CN110377051A - A kind of time-varying formation applied to unmanned aerial vehicle group is swarmed control method - Google Patents
A kind of time-varying formation applied to unmanned aerial vehicle group is swarmed control method Download PDFInfo
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Abstract
The time-varying formation that the invention discloses a kind of applied to unmanned aerial vehicle group is swarmed control method, and exercise data and communication topological relation that unmanned plane is formed into columns are obtained；Establish the multiple agent kinematics model of unmanned plane；Construction time-varying formation is swarmed control algolithm；The virtual leader's model of average value-of unmanned aerial vehicle group is constructed to analyze formation stability, and realizes that the time-varying of unmanned aerial vehicle group is formed into columns on this basis.The control method has many advantages, such as to be easily achieved, and formation control effect is good, is able to achieve the formation of unmanned aerial vehicle group formation, scaling, the time-varying fleet operation of rotation and direction transformation.
Description
Technical Field
The invention belongs to the technical field of decentralized control of multi-agent models, and particularly relates to a time-varying formation bee-hive control method applied to an unmanned aerial vehicle cluster.
Background
Unmanned aerial vehicles are from the military field, have entered the rapid development phase after decades of development, and their kind is various, and the application field is constantly expanded, and the task type is extensive. In the military aspect, the unmanned aerial vehicle can perform reconnaissance and monitoring under the condition of complex terrain, and can be used for monitoring, rescuing the hostage, resisting terrorist action and the like of the internal condition of a building under a special environment; the system is mainly used for monitoring and surveying disasters such as flood, forest fire, earthquake and the like in the civil aspect, civil aviation shooting, entertainment shooting and the like.
Along with the deepening and acceleration of informatization and intellectualization, the application environment is complex and changeable, a single unmanned aerial vehicle cannot complete certain tasks, or a single unmanned aerial vehicle is expensive, a large-scale, low-cost and multifunctional miniature unmanned aerial vehicle cluster replaces the single unmanned aerial vehicle, and multi-task cooperation is realized through technologies such as aerial communication networking, autonomous control, crowd's intelligence decision-making and the like. The formation of the unmanned aerial vehicle group refers to that when the unmanned aerial vehicle group moves cooperatively, the geometric queue form of each individual is kept fixed or changed as required, and condition constraints such as obstacle avoidance, collision avoidance and the like are completed. In actual engineering, when the unmanned aerial vehicles execute tasks in a formation mode, the unmanned aerial vehicles can mutually influence each other, formation, maintenance, formation and the like in a movement process are needed, and operations of information acquisition, exchange, calculation, control and the like between the unmanned aerial vehicles are achieved. The current main formation control algorithms include: 1) the artificial potential field method expresses corresponding formation by defining artificial potential fields, but the formation is single, different formations need to define different artificial potential fields, and the formation is too complex; 2) the virtual structure method expresses the formation through a virtual space structure, and the application range of the method is easily limited by the virtual structure; 3) the navigator-follower method has large dependence on the navigator, and the guide failure of the navigator can cause the formation control failure; 4) based on behavioral methods, it is very difficult to mathematically analyze and control group behaviors if they are not well defined.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problem that the formation of the unmanned aerial vehicle cluster is difficult to control in the prior art, the invention provides a time-varying formation bee-congestion control method applied to the unmanned aerial vehicle cluster.
The technical scheme is as follows: a time-varying formation bee-jamming control method applied to an unmanned aerial vehicle cluster comprises the following steps:
(1) acquiring the motion data and communication topological relation of each unmanned aerial vehicle in the unmanned aerial vehicle cluster, wherein the motion data comprises position, speed and acceleration vectors; the communication topological relation is used for establishing a wireless data communication network between the unmanned aerial vehicles;
(2) establishing a multi-agent kinematics model of the unmanned aerial vehicle cluster according to the motion data of each unmanned aerial vehicle;
(3) calculating the acceleration operation amount of each unmanned aerial vehicle at the next moment by using the motion data of each unmanned aerial vehicle at the current moment and the multi-agent kinematics model, and constructing the acceleration operation amount of each unmanned aerial vehicleSet of acceleration control operation amounts { u } of machine1,u2,…,uNNext moment unmanned plane acceleration operation amount uiThe unmanned aerial vehicle is a superposition of a first component vector, a second component vector and a third component vector, wherein the first component vector is the attraction or repulsion of the unmanned aerial vehicle on the position relation of other unmanned aerial vehicles, the second component vector is the speed driving force of the unmanned aerial vehicle, the third component vector is the guiding force of a virtual leader, and the unmanned aerial vehicles transmit data through a wireless data communication network;
(4) the time-varying formation operation of the unmanned aerial vehicle group is realized by changing the attraction force or the repulsion force, the driving force and the guiding force.
Further, in step (3), the first component ui,1Comprises the following steps:
wherein,i denotes the ith drone, j denotes the jth drone; gamma rayσ＞0，dσ＞0，γσ、dσRespectively the upper limit and the lower limit of the adjacent distance between the unmanned aerial vehicles, qiPosition representing drone i, NiGeometric reference numerals, n, representing other drones having an adjacent relationship with the ith droneijIs a position gradient vector, and eta and epsilon are given control parameters; m (t) is a time-varying weighting matrix;
|M(t)z|σis a weighted distance measure between drones i, j,
a, b and c are constants and satisfy 0 < a < b,
the potential function ρ (·,) is used to represent the magnitude of the attraction/repulsion force between drones i, j, given by
Further, in step (3), the second component ui,2Comprises the following steps:
wherein, pirepresents the speed of drone i; a isijRepresenting an adjacency of the drones; Δ q ofijRepresenting the position difference vector between drones.
Further, in step (3), a motion model of the virtual leader is constructed first, and then the third vector component is calculated, where the motion model of the virtual leader is described as:wherein q isr、pr、urRespectively representing the position, the speed and the acceleration control quantity of the virtual leader;
third component ui,3Comprises the following steps:
ui,3＝ΦTΦ(qi-qr)+Ψ(pi-pr)
where phi, psi are constant matrices, phi is non-singular and 0 < psiT＝Ψ。
Further, in step (4), the time-varying formation of the drone swarm includes shaping, scaling, rotating and direction transforming of the queue, and the relation is mathematically defined as:
here, m (t) is a time-varying weighting matrix, the neighborhood radius γ and the desired distance d are given values, and p × is a desired velocity vector.
Further, in the step (4), the method is advantageousBy selection of a time-varying weighting matrix M (t) and an acceleration control u of the virtual leaderrAnd the formation of the unmanned aerial vehicle cluster is realized.
Further, in the step (4), M (t) is selected as a piecewise constant matrix
A constant matrix M of each segment of M (t)iSetting the order of progressive increase or decrease according to the norm size, and controlling the size of the formation of the unmanned aerial vehicle group to decrease or increase;
a constant matrix M of each segment of M (t)iAnd gradually changing the set direction of the characteristic vector in a time period to control the unmanned aerial vehicle group to roll.
Further, in step (4), a time-varying weighting matrix M (t) is used for ui,1And ui,2The subspace transformation of (1) controls u by designing M (t)i,1And ui,2The fleet of drones is formed into motion states that are limited to 1-dimensional or 2-dimensional spaces.
Further, in the step (4), the acceleration control amount u of the virtual leader is usedrChanging the direction and magnitude of the speed in the pilot information acting on the control operation ui,3And then, controlling the direction change of the formation of the unmanned aerial vehicle group.
Further, in the step (2), calculating the average value of the position and the speed of each unmanned aerial vehicle, and combining the virtual leader model to form an average value-virtual leader model of the state space equation; and judging the formation stability through the average value-characteristic value of the virtual leader model in the step (4).
Has the advantages that: compared with the prior art, the time-varying formation bee-congestion control method applied to the unmanned aerial vehicle group can realize formation control of the unmanned aerial vehicle group through selection and adjustment of the time-varying weighting matrix, is convenient and fast to operate time-varying formation of formation generation, scaling, rotation and direction change, overcomes the defects that the formation of the unmanned aerial vehicle group is single and the formation characteristics are difficult to adjust in the prior art, is easy to realize by a computer, has wide applicable scenes, is good in real-time performance, determines the control effect and the like.
Drawings
FIG. 1 is a schematic block diagram of a time-varying formation control method flow of an unmanned aerial vehicle fleet of the present invention;
fig. 2 is a schematic diagram of formation of a drone swarm in the method provided in this embodiment;
FIG. 3 is a schematic diagram of an unmanned aerial vehicle fleet formation for setting a virtual leader in the method provided in this embodiment;
fig. 4 is a schematic diagram illustrating a scaling effect of the formation of the drone group in the method according to the present embodiment;
FIG. 5 is a schematic view illustrating a subspace flight effect of a formation of an unmanned aerial vehicle group in the method according to this embodiment;
fig. 6 is a schematic diagram illustrating a rotation effect of the formation of the drone group in the method according to the present embodiment;
fig. 7 is a schematic diagram illustrating a direction changing effect of the formation of the unmanned aerial vehicle group in the method provided by this embodiment.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
Fig. 1 is a schematic block diagram of a flow of a time-varying formation control method applied to an unmanned aerial vehicle cluster. Considering a fleet of 4 drones (numbered UAV1, UAV2, UAV3, UAV4 in that order), the implementation procedure comprises the following steps:
step 1: the method comprises the steps of obtaining the motion data and communication topological relation of formation of the unmanned aerial vehicles, forming a mobile data network of the unmanned aerial vehicle cluster according to the motion data and communication topological relation, assisting in completing transmission of position and speed data of the unmanned aerial vehicles, and calculating and distributing and implementing formation control quantity. The motion data includes position, velocity and acceleration vectors.
In the specific calculation process, the topology of inter-individual communication is described as a graph G ═ V, E, a }, where V ═ 1, 2.., N } represents the set of unmanned aerial vehicle nodes in the graph G,a set for expressing the communication connection relation among the unmanned aerial vehicles, A is an adjacent matrix for expressing the mutual position relation among the unmanned aerial vehicles. The subscript set of a group of all unmanned aerial vehicles which have connection relation with a common unmanned aerial vehicle node i is Ni＝{j∈V:aijNot equal to 0 ≠ j ∈ V ∈ E }. (i × j) is defined as the connection relationship of unmanned planes i and j which are γ -neighborhoods of each other, and the two can acquire the position and the speed of the other. the topology graph G of the unmanned aerial vehicle group network at the time t isTherefore, the relationship between the motion data of the unmanned aerial vehicle formation and the communication topology is obtained at a certain moment, namely, the graph G at the moment is calculated according to the neighborhood relationship of the nodes of the unmanned aerial vehicles.
Step 2: and establishing a multi-agent kinematics model of the unmanned aerial vehicle cluster according to the motion data of each unmanned aerial vehicle. And calculating the average value of the position and the speed of each unmanned aerial vehicle, and combining the virtual leader model to form an average value-virtual leader model of the state space equation.
The kinematic model of drone i is as follows:
wherein q isi，pi，uiRespectively representing the position, velocity and acceleration vectors of drone i.
And step 3: and constructing a bee-owned formation control algorithm. The control algorithm constructs the acceleration u of the unmanned aerial vehicle iiAnd a multi-agent bee-space distributed protocol is adopted. That is, uiThe unmanned aerial vehicle is determined by combining position and speed information of adjacent multi-agent nodes of other unmanned aerial vehicles after the unmanned aerial vehicle is represented by a multi-agent kinematic model. Calculating the acceleration operation amount of each unmanned aerial vehicle at the next moment by using the motion data of each unmanned aerial vehicle at the current moment and the multi-agent kinematics model, and constructing a set { u } of the acceleration control operation amount of each unmanned aerial vehicle1,u2,…,uNNext moment unmanned plane acceleration operation amount uiThe first component vector is the superposition of the first component vector, the second component vector and the third component vector, the first component vector is the attraction or repulsion of the unmanned aerial vehicle on the position relation of other unmanned aerial vehicles, and the second component vector is the superposition of the first component vector, the second component vector and the third component vectorThe component vector is the speed driving force of this unmanned aerial vehicle, and the third component vector is virtual leader's guiding force. Wherein, data transmission is realized through wireless communication network between each unmanned aerial vehicle. The method specifically comprises the following steps:
step 3-1: determining a gravitation and repulsion function of the position operation of the unmanned aerial vehicle among the unmanned aerial vehicles, namely a first component vector u according to the position information of the unmanned aerial vehicle node at the current moment, the neighborhood radius gamma and the expected distance di,1Comprises the following steps:
wherein,
i denotes the ith drone, j denotes the jth drone; gamma rayσ＞0，dσ＞0，γσ、dσRespectively the upper limit and the lower limit of the adjacent distance between the unmanned aerial vehicles, qiRepresents the position of drone i, m (t) is a time-varying weighting matrix;
|M(t)z|σis a weighted distance measure between drones i, j,
a, b and c are constants and satisfy 0 < a < b,
the potential function ρ (·,) is used to represent the magnitude of the attraction/repulsion force between drones i, j, given by
This component realizes preventing unmanned aerial vehicle from separating from the formation and the effect of avoiding colliding with each other. Specifically, when the position difference distance between two unmanned planes i and j satisfies 0 < | M (t) Δ qij|σ＜dσWhen u is turned oni,1Will follow the positionIncreasing the difference distance and decreasing at | M (t) Δ qij|σWhen the maximum value is reached, the control action is to generate repulsive force between the unmanned aerial vehicles; when the distance of the position difference between the two unmanned planes i and j satisfies dσ＜|M(t)Δqij|σ＜γσWhen u is turned oni,1Increases with increasing distance of position difference, at | M (t) Δ qij|σ＝dσThe time tends to the minimum value, and the control action is to generate attraction between the unmanned aerial vehicles; when the position difference is in | M (t) Δ qij|σ＜γσWhen u is turned oni,1The adjustment effect is lost. In summary, ui,1Under the action of the force, the unmanned aerial vehicle group finally reaches a balance state, the resultant force borne by each machine is zero, and the relative position between the machines is not changed any more.
Step 3-2: obtaining the speed information of each unmanned aerial vehicle in the current unmanned aerial vehicle cluster, and constructing an adjusting function which facilitates the speed of each unmanned aerial vehicle to be consistent, namely a second component ui,2Comprises the following steps:
wherein,
pirepresenting the speed of drone i.
It can be seen that ui,2Speed difference p between unmanned aerial vehicle membersj-piAnd (4) in proportion. | pj-piThe larger is |, ui,2The larger. In addition, when the group speeds of the unmanned aerial vehicles reach the same, namely pj＝piWhen u is turned oni,2When it is 0, the speed adjustment amount disappears. At ui,1And ui,2Under the combined action of the two components, the unmanned aerial vehicle group finally reaches a bee-holding state, namely, the relative distance between the machines is fixed at | M (t) delta qijThe speeds of the machines are the same, as shown in fig. 2.
Step 3-3: and introducing a virtual leader in the unmanned aerial vehicle cluster, and keeping the formation of the unmanned aerial vehicles while the rest unmanned aerial vehicles follow the movement information change of the virtual leader. The virtual leader motion model may be described as:
wherein q isr、pr、urRespectively representing the position, velocity and acceleration control of the virtual leader.
The following adjustment function of each drone to the virtual leader, i.e. the third vector ui,3Comprises the following steps:
ui,3＝ΦTΦ(qi-qr)+Ψ(pi-pr) (6)
where phi, psi are constant matrices, phi is non-singular and 0 < psiT＝Ψ。
The virtual leader is not a physical drone, but merely acts as a built-in mathematical model in the drone navigation program module, which provides motion guidance information for the individual based on real-time drone flight parameters.
Step 3-4: acceleration operation amount of unmanned aerial vehicle i at next moment
ui＝ui,1+ui,2+ui,3 (7)
Position adjusting force u for attracting/repelling each unmanned aerial vehicle by other members in position relationi,1Velocity driving force ui,2And the guiding force u of the virtual leaderi,3. Under their combined action, the members follow the virtual leader and form a pre-set bee-hive formation in the process, as shown in FIG. 3.
Step 3-5: the mean value of the unmanned aerial vehicle group-the stable design of the virtual leader model ensures the stability of the formation of the unmanned aerial vehicle group. The design of tranquilization is represented by formula ui＝ui,1+ui,2+ui,3Defined acceleration control operation amount u of each unmanned aerial vehicleiAnd (4) realizing.
And 4, step 4: selection using a time-varying weighting matrix M (t) and a virtual leader acceleration control urThe formation, the scaling, the rotation and the direction transformation of the formation of the unmanned aerial vehicle group are realized, and the formation is judged through the characteristic value of the mean value-virtual leader modelAnd (4) stability.
The mathematical definition of the time-varying formation of the drone swarm is a relational expression:
using selection of a time-varying weighting matrix M (t) and an acceleration control u of the virtual leaderrThe formation of the unmanned aerial vehicle cluster is realized, and the formation comprises the following contents:
(a) using a time-varying weighting matrix M (t) at ui,1And ui,2The gain adjustment function in the method realizes the formation and scaling of the unmanned aerial vehicle fleet. For example, M (t) is selected as a piecewise constant matrix
A constant matrix M of each segment of M (t)iWhen the norm is gradually increased or decreased in the order of magnitude, u is controlled accordinglyi,1And ui,2The shrinking or expanding effect of the formation scale of the unmanned aerial vehicle group is generated, as shown in fig. 4.
(b) Using a time-varying weighting matrix M (t) at ui,1And ui,2The sub-space transformation in (1) can change the formation configuration of the unmanned aerial vehicle cluster by designing M (t), thereby realizing time-varying formation control. For example, using M (t) matrix rank variation, in control operation ui,1And ui,2The robot cluster is then formed into a motion state limited to a 1-dimensional (linear) or 2-dimensional space (planar), as shown in fig. 5.
(c) Using a time-varying weighting matrix M (t) at ui,1And ui,2The conversion function of the characteristic vector realizes the rolling of the unmanned aerial vehicle group formation. For example, M (t) is selected as a piecewise constant matrix
Wherein M isiThe set direction of the characteristic vector is changed gradually in a time period, the formation of the unmanned aerial vehicle group is controlled to roll,as shown in fig. 6.
(d) Acceleration control u using virtual leaderrThe direction and magnitude of the speed in the pilot information acting on the control operation u can be changedi,3And then, the direction and the size of the group velocity of the unmanned aerial vehicles are changed to the expected direction and size, so that the direction change is realized, as shown in fig. 7.
Claims (10)
1. A time-varying formation bee-jamming control method applied to an unmanned aerial vehicle cluster is characterized by comprising the following steps:
(1) acquiring the motion data and communication topological relation of each unmanned aerial vehicle in the unmanned aerial vehicle cluster, wherein the motion data comprises position, speed and acceleration vectors; the communication topological relation is used for establishing a wireless data communication network between the unmanned aerial vehicles;
(2) establishing a multi-agent kinematics model of the unmanned aerial vehicle cluster according to the motion data of each unmanned aerial vehicle;
(3) calculating the acceleration operation amount of each unmanned aerial vehicle at the next moment by using the motion data of each unmanned aerial vehicle at the current moment and the multi-agent kinematics model, and constructing a set { u } of the acceleration control operation amount of each unmanned aerial vehicle1,u2,…,uNNext moment unmanned plane acceleration operation amount uiThe unmanned aerial vehicle is a superposition of a first component vector, a second component vector and a third component vector, wherein the first component vector is the attraction or repulsion of the unmanned aerial vehicle on the position relation of other unmanned aerial vehicles, the second component vector is the speed driving force of the unmanned aerial vehicle, the third component vector is the guiding force of a virtual leader, and the unmanned aerial vehicles transmit data through a wireless data communication network;
(4) the time-varying formation operation of the unmanned aerial vehicle group is realized by changing the attraction force or the repulsion force, the driving force and the guiding force.
2. The time-varying formation bee-jamming control method applied to unmanned aerial vehicle group according to claim 1, wherein in step (3), the first component vector ui,1Comprises the following steps:
wherein,z＝qi-qji denotes the ith drone, j denotes the jth drone; gamma rayσ＞0，dσ＞0，γσ、dσRespectively the upper limit and the lower limit of the adjacent distance between the unmanned aerial vehicles, qiPosition representing drone i, NiGeometric reference numerals, n, representing other drones having an adjacent relationship with the ith droneijIs a position gradient vector, and eta and epsilon are given control parameters; m (t) is a time-varying weighting matrix;
|M(t)z|σis a weighted distance measure between drones i, j,
a, b and c are constants and satisfy 0 < a < b,
the potential function ρ (·,) is used to represent the magnitude of the attraction/repulsion force between drones i, j, given by
3. The time-varying formation bee-jamming control method applied to unmanned aerial vehicle group according to claim 2, wherein in step (3), the second component vector ui,2Comprises the following steps:
wherein,
pirepresents the speed of drone i; a isijRepresenting an adjacency of the drones; Δ q ofijRepresenting the position difference vector between drones.
4. The time-varying formation bee-holding control method applied to the unmanned aerial vehicle group as claimed in claim 2, wherein in the step (3), a motion model of the virtual leader is constructed and then a third component vector is calculated,
the motion model of the virtual leader is described as:wherein q isr、pr、urRespectively representing the position, the speed and the acceleration control quantity of the virtual leader;
third component ui,3Comprises the following steps:
ui,3＝ΦTΦ(qi-qr)+Ψ(pi-pr)
where phi, psi are constant matrices, phi is non-singular and 0 < psiT＝Ψ。
5. The time-varying formation bee-congestion control method applied to the unmanned aerial vehicle group as claimed in claim 4, wherein in the step (4), the time-varying formation of the unmanned aerial vehicle group comprises formation, scaling, rotation and direction transformation of the queue, and the relation is mathematically defined as follows:
here, m (t) is a time-varying weighting matrix, the neighborhood radius γ and the desired distance d are given values, and p × is a desired velocity vector.
6. The time-varying formation bee-holding control method applied to the unmanned aerial vehicle group as claimed in claim 4, wherein in the step (4), the selection of the time-varying weighting matrix M (t) and the acceleration control amount u of the virtual leader are utilizedrImplementation ofFormation of unmanned aerial vehicle group.
7. The method as claimed in claim 6, wherein in step (4), M (t) is selected as a piecewise constant matrix
A constant matrix M of each segment of M (t)iSetting the order of progressive increase or decrease according to the norm size, and controlling the size of the formation of the unmanned aerial vehicle group to decrease or increase;
a constant matrix M of each segment of M (t)iAnd gradually changing the set direction of the characteristic vector in a time period to control the unmanned aerial vehicle group to roll.
8. The method as claimed in claim 6, wherein the step (4) utilizes a time-varying weighting matrix M (t) to control the time-varying queueing congestion in ui,1And ui,2The subspace transformation of (1) controls u by designing M (t)i,1And ui,2The fleet of drones is formed into motion states that are limited to 1-dimensional or 2-dimensional spaces.
9. The time-varying formation bee-holding control method applied to the unmanned aerial vehicle group as claimed in claim 6, wherein in the step (4), the acceleration control amount u of the virtual leader is usedrChanging the direction and magnitude of the speed in the pilot information acting on the control operation ui,3And then, controlling the direction change of the formation of the unmanned aerial vehicle group.
10. The time-varying formation bee-holding control method applied to the unmanned aerial vehicle group as claimed in claim 1, wherein in the step (2), the average value-virtual leader model of the state space equation is formed by calculating the average value of the position and the speed of each unmanned aerial vehicle and combining the virtual leader model; and judging the formation stability through the average value-characteristic value of the virtual leader model in the step (4).
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| CN113110593A (en) * | 2021-05-06 | 2021-07-13 | 西北工业大学 | Flight formation cooperative self-adaptive control method based on virtual structure and estimation information transmission |
| CN113359812A (en) * | 2021-05-11 | 2021-09-07 | 中国电子科技集团公司电子科学研究院 | Unmanned aerial vehicle cluster control method and device and readable storage medium |
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| CN117762166A (en) * | 2024-02-22 | 2024-03-26 | 杭州牧星科技有限公司 | multi-unmanned aerial vehicle cluster formation cooperative control method and system thereof |
| CN117762166B (en) * | 2024-02-22 | 2024-06-11 | 杭州牧星科技有限公司 | Multi-unmanned aerial vehicle cluster formation cooperative control method and system thereof |
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