CN111596688B - Multi-unmanned aerial vehicle formation consistency control method based on continuous communication - Google Patents

Multi-unmanned aerial vehicle formation consistency control method based on continuous communication Download PDF

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CN111596688B
CN111596688B CN202010463656.2A CN202010463656A CN111596688B CN 111596688 B CN111596688 B CN 111596688B CN 202010463656 A CN202010463656 A CN 202010463656A CN 111596688 B CN111596688 B CN 111596688B
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于剑桥
郑世钰
陈曦
李佳迅
郭斐然
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Beijing Institute of Technology BIT
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Abstract

The invention provides a multi-unmanned aerial vehicle formation consistency control method based on continuous communication, which is suitable for various communication network topological structure forms, avoids the network topological form of connecting all unmanned aerial vehicles and a control center in centralized control, and the flexibility of communication network selection, and brings better expandability, fault tolerance and adaptability to unmanned aerial vehicle formation application; the distributed architecture removes a control center in a centralized control architecture, so that the phenomenon of failure of formation due to damage of central control nodes does not exist; the calculation tasks of the unmanned aerial vehicle formation are controlled and dispersed to the onboard computers of the member unmanned aerial vehicles by the control center, so that the overall calculation capacity of the formation is greatly improved, and the performance bottleneck caused by the calculation capacity is relieved; unmanned aerial vehicle need not to communicate with control center, only need with member unmanned aerial vehicle between keep communicate can, greatly reduced network traffic to communication distance is far less than with control center communication distance between unmanned aerial vehicle and the member of formation, communication interference killing feature and reliability improve by a wide margin.

Description

Multi-unmanned aerial vehicle formation consistency control method based on continuous communication
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle guidance control, and particularly relates to a multi-unmanned aerial vehicle formation consistency control method based on continuous communication.
Background
The formation flying of multiple unmanned aerial vehicles means that multiple unmanned aerial vehicles are arranged into a certain formation and fly together. The formation control technology runs through the whole process of formation flight of multiple unmanned aerial vehicles, and has important significance for smooth development and completion of tasks.
Currently, a centralized architecture control method is mostly adopted for unmanned aerial vehicle formation control. The centralized control architecture needs to be provided with a control center which controls global state information, is responsible for control algorithm resolving and instruction generation, and sends instructions to the member unmanned aerial vehicles through communication links. The member unmanned aerial vehicle responds to the instruction of the control center, and control functions of formation, maintenance, transformation and the like are achieved. The control center can be arranged on the ground and also can be carried in the formation unmanned aerial vehicle. The centralized control architecture can process the unmanned aerial vehicle formation control problem globally, and is widely applied to various unmanned aerial vehicle formation control applications at present.
However, the formation control method with a centralized architecture is difficult to be applied to a task scene with signal interference and complex and variable environment. The main factors of the restriction include the following three factors.
(1) The centralized architecture control relies heavily on reliable communication between the unmanned aerial vehicle and the control center. The unmanned aerial vehicle formation and the control center need to transmit state information and control instructions through a stable and reliable communication link. With the increase of the number of the unmanned planes in the formation, the transmission amount of the network is increased remarkably, which puts high requirements on the communication bandwidth and the anti-interference performance of the system. In the military and civilian fields, unmanned aerial vehicle formation will be applied under the harsher flight environment. For example: under the future informatization, networking and system countermeasure combat environment, electromagnetic interference is a main mode of unmanned aerial vehicle cluster countermeasure; the unmanned aerial vehicle formation inevitably receives strong electromagnetic environment interference in the electric power inspection application.
(2) The control center becomes a system performance bottleneck. The global state information and the control signals of the formation are all centralized in the control center, and if the processing capacity of the control center reaches the upper limit, the performance of the formation system is further improved. In addition, if the control center fails or is destroyed by an attack, the whole formation system is paralyzed, and the robustness of the system structure is poor.
(3) The control center is difficult to ensure the real-time performance of the system. Because the calculation of all formation control is concentrated in the control center, the calculation amount is large, a large amount of calculation time is usually consumed for solving the complex unmanned aerial vehicle formation control, and the real-time requirement cannot be met.
The above factors severely restrict the application of the centralized-architecture unmanned aerial vehicle formation in the real scene. In the face of increasingly complex unmanned aerial vehicle formation application scenes, a distributed formation control method is needed to be established, and the problems of communication dependence, performance bottleneck and instantaneity of an unmanned aerial vehicle control center are solved. The multi-unmanned aerial vehicle continuous communication consistency control algorithm can be applied to the construction of an unmanned aerial vehicle formation control system with a distributed architecture.
Disclosure of Invention
In view of this, the present invention aims to provide a method for controlling formation consistency of multiple unmanned aerial vehicles based on continuous communication, in which a state consistency controller is constructed by exchanging cooperative state information among the formation unmanned aerial vehicles, and a control instruction is generated by distributed calculation to control one or more states of the unmanned aerial vehicles to be consistent. The method can be used as a basic controller of an unmanned aerial vehicle formation control architecture, can flexibly select a communication network topological structure, and effectively avoids the problems brought by control center nodes.
A multi-unmanned aerial vehicle formation consistency control method based on continuous communication comprises the following steps:
step 1, establishing a communication network among N unmanned aerial vehicles, and using a Laplacian matrix
Figure BDA0002511913630000021
Describing a communication topology between unmanned aerial vehicles; wherein N is more than or equal to 2;
step 2, selecting at least one flight state quantity of the unmanned aerial vehicle to form a state vector xiAs a cooperative state, a dynamic model of the unmanned aerial vehicle is obtained
Figure BDA0002511913630000022
Designing a distributed consistency controller:
Figure BDA0002511913630000023
wherein u isiThe control input vector of the ith unmanned aerial vehicle is A, a is a system matrix of a general linear system, B is a control matrix, and the matrices (A, B) are controllable; c is a positive gain coefficient, K is a gain matrix, aijAssociating adjacency matrices for communication networks
Figure BDA0002511913630000024
The element in (b) represents the communication relationship between the ith unmanned aerial vehicle and the jth unmanned aerial vehicle, and when the information of the unmanned aerial vehicle j can be received by the unmanned aerial vehicle i, aij1, otherwiseij＝0；
Step 3, calculating the lower bound of the gain matrix K and the gain coefficient c of the controller, designing and selecting parameters of proper gain to meet the stability requirement, and specifically:
the gain factor should satisfy
Figure BDA0002511913630000025
Figure BDA0002511913630000026
Is a Laplacian matrix
Figure BDA0002511913630000027
Algebraic connectivity of;
obtaining a positive definite matrix P by solving the following algebraic Riccati equation, and calculating to obtain a gain matrix K-B in the algorithmTP：
PA+ATP-PBBTP+In＝0
Wherein, InRepresenting an identity matrix;
step 4, each unmanned aerial vehicle acquires the cooperative state of the unmanned aerial vehicles close to the nodes in real time through the communication network, and respective control input vector u is calculated according to the cooperative stateiAnd finally, the unmanned aerial vehicle automatic pilot executes the control to realize the formation consistency control of the unmanned aerial vehicles.
The invention has the following beneficial effects:
the invention provides a multi-unmanned aerial vehicle formation consistency control method based on continuous communication, which is applied to unmanned aerial vehicle cooperative control, wherein the unmanned aerial vehicles have equal status, the executed control algorithms are the same, a central control node does not exist, and the method belongs to a distributed control architecture; the unmanned aerial vehicle control system can adapt to various communication network topological structure forms, and avoids a network topological form of connecting all unmanned aerial vehicles and a control center in centralized control. Due to the flexibility of communication network selection, better expandability, fault tolerance and adaptability are brought to the formation application of the unmanned aerial vehicles; the distributed control architecture removes a control center in a centralized control architecture, so that the phenomenon of failure of formation due to damage of central control nodes does not exist; the calculation tasks of the unmanned aerial vehicle formation are controlled and dispersed to the onboard computers of the member unmanned aerial vehicles by the control center, so that the overall calculation capacity of the formation is greatly improved, and the performance bottleneck caused by the calculation capacity is relieved; the unmanned aerial vehicle does not need to communicate with the control center, and only needs to keep communication with the member unmanned aerial vehicle, so that network communication traffic is greatly reduced. And the communication distance between the unmanned aerial vehicle and the formation member is far shorter than the communication distance with the control center, and the communication anti-interference capability and the reliability of the unmanned aerial vehicle formation are greatly improved.
Drawings
Fig. 1 is a schematic diagram of an unmanned aerial vehicle communication topology;
FIG. 2 is a schematic diagram of multi-drone coherence control;
FIG. 3 is a schematic diagram of formation of multiple drones;
fig. 4 is a block diagram of a multi-drone distributed cooperative formation control structure.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
Step 1: and establishing a communication network among the N unmanned aerial vehicles to clarify the communication relation among the unmanned aerial vehicles. The communication network topology among the N unmanned aerial vehicles should meet the strong communication condition.
Step 2: selecting a certain flight state quantity of the unmanned aerial vehicle as a cooperative state according to a continuous communication consistency algorithm
Figure BDA0002511913630000031
A distributed consistency controller is designed. Where c is a positive gain coefficient, K is a gain matrix, aijAssociating adjacency matrices for communication networks
Figure BDA0002511913630000032
Of (1). Drawing (A)
Figure BDA0002511913630000033
Associated adjacency matrices
Figure BDA0002511913630000034
Is defined as: when (v)j,vi) E epsilon, i.e. when node i receives the information of node j, aij>0; when in use
Figure BDA0002511913630000035
When a isij0. A since the existence of self-to-self edge is not allowedii0. A in the adjacency matrixijThe value is called the weight of the edge, and if the weight of the edge has no specific practical meaning, when (v)j,vi) When epsilon is epsilon, a is takenij＝1。
And step 3: and calculating the lower bound of the gain matrix and the gain coefficient of the controller, and designing and selecting parameters with proper gain to meet the requirement of algorithm stability. The gain factor should satisfy
Figure BDA0002511913630000036
Figure BDA0002511913630000037
For communication with a communication network
Figure BDA0002511913630000038
Associated Laplacian matrix
Figure BDA0002511913630000039
Algebraic connectivity of. Gain matrix K ═ BTP, the positive definite matrix P is a solution that satisfies the algebraic ricati equation.
And 4, step 4: each unmanned aerial vehicle acquires the cooperative state of the unmanned aerial vehicles close to the nodes in real time through a communication network, and calculates and generates a consistency control instruction in a distributed manner according to the cooperative state;
and 5: and transmitting the generated consistency control instruction to the next control link of the unmanned aerial vehicle, and finally executing the control by an automatic pilot of the unmanned aerial vehicle to realize the state consistency control of the unmanned aerial vehicle.
Step 6: in the multi-drone execution consistency control, step 4 and step 5 should be executed in each controller beat until the multi-drone consistency control state ends.
Example (b):
step 1, taking the number N of the unmanned aerial vehicles as 4 as an example, explaining a specific implementation mode of the consistency algorithm. First, a communication network needs to be established for the drone before implementing the coherence control algorithm. The consistency algorithm realizes the consistency of the states of the multiple intelligent agents by generating control instructions in a distributed manner, and obviously, the premise that the intelligent agents acquire the state information of other intelligent agents through communication is to realize consistency control. The communication topology structure can influence the stability of the consistency algorithm, and the system communication topology can be clearly described by combining the matrix theory of the graph. Communication connection relations among 4 unmanned aerial vehicles are shown in fig. 1, and the unidirectional arrows indicate that communication among the unmanned aerial vehicles is unidirectional. The Laplacian matrix describing the communication topology is:
Figure BDA0002511913630000041
by analyzing the Laplacian matrix, whether the communication network structure meets the stability requirement of the consistency algorithm can be analyzed and judged.
Further, a certain flight state of the unmanned aerial vehicle is selected, and a distributed consistency controller is designed according to a consistency control algorithm form. Unmanned aerial vehicle flight state quantity includes: flight speed, altitude, course angle, horizontal position, etc., different state quantities follow different dynamics. And establishing a dynamic model for the selected unmanned aerial vehicle state, and simplifying the dynamic model into a general linear system model.
Figure BDA0002511913630000042
Wherein i 1i＝[xi1,...xin]T∈RnFor an agent viState vector of ui∈RpThe input vector is controlled. A is a system matrix of a general linear system, B is a control matrix, and the matrices (A, B) are controllable. The consistency control algorithm provided by the invention aims at designing a consistency controller for an intelligent agent with general linear system dynamics. The consistency control algorithm is as follows:
Figure BDA0002511913630000043
further, a positive definite matrix P is obtained by solving an algebraic Riccati equation (4), and a gain matrix K in the algorithm is obtained through calculation, wherein the gain matrix K is equal to-BTP。
PA+ATP-PBBTP+In＝0 (4)
To introduce the lower limit of the gain factor, the following concept is introduced. If communication topology map
Figure BDA0002511913630000051
Is provided with a Laplacian matrix
Figure BDA0002511913630000052
The strong connection diagram of (2) has a positive vector r ═ r1,...rN]TSatisfy the requirement of
Figure BDA0002511913630000053
And is
Figure BDA0002511913630000054
A general algebraic connectivity is defined as a real number
Figure BDA0002511913630000055
In the formula
Figure BDA0002511913630000056
R＝diag(r1,r2,...,rN)TAnd x is xTArbitrary state vector where r is 0 and x is not equal to 0. Determining the lower boundary of the gain coefficient by calculating the algebraic connectivity of the Laplacian matrix
Figure BDA0002511913630000057
And a proper gain coefficient is designed according to the method, so that the control parameters are ensured to meet the stability condition of the consistency algorithm.
The stability of the consistency algorithm is demonstrated below:
firstly, the degree of deviation between the states of the multiple agents is introducedA consistency error of (2). And deducing consistency error dynamics according to a control algorithm and intelligent body dynamics, and further introducing stability certification. Let r be ═ r1,...rN]T∈RN×1Is prepared by reacting with
Figure BDA0002511913630000058
A left eigenvector associated with zero eigenvalue, satisfying r T1 is 1. Defining a consistency error vector ξi(t)∈Rn×1Comprises the following steps:
Figure BDA0002511913630000059
the formula (6) can be written into a stack vector shorthand form by using the Kronecker product operation
Figure BDA00025119136300000510
Wherein x is ═ x1,...,xN]T，ξ＝[ξ1,...,ξN]TAnd xi ∈ RNn×1. The formula (7) can be rewritten into by matrix representation
Figure BDA00025119136300000511
In the formula
Figure BDA00025119136300000512
From rTAs can be seen from the definition of (1), 0 is a matrix
Figure BDA00025119136300000513
Figure BDA00025119136300000513
1 is the right eigenvector associated with it, and 1 is another eigenvalue of order N-1. Due to the fact that
Figure BDA00025119136300000514
Can be easily seen
Figure BDA00025119136300000515
Then, from the formula (8), if and only if x1＝...＝xNWhen, xi is 0. Therefore xi can be regarded as a consistency error vector, and the consistency error is zero only when the state quantities of the multi-agent are the same. The consistency problem translates to ξ → 0 when t → ∞.
Substituting the consistency control algorithm formula (3) into the intelligent agent dynamics (2) to obtain the controlled dynamics equation of the intelligent agent
Figure BDA00025119136300000516
Written as stack vector shorthand form
Figure BDA0002511913630000061
The derivatives of both sides of the formula (7) with time are obtained and are substituted into the formula (11) for arrangement to obtain a consistency error kinetic equation
Figure BDA0002511913630000062
Namely, it is
Figure BDA0002511913630000063
Selecting a Lyapunov function as follows:
Figure BDA0002511913630000064
wherein R is a positive diagonal matrix, defined by formula (5). Lyapunov function V1Is positive, for V1Derivation along the trajectory of equation (11)
Figure BDA0002511913630000065
Substituting the gain matrix K ═ BTP
Figure BDA0002511913630000066
For the second term in equation (16), the algebraic connectivity defined in equation 5 is
Figure BDA0002511913630000067
Only need to select
Figure BDA0002511913630000068
The following inequality can be obtained
Figure BDA0002511913630000069
By substituting formula (18) for formula (14)
Figure BDA00025119136300000610
The positive definite matrix P is the solution of algebraic Riccati equation satisfying the formula (4), and is obtained by substituting the equation
Figure BDA00025119136300000611
Thus, the system is progressively stable and the consistency problem is solved.
After the design and parameter setting of the distributed consistency controller are completed, the controller can be applied to unmanned aerial vehicle consistency control. Multiple drone coherence control as shown in fig. 2, the initial flight conditions of multiple drones are shown, for example the heading angle χ of drone 11Velocity V1. In the figure byDotted lines connecting dots represent formation of unmanned aerial vehicles, and the geometric center of the formation is located in the center of mass of the unmanned aerial vehicle 1. OxfyfFor formation of formations in the horizontal plane, OxfAxis coincides with 1 velocity vector of unmanned plane, OxgygIs a ground coordinate system in the horizontal plane. Course angle, speed have the difference between unmanned aerial vehicle, therefore unmanned aerial vehicle can't form stable formation, and unmanned aerial vehicle relative position also can't satisfy formation requirement. The controller designed by using the consistency algorithm can realize gradual consistency of states such as the heading angle, the speed and the like of the unmanned aerial vehicle, and lays a foundation for formation flight. By controlling the relative position between the unmanned aerial vehicles to be consistent with the formation shape, the unmanned aerial vehicles can fly in the formation.
The formation of the multiple unmanned aerial vehicles is shown in fig. 3. By defining the geometric center and the coordinate system of the formation, the distance between the formation member unmanned aerial vehicle and the formation center is set, and the formation of the unmanned aerial vehicle in the space can be accurately described. The vector expected by the relative formation geometric center of unmanned aerial vehicle i and unmanned aerial vehicle j in formation is ri T＝[xif,yif,zif]T，rj T＝[xjf,yjf,zjf]T. A plurality of such vectors to be formed into a formation form description matrix.
Figure BDA0002511913630000071
Fig. 4 is a block diagram of a control structure of distributed cooperative formation of multiple unmanned aerial vehicles, which visually describes a composition structure and a control instruction transmission sequence of a control system in the cooperative formation of the unmanned aerial vehicles. The multi-unmanned aerial vehicle continuous communication consistency control algorithm provided by the invention can be used in the links of unmanned aerial vehicle state cooperative control and unmanned aerial vehicle formation control shown in fig. 4. When the algorithm is applied, firstly, the unmanned aerial vehicle needs to acquire the cooperative state information in real time through a communication network, and transmit the cooperative state information to the corresponding consistency controller. Secondly, the consistency controller utilizes the cooperative state and the state of the unmanned aerial vehicle to calculate and generate a control instruction, and the control instruction is transmitted to the unmanned aerial vehicle automatic pilot after conversion. Then, the automatic pilot of the unmanned aerial vehicle executes the control command, so that the flight state of the unmanned aerial vehicle changes. And finally, the unmanned aerial vehicle issues the flight state information of the unmanned aerial vehicle through a communication network, and the flight state information is used as the cooperative state information of the rest unmanned aerial vehicles in the formation.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (1)

1. A multi-unmanned aerial vehicle formation consistency control method based on continuous communication is characterized by comprising the following steps:
step 1, establishing a communication network among N unmanned aerial vehicles, and using a Laplacian matrix
Figure FDA0002511913620000011
Describing a communication topology between unmanned aerial vehicles; wherein N is more than or equal to 2;
step 2, selecting at least one flight state quantity of the unmanned aerial vehicle to form a state vector xiAs a cooperative state, a dynamic model of the unmanned aerial vehicle is obtained
Figure FDA0002511913620000012
Designing a distributed consistency controller:
Figure FDA0002511913620000013
wherein u isiThe control input vector of the ith unmanned aerial vehicle is A, a is a system matrix of a general linear system, B is a control matrix, and the matrices (A, B) are controllable; c is a positive gain coefficient, K is a gain matrix, aijAssociating adjacency matrices for communication networks
Figure FDA0002511913620000014
Element (ii) represents the ithThe communication relation between the man-machine and the jth unmanned aerial vehicle, when the unmanned aerial vehicle i can receive the information of the unmanned aerial vehicle j, aij1, otherwiseij＝0；
Step 3, calculating the lower bound of the gain matrix K and the gain coefficient c of the controller, designing and selecting parameters of proper gain to meet the stability requirement, and specifically:
the gain factor should satisfy
Figure FDA0002511913620000015
Figure FDA0002511913620000016
Is a Laplacian matrix
Figure FDA0002511913620000017
Algebraic connectivity of;
obtaining a positive definite matrix P by solving the following algebraic Riccati equation, and calculating to obtain a gain matrix K-B in the algorithmTP：
PA+ATP-PBBTP+In＝0
Wherein, InRepresenting an identity matrix;
step 4, each unmanned aerial vehicle acquires the cooperative state of the unmanned aerial vehicles close to the nodes in real time through the communication network, and respective control input vector u is calculated according to the cooperative stateiAnd finally, the unmanned aerial vehicle automatic pilot executes the control to realize the state consistency control of the unmanned aerial vehicle.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108845590A (en) * 2018-07-06 2018-11-20 哈尔滨工业大学（威海） A kind of multiple no-manned plane under time delay environment cooperates with formation control method
CN109582036A (en) * 2018-12-03 2019-04-05 南京航空航天大学 Quadrotor drone consistency formation control method
CN110196554A (en) * 2019-05-27 2019-09-03 重庆邮电大学 A kind of safety compliance control method of multi-agent system
CN110673649A (en) * 2019-11-12 2020-01-10 哈尔滨工业大学（深圳） Unmanned aerial vehicle formation consistency control method, system and device under time-varying channel based on topology optimization and storage medium

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8186589B2 (en) * 2006-01-11 2012-05-29 Carmel-Haifa University Economic Corporation Ltd. UAV decision and control system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108845590A (en) * 2018-07-06 2018-11-20 哈尔滨工业大学（威海） A kind of multiple no-manned plane under time delay environment cooperates with formation control method
CN109582036A (en) * 2018-12-03 2019-04-05 南京航空航天大学 Quadrotor drone consistency formation control method
CN110196554A (en) * 2019-05-27 2019-09-03 重庆邮电大学 A kind of safety compliance control method of multi-agent system
CN110673649A (en) * 2019-11-12 2020-01-10 哈尔滨工业大学（深圳） Unmanned aerial vehicle formation consistency control method, system and device under time-varying channel based on topology optimization and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
切换通信拓扑条件下的无人机集群构型变换控制;刘流 等;《兵工学报》;20190531;第40卷(第5期);第996-1002页 *

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