicle _b 。

In implementation, χ= [ y ] is the system continuous state quantity _c ψ v _y γ] ^T A system matrix F is preset, an input matrix B is preset for the input quantity u of the continuous track control model, a disturbance matrix H is preset for the equivalent rear wheel steering angle d, and a system continuous state quantity χ= [ y ] _c ψ v _y γ] ^T An output matrix C is preset.

The expression of the continuous track control model is as follows:

wherein, the upper part of χ is added with a point representation to calculate the first derivative of the continuous state quantity of the system with respect to time.

Specifically, the expressions of the system matrix F, the input matrix B, the disturbance matrix H, and the output matrix C are as follows:

s203, discretizing the continuous track control model to generate a discrete track control model.

After the continuous track control model is obtained in step S202, in order to facilitate track control of the autonomous vehicle according to each sampling time point, discretization needs to be performed on the continuous track control model; in this embodiment, the continuous track control model is discretized by adopting a forward euler method, so as to obtain a discrete track control model, and specifically, the expression of the discrete track control model is as follows:

it should be explained that G is a system matrix of the discrete track control model, Q is an input matrix of the discrete track control model, R is a disturbance matrix of the discrete track control model, and k represents k time.

Further:

G＝FT+I _4×4 ...................................................(9)

Q＝BT......................................................(10)

R＝HT......................................................(11)

wherein F is the system matrix of the continuous track control model, T is the sampling time, I _4×4 And B is an input matrix of the continuous track control model, and H is a disturbance matrix of the continuous track control model.

S300, acquiring front axle midpoint coordinates of a vehicle expected track and a front axle midpoint of the vehicle, generating an expected lateral movement distance sequence based on the vehicle expected track, and calculating a track tracking error based on the front axle midpoint coordinates and the expected lateral movement distance sequence.

Specifically, S300 includes the following steps:

s301, discretizing the system continuous state quantity to generate a system discrete state quantity.

In the implementation, in order to facilitate real-time acquisition of the state of the automatic driving vehicle, a plurality of continuous sampling moments are preset, after the system continuous state quantity is acquired in the step S201, further, time discretization processing is performed on the system continuous state quantity according to the preset sampling moments, so as to obtain the system discrete state quantity;

s302, generating an expected track point coordinate sequence based on an expected track of the vehicle and a preset sampling time.

Referring to fig. 3, during the traveling process of the autopilot vehicle, the surrounding environment is detected, so as to generate a corresponding expected track of the vehicle, namely, the expected track of the autopilot vehicle; after the expected track of the vehicle is obtained, controlling the automatic driving vehicle to travel according to the expected track of the vehicle; in the process of controlling the automatic driving vehicle to travel, the latest state is that the midpoint of the front axle of the automatic driving vehicle can always move along the expected track; however, due to the influence of the additive noise of the system caused by the suspension system, the midpoint of the front axle of the vehicle does not always move along the expected track, so that the midpoint of the front axle of the vehicle is controlled to be close to the expected track of the vehicle as much as possible, and so that the coordinates of the midpoint of the front axle of the vehicle at each sampling moment need to be acquired, and each sampling moment corresponds to the coordinates of the midpoint of the front axle of the vehicle and is marked as the coordinates (x (k), y (k)) of the midpoint of the front axle of the vehicle. In order to facilitate the acquisition of the position information of the midpoint of the front axle of the vehicle in real time, the coordinates of the midpoint of the front axle of the vehicle are acquired once at each sampling time.

After the expected track of the vehicle is obtained, further obtaining a coordinate set of a plurality of continuous sampling moments corresponding to the expected track of the vehicle, and recording the coordinate set as a coordinate sequence of the expected track point; the coordinates of the points in the front axle of the vehicle at each sampling moment correspond to an expected track of the vehicle, namely a sequence of coordinates of the points of the expected track.

S303, generating a pretightening distance based on a front axle midpoint coordinate of the front axle midpoint of the vehicle and a desired track point coordinate sequence.

Specifically, the step of generating the pretightening distance includes:

s303a, acquiring a first element in a desired track point coordinate sequence corresponding to a front axle midpoint coordinate of a front axle midpoint of the vehicle, and recording the first element as a first desired track point coordinate.

After the front-axis midpoint coordinates (x (k), y (k)) of the front-axis midpoint of the vehicle are determined, the first element in the desired trajectory point coordinate sequence at the same time as the front-axis midpoint coordinates is further acquired and recorded as the first desired trajectory point coordinates (x(s) _p,i ),y(s _p,i ))。

And S303b, calculating the pretightening distance according to the first expected track point coordinate and the front axle midpoint coordinate of the corresponding front axle midpoint of the vehicle.

Determining a front-axis midpoint coordinate (x (k), y (k)) of a front-axis midpoint of the vehicle and a first expected trajectory point coordinate (x(s) _p,i ),y(s _p,i ) Further, a front-axis midpoint coordinate (x (k), y (k)) of the vehicle front-axis midpoint and a first desired trajectory point coordinate (x(s) _p,i ),y(s _p,i ) Distance between two pairs, denoted as pretarget distance S _p,i Pretarget distance S _p,i I.e. the distance of movement required for the midpoint of the front axle of the vehicle of the autonomous vehicle to be ready to return to the desired trajectory of the vehicle; pretarget distance s _p,i ＝v _x (t _p +iT), where t _p For the current time, i=0, 1,2,3 … …, T is the sampling interval.

S304, processing the discrete state quantity of the system, the front axis midpoint coordinate and the pre-aiming distance to generate a desired lateral movement distance sequence.

The discrete state quantity χ (k) = [ y ] of the system is obtained through step S301 _c (k)ψ(k)v _y (k)γ(k)] ^T And the front axis midpoint coordinates (x (k), y (k)) and the pretightening distance S are obtained through the steps S302 and S303 _p,i ＝v _x (t _p +it), further according to the discrete state quantity χ (k) = [ y ] _c (k)ψ(k)v _y (k)γ(k)] ^T Front axis midpoint coordinates (x (k), y (k)) and pretightening distance s _p,i ＝v _x (t _p +it) calculates the desired lateral movement distance sequence, specifically, the desired lateral movement distance sequence is expressed as follows:

y _d (k+i)＝(y(s _p,i )-y(k))cosψ(k)-(x(s _p,i )-x(k))sinψ(k)...................(12)

s305, acquiring the ordinate of the midpoint coordinate of the front axle of the vehicle, and recording the ordinate as the ordinate of the midpoint of the front axle.

After the front axle midpoint coordinates of the front axle midpoint of the vehicle are obtained in step S302, the ordinate of the front axle midpoint coordinates is further obtained and is recorded as the front axle midpoint ordinate y _b (k)。

S306, acquiring a first element in the expected lateral movement distance sequence, and recording the first element as a first expected lateral movement distance.

Acquiring a desired lateral movement distance sequence y through S304 _d (k+i) further acquiring a desired lateral movement distance sequence y _d The first element in (k+i) is noted as the first desired lateral movement distance y _d (k)。

S307, generating a trajectory tracking error based on the front axis midpoint ordinate and the first desired lateral movement distance.

The longitudinal coordinate y of the midpoint of the front axle is obtained through the step S305 _b (k) And obtains a first expected lateral movement distance y through S306 _d (k) Then, the ordinate y of the midpoint of the front axle is further calculated _b (k) Distance y from first desired lateral movement _d (k) The difference value between the two values, so as to obtain a track tracking error e (k), wherein the track tracking error e (k) is expressed as follows:

e(k)＝y _b (k)-y _d (k)..............................................(13)

it should be noted that, if the track tracking error e (k) gradually converges in the neighborhood of zero, the closer the actual running track of the autonomous vehicle is to the sliding mode surface, that is, the more stable the autonomous vehicle runs according to the expected track of the vehicle, the no buffeting phenomenon will occur.

S400, establishing a sliding mode track control logic based on the expected lateral movement distance sequence, the track tracking error, the discrete track control model and preset design parameters, and performing sliding mode control on the vehicle according to the sliding mode track control logic.

After the track tracking error e (k) is obtained in step S307, the track tracking error e (k) is taken as a discrete sliding mode surface σ (k), and specifically, the expression of σ (k) is as follows:

σ(k)＝e(k)...................................................(14)

acquisition of the desired lateral movement distance sequence y _d After (k+i), the track tracking error e (k) and the discrete track control model, the sliding mode track tracking control logic u (k) is further established according to preset design parameters, and specifically, the expression of the sliding mode track tracking control logic u (k) is as follows:

it should be noted that sgn () is a sign function, λ ₁ And lambda is ₂ Is a preset design parameter and lambda ₁ And lambda is ₂ Are all larger than 0 and are not smaller than 0,for the system additive noise estimation determined by the one-step delay estimation method, R is a disturbance matrix, and is specifically:

and in order to make the trajectory tracking error e (k) asymptotically converge in the neighborhood of zero, in the present embodiment, the following condition is also satisfied by the formula (16):

|CR||d(k)-d(k-1)|＜λ ₃ ＜λ ₂ ........................................(17)

wherein lambda is ₃ Is also a preset design parameter, lambda ₃ Also greater than zero; lambda is the sum of the values of lambda ₁ 、λ ₂ And lambda is ₃ The specific numerical values of (3) are obtained by simulation experiments or real vehicle experiments which are performed in advance.

Equation (16) the trajectory tracking error e (k) may converge in the vicinity of zero in the case where the condition as listed in equation (17) is satisfied; and establishing a sliding mode track tracking control logic u (k) based on the track tracking error e (k) at the moment, and performing sliding mode control on the automatic driving vehicle according to the sliding mode track tracking control logic u (k), so that the buffeting phenomenon of the automatic driving vehicle is conveniently solved.

Further, it is demonstrated that the process in the vicinity where the trajectory tracking error e (k) at this time asymptotically converges to zero is as follows:

from equation (8) and equation (14):

σ(k+1)＝e(k+1)＝y _b (k+1)-y _d (k+1)＝C(Gχ(k)+Qu(k)+Rd(k))-y _d (k+1).....(18)

substituting equation (15) into equation (18) yields:

σ(k+1)＝λ ₁ σ(k)-λ ₂ sgn(σ(k))+CR(d(k)-d(k-1)).......................(19)

(1) When sigma (k) is not less than lambda ₂ +λ ₃ When the method is used, the following steps are included:

σ(k+1)-σ(k)＝(λ ₁ -1)σ(k)-λ ₂ +CR(d(k)-d(k-1))＜0...................(20)

σ(k+1)+σ(k)＝(λ ₁ +1)σ(k)-λ ₂ +CR(d(k)-d(k-1))＞0...................(21)

thus, when σ (k) > λ ₂ +λ ₃ When the method is used, the following steps are included:

σ(k+1) ² ＜σ(k) ² ................................................(22)

(2) When sigma (k) is less than or equal to-lambda ₂ -λ ₃ When the method is used, the following steps are included:

σ(k+1)-σ(k)＝(λ ₁ -1)σ(k)+λ ₂ +CR(d(k)-d(k-1))＞0...................(23)

σ(k+1)+σ(k)＝(λ ₁ +1)σ(k)+λ ₂ +CR(d(k)-d(k-1))＜0...................(24)

thus, when σ (k). Ltoreq. - λ ₂ -λ ₃ When the method is used, the following steps are included:

σ(k+1) ² ＜σ(k) ² ................................................(25)

(3) When 0 < sigma (k) < lambda ₂ +λ ₃ When the method is used, the following steps are included:

σ(k+1)＝λ ₁ σ(k)-λ ₂ +CR(d(k)-d(k-1))＜

λ ₁ (λ ₂ +λ ₃ )-λ ₂ +CR(d(k)-d(k-1))＜λ ₂ +λ ₃ ............................(26)

σ(k+1)＝λ ₁ σ(k)-λ ₂ +CR(d(k)-d(k-1))＞

-λ ₂ +CR(d(k)-d(k-1))＞-λ ₂ -λ ₃ .............................(27)

thus, when 0 < sigma (k) < lambda ₂ +λ ₃ When the method is used, the following steps are included:

|σ(k+1)|＜λ ₂ +λ ₃ ...............................................(28)

(4) When-lambda ₂ -λ ₃ When < sigma (k) < 0, there are:

σ(k+1)＝λ ₁ σ(k)+λ ₂ +CR(d(k)-d(k-1))＞

σ(k)+λ ₂ +CR(d(k)-d(k-1))＞-λ ₂ -λ ₃ .............................(29)

σ(k+1)＝λ ₁ σ(k)+λ ₂ +CR(d(k)-d(k-1))＜

λ ₂ +CR(d(k)-d(k-1))＜λ ₂ +λ ₃ .............................(30)

thus, when-lambda ₂ -λ ₃ When < sigma (k) < 0, there are:

σ(k+1)|＜λ ₂ +λ ₃ ...............................................(31)

combining equation (22), equation (25), equation (28), and equation (31), one can obtain:

the above completes the proof that the track-following error e (k) gradually converges to the neighborhood of zero.

FIG. 1 is a flow chart of a sliding mode track tracking control method in an embodiment. It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows; the steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders; and at least some of the steps in fig. 1 may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur in sequence, but may be performed alternately or alternately with at least some of the other steps or sub-steps of other steps.

The embodiment of the application also provides a sliding mode track tracking control device, referring to fig. 4, the sliding mode track tracking control device comprises:

the sliding mode control module is used for establishing sliding mode track tracking control logic based on the expected lateral movement distance sequence, the track tracking error, the discrete track control model and preset design parameters, and carrying out sliding mode control on the vehicle according to the sliding mode track tracking control logic.

It should be noted that, the technical solution for solving the technical problem provided by the sliding-mode track tracking control device is similar to the technical solution defined by the sliding-mode track tracking control method, and the technical solution provided by the sliding-mode track tracking control device is not repeated here.

Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application, and referring to fig. 5, a computer device 60 includes a memory 602, a processor 601, and a computer program stored in the memory 602 and capable of running on the processor, where the processor 601 implements the method in the above embodiment when executing the program. FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present application. The computer device 60 shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the application. As shown in FIG. 5, the computer device 60 is in the form of a general purpose computing device. The components of the computer device 60 may include, but are not limited to: one or more processors 601, a system memory 602, and a bus 603 that connects the different system components (including the system memory 602 and the processor 601).

Bus 603 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Computer device 60 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 60 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 602 may include computer system readable media in the form of volatile memory such as Random Access Memory (RAM) 604 and/or cache memory 605. The computer device 60 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 606 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 603 through one or more data medium interfaces. The system memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the application.

A program/utility 608 having a set (at least one) of program modules 607 may be stored in, for example, system memory 602, such program modules 607 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 607 generally perform the functions and/or methods of the described embodiments of the application.

The computer device 60 may also communicate with one or more external devices 609 (e.g., keyboard, pointing device, display 610, etc.), one or more devices that enable a user to interact with the device, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 60 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 611. Moreover, the computer device 60 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 612. As shown in fig. 5, the network adapter 612 communicates with other modules of the computer device 60 over the bus 603. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer device 60, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

The processor 601 executes various functional applications and data processing by running programs stored in the system memory 602.

The embodiment of the present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of the above embodiment.

The computer storage media of embodiments of the application may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements, combinations, and substitutions can be made by those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.

Claims

1. The sliding mode track tracking control method is characterized by comprising the following steps of:

2. A method according to claim 1, wherein said establishing a vehicle kinematic model based on vehicle kinematic parameters comprises:

3. A method according to claim 1, wherein said building a discrete trajectory control model based on said vehicle kinematics model, said vehicle dynamics model and said equivalent rear wheel steering angle comprises:

4. A method according to claim 3, wherein the state quantity of the vehicle kinematic model on which the system continuous state quantity is generated comprises at least one of a vehicle centroid ordinate and a vehicle yaw angle; the state quantity of the vehicle dynamics model on which the system continuous state quantity is generated includes at least one of a vehicle lateral speed and a yaw rate.

5. A method according to claim 3, wherein said generating a desired sequence of lateral movement distances based on said desired trajectory of the vehicle comprises:

6. A method according to claim 5, wherein said generating a pretightening distance based on said front axle midpoint coordinates of said vehicle front axle midpoint and said desired track point coordinate sequence comprises:

7. A method according to claim 1, wherein said calculating a trajectory tracking error based on said current time instant coordinates and said sequence of desired lateral movement distances comprises:

8. A slip form trajectory tracking control device, comprising:

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-7 when the program is executed by the processor.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.