CN115542813A

CN115542813A - Unmanned vehicle control method, device, electronic equipment and storage medium

Info

Publication number: CN115542813A
Application number: CN202211248390.5A
Authority: CN
Inventors: 许男; 胡敏; 丁海涛; 张岳韬; 裴双红
Original assignee: Jilin University; Dongfeng Motor Group Co Ltd
Current assignee: Jilin University; Dongfeng Motor Group Co Ltd
Priority date: 2022-10-12
Filing date: 2022-10-12
Publication date: 2022-12-30

Abstract

The disclosure provides a method and a device for controlling an unmanned vehicle, electronic equipment and a storage medium, and relates to the technical field of unmanned driving. The method comprises the following steps: acquiring the track tracking precision, stability index and wheel slip rate of the unmanned vehicle under different operating conditions; determining the front wheel rotation angle and the wheel moment of the unmanned vehicle which tracks the target speed and runs by referring to the track information in a stable state under different running conditions according to the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions; and controlling the unmanned vehicle to run according to the determined front wheel turning angle and the determined wheel torque. The method and the device can comprehensively consider the vehicle stability and the tracking precision of the unmanned vehicle under different running conditions to control the running of the unmanned vehicle, and can realize the integrated coordination control of the trajectory tracking and the stability adapting to different running conditions.

Description

Unmanned vehicle control method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of unmanned technologies, and in particular, to a method and an apparatus for controlling an unmanned vehicle, an electronic device, and a storage medium.

Background

With the continuous development of electronic information and communication technology and the continuous improvement of related novel infrastructure construction, the process of electric operation, intellectualization and networking of vehicles is accelerated. Compared with the traditional vehicle, the unmanned electric vehicle with four wheels driven independently has remarkable advantages in the aspects of reducing environmental pollution, reducing energy consumption, relieving traffic jam, improving road utilization rate, reducing vehicle safety accidents, improving vehicle safety and the like, and provides a solution for meeting the safe, comfortable and convenient daily life needs of people and the efficient, energy-saving and intelligent travel needs.

The unmanned vehicle is a complex system and mainly comprises four core key technologies of environment perception, behavior decision, motion planning and trajectory tracking control. The track tracking control is based on road environment information and reference track information obtained by a perception planning layer, combines the self state of a vehicle, and realizes the tracking of a reference track and a target speed while ensuring the stable running of the vehicle by controlling the steering, acceleration or braking of the vehicle. Therefore, the trajectory tracking control as a functional module for controlling the vehicle to generate actual actions is very important for ensuring the stability, safety and comfort of the vehicle.

At present, a trajectory tracking control scheme for an unmanned vehicle is mostly based on a conventional working condition of a good road surface with a medium-low vehicle speed and a fixed weight, and the control of stability and an expected vehicle speed is rarely considered simultaneously or the stability control and the trajectory tracking control are considered separately, so that once the running working condition of the vehicle changes, the controller is difficult to comprehensively consider the weight priorities of a plurality of control targets such as stability and tracking accuracy, the trajectory tracking accuracy is obviously reduced, even dangerous working conditions such as sideslip and instability of the vehicle can be caused, and the unmanned vehicle is difficult to adapt to a complex and variable real road traffic environment. Therefore, designing a track tracking and stability coordination control method for unmanned vehicles (e.g., four-wheel independent drive unmanned electric vehicles) oriented to different operating conditions to improve the track tracking accuracy, stability and operating condition adaptability of the unmanned vehicles under different operating conditions is a technical problem to be solved urgently in the field.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The present disclosure provides a method and an apparatus for controlling an unmanned vehicle, an electronic device, and a storage medium, which at least to some extent overcome the technical problem in the related art that it is difficult to achieve coordinated control of trajectory tracking accuracy and stability of the unmanned vehicle under different operating conditions.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to an aspect of the present disclosure, there is provided a method of controlling an unmanned vehicle, the method including: acquiring the track tracking precision, stability indexes and wheel slip rate of the unmanned vehicle under different running conditions; determining a front wheel corner and a wheel moment of the unmanned vehicle running at a stable state tracking target speed and reference track information under different running conditions according to the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions; and controlling the unmanned vehicle to run according to the determined front wheel rotation angle and the determined wheel torque.

According to another aspect of the present disclosure, there is also provided an unmanned vehicle control apparatus including: the state information acquisition module is used for acquiring the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions; the control quantity determining module is used for determining the front wheel rotation angle and the wheel moment of the unmanned vehicle which tracks the target vehicle speed and runs with reference track information in a stable state under different running conditions according to the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions; and the control module is used for controlling the unmanned vehicle to run according to the determined front wheel rotating angle and the determined wheel torque.

According to another aspect of the present disclosure, there is also provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the above-described unmanned vehicle control methods via execution of the executable instructions.

According to another aspect of the present disclosure, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the unmanned vehicle control method of any of the above.

According to the unmanned vehicle control method, the unmanned vehicle control device, the unmanned vehicle control electronic equipment and the computer readable storage medium, the track tracking accuracy, the stability index and the wheel slip rate of the unmanned vehicle under different operation conditions are obtained, and then the front wheel rotation angle and the wheel moment of the unmanned vehicle which tracks the target vehicle speed and runs according to the reference track information in a stable state under different operation conditions are determined according to the track tracking accuracy, the stability index and the wheel slip rate of the unmanned vehicle under different operation conditions, so that the unmanned vehicle is controlled to run according to the determined front wheel rotation angle and the determined wheel moment. By the aid of the method and the device, the driving of the unmanned vehicle can be controlled by comprehensively considering the vehicle stability and tracking precision of the unmanned vehicle under different operating conditions, and integrated coordination control of trajectory tracking and stability suitable for different operating conditions can be realized.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

FIG. 1 illustrates a flow chart of a method for controlling an unmanned vehicle in an embodiment of the disclosure;

FIG. 2 is a schematic diagram illustrating an architecture for implementing a method for controlling an unmanned vehicle according to an embodiment of the present disclosure;

FIG. 3 illustrates a schematic diagram of a longitudinal PID motion controller in an embodiment of the disclosure;

FIG. 4 is a schematic diagram illustrating a vehicle and path relative position model in an embodiment of the disclosure;

FIG. 5 is a schematic diagram illustrating a seven degree-of-freedom dual-track vehicle dynamics model in an embodiment of the present disclosure;

FIG. 6 is a schematic view of a tire mold model in an embodiment of the present disclosure;

FIG. 7 is a schematic diagram illustrating a stability indicator design based on a tire slip angle phase plane in an embodiment of the present disclosure;

FIG. 8 is a schematic diagram illustrating an adaptive adjustment curve for tracking accuracy, maneuverability and stability weights of a vehicle trajectory according to an embodiment of the present disclosure;

FIG. 9 is a schematic diagram illustrating a four wheel slip ratio deviation weight adaptive tuning curve in an embodiment of the present disclosure;

FIG. 10 is a schematic diagram illustrating an unmanned vehicle control arrangement in an embodiment of the present disclosure;

FIG. 11 shows a block diagram of an electronic device in an embodiment of the disclosure;

FIG. 12 is a schematic diagram of a computer-readable storage medium in an embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

Specific embodiments of the disclosed embodiments are described in detail below with reference to the accompanying drawings.

In order to solve the problem of coordinated control of the trajectory tracking precision and the stability of the vehicle under different operating conditions, the embodiment of the disclosure provides a control method of the unmanned vehicle, which can be applied to, but is not limited to, the coordinated control of the trajectory tracking and the stability of the four-wheel independent drive electric unmanned vehicle.

It should be noted that the unmanned vehicle control method provided in the embodiments of the present disclosure may be executed by any electronic device having a computing processing capability. In some embodiments, the unmanned vehicle control method provided in embodiments of the present disclosure may be performed by an in-vehicle control apparatus; in other embodiments, the unmanned vehicle control methods provided in embodiments of the present disclosure may be performed by a remote control device; in other embodiments, the unmanned vehicle control method provided in the embodiments of the present disclosure may also be implemented by an in-vehicle control device and a remote control device in an interactive manner.

Fig. 1 shows a flowchart of a method for controlling an unmanned vehicle according to an embodiment of the present disclosure, and as shown in fig. 1, the method for controlling an unmanned vehicle according to an embodiment of the present disclosure includes the following steps:

s102, obtaining the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions.

It should be noted that the unmanned vehicle in the embodiments of the present disclosure may be, but is not limited to, an electric unmanned vehicle with four wheels independently driven, and the four wheels independently driven electric unmanned vehicle is taken as an example in each embodiment of the present disclosure for explanation. Stability control and trajectory tracking control are separately considered in control of an unmanned vehicle in the related art, and once the working condition of the vehicle changes, either trajectory tracking accuracy of the vehicle may be reduced or dangerous situations such as sideslip and instability of the vehicle may occur. The four-wheel slip rate deviation can reflect the rolling state of the wheels, in the embodiment of the disclosure, the track tracking accuracy, the stability index and the wheel slip rate of the unmanned vehicle under different operation conditions are obtained, the control targets can be integrated to realize the control of the unmanned vehicle,

and S104, determining the front wheel rotation angle and the wheel moment of the unmanned vehicle running in a stable state to track the target speed and reference track information under different running conditions according to the track tracking accuracy, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions.

In a specific implementation, the step S104 may be implemented by: constructing a target function by taking the track tracking precision, the stability index and the wheel slip rate as targets; determining constraint conditions of the objective function; and determining the front wheel rotation angle and the wheel torque of the unmanned vehicle running in a stable state to track the target vehicle speed and the reference track information under different running conditions according to the target function and the constraint condition.

In some embodiments, the objective function is:

wherein,

u＝[δ _f T _fl T _fr T _rl T _rr s _v s _r ] ^T (2)

wherein J represents a function value of the objective function; k represents a time; n is a radical of hydrogen _p Representing a prediction time domain; eta ^k Representing the actual output quantity at time k; eta ^k _ref Representing a reference output quantity at time k; u. of ^k Representing the actual control quantity at time k; v ^k A reference control amount indicating a time k; u. of ^k-1 Representing the actual control quantity at the time k-1; q, S, R and P represent weight coefficients; delta _f Indicating a front wheel turning angle; t is _fl Indicating a left front wheel longitudinal drive or braking torque; t is _fr Representing the right front wheel longitudinal drive or braking torque; t is _rl Indicating a left rear wheel longitudinal drive or braking torque; t is _rr Representing the right rear wheel longitudinal drive or braking torque; s _v And s _r Represents a relaxation variable; v. of _y Represents lateral velocity; r represents a yaw rate; e.g. of the type _y Indicating a lateral displacement deviation;

indicating a course angle deviation; e.g. of the type _wfl Representing a left front wheel slip ratio deviation; e.g. of the type _wfr Representing a right front wheel slip ratio deviation; e.g. of a cylinder _wrl Representing left rear wheel slip ratio deviation; e.g. of the type _wrr Representing a right rear wheel slip ratio deviation; the first element in V represents a reference front wheel corner and takes the value of 0; t is _all Represents the total drive or braking torque; f _zf Representing the front axle vertical load; f _zr Indicating the rear axle vertical load.

In some embodiments, in the present disclosure, the constraint condition for optimizing the objective function may include:

constraint conditions of the front wheel turning angle:

δ _fmin ≤δ _f ≤δ _fmax (5)

constraints of four tyre longitudinal driving or braking torques:

constraint conditions of front wheel steering angle increment:

-Δδ _fmax ≤Δδ _f ≤Δδ _fmax (7)

constraints for four tire longitudinal drive or brake torque increments:

-ΔT _ij,max ≤ΔT _ij ≤ΔT _ij,max (8)

safe phase plane constraint conditions:

M|ξ _k |≤E+s _k (9)

wherein,

M＝[M ₁ |0 _2×8 ]，

E＝[α ^sat r ^sat ]，

s _k is not less than 0 and s _k ＝su _k ,s＝[0 _2×5 |I _2×2 ]；

Wherein, delta _f Indicating a front wheel turning angle; delta _fmin Represents the minimum value of the front wheel turning angle; delta _fmax Represents the maximum value of the front wheel turning angle; t is _ij Represents the longitudinal driving or braking moments of the four wheels, ij = fl, fr, rl, rr, respectively representing the left front wheel, the right front wheel, the left rear wheel, the right rear wheel; t is _max Represents the maximum driving or braking torque output by the motor;

represents the maximum longitudinal force of the four wheels; f _yij Represents the lateral force of four wheels;

represents the maximum lateral force of four wheels; r _e Representing the effective radius of the wheel; delta delta _f Indicating a front wheel steering angle increment; delta delta _fmax A maximum value representing a front wheel steering angle increment; delta T _ij Represents four wheel longitudinal drive or brake torque increments; delta T _ij,max Represents the maximum value of the four wheel longitudinal drive or brake torque increments; m represents a construction matrix related to safety phase plane constraint; xi _k Representing the state quantity after dispersion; v. of _y Represents lateral velocity; r represents a yaw rate; e.g. of the type _y Indicating a lateral displacement deviation;

indicating a course angle deviation; e.g. of the type _wfl Representing a left front wheel slip ratio deviation; e.g. of the type _wfr Representing a right front wheel slip ratio deviation; e.g. of a cylinder _wrl Representing the left rear wheel slip ratio deviation; e.g. of a cylinder _wrr Representing a right rear wheel slip ratio deviation; alpha (alpha) ("alpha") _f Representing a front axle wheel side slip angle; alpha is alpha _r Representing a rear axle wheel side slip angle; e represents a curvature factor; v. of _x Representing a longitudinal vehicle speed; l. the _r Representing the distance of the vehicle's center of mass to the rear axle; alpha is alpha ^sat Representing a rear wheel side slip angle saturation value; beta is a ^sat Representing a centroid slip angle; r is a radical of hydrogen ^sat Representing a yaw rate; s _k Represents a relaxation variable; s represents a coefficient; u. of _k Representing the discrete control quantity; i denotes an identity matrix.

In some embodiments, the weight coefficients of the output quantities are adaptively adjusted by the following formula:

wherein,

wherein Q represents a matrix sign of the output quantity weight coefficient;

representing lateral velocity v _y The weight coefficient of (a); q _r A weight coefficient indicating the yaw rate r;

indicating the lateral displacement deviation e _y The weight coefficient of (a);

indicating course angle deviation

The weight coefficient of (a);

weighting coefficients representing four wheel slip ratio deviations ij = fl, fr, rl, rr representing a left front wheel, a right front wheel, a left rear wheel, and a right rear wheel, respectively; ε represents a stability index; kappa type _ij Represents the slip rates of the four wheels; s is ₁ ＝400；s ₂ ＝5；s ₃ ＝400；s ₄ ＝0.65；s ₅ ＝500；s ₆ ＝0.65；s ₇ ＝500；s ₈ ＝0.67；a ₁ ＝1.5；a ₂ ＝1.5；b ₁ ＝1.2；b ₂ ＝1.2；a' ₁ ＝30；b' ₁ ＝3；c' ₁ ＝0.5；range＝0.15。

It should be noted that s in the examples of the present disclosure ₁ 、s ₂ 、s ₃ 、s ₄ 、s ₅ 、s ₆ 、s ₇ 、s ₈ 、a ₁ 、a ₂ 、b ₁ 、b ₂ 、a' ₁ 、b' ₁ 、c' ₁ The value of range is a group of reference values given according to the debugging experience of the system, and due to different structural parameters and the like of the controlled object, the range has the capability of being used in practiceTechnicians can debug according to the system characteristics of the technicians to obtain a better control effect.

In some embodiments, the weight coefficient of the control amount is adaptively adjusted by the following formula:

wherein,

the weight coefficient of the control quantity increment is adaptively adjusted through the following formula:

wherein, R represents the matrix symbol of the weight coefficient of the control quantity;

a weight coefficient indicating a front wheel turning angle;

a weight coefficient representing a left front wheel longitudinal drive or braking torque;

a weighting factor representing a longitudinal driving or braking torque of the front right wheel;

a weight factor representing a left rear wheel longitudinal drive or braking torque;

a weight coefficient representing a longitudinal driving or braking torque of the right rear wheel;

and

representing the weight coefficient corresponding to the relaxation variable increment; s represents a matrix symbol of the control quantity increment weight coefficient;

a weight coefficient representing a front wheel steering angle increment;

a weighting factor representing the left front wheel longitudinal drive or brake torque increment;

a weighting factor representing a right front wheel longitudinal drive or brake torque increment;

a weight factor representing a left rear wheel longitudinal drive or brake torque increment;

a weight factor representing a right rear wheel longitudinal drive or braking torque increment; a' ₂ ＝15；b' ₂ ＝3；c' ₂ ＝0.6；rangeR＝300。

Note that a 'in the embodiment of the present disclosure' ₂ 、b' ₂ 、c' ₂ And the value of range R is a group of reference values given according to the debugging experience of the self system, and due to different structural parameters and the like of the controlled object, the technical personnel can carry out debugging according to the characteristics of the self system during actual use so as to obtain better control effect.

In some embodiments, the weight coefficient of the slack variable is adaptively adjusted by the following formula:

P＝[0 _1×5 σ _v ^P σ _r ^P ] (15)

wherein P represents a matrix symbol;

represents the relaxation variable s _v The weight coefficient of (a);

represents the relaxation variable s _r The weight coefficient of (c).

And S106, controlling the unmanned vehicle to run according to the determined front wheel rotation angle and the determined wheel moment.

It should be noted that the control variables for controlling the driving of the unmanned vehicle are mainly the front wheel rotation angle and the wheel torque, which includes the longitudinal driving or braking torque of each wheel. In the above step S106, the front wheel rotation angle and the wheel torque for controlling the unmanned vehicle to run are determined by comprehensively considering the trajectory tracking accuracy, the stability index and the wheel slip rate of the unmanned vehicle under different operating conditions, and therefore, the unmanned vehicle control method provided in the embodiment of the present disclosure can ensure that the target vehicle speed and the reference trajectory are tracked in a stable state under different operating conditions of the vehicle, which not only meets the stability requirement of the vehicle, but also meets the trajectory tracking accuracy of the vehicle.

As can be seen from the above, in the unmanned vehicle control method provided in the embodiment of the present disclosure, the track tracking accuracy, the stability index, and the wheel slip rate of the unmanned vehicle under different operating conditions are obtained, and then the front wheel rotation angle and the wheel torque of the unmanned vehicle, which tracks the target vehicle speed and refers to the track information in a stable state, are determined according to the track tracking accuracy, the stability index, and the wheel slip rate of the unmanned vehicle under different operating conditions, so as to control the unmanned vehicle to run according to the determined front wheel rotation angle and the determined wheel torque. By the aid of the method and the device, the driving of the unmanned vehicle can be controlled by comprehensively considering the vehicle stability and tracking precision of the unmanned vehicle under different operating conditions, and integrated coordination control of trajectory tracking and stability suitable for different operating conditions can be realized.

In some embodiments, the unmanned vehicle control method provided in embodiments of the present disclosure may further include the steps of: acquiring target speed and reference track information of the unmanned vehicle; determining a front wheel corner of the unmanned vehicle running along with the target speed and the reference track information according to the target speed and the reference track information of the unmanned vehicle; determining the total driving or braking torque required by the unmanned vehicle to track the target speed according to the target speed and the actual speed of the unmanned vehicle; distributing the total driving or braking torque required by the unmanned vehicle to track the target speed to each wheel according to the vertical load distribution condition of the front and rear shafts of the unmanned vehicle to obtain the longitudinal driving or braking torque of each wheel; and according to the wheel slip rate of the unmanned vehicle under different running conditions, the longitudinal driving or braking torque of each wheel is restrained.

Further, in some embodiments, the total drive or braking torque required by the unmanned vehicle to track the target vehicle speed may be determined by the following equation:

T _des ＝k ₁ (v _x -v _xdes )+k ₂ ∫(v _x -v _xdes )dt (16)

wherein, T _des Represents the total drive or braking torque; v. of _x Representing an actual vehicle speed; v. of _xdes Representing a target vehicle speed; k is a radical of formula ₁ Represents a scale factor; k is a radical of formula ₂ Representing the integral coefficient.

Further, in some embodiments, the unmanned vehicle control method provided in embodiments of the present disclosure may perform the limiting process on the total driving or braking torque required for the unmanned vehicle to track the target vehicle speed by: acquiring the maximum driving or braking torque output by a motor on the unmanned vehicle; and carrying out amplitude limiting processing on the total driving or braking torque required by the unmanned vehicle for tracking the target speed according to the maximum driving or braking torque output by the motor on the unmanned vehicle.

In some embodiments, the total drive or brake torque required for the unmanned vehicle to track the target vehicle speed is limited by the following formula:

wherein, T _all Representing the total driving or braking torque after amplitude limiting; t is a unit of _des Representing the total driving or braking torque before the amplitude limiting process; t is _max Represents the maximum driving or braking torque output by the single motor; n represents the number of motors outputting a driving or braking torque.

In some embodiments, the unmanned vehicle control method provided in embodiments of the present disclosure may further include the steps of: acquiring lateral displacement deviation and course angle deviation of the unmanned vehicle tracking target speed and reference track information under different operating conditions; and determining the track tracking precision of the unmanned vehicle for tracking the target speed and the reference track information under different operating conditions according to the lateral displacement deviation and the course angle deviation of the unmanned vehicle for tracking the target speed and the reference track information under different operating conditions.

In some embodiments, the unmanned vehicle control method provided in embodiments of the present disclosure may further include the steps of: acquiring lateral speed, yaw angular speed and front and rear axle wheel slip angles of the unmanned vehicle under different operating conditions; and determining the stability index of the unmanned vehicle for tracking the target vehicle speed and the reference track information under different operation conditions according to the lateral vehicle speed, the yaw angular velocity and the front and rear axle wheel slip angles of the unmanned vehicle under different operation conditions.

In some embodiments, the stability index of the unmanned vehicle running along the target vehicle speed and the reference track information under different running conditions is determined by the following formula:

wherein R is ₁ ＝max(|α _f,sat |,|α _r,sat |) represents the larger of the front wheel saturated slip angle and the rear wheel saturated slip angle;

(α _f,s ,α _r,s ) Representing saddle point coordinates closer to the origin;

representing the distance of the current state of the unmanned vehicle from the origin position.

In some embodiments, the method further comprises: determining lateral speed, yaw angular speed, lateral displacement deviation, course angle deviation, wheel slip rate and front and rear axle wheel slip angles as state variables, and establishing a state space expression; and self-adaptively adjusting the weight coefficients of the lateral speed, the yaw rate and the tracking deviation amount according to the vehicle stable state of the unmanned vehicle under different operation conditions.

Further, in some embodiments, the state space expression is established as:

wherein,

wherein,

indicating a change speed of the state quantity; ξ represents the state quantity; u represents a control amount; f () represents a functional relationship between a change speed of the state quantity and the control quantity; η represents an output quantity; h () represents a functional relationship between the output quantity and the state quantity; alpha (alpha) ("alpha") _f Representing a front axle wheel side slip angle; alpha (alpha) ("alpha") _r Indicating the rear axle wheel side slip angle.

Further, in some embodiments, the weighting coefficients for the lateral vehicle speed, yaw rate, and tracking offset may be adaptively adjusted by: if the unmanned vehicle is in a stable state, reducing the weight coefficient of the lateral vehicle speed, and increasing the weight coefficient of the yaw angle and the tracking deviation amount; and if the unmanned vehicle is in an unstable state, increasing the weight coefficient of the lateral vehicle speed and reducing the weight coefficient of the yaw angle and the tracking deviation amount.

Fig. 2 shows a specific implementation architecture of a method for controlling an unmanned vehicle in an embodiment of the present disclosure, where the architecture may be applied to, but not limited to, track following and stability coordination control of an electric unmanned vehicle with four independent drives facing different operation conditions, and as shown in fig. 2, the general idea is: firstly, obtaining a target vehicle speed v based on an upper movement planning layer _xref And reference track information Y _ref And

and real-time vehicle state information, and the required total driving torque T is obtained by designing a PID controller to track the change of the target vehicle speed _all That is, the total torque demand of the driver is met, and then the total driving torque is distributed according to the vertical load distribution condition of the front axle and the rear axle to obtain a reference four-wheel torque value T _ref . Then, an integrated controller based on an MPC is designed, lateral displacement deviation and course angle deviation representing tracking accuracy are selected, lateral speed and yaw speed describing vehicle yaw dynamic characteristics are selected, four-wheel slip rate deviation representing a tire rolling state is selected as state quantity, in addition, in order to further ensure the stability of the vehicle, front and rear axle tire slip angles are selected as state quantity, the stable state of the vehicle is judged in real time through stability evaluation indexes designed based on front and rear wheel slip angle phase planes, a set of weight adaptive adjustment strategies based on the track tracking accuracy, maneuverability and stability targets of the stability evaluation indexes are designed, according to real-time tire slip rate information, hyperbolic functions are introduced to correspondingly adjust the weight of the four-wheel slip rate deviation, corresponding turning angles and moments are decided, and the reference track and the target vehicle speed are tracked while the stable running of the vehicle is ensured.

In specific implementation, the method can comprise the following steps:

1) Establishing a PI longitudinal motion controller, and calculating the total driving or braking torque required by the unmanned vehicle for tracking the target speed and the reference track information:

as shown in fig. 3, the total drive torque required to track the desired vehicle speed is calculated using the proportional-integral principle based on the deviation between the current vehicle speed (actual vehicle speed) and the desired vehicle speed (target vehicle speed), and the specific calculation formula is shown in formula (16), and will not be described herein again.

Then, according to the maximum driving or braking torque T which can be output by the single motor _max Limiting the total driving or braking torque to obtain the actual total driving or braking torque T _all Comprises the following steps:

thereafter, the total driving/braking torque T is adjusted according to the load distribution of the front and rear axles _all The distribution is performed such that the left and right wheels are equally distributed as the reference control amount V as shown in the above equation (4).

2) An integrated controller based on MPC is established to realize the coordinated control of trajectory tracking and stability:

the process of establishing the prediction model is described in detail with reference to the vehicle-path relative position model shown in fig. 4 and the seven-degree-of-freedom two-rail vehicle dynamics model shown in fig. 5:

a1, establishing a state space expression:

calculating lateral displacement and course angle deviation derivatives based on the relative position model of the vehicle and the path, wherein the calculation formulas are respectively as follows:

the lateral vehicle speed, the deviation between the yaw angular velocity and the four-wheel slip rate and the change rate of the yaw angle of the seven-degree-of-freedom vehicle dynamic model are calculated, and the calculation formulas are respectively as follows:

wherein Mz is the external yaw moment generated by the four-wheel longitudinal force around the vehicle centroid, and is specifically expressed as:

wherein κ (ρ) is a road curvature; m is the mass of the whole vehicle; i is _z Is the moment of inertia, v, of the vehicle about the z-axis _x 、v _y Longitudinal and lateral vehicle speeds, r yaw rate, delta front wheel angle, F _xij ,F _yij Tire longitudinal and lateral forces, respectively, where subscripts ij = fl, fr, rl, rr denote left front wheel, right front wheel, left rear wheel, right rear wheel, respectively; l _a 、l _b The distance from the center of mass to the front and rear axes, respectively, and c is the vehicle wheel tread.

The four wheel slip rate deviation derivative is:

the desired angular tire rotation speeds are:

the desired angular acceleration of the tire is then:

therein, ζ _r ＝-ζ _l ＝1；

The actual angular acceleration of the tire is:

wherein, ω is _ij For the actual angular velocity of rotation of the tyre, T _ij For four wheel moment, I _z Is moment of inertia, R _e Is the effective radius of the wheel;

the four-wheel sidewall slip angle can be written as:

therein, ζ _f ＝-ζ _r ＝1,δ _f ＝δ,δ _r ＝0；

The wheel sidewall slip angle is derived as:

wherein,

by the above formula derivation, a state space expression as shown in the above formula (19) can be established.

In order to ensure that the vehicle can track the reference track well while running stably, the determined state variable is shown in the formula (20) above; the selected control quantity is shown in the formula (2); the selected output is shown in the above formula (3); the selected disturbance amount is shown in equation (22).

Wherein, a _x In the form of a longitudinal acceleration, the acceleration,

the resulting four wheel longitudinal force is estimated.

A2, performing linearization processing on the state space expression to obtain a linear time-varying system:

the tire stress plays an important role in the steering stability and the smoothness of a vehicle, a unified tire model (UniTire model) shown in fig. 6 in the embodiment of the present disclosure describes the stress of a tire under different operating conditions, and the model can uniformly express the longitudinal, sliding and lateral deviation characteristics of the tire under pure and compound operating conditions, and the specific expression form is as follows:

wherein,

is a dimensionless total tangential force; f _x And F _y Representing the actual longitudinal and lateral forces, respectively; e is a curvature factor; μ is the directional coefficient of friction; f _z Is a vertical load; phi is a _x ，φ _y And φ represents the relative longitudinal, lateral and overall slip ratios, respectively, defined as:

wherein, K _x 、K _y Respectively, the longitudinal and lateral stiffness of the tire; f _z Is a vertical load; mu.s _x ，μ _y Coefficient of friction, S, in the longitudinal and lateral directions, respectively _x And S _y The calculation formulas are respectively the longitudinal slip rate and the lateral slip rate of the tire, and are as follows:

wherein R is _e To effectively rollA dynamic radius; v _sx And V _sy Respectively representing longitudinal and lateral slip speeds.

And substituting the tire force into a state space equation to obtain a nonlinear vehicle dynamic model as a prediction model.

In order to meet the real-time requirement of the controller in the practical application process, the model (state space expression) must be subjected to proper linearization processing. According to Taylor's expansion formula, at the working point (xi) ₀ (k)、u ₀ (k)、w ₀ (k) Taylor expansion is performed, only the first term is kept, and all higher-order terms are ignored, so that the following linear time-varying system is obtained:

wherein A (t) represents a coefficient matrix of the state quantity, B (t) represents a coefficient matrix of the control quantity, and D (t) represents a coefficient matrix of the disturbance quantity;

a3, discretizing the state space expression by a first-order difference quotient method to obtain a discretized state space expression:

ξ(k+1)＝A(k)ξ(k)+B(k)u(k)+D(k)w(k) (39)

where ξ (k) represents the state quantity after discretization, u (k) represents the controlled quantity after discretization, a (k) represents the state quantity coefficient matrix after discretization, and a (k) = I + a (T) T, B (k) and D (k) represent the controlled quantity after discretization and the disturbance quantity coefficient matrix, respectively, and B (k) = B (T) T and D (k) = D (T) T are satisfied; wherein I represents an identity matrix; t denotes a sampling period.

Because the system matrix dimension is large, in order to reduce the huge calculation amount of manual derivation operation and reduce the error rate, the state quantity, the control quantity and disturbance quantity coefficient matrixes A (k), B (k) and D (k) are solved by adopting the Jacobian function of matlab;

and A4, calculating expected reference output quantity:

taking the current-time vehicle speed and the front wheel steering angle decided by the controller at the previous time as input, and calculating by using a two-degree-of-freedom vehicle model to obtain an expected yaw rate as follows:

in the formula, r _des In the desired yaw rate, r ₀ And r _max Respectively, the target yaw angular velocity and the maximum value thereof are specifically expressed as follows:

wherein mu is a road surface adhesion coefficient; c _r Rear wheel stiffness; c _f Front wheel stiffness; l _f The distance from the center of mass of the vehicle to the front axle; l. the _r Is the distance from the vehicle center of mass to the rear axle.

When the centroid slip angle β is small, the desired lateral vehicle speed is defined as the actual vehicle lateral speed, and once the centroid slip angle increases and exceeds a certain threshold, the desired lateral vehicle speed is set to zero, i.e.:

wherein the centroid slip angle threshold is: beta is a beta _max = atan (0.02 μ g), expected values of lateral displacement and heading angle deviation characterizing tracking accuracy are 0, i.e.:

in order to make the tire in a pure rolling state as much as possible, the desired rotation speed is set to a rotation speed in the pure rolling state of the tire, and the slip ratio of the wheel is controlled to prevent the wheel from excessively slipping while controlling the handling stability of the vehicle, so that the desired value of the four-wheel slip ratio deviation is 0, that is: e.g. of the type _wij,des ＝0。

The total reference output is therefore:

wherein eta _ref Representing a desired reference output quantity; v. of _ydes Indicating a desired lateral velocity; r is a radical of hydrogen _des Representing a desired yaw rate; e.g. of the type _ydes Indicating a desired lateral displacement deviation;

indicating a desired heading angle deviation; e.g. of the type _wfl,des Indicating a desired left front wheel slip ratio deviation; e.g. of the type _wfr,des Indicating a desired right front wheel slip ratio deviation; e.g. of the type _wrl,des Indicating a desired left rear wheel slip ratio deviation; e.g. of the type _wrr,des Indicating the desired right rear wheel slip ratio deviation.

A5, establishing an objective function:

assuming that the current moment is k, establishing an objective function shown in the formula (1) for calculating a front wheel corner and a four-wheel moment required by a vehicle for quickly and stably tracking a reference track, wherein the first term is punishment on deviation between actual output and reference output and is used for ensuring track tracking precision and vehicle stability, the second term is punishment on deviation between a front wheel corner amplitude and the deviation between the actual torque output and the reference value, the corner is prevented from being too large, meanwhile, the moment decided by a controller under a conventional working condition is enabled to track the change of a driver expected value as much as possible, tracking of an expected vehicle speed is better realized, the third term is punishment on control quantity increment, stable change of an actuator is ensured, and the fourth term is punishment on a safety phase plane relaxation variable, so that feasibility of solving of the controller is ensured.

A6, considering relevant constraint conditions:

first, the physical constraint and the friction ellipse constraint of the actuator are considered, and the front wheel rotation angle and the four-wheel torque range are given, as shown in the above equation (5) and equation (6), respectively.

In order to ensure smooth change of the actuator, improve the comfort of the whole vehicle and the comprehensive control effect, the increment of the controlled variable is restrained, and the restraint formula is shown as the formula (7) and the formula (8).

To ensure vehicle stability, the centroid slip angle β is derived based on the peak slip angle ^e And yaw rate r ^e The safety phase plane constraint is designed as shown in the above equation (9), and will not be described herein.

A7, calculating a vehicle stability evaluation index:

the quantitative stability evaluation index based on the front and rear wheel side slip angle phase plane is used as shown in equation (18).

In view of the stability index design based on the tire slip angle phase plane in conjunction with FIG. 7, region 3 represents the stability region, which is centered at the origin with a radius R ₁ Circle of (2), R ₁ ＝max(|α _f,sat |,|α _r,sat I) represents a larger value of the saturated slip angle of the front wheel or the rear wheel, the vehicle has better stability in the region, and the track tracking precision and the maneuverability of the vehicle are taken as main control targets; region 2 represents the transition region, which is centered at the origin and has a radius

The vehicle may be unstable when the state is in this region because the front-rear wheel side slip angle of the vehicle reaches a large value and a portion exceeds a saturation value. Region 1 is defined as the unstable region, which is centered at the origin and has a radius of

Of the center point (alpha) _f,s ,α _r,s ) The coordinate of the saddle point closer to the origin is shown, the side deflection angle of the front wheel or the rear wheel enters a severe descending region, and the vehicle easily loses the steering capability or is in a 'tail flick' state, so that the vehicle is in an unstable region.

Indicating the distance from the current vehicle actual state to the origin position.

When epsilon is (0, 1), the vehicle state is stable, the stability degree is higher when epsilon is larger, when epsilon is (-1, 0), the vehicle state is in the transition region, the vehicle has the possibility of instability, and when epsilon is (2, -1), the vehicle is in the limit state and is about to be unstable.

A8, weight self-adaptive adjustment strategy based on quantitative stability evaluation indexes:

the track tracking precision is similar to the controllability control target of the vehicle, and the controllability and the stability control target are usually contradictory, so that in order to better meet the weight priority of the vehicle on the control targets such as the track tracking precision, the controllability, the stability and the like under different operation conditions, a set of weight adaptive adjustment strategy based on the stability evaluation index and hyperbolic tangent slip ratio deviation weight adaptive adjustment based on the four-wheel slip ratio are designed, and the formula (11) is specifically shown.

In combination with the tracking accuracy, maneuverability and stability weight adaptive graph of fig. 8, when epsilon is more than or equal to 0.2, the vehicle is in a stable state, and the track tracking accuracy and the vehicle maneuverability are used as main control targets, so that the lateral vehicle speed weight is small, and the yaw rate and the tracking deviation amount weight are large; at epsilon < 0.2, the stability of the vehicle gradually deteriorates as epsilon decreases, so the lateral vehicle speed weight is gradually increased, and the yaw rate and tracking deviation amount weight are correspondingly decreased, thus realizing adaptive adjustment. In the embodiment of the disclosure, a hyperbolic function is introduced to adaptively adjust the four-wheel slip ratio deviation weight, and in combination with the curve shown in fig. 9, when the slip ratio is smaller, a smaller weight is given, and after the slip ratio is increased, the weight is correspondingly and rapidly increased, so that the slip ratio is restricted in a smaller range, thereby preventing wheel slip and further improving the safety of vehicle driving.

Thus, the final output quantity weight is:

the weight of the control quantity based on the moment increment is adjusted in an adaptive mode, and is specifically shown in the formula (13) above; the control amount weight is as shown in the above equation (12); the control increment weight is as shown in equation (14) above; the relaxation variable weight is as shown in equation (15) above.

A9, optimization solution of an objective function:

based on the objective function of the formula (4), actuator constraint and safety phase plane constraint are comprehensively considered, and optimization solution is carried out through a qpOASES resolver, namely:

solving the objective function to obtain a series of control input increments and relaxation variables in the control time domain:

wherein Δ u represents a control amount increment;

a control amount increment indicating time t;

represents the control amount increment at the time t + 1;

represents t + N _c -a control quantity increment at time 1; sigma _v And σ _r Representing the relaxation variable.

And adding the control quantity corresponding to the first element in the control sequence and the previous moment to obtain the final control quantity.

And A10, repeating the steps A1-A9 at the moment of t +1, and repeating the rolling optimization in such a way to realize the tracking of the reference track.

As can be seen from the above, the control method for the unmanned vehicle provided in the embodiment of the present disclosure adopts an integrated control method based on the MPC, comprehensively considers a trajectory tracking precision, a vehicle stability control target and a four-wheel slip rate control target, determines the vehicle operation state through a stability evaluation index designed based on a front-wheel side deflection angle phase plane and a rear-wheel side deflection angle phase plane, correspondingly performs real-time adaptive adjustment on multiple control target weights of the MPC, and finally decides a corresponding corner and four-wheel moment, thereby implementing integrated coordination control of trajectory tracking and stability adapted to different operation conditions.

Compared with the existing trajectory tracking controller, the unmanned vehicle control method provided by the embodiment of the disclosure has the following advantages:

1) The four-wheel independent drive unmanned electric vehicle track tracking and stability coordination control method oriented to different running conditions can ensure that a vehicle has higher stability while tracking a reference track.

2) The integrated controller for different operating conditions is designed based on a model predictive control algorithm, the control targets of the trajectory tracking precision, the vehicle stability and the four-wheel slip ratio under different operating conditions, the physical constraint of an actuator, the friction ellipse constraint and the phase plane safety constraint can be comprehensively considered, the relatively optimal control quantity is decided, and the integrated controller has better comprehensive control performance.

3) A set of weight adaptive adjustment strategies of different control targets are designed based on quantized stability evaluation indexes to coordinate the track tracking precision and the weight priority of a vehicle maneuverability target and a vehicle stability target under different operation conditions, a hyperbolic function is introduced to carry out adaptive adjustment on the four-wheel slip rate deviation weight, tire slip is prevented, the safety performance of the vehicle is further improved, and the adaptive adjustment strategy has good adaptability and robustness to different operation conditions.

4) Phase plane safety constraints are designed based on corresponding yaw velocity and centroid slip angle when the rear wheel slip angle is saturated, and the stability of the vehicle is further guaranteed; and by introducing a relaxation variable, the controller can still find a feasible solution under the limit working condition, namely, the vehicle is allowed to temporarily exceed a safety constraint boundary to a small extent, so that the control performance of the whole vehicle is ensured, and the vehicle is prevented from being out of control caused by the solving failure of the controller.

Based on the same inventive concept, the disclosed embodiment also provides a control device of the unmanned vehicle, as described in the following embodiments. Because the principle of solving the problem of the embodiment of the apparatus is similar to that of the embodiment of the method, reference may be made to the implementation of the embodiment of the apparatus, and repeated descriptions are omitted.

Fig. 10 shows a schematic diagram of a control device for an unmanned vehicle according to an embodiment of the present disclosure, and as shown in fig. 10, the control device includes: a status information acquisition module 1001, a control amount determination module 1002, and a control module 1003.

The state information obtaining module 1001 is configured to obtain track tracking accuracy, stability indexes and wheel slip rate of the unmanned vehicle under different operating conditions; the control quantity determining module 1002 is configured to determine a front wheel rotation angle and a wheel torque of the unmanned vehicle, which tracks the target vehicle speed and runs according to the reference track information in a stable state under different operating conditions, according to the track tracking accuracy, the stability index and the wheel slip ratio of the unmanned vehicle under different operating conditions; and the control module 1003 is used for controlling the unmanned vehicle to run according to the determined front wheel turning angle and the determined wheel torque.

It should be noted here that the state information obtaining module 1001, the control amount determining module 1002, and the control module 1003 correspond to S102 to S106 in the method embodiment, and the modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure of the method embodiment. It should be noted that the modules described above as part of an apparatus may be implemented in a computer system such as a set of computer-executable instructions.

In some embodiments, determining the front wheel rotation angle and the wheel torque of the unmanned vehicle running in a stable state to track the target vehicle speed and reference track information under different running conditions according to the track tracking accuracy, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions comprises: constructing a target function by taking the track tracking precision, the stability index and the wheel slip rate as targets; determining constraint conditions of an objective function; and determining the front wheel rotation angle and the wheel torque of the unmanned vehicle running in a stable state to track the target vehicle speed and the reference track information under different running conditions according to the target function and the constraint condition.

In some embodiments, the control amount determining module 1002 is further configured to: acquiring target vehicle speed and reference track information of the unmanned vehicle; determining a front wheel corner of the unmanned vehicle running along with the target speed and the reference track information according to the target speed and the reference track information of the unmanned vehicle; determining the total driving or braking torque required by the unmanned vehicle to track the target speed according to the target speed and the actual speed of the unmanned vehicle; distributing the total driving or braking torque required by the unmanned vehicle to track the target speed to each wheel according to the vertical load distribution condition of the front and rear shafts of the unmanned vehicle to obtain the longitudinal driving or braking torque of each wheel; and according to the wheel slip rate of the unmanned vehicle under different running conditions, the longitudinal driving or braking torque of each wheel is restrained.

In some embodiments, the control amount determining module 1002 is further configured to: obtaining the maximum driving or braking torque output by a motor on the unmanned vehicle; and carrying out amplitude limiting processing on the total driving or braking torque required by the unmanned vehicle for tracking the target speed according to the maximum driving or braking torque output by the motor on the unmanned vehicle.

In some embodiments, the control amount determining module 1002 is further configured to: acquiring lateral displacement deviation and course angle deviation of the unmanned vehicle tracking target speed and reference track information under different operating conditions; and determining the track tracking precision of the unmanned vehicle for tracking the target speed and the reference track information under different operating conditions according to the lateral displacement deviation and the course angle deviation of the unmanned vehicle for tracking the target speed and the reference track information under different operating conditions.

In some embodiments, the control amount determining module 1002 is further configured to: acquiring lateral speed, yaw angular speed and front and rear axle wheel slip angles of the unmanned vehicle under different operating conditions; and determining the stability index of the unmanned vehicle for tracking the target vehicle speed and the reference track information under different operation conditions according to the lateral vehicle speed, the yaw angular velocity and the front and rear axle wheel slip angles of the unmanned vehicle under different operation conditions.

In some embodiments, the control amount determining module 1002 is further configured to: determining lateral speed, yaw angular speed, lateral displacement deviation, course angle deviation, wheel slip rate and front and rear axle wheel slip angles as state variables, and establishing a state space expression; and self-adaptively adjusting the weight coefficients of the lateral speed, the yaw rate and the tracking deviation amount according to the vehicle stable state of the unmanned vehicle under different operation conditions.

In some embodiments, the control amount determining module 1002 is further configured to: if the unmanned vehicle is in a stable state, reducing the weight coefficient of the lateral vehicle speed, and increasing the weight coefficient of the yaw angle and the tracking deviation amount; and if the unmanned vehicle is in an unstable state, increasing the weight coefficient of the lateral vehicle speed and reducing the weight coefficient of the yaw angle and the tracking deviation.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 1100 according to this embodiment of the disclosure is described below with reference to fig. 11. The electronic device 1100 shown in fig. 11 is only an example and should not impose any limitations on the functionality or scope of use of embodiments of the present disclosure.

As shown in fig. 11, electronic device 1100 is embodied in the form of a general purpose computing device. The components of the electronic device 1100 may include, but are not limited to: the at least one processing unit 1110, the at least one memory unit 1120, and a bus 1130 that couples various system components including the memory unit 1120 and the processing unit 1110.

Wherein the storage unit stores program code that is executable by the processing unit 1110 to cause the processing unit 1110 to perform steps according to various exemplary embodiments of the present disclosure as described in the above section "exemplary methods" of this specification. For example, the processing unit 1110 may perform the following steps of the above-described method embodiment: acquiring the track tracking precision, stability indexes and wheel slip rate of the unmanned vehicle under different running conditions; determining the front wheel rotation angle and the wheel moment of the unmanned vehicle which tracks the target speed and runs by referring to the track information in a stable state under different running conditions according to the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions; and controlling the unmanned vehicle to run according to the determined front wheel rotation angle and the determined wheel torque.

The storage unit 1120 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM) 11201 and/or a cache memory unit 11202, and may further include a read only memory unit (ROM) 11203.

Storage unit 1120 may also include a program/utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 1130 may be representative of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 1100 may also communicate with one or more external devices 1140 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1100, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1100 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 1150. Also, the electronic device 1100 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1160. As shown, the network adapter 1160 communicates with the other modules of the electronic device 1100 over the bus 1130. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, and may also be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, which may be a readable signal medium or a readable storage medium. Fig. 12 is a schematic diagram of a computer-readable storage medium in an embodiment of the disclosure, and as shown in fig. 12, the computer-readable storage medium 1200 has a program product stored thereon, which is capable of implementing the above-mentioned method of the disclosure. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure as described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.

More specific examples of the computer-readable storage medium in the present disclosure may include, but are not limited to: 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 the present disclosure, a computer readable storage medium may include a propagated data signal with readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a 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.

Alternatively, program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

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

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken into multiple step executions, etc.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method of controlling an unmanned vehicle, comprising:

acquiring the track tracking precision, stability indexes and wheel slip rate of the unmanned vehicle under different running conditions;

determining a front wheel corner and a wheel moment of the unmanned vehicle running at a stable state tracking target speed and reference track information under different running conditions according to the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions;

and controlling the unmanned vehicle to run according to the determined front wheel rotation angle and the determined wheel torque.

2. The unmanned vehicle control method of claim 1, wherein determining a front wheel rotation angle and a wheel moment at which the unmanned vehicle tracks a target vehicle speed and reference trajectory information in a stable state under different operation conditions according to the trajectory tracking accuracy, the stability index and the wheel slip rate of the unmanned vehicle under the different operation conditions comprises:

constructing a target function by taking the track tracking precision, the stability index and the wheel slip rate as targets;

determining constraints of the objective function;

and determining the front wheel rotation angle and the wheel torque of the unmanned vehicle running in a stable state tracking target speed and reference track information under different running conditions according to the target function and the constraint condition.

3. The unmanned vehicle control method of claim 1, further comprising:

acquiring target speed and reference track information of the unmanned vehicle;

determining a front wheel corner of the unmanned vehicle running along with the target speed and the reference track information according to the target speed and the reference track information of the unmanned vehicle;

determining the total driving or braking torque required by the unmanned vehicle to track the target speed according to the target speed and the actual speed of the unmanned vehicle;

distributing the total driving or braking torque required by the unmanned vehicle for tracking the target speed to each wheel according to the vertical load distribution condition of the front and rear shafts of the unmanned vehicle to obtain the longitudinal driving or braking torque of each wheel;

and according to the wheel slip rates of the unmanned vehicle under different operating conditions, constraining the longitudinal driving or braking torque of each wheel.

4. The unmanned vehicle control method of claim 3, further comprising, after determining a total drive or braking torque required by the unmanned vehicle to track the target vehicle speed based on the target vehicle speed and an actual vehicle speed of the unmanned vehicle, the method further comprising:

obtaining a maximum driving or braking torque output by a motor on the unmanned vehicle;

and carrying out amplitude limiting processing on the total driving or braking torque required by the unmanned vehicle for tracking the target vehicle speed according to the maximum driving or braking torque output by the motor on the unmanned vehicle.

5. The unmanned vehicle control method of claim 1, further comprising:

acquiring lateral displacement deviation and course angle deviation of the unmanned vehicle tracking target speed and reference track information under different operating conditions;

and determining the track tracking precision of the unmanned vehicle for tracking the target vehicle speed and the reference track information under different operating conditions according to the lateral displacement deviation and the course angle deviation of the unmanned vehicle for tracking the target vehicle speed and the reference track information under different operating conditions.

6. The unmanned vehicle control method of claim 1, further comprising:

acquiring lateral speed, yaw angular speed and front and rear axle wheel slip angles of the unmanned vehicle under different operating conditions;

and determining the stability index of the unmanned vehicle for tracking the target vehicle speed and the reference track information under different operation conditions according to the lateral vehicle speed, the yaw rate and the front and rear axle wheel slip angles of the unmanned vehicle under different operation conditions.

7. The unmanned vehicle control method of claim 1, further comprising:

determining lateral speed, yaw rate, lateral displacement deviation, course angle deviation, wheel slip rate and front and rear axle wheel slip angles as state variables, and establishing a state space expression;

and adaptively adjusting the weight coefficients of the lateral speed, the yaw rate and the tracking deviation amount according to the vehicle stable state of the unmanned vehicle under different operation conditions.

8. An unmanned vehicle control apparatus, comprising:

the state information acquisition module is used for acquiring the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions;

the control quantity determining module is used for determining the front wheel rotation angle and the wheel moment of the unmanned vehicle which tracks the target vehicle speed and runs with reference track information in a stable state under different running conditions according to the track tracking precision, the stability index and the wheel slip rate of the unmanned vehicle under different running conditions;

and the control module is used for controlling the unmanned vehicle to run according to the determined front wheel rotating angle and the determined wheel torque.

9. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the unmanned vehicle control method of any of claims 1-7 via execution of the executable instructions.

10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, is adapted to carry out the unmanned vehicle control method of any one of claims 1-7.