CN117826590A - Unmanned vehicle formation control method and system based on prepositive following topological structure - Google Patents
Unmanned vehicle formation control method and system based on prepositive following topological structure Download PDFInfo
- Publication number
- CN117826590A CN117826590A CN202311806735.9A CN202311806735A CN117826590A CN 117826590 A CN117826590 A CN 117826590A CN 202311806735 A CN202311806735 A CN 202311806735A CN 117826590 A CN117826590 A CN 117826590A
- Authority
- CN
- China
- Prior art keywords
- virtual point
- following
- follower
- error
- formation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000015572 biosynthetic process Effects 0.000 title claims abstract description 101
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 30
- 238000012545 processing Methods 0.000 claims abstract description 11
- 230000009467 reduction Effects 0.000 claims abstract description 10
- 230000006870 function Effects 0.000 claims description 19
- 238000013178 mathematical model Methods 0.000 claims description 17
- 238000004891 communication Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 13
- 238000012546 transfer Methods 0.000 claims description 13
- 238000013507 mapping Methods 0.000 claims description 6
- 238000013459 approach Methods 0.000 claims description 4
- 230000003189 isokinetic effect Effects 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 230000008859 change Effects 0.000 abstract description 9
- 230000001133 acceleration Effects 0.000 abstract description 5
- 230000009471 action Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 12
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 238000009795 derivation Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides an unmanned vehicle formation control method and system based on a front following topological structure, which belong to the technical field of automatic driving and intelligent transportation, and detect the speed and angular speed of a front virtual point in real time; establishing a virtual point-pilot following formation model of a pilot leader, a follower followers and virtual points; performing dimension reduction processing on the controlled quantity of the virtual point-pilot following formation model; calculating the output quantity of the sliding mode control law according to the input quantity; and estimating and correcting the uncertain parameters according to the control law. According to the invention, based on the prepositive following topological structure, the piloting prepositive following topological structure is provided, a topological sliding mode control algorithm is designed, the influence of sliding mode control buffeting on a system is reduced, and the chain stability of vehicle formation is ensured; the unmanned formation control system has good control performance under the scene of no abrupt change of the acceleration of the pilot vehicle, abrupt change of the acceleration and combined action of noise interference, and has important application significance for the unmanned formation control system which can be used in actual scenes.
Description
Technical Field
The invention relates to the technical field of automatic driving and intelligent transportation, in particular to an unmanned vehicle formation control method and system based on a front following topological structure.
Background
The nonlinear control method for formation comprises the following steps: control method based on back-stepping method, model predictive control, robust control, cooperative fault-tolerant control and H ∞ Control, adaptive integral sliding mode control, consistency-based distributed control, artificial neural network control, sliding mode control, and the like.
When the model order is higher, the design of the controller is more complicated, and the structure is too complex. The model prediction control has higher requirement on the accuracy of the model, different cost functions are designed according to different application scenes, and meanwhile, the parameter adjustment difficulty is high. Robust control is difficult to work in an optimal state, so steady state accuracy of the system can be poor. H ∞ The design process of the controller is complicated. The self-adaptive control is suitable for a type of system with unknown controlled object characteristics or large disturbance characteristic change range and high performance index maintenance requirement, but the design of the system is not systematic, and a control target cannot be defined. The neural network can approximate any nonlinear function with any accuracy and has global approximation capability, but when training samples are increased, the complexity of the network increases, resulting in an increase in the amount of computation. The sliding mode control can overcome uncertainty of the system, has strong robustness to external interference and unmodeled dynamics, and particularly has good control effect on control of a nonlinear system.
Compared with the existing front-end following topology, the piloting front-end following topology has the following advantages: all the formation vehicles can obtain information of the piloting vehicles, and have better perception capability on the road conditions in front; when the formation vehicle can not acquire the information of the vehicle in front of the formation vehicle, the information of the pilot vehicle can still be acquired, and the network structure redundancy is better.
Disclosure of Invention
The invention aims to provide an unmanned vehicle formation control method and system based on a front following topological structure, which are used for solving at least one technical problem in the background technology.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the invention provides an unmanned vehicle formation sliding mode control method based on a front following topological structure, which comprises the following steps:
detecting the speed and the angular speed of a front virtual point in real time;
establishing a virtual point-pilot following formation model of a pilot leader, a follower followers and virtual points;
performing dimension reduction processing on the controlled quantity of the virtual point-pilot following formation model;
calculating the output quantity of the sliding mode control law according to the input quantity;
estimating and correcting the uncertain parameters according to the control law;
the input quantity is the linear speed and the angular speed of a front virtual point, the longitudinal error, the transverse error and the angular error of the current vehicle and the front vehicle, the output quantity is the linear speed and the angular speed of the current vehicle, and the two variables are acted on the unmanned vehicle formation kinematics model to form a complete closed loop management system.
Further, establishing a virtual point-pilot following formation model comprising a pilot vehicle leader, a following vehicle followers and a mathematical model of the virtual point;
the mathematical model of leader is:
the mathematical model of the follower is:
the geometric relationship between the virtual point and the leader is as follows:
global lateral position error x of a follower with a virtual point e Global longitudinal position error y e And global yaw angle error θ e The method comprises the following steps:
converting global errors in an x-y coordinate system into local errors in an x '-y' coordinate system, and establishing an x '-y' local coordinate system by taking a follower as a center; the coordinate system is converted into:
wherein e 1 Represents a lateral error, e 2 Represented as longitudinal error, e 3 Representing yaw angle error;
deriving the transformed coordinate system to obtain an error kinematics equation of the follower under the x '-y' coordinate system:
the linear velocity and angular velocity of the virtual point can be obtained by deriving the mathematical model of the follower:
the linear velocity of the virtual point can be expressed as:
since the geometric centers of the virtual point and the leader are always on a straight line, θ m ＝θ l The angle of the virtual point is:the angular velocity of the virtual point can be obtained by deriving the angle as follows:
wherein, x and y respectively represent the x axis and the y axis in the global coordinate system, x ', y' respectively represent the x axis and the y axis of the local coordinate system with the follower center as the origin, G l (x l ,y l ) Is the center point coordinates of the leader, x l Is the abscissa, y l Is the ordinate, gm (xm, ym) is the coordinate of the virtual point, x m Is the abscissa, y m Is the ordinate, G f (x f ,y f ) Is the center point coordinate of the follower, x f In abscissa, y f On the ordinate, v l ，v f Indicating the speeds of leader and follower, ω, respectively l ，ω f Respectively represent the angular velocity of the leader and the follower, θ l ，θ f The yaw angles of the leader and the follower are respectively indicated, and L represents the time-varying safety distance.
Further, the controlled quantity is subjected to dimension reduction processing, the transverse position error and the yaw angle error are considered together, and the error variable is modified as follows:
furthermore, the sliding mode control algorithm is adopted to control the followers, and in the vehicle formation under the front following topological structure, each of the followers can only obtain the position, speed and angular speed information of the front vehicle, and the sliding mode surface is as follows:
wherein the method comprises the steps ofAnd alpha is i ＞0(i＝1,2)。
And (3) deriving a sliding mode surface to obtain:
then:
the isokinetic approach law was chosen as follows:
wherein the method comprises the steps ofAnd epsilon i ＞0(i＝1,2)。
Then, the sliding mode control law based on the front-end following topology is:
v f ＝v m cose 3 +e 2 ω f +α 1 e 1 +ε 1 sat(s 1 )
further, as the unmanned vehicle formation is a whole, when the front vehicle receives interference, the expansion of the interference error along the vehicle flow direction is avoided as much as possible, namely the chain stability of the vehicle formation is ensured; error transfer function G between vehicles i (s)＝E i+1 (s)/E i (s) (i=1,., n) all satisfy ||g i When the I is less than or equal to 1, the chain stability of the unmanned vehicle formation can be ensured;
let it be at T i At time (i=1, 2,., n), when the sliding surface s of the follower i (i=1, 2, 3) converges, there is a time T m So that when T m ＝max{T 1 ,T 2 ,...,T n "t.gtoreq.T m When the sliding mode surfaces of the following are all converged to 0, the following formulas are established:
s i (t)＝s i+1 (t)
carrying the sliding mode surface into the above mode to perform Laplace transformation to obtain:
the error transfer function is:
the modulus of the error transfer function is:
then let I G i The I is less than or equal to 1, and the parameter setting meets A i+1 ≥A i The chain stability of unmanned vehicle formation can be ensured.
Further, after the uncertain parameters are estimated and corrected by adopting a self-adaptive algorithm, controlling the followers by adopting a sliding mode control algorithm based on a front follow topology structure;
the original self-adaptation law is:
to preventExcessive control input signal is excessive or +.>In the case of (2) it is necessary to let +.>The variation of (2) is->In the range, a mapping self-adaptive algorithm is adopted to correct the original self-adaptive law:
wherein the method comprises the steps of
In a second aspect, the present invention provides an unmanned vehicle formation control system based on a front-end following topology, comprising:
the sensor module is used for detecting the speed and the angular speed of the front virtual point in real time;
the model building module is used for building a virtual point-pilot following formation model of a pilot leader, a follower followers and virtual points;
the model conversion module is used for performing dimension reduction processing on the controlled quantity of the virtual point-pilot following formation model;
the controller module calculates the output quantity of the sliding mode control law according to the input quantity;
the self-adaptive estimation correction module is used for estimating and correcting the uncertain parameters according to the control law;
the management module inputs linear speed and angular speed of a front virtual point, longitudinal error, transverse error and angle error of a current vehicle and a front vehicle, outputs linear speed and angular speed of the current vehicle, and the two variables act on an unmanned vehicle formation kinematic model to form a complete closed-loop management system.
In a third aspect, the present invention provides a non-transitory computer readable storage medium for storing computer instructions which, when executed by a processor, implement the unmanned vehicle formation control method based on the leading following topology according to the first aspect.
In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, the processor and the memory being in communication with each other, the memory storing program instructions executable by the processor, the processor invoking the program instructions to perform the unmanned vehicle formation control method based on a lead following topology as described in the first aspect.
In a fifth aspect, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the instructions for implementing the unmanned vehicle formation control method based on the front-end following topology according to the first aspect.
The invention has the beneficial effects that: based on the front following topological structure, a pilot front following topological structure is provided, and a topological sliding mode control algorithm is designed. The influence of slip-form control buffeting on a system is reduced, and the chain stability of vehicle formation is ensured. The invention can obtain good control performance under the scene of no abrupt change of the acceleration of the pilot vehicle, abrupt change of the acceleration and combined action of the abrupt change of the acceleration and noise interference. This has important application meaning to unmanned formation control system that can be used to actual scene.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an unmanned vehicle formation control method based on a front-end following topology structure according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a front-end following topology formation structure according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a soft-hit wall virtual point-pilot following formation model according to an embodiment of the present invention.
Fig. 4 is a functional schematic diagram of an unmanned vehicle formation control system based on a front-end following topology according to an embodiment of the present invention.
Fig. 5 is a functional block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements throughout or elements having like or similar functionality. The embodiments described below by way of the drawings are exemplary only and should not be construed as limiting the invention.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and/or groups thereof.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
In order that the invention may be readily understood, a further description of the invention will be rendered by reference to specific embodiments that are illustrated in the appended drawings and are not to be construed as limiting embodiments of the invention.
It will be appreciated by those skilled in the art that the drawings are merely schematic representations of examples and that the elements of the drawings are not necessarily required to practice the invention.
Example 1
In this embodiment 1, a method for controlling formation of unmanned vehicles based on a front-end following topology structure is provided, and includes: a soft collision wall virtual point following strategy is provided. The strategy introduces a virtual point between two vehicles, separates the two vehicles in different sections, is a desired point of a rear vehicle, and maintains a time-varying safety distance with a front vehicle; combining the advantages of the soft-hit wall virtual point following strategy and the pilot following structure, and establishing a virtual point-pilot following formation model based on a soft-hit wall mode; based on the front-mounted following topological structure, a sliding mode control algorithm is adopted to control the following vehicle to track the track, speed and angular speed of the piloting vehicle; in the actual environment, uncertainty of speed information of the pilot vehicle caused by packet loss of communication of the pilot vehicle is considered, and the uncertainty can directly influence the uncertainty of the speed information of the virtual point, so that the first following vehicle cannot accurately track the motion state of the pilot vehicle. The invention designs a mapping self-adaptive algorithm for estimating and correcting the speed of a virtual point. The flow chart of the algorithm and the schematic diagram of the front-end following topology formation structure in this embodiment are shown in fig. 1 and fig. 2.
Firstly, an unmanned vehicle formation model is established, which comprises the following steps:
a pilot vehicle (leader), a follower vehicle (follower), and a mathematical model of a virtual point.
Taking a leader and a follower as an example to establish a kinematic mathematical model, and keeping the same kinematic relation between the following vehicles and the vehicles in front, thereby forming the kinematic model of the whole unmanned vehicle formation. The soft wall virtual point pilot following formation model is shown in fig. 3:
in fig. 3, leader is a pilot vehicle and follower is a follower vehicle. The argument time t is omitted for ease of model building and formula derivation. The physical meaning of each variable in fig. 3 is described as follows:
x and y respectively represent the x axis and the y axis in the global coordinate system, and x ', y' respectively representTable x-axis and y-axis of local coordinate system with follower center as origin, G l (x l ,y l ) Is the center point coordinates of the leader, x l Is the abscissa (unit: m), y l Is the ordinate (unit: m), G m (x m ,y m ) Is the coordinates of the virtual point, x m Is the abscissa (unit: m), y m Is the ordinate (unit: m), G f (x f ,y f ) For the center point coordinates of the follower, xf is the abscissa (unit: m), y f In ordinate (unit: m), v l ，v f The speeds of leader and follower (units: m/s), ω, respectively l ，ω f Angular velocities (units: °/s), θ, of the leader and the follower, respectively l ，θ f Yaw angles (units: °) of the leader and the follower, respectively, and L represents a time-varying safety distance (units: m).
(1) The mathematical model of leader is:
wherein the yaw angle θ l ∈(-40°,+40°)。
(2) The mathematical model of the follower is:
wherein the yaw angle θ l ∈(-40°,+40°)。
(3) The geometric relationship between the virtual point and the leader is as follows:
wherein l=0.851 v l +1.5。
(4) Global lateral position error x of a follower with a virtual point e Global longitudinal position error y e And global yaw angle error θ e The method comprises the following steps:
the global error in the x-y coordinate system is converted into a local error in the x '-y' coordinate system, and the x '-y' local coordinate system is built by taking the follower as the center. The coordinate system is converted into:
wherein e 1 Represents a lateral error, e 2 Represented as longitudinal error, e 3 Representing yaw angle error.
The derivation of equation (5) can obtain the error kinematics equation of the follower in the x '-y' coordinate system as follows:
(5) The linear and angular velocities of the virtual points can be obtained by deriving the equation (3):
the linear velocity of the virtual point can be expressed as:
since the geometric centers of the virtual point and the leader are always on a straight line, θ m ＝θ l The angle of the virtual point is:the angular velocity of the virtual point can be obtained by deriving the angle as follows:
for an actual vehicle, the linear speed v of the vehicle can be controlled only through an accelerator, a brake and a steering wheel f And yaw rate omega f However, in the established kinematic model, there are three controlled variables, namely, three error components shown in the formula (5), which makes the designed control system an underactuated system, so that the controlled variables of the system need to be subjected to dimension reduction treatment. Taking the lateral position error and the yaw angle error into account, the error variable is modified to the form:
after the model is built, the sliding mode control algorithm is adopted to control the followers in consideration of the nonlinear characteristics of the unmanned vehicle formation model, and the sliding mode control algorithm has the advantages of high response speed, insensitivity to system parameter change and external disturbance, simple physical structure and easiness in realization, and meanwhile, no need of on-line identification of system parameters, and is suitable for a nonlinear control system with high requirements on robustness.
In vehicle formation in the lead following topology, since each follower can only obtain position, speed, and angular velocity information of the preceding vehicle, the control laws are designed by taking follower 1 as an example, the control laws of follower 2 and follower 3, and so on. The sliding mode surface is designed as follows:
wherein the method comprises the steps ofAnd alpha is i ＞0(i＝1,2)。
Deriving the formula (10):
bringing formula (10) into formula (11) yields:
the isokinetic approach law was chosen as follows:
wherein the method comprises the steps ofAnd epsilon i ＞0(i＝1,2)。
From equations (12) and (13), the sliding mode control law based on the lead-following topology can be deduced as follows:
v f ＝v m cose 3 +e 2 ω f +α 1 e 1 +ε 1 sat(s 1 )
because unmanned vehicles are formed as a whole, when the front vehicles are interfered, the interference errors are avoided to be expanded along the direction of the vehicle flow as much as possible, and the chain stability of the vehicle formation is ensured. Error transfer function G between vehicles i (s)＝E i+1 (s)/E i (s) (i=1,., n) all satisfy ||g i When the I is less than or equal to 1, the chain stability of unmanned vehicle formation can be ensured.
Let it be at T i At time (i=1, 2,., n), when the sliding surface s of the follower i (i=1, 2, 3) converges, there is a time T m So that when T m ＝max{T 1 ,T 2 ,...,T n "t.gtoreq.T m When the sliding mode surfaces of the following are all converged to 0, the following formulas are established:
s i (t)＝s i+1 (t) (15)
carrying out Laplace transformation by taking the formula (10) into the formula (15), and obtaining the following components:
the error transfer function is:
the modulus of the error transfer function is:
let I G i And (3) the I is less than or equal to 1, and the following steps are obtained:
A i+1 ≥A i (19)
as long as the formula (19) is satisfied when the parameters are set, the chain stability of the unmanned vehicle formation can be ensured.
For the uncertainty of the speed of the leader caused by the influence of communication packet loss, the uncertainty of the speed of the first virtual point can be directly caused, so that the speed information of the virtual point can not be accurately acquired by the follower 1, and the formation control effect is influenced. After the self-adaptive algorithm is adopted to estimate and correct the uncertain parameters (the linear speed of the virtual point), the sliding mode control algorithm is adopted to control the followers based on the front follow topology structure.
The original self-adaptation law is:
to preventExcessive control input signal is excessive or +.>In the case of (2) it is necessary to let +.>The variation of (2) is->In the range, a mapping self-adaptive algorithm is adopted to correct the original self-adaptive law:
/>
wherein the method comprises the steps of
Example 2
With the increasing maturity of the automatic driving technology, when the unmanned driving technology is integrated into highway traffic, the unmanned driving technology can be operated in a vehicle formation mode. In recent years, unmanned vehicle driving formation is widely focused by researchers in the fields of automatic driving, intelligent transportation and the like, and the unmanned vehicle driving formation technology has great application potential in the aspects of improving traffic capacity, improving driving safety, reducing fuel consumption and the like. Meanwhile, in a formation driving mode, the behavior of the vehicle is simple, only the motion state of the front vehicle needs to be accurately tracked, and compared with the motion state (lane changing, overtaking, obstacle avoidance and the like) of a single automatic driving vehicle, the formation is safer and more efficient. How to ensure the stability of unmanned vehicle formation; ensuring that each vehicle can follow the front vehicle at a smaller safety distance; the total control error (longitudinal error, transverse error and angle error) of each vehicle is guaranteed to converge to zero as soon as possible. A safe and reliable control method is needed.
First, in this embodiment, an unmanned vehicle formation control device based on a front-end following topology is provided, where the device is configured to execute an unmanned vehicle formation control method based on a front-end following topology, and a specific flow of the unmanned vehicle formation control method may be seen in fig. 4, where the device includes:
the sensor module is used for detecting the speed and the angular speed of the front virtual point in real time;
the model building module is used for building a leader, followers mathematical model of the virtual points;
the model conversion module is used for performing dimension reduction treatment on the controlled quantity of the system;
the controller module calculates the output quantity of the sliding mode control law according to the input quantity;
the self-adaptive estimation correction module is used for estimating and correcting the uncertain parameters according to the control law;
the management module inputs linear speed and angular speed of a front virtual point, longitudinal error, transverse error and angle error of a current vehicle and a front vehicle, outputs linear speed and angular speed of the current vehicle, and the two variables act on an unmanned vehicle formation kinematic model to form a complete closed-loop management system.
Secondly, in this embodiment 3, the unmanned vehicle formation control method based on the leading following topology structure is implemented by using the above device, and the specific flow thereof may be seen in fig. 2, and the method includes: based on the principle of driving permission in train speed supervision control and the safe following mode of 'crashing soft wall' in moving block, a virtual point following strategy of soft crashing wall is provided. The strategy introduces a virtual point between two vehicles, separates the two vehicles in different sections, is a desired point of a rear vehicle, and maintains a time-varying safety distance with a front vehicle; combining the advantages of the soft-hit wall virtual point following strategy and the pilot following structure, and establishing a virtual point-pilot following formation model based on a soft-hit wall mode; based on the front-end following topology structure, a sliding mode control algorithm is adopted to control the tracks, speeds and angular speeds of the follow-up readers (three follow-up readers are taken as an example in the embodiment);
after the model is built, the sliding mode control algorithm is adopted to control the followers in consideration of the nonlinear characteristics of the unmanned vehicle formation model, and the sliding mode control algorithm has the advantages of high response speed, insensitivity to system parameter change and external disturbance, simple physical structure and easiness in realization, and meanwhile, no need of on-line identification of system parameters, and is suitable for a nonlinear control system with high requirements on robustness.
Because unmanned vehicles are formed as a whole, when the front vehicles are interfered, the interference errors are avoided to be expanded along the direction of the vehicle flow as much as possible, and the chain stability of the vehicle formation is ensured. Error transfer function G between vehicles i (s)＝E i+1 (s)/E i (s) (i=1,., n) all satisfy ||g i When the I is less than or equal to 1, the chain stability of unmanned vehicle formation can be ensured.
For the uncertainty of the speed of the leader caused by the influence of communication packet loss, the uncertainty of the speed of the first virtual point can be directly caused, so that the speed information of the virtual point can not be accurately acquired by the follower 1, and the formation control effect is influenced. After the self-adaptive algorithm is adopted to estimate and correct the uncertain parameters (the linear speed of the virtual point), the sliding mode control algorithm is adopted to control the followers based on the front follow topology structure.
The whole calculation flow can be realized in a formation control device, the control device can carry out unidirectional communication with a rear vehicle, and a schematic diagram of the unmanned vehicle formation communication topology structure in the invention is shown in figure 3.
Based on the content of the above embodiments, as an alternative embodiment, an unmanned vehicle formation model should be built first, including:
leader, followers and mathematical models of virtual points.
Taking a leader and a follower as an example to establish a kinematic mathematical model, and keeping the same kinematic relation between the following vehicles and the vehicles in front, thereby forming the kinematic model of the whole unmanned vehicle formation. The soft wall virtual point pilot following formation model is shown in fig. 3:
in fig. 3, leader is a pilot vehicle and follower is a follower vehicle. The argument time t is omitted for ease of model building and formula derivation. The physical meaning of each variable in fig. 3 is described as follows:
x and y respectively represent the x-axis and the y-axis in the global coordinate system, and x ', y' respectively represent the x-axis and the y-axis of the local coordinate system with the follower center as the origin, G l (x l ,y l ) Is the center point coordinates of the leader, x l Is the abscissa (unit: m), y l Is the ordinate (unit: m), G m (x m ,y m ) Is the coordinates of the virtual point, x m Is the abscissa (unit: m), y m Is the ordinate (unit: m), gf (xf, yf) is the center point coordinate of the follower, xf is the abscissa (unit: m), y f In ordinate (unit: m), v l ，v f The speeds of leader and follower (units: m/s), ω, respectively l ，ω f Angular velocities (units: °/s), θ, of the leader and the follower, respectively l ，θ f Yaw angles (units: °) of the leader and the follower, respectively, and L represents a time-varying safety distance (units: m).
(1) The mathematical model of leader is:
wherein the yaw angle θ l ∈(-40°,+40°)。
(2) The mathematical model of the follower is:
wherein the yaw angle θ l ∈(-40°,+40°)。
(3) The geometric relationship between the virtual point and the leader is as follows:
wherein l=0.851 v l +1.5。
(4) Global lateral position error x of a follower with a virtual point e Global longitudinal position error y e And global yaw angle error θ e The method comprises the following steps:
the global error in the x-y coordinate system is converted into a local error in the x '-y' coordinate system, and the x '-y' local coordinate system is built by taking the follower as the center. The coordinate system is converted into:
wherein e 1 Represents a lateral error, e 2 Represented as longitudinal error, e 3 Representing yaw angle error.
The derivation of equation (5) can obtain the error kinematics equation of the follower in the x '-y' coordinate system as follows:
(5) The linear and angular velocities of the virtual points can be obtained by deriving the equation (3):
the linear velocity of the virtual point can be expressed as:
since the geometric centers of the virtual point and the leader are always on a straight line, θ m ＝θ l The angle of the virtual point is:the angular velocity of the virtual point can be obtained by deriving the angle as follows:
for an actual vehicle, the linear speed v of the vehicle can be controlled only through an accelerator, a brake and a steering wheel f And yaw rate omega f However, in the established kinematic model, there are three controlled variables, namely, three error components shown in the formula (5), which makes the designed control system an underactuated system, so that the controlled variables of the system need to be subjected to dimension reduction treatment. Taking the lateral position error and the yaw angle error into account, the error variable is modified to the form:
after the model is built, the sliding mode control algorithm is adopted to control the followers in consideration of the nonlinear characteristics of the unmanned vehicle formation model, and the sliding mode control algorithm has the advantages of high response speed, insensitivity to system parameter change and external disturbance, simple physical structure and easiness in realization, and meanwhile, no need of on-line identification of system parameters, and is suitable for a nonlinear control system with high requirements on robustness.
In vehicle formation in the lead following topology, since each follower can only obtain position, speed, and angular velocity information of the preceding vehicle, the control laws are designed by taking follower 1 as an example, the control laws of follower 2 and follower 3, and so on. The sliding mode surface is designed as follows:
wherein the method comprises the steps ofAnd alpha is i ＞0(i＝1,2)。
Deriving the formula (10):
bringing formula (10) into formula (11) yields:
the isokinetic approach law was chosen as follows:
wherein the method comprises the steps ofAnd epsilon i ＞0(i＝1,2)。
From equations (12) and (13), the sliding mode control law based on the lead-following topology can be deduced as follows:
v f ＝v m cose 3 +e 2 ω f +α 1 e 1 +ε 1 sat(s 1 )
because unmanned vehicles are formed as a whole, when the front vehicles are interfered, the interference errors are avoided to be expanded along the direction of the vehicle flow as much as possible, and the chain stability of the vehicle formation is ensured. Error transfer function G between vehicles i (s)＝E i+1 (s)/E i (s) (i=1,., n) all satisfy ||g i When the I is less than or equal to 1, the chain stability of unmanned vehicle formation can be ensured.
Let it be at T i At time (i=1, 2,., n), when the sliding surface s of the follower i (i=1, 2, 3) converges, there is a time T m So that when T m ＝max{T 1 ,T 2 ,...,T n "t.gtoreq.T m When the sliding mode surfaces of the following are all converged to 0, the following formulas are established:
s i (t)＝s i+1 (t) (15)
carrying out Laplace transformation by taking the formula (10) into the formula (15), and obtaining the following components:
the error transfer function is:
the modulus of the error transfer function is:
let I G i And (3) the I is less than or equal to 1, and the following steps are obtained:
A i+1 ≥A i (19)
as long as the formula (19) is satisfied when the parameters are set, the chain stability of the unmanned vehicle formation can be ensured.
For the uncertainty of the speed of the leader caused by the influence of communication packet loss, the uncertainty of the speed of the first virtual point can be directly caused, so that the speed information of the virtual point can not be accurately acquired by the follower 1, and the formation control effect is influenced. After the self-adaptive algorithm is adopted to estimate and correct the uncertain parameters (the linear speed of the virtual point), the sliding mode control algorithm is adopted to control the followers based on the front follow topology structure.
The original self-adaptation law is:
to preventExcessive control input signal is excessive or +.>In the case of (2) it is necessary to let +.>The variation of (2) is->In the range, a mapping self-adaptive algorithm is adopted to correct the original self-adaptive law:
/>
wherein the method comprises the steps of
Example 3
Embodiment 3 provides a non-transitory computer readable storage medium storing computer instructions capable of executing any one of the various possible implementations of the first aspect, and finally controlling the followers to accurately track the movement state of the leader according to the unmanned vehicle formation control method based on the front-end following topology as described above.
Example 4
The embodiment 4 provides a computer device, which includes a memory and a processor, where the processor and the memory are in communication with each other, the memory stores a program instruction that can be executed by the processor, and the processor invokes the program instruction to execute an unmanned vehicle formation control method based on a front-end following topology structure, and finally controls the followers to accurately track the movement state of the leader.
Example 5
Embodiment 5 provides an electronic device, at least one processor, and at least one memory in communication with the processor, where the memory may store all program instructions executable by the processor, and the processor invokes the program of instructions to be able to execute any of the various possible implementations of the first aspect, and ultimately control the followers to accurately track the motion state of the leader.
As shown in fig. 5, the electronic device may include: a processor (processor), a communication interface (communication interface), a memory (memory) and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus. The processor may schedule logic instructions in the memory to perform the following method: 1) Based on the principle of driving permission in train speed supervision control and the safe following mode of 'crashing soft wall' in moving block, a virtual point following strategy of soft crashing wall is provided. The strategy introduces a virtual point between two vehicles, separates the two vehicles in different sections, is a desired point of a rear vehicle, and maintains a time-varying safety distance with a front vehicle; 2) Combining the advantages of the soft-hit wall virtual point following strategy and the pilot following structure, and establishing a virtual point-pilot following formation model based on a soft-hit wall mode; 3) Based on the front-end following topology structure, a sliding mode control algorithm is adopted to control the tracks, speeds and angular speeds of the followers (three followers are taken as examples in the text) to track the followers (one leader is taken as an example in the text); 4) Considering the uncertainty of the speed information caused by the packet loss of the leader communication in the actual environment, the uncertainty can directly influence the uncertainty of the speed information of the virtual point, so that the first follower cannot accurately track the motion state of the leader. And estimating and correcting the speed of the virtual point by adopting a mapping self-adaptive algorithm.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it should be understood that various changes and modifications could be made by one skilled in the art without the need for inventive faculty, which would fall within the scope of the invention.
Claims (10)
1. The unmanned vehicle formation control method based on the front-end following topological structure is characterized by comprising the following steps of:
detecting the speed and the angular speed of a front virtual point in real time;
establishing a virtual point-pilot following formation model of a pilot leader, a follower followers and virtual points;
performing dimension reduction processing on the controlled quantity of the virtual point-pilot following formation model;
calculating the output quantity of the sliding mode control law according to the input quantity;
estimating and correcting the uncertain parameters according to the control law;
the input quantity is the linear speed and the angular speed of a front virtual point, the longitudinal error, the transverse error and the angular error of the current vehicle and the front vehicle, the output quantity is the linear speed and the angular speed of the current vehicle, and the two variables are acted on the unmanned vehicle formation kinematics model to form a complete closed loop management system.
2. The unmanned vehicle formation control method based on the front-end following topology according to claim 1, wherein the virtual point-pilot following formation model is built by a mathematical model including a pilot vehicle leader, a following vehicle followers and a virtual point;
the mathematical model of leader is:
the mathematical model of the follower is:
the geometric relationship between the virtual point and the leader is as follows:
global lateral position error x of a follower with a virtual point e Global longitudinal position error y e And global yaw angle error θ e The method comprises the following steps:
converting global errors in an x-y coordinate system into local errors in an x '-y' coordinate system, and establishing an x '-y' local coordinate system by taking a follower as a center; the coordinate system is converted into:
wherein e 1 Represents a lateral error, e 2 Represented as longitudinal error, e 3 Representing yaw angle error;
deriving the transformed coordinate system to obtain an error kinematics equation of the follower under the x '-y' coordinate system:
the linear velocity and angular velocity of the virtual point can be obtained by deriving the mathematical model of the follower:
the linear velocity of the virtual point can be expressed as:
since the geometric centers of the virtual point and the leader are always on a straight line, θ m ＝θ l The angle of the virtual point is:the angular velocity of the virtual point can be obtained by deriving the angle as follows:
wherein, x and y respectively represent the x axis and the y axis in the global coordinate system, x ', y' respectively represent the x axis and the y axis of the local coordinate system with the follower center as the origin, G l (x l ,y l ) Is the center point coordinates of the leader, x l Is the abscissa, y l Is the ordinate, G m (x m ,y m ) Is the coordinates of the virtual point, x m Is the abscissa, y m Is the ordinate, G f (x f ,y f ) Is the center point coordinate of the follower, x f In abscissa, y f On the ordinate, v l ，v f Indicating the speeds of leader and follower, ω, respectively l ，ω f Respectively represent the angular velocity of the leader and the follower, θ l ，θ f The yaw angles of the leader and the follower are respectively indicated, and L represents the time-varying safety distance.
3. The unmanned vehicle formation control method based on the front-end following topology according to claim 2, wherein the controlled quantity is subjected to dimension reduction processing, the lateral position error and the yaw angle error are considered together, and the error variable is modified as follows:
4. the unmanned vehicle formation control method based on the front-end following topology according to claim 3, wherein the sliding mode control algorithm is used to control the follows, and in the vehicle formation under the front-end following topology, since each follower can only obtain the position, speed and angular speed information of the front vehicle, the sliding mode surface is:
wherein the method comprises the steps ofAnd alpha is i ＞0(i＝1,2)。
And (3) deriving a sliding mode surface to obtain:
then:
the isokinetic approach law was chosen as follows:
wherein the method comprises the steps ofAnd epsilon i ＞0(i＝1,2)。
Then, the sliding mode control law based on the front-end following topology is:
v f ＝v m cose 3 +e 2 ω f +α 1 e 1 +ε 1 sat(s 1 )
5. the unmanned vehicle formation control method based on the front-end following topological structure according to claim 4, wherein the unmanned vehicle formation is a whole, so that when the front vehicle receives interference, the chain stability of the vehicle formation is ensured by avoiding expansion of interference errors along the direction of the vehicle flow as much as possible; error transfer function G between vehicles i (s)＝E i+1 (s)/E i (s) (i=1,., n) all satisfy ||g i When the level is less than or equal to 1, the protection can be ensuredChain stability of unmanned vehicle formation;
let it be at T i At time (i=1, 2,., n), when the sliding surface s of the follower i (i=1, 2, 3) converges, there is a time T m So that when T m ＝max{T 1 ,T 2 ,...,T n "t.gtoreq.T m When the sliding mode surfaces of the following are all converged to 0, the following formulas are established:
s i (t)＝s i+1 (t)
carrying the sliding mode surface into the above mode to perform Laplace transformation to obtain:
the error transfer function is:
the modulus of the error transfer function is:
then let I G i The I is less than or equal to 1, and the parameter setting meets A i+1 ≥A i The chain stability of unmanned vehicle formation can be ensured.
6. The unmanned vehicle formation control method based on the front-end following topology according to claim 5, wherein after the uncertain parameters are estimated and corrected by adopting an adaptive algorithm, the followers are controlled by adopting a sliding mode control algorithm based on the front-end following topology;
the original self-adaptation law is:
to preventExcessive control input signal is excessive or +.>In the case of (2) it is necessary to let +.>The variation of [ v ] mmin ,v mmax ]In the range, a mapping self-adaptive algorithm is adopted to correct the original self-adaptive law:
wherein the method comprises the steps of
7. Unmanned vehicle formation control system based on leading topology structure that follows, characterized by comprising:
the sensor module is used for detecting the speed and the angular speed of the front virtual point in real time;
the model building module is used for building a virtual point-pilot following formation model of a pilot leader, a follower followers and virtual points;
the model conversion module is used for performing dimension reduction processing on the controlled quantity of the virtual point-pilot following formation model;
the controller module calculates the output quantity of the sliding mode control law according to the input quantity;
the self-adaptive estimation correction module is used for estimating and correcting the uncertain parameters according to the control law;
the management module inputs linear speed and angular speed of a front virtual point, longitudinal error, transverse error and angle error of a current vehicle and a front vehicle, outputs linear speed and angular speed of the current vehicle, and the two variables act on an unmanned vehicle formation kinematic model to form a complete closed-loop management system.
8. A non-transitory computer readable storage medium storing computer instructions which, when executed by a processor, implement the unmanned vehicle formation control method based on a lead following topology of any of claims 1-6.
9. A computer device comprising a memory and a processor, the processor and the memory being in communication with each other, the memory storing program instructions executable by the processor, the processor invoking the program instructions to perform the lead following topology based unmanned vehicle formation control method of any of claims 1-6.
10. An electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and wherein the computer program is stored in the memory, which processor executes the computer program stored in the memory when the electronic device is running, to cause the electronic device to execute instructions for implementing the unmanned vehicle formation control method based on the lead following topology according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311806735.9A CN117826590A (en) | 2023-12-26 | 2023-12-26 | Unmanned vehicle formation control method and system based on prepositive following topological structure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311806735.9A CN117826590A (en) | 2023-12-26 | 2023-12-26 | Unmanned vehicle formation control method and system based on prepositive following topological structure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117826590A true CN117826590A (en) | 2024-04-05 |
Family
ID=90510746
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311806735.9A Pending CN117826590A (en) | 2023-12-26 | 2023-12-26 | Unmanned vehicle formation control method and system based on prepositive following topological structure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117826590A (en) |
-
2023
- 2023-12-26 CN CN202311806735.9A patent/CN117826590A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tang et al. | An improved kinematic model predictive control for high-speed path tracking of autonomous vehicles | |
| CN113386781B (en) | Intelligent vehicle track tracking control method based on data-driven vehicle dynamics model | |
| Cao et al. | Trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics | |
| CN107415939B (en) | Steering stability control method for distributed driving electric automobile | |
| CN107831761B (en) | Path tracking control method of intelligent vehicle | |
| CN110780594B (en) | Path tracking method and system of intelligent vehicle | |
| CN110162046B (en) | Unmanned vehicle path following method based on event trigger type model predictive control | |
| CN109131325B (en) | Lane keeping control method for three-dimensional extension preview switching of intelligent driving automobile | |
| CN105676643B (en) | A kind of intelligent automobile turns to and braking self-adaptive wavelet base method | |
| CN107015477A (en) | Vehicle route tracking H ∞ control methods based on feedback of status | |
| WO2008072515A1 (en) | Vehicle control device | |
| CN111123701B (en) | Automatic driving path tracking anti-interference control method based on pipeline prediction model | |
| CN112937551B (en) | Vehicle control method and system considering input characteristics of driver | |
| CN114148320B (en) | Path tracking control method and equipment | |
| CN112622895B (en) | Prediction control method applied to trajectory control of automatic driving | |
| CN114228690B (en) | Automatic driving vehicle roll control method based on DDPG and iterative control | |
| CN116560371A (en) | Self-adaptive model predictive control-based automatic driving vehicle path tracking method | |
| CN113126623A (en) | Adaptive dynamic sliding mode automatic driving vehicle path tracking control method considering input saturation | |
| CN114942642A (en) | Unmanned automobile track planning method | |
| CN114852085A (en) | Automatic vehicle driving track planning method based on road right invasion degree | |
| CN117826590A (en) | Unmanned vehicle formation control method and system based on prepositive following topological structure | |
| CN115525054A (en) | Large-scale industrial park unmanned sweeper edge path tracking control method and system | |
| Khalifa et al. | Vehicles platooning in urban environments: Integrated consensus-based longitudinal control with gap closure maneuvering and collision avoidance capabilities | |
| CN115042770A (en) | Vehicle queue transverse control method based on distributed robust model prediction | |
| CN114179818A (en) | Intelligent automobile transverse control method based on adaptive preview time and sliding mode control |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |