CN113050697A - Unmanned aerial vehicle cluster consistency cooperative control method based on time Petri network - Google Patents
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Abstract
The invention discloses a time Petri network-based unmanned aerial vehicle cluster consistency cooperative control method, and belongs to the field of unmanned aerial vehicle intelligent control. The method comprises the following steps: s1: establishing a communication topology; s2: making a related constraint rule of unmanned aerial vehicle control information stream transmission; s3: establishing a time Petri network of the unmanned aerial vehicle cluster; s4: a consistent cooperation scheme is formulated, and state information of neighbor nodes is obtained; s5: under constraint conditions, calculating an expected steady-state tracking error, designing a performance function index, performing performance transformation to obtain an error model, and solving to realize consistency control; s6: and the unmanned aerial vehicle brings the solved transition time of the unmanned aerial vehicle cluster into a time Petri network, and predicts the transition strategies of the unmanned aerial vehicle in different environments. The method can realize the automatic flight control of the unmanned aerial vehicle cluster, improve the precision of the unmanned aerial vehicle cluster control, achieve the expected steady-state performance and consistency target and ensure the control safety of the unmanned aerial vehicle cluster in a complex environment.
Description
Technical Field
The invention relates to a time Petri network-based unmanned aerial vehicle cluster consistency cooperative control method, belongs to the field of unmanned aerial vehicle intelligent control, and is particularly suitable for the field of time Petri network-based unmanned aerial vehicle cluster consistency cooperative control.
Background
With the increasing maturity of artificial intelligence, big data analysis and processing technology, wireless communication technology and the like, unmanned aerial vehicle group has been widely applied to civil and military fields such as logistics, electric power, public safety, emergency command, battlefield confrontation and the like. Under the multi-source heterogeneous complex environment, the realization of the consistent cooperative control of the automatic control flight of the unmanned aerial vehicle cluster is particularly important. A formalized modeling and analyzing method with strict mathematical logic foundation is adopted to predict the cooperative control strategy of the unmanned aerial vehicle cluster, which is beneficial to enhancing the safety of the system.
The Petri network is one of formal methods and is commonly used for modeling simulation and analysis of a discrete parallel system; the Time Petri Network (TPN) developed on the basis is added with the transition time interval, can provide more accurate description for the dynamic process of the discrete time system, and can be used for well solving the problem of unmanned aerial vehicle cluster control simulation.
The consistency problem is used as the basis of the distributed cooperative control of the unmanned aerial vehicle group, and has important practical significance and theoretical value. The consistency protocol is a rule for interaction and information transmission between unmanned aerial vehicles, and describes an information interaction process between each unmanned aerial vehicle and adjacent unmanned aerial vehicles. When a fleet of drones is to collaborate on a task, the control strategy is effective in being able to cope with a variety of unpredictable situations and suddenly changing circumstances and to agree with the task expectations. Therefore, the consistency of the unmanned aerial vehicle cluster is a first condition for realizing cooperative control.
In summary, although the time Petri net and the consistency control problem are applied to each other at present, the time Petri net is mainly reflected in the macro research on the control strategy, and the consistency control is mainly reflected in the micro control on a specific operation of the unmanned aerial vehicle cluster, and lacks of the overall control, so that it is important to organically combine the two to realize the control on the unmanned aerial vehicle cluster.
Disclosure of Invention
Aiming at the blank of the prior art, the invention provides a time Petri network-based unmanned aerial vehicle cluster consistency cooperative control method, aiming at macroscopically utilizing the time Petri network to realize the overall strategy control of the unmanned aerial vehicle cluster, microscopically improving the precision of the unmanned aerial vehicle cluster control, achieving the expected steady-state performance and consistency target and ensuring the safety of the unmanned aerial vehicle cluster control.
In order to achieve the purpose, the invention provides the following technical scheme:
the time Petri network-based unmanned aerial vehicle cluster consistency cooperative control method comprises the following steps:
s1: establishing a communication topology by taking each unmanned aerial vehicle of the unmanned aerial vehicle cluster as a communication node;
s2: acquiring state information and communication topology conditions of the unmanned aerial vehicle in real time through communication, and making a related constraint rule of unmanned aerial vehicle control information stream transmission;
s3: establishing a time Petri network of the unmanned aerial vehicle cluster based on the state information of the unmanned aerial vehicle and the related constraint rule of unmanned aerial vehicle control information stream transmission, and performing frame formal description of state and transition;
s4: establishing a consistent cooperation scheme aiming at the communication relation of each unmanned aerial vehicle in the unmanned aerial vehicle cluster communication topology, wherein the unmanned aerial vehicles communicate with each other according to the communication topology so as to acquire the state information of the neighbor nodes;
s5: combining the specific working environment of the unmanned aerial vehicle cluster and the self constraint condition limit, calculating an expected steady-state tracking error by each unmanned aerial vehicle according to the state information of the unmanned aerial vehicle and the state information of the neighbor nodes, designing a performance function index according to a consistency cooperation scheme, carrying out performance transformation on the tracking error, solving the transition time of the unmanned aerial vehicle cluster, and realizing the consistency control of the unmanned aerial vehicle;
s6: and (4) bringing the solved transition time of the unmanned aerial vehicle cluster into a time Petri network, and predicting the transition strategies of the unmanned aerial vehicles in different environments.
Further, the communication topology described in step S1 may be set to be one of a directed type or an undirected type according to the requirement of information circulation and exchange inside the drone group, or one or more of connection modes of leader-follower communication, neighbor communication, and designated object communication may be set according to the connection relationship of the drone group, or one of a fixed topology and a switched topology may be selected according to the information of the application scale, the optimization control, and the power and energy consumption of the drone group.
Further, the state information of the drone in step S2 includes a unique identification name Id-name, Time, and attribute At of the drone; the time comprises the response time t of the unmanned aerial vehiclepAnd a system global time tg(ii) a SaidThe drone attributes contain all states P ═ { P that the drone sensors may existiI ═ 1, 2, …, m } and all operations T ═ { τ) for drones j1, 2, …, n }; and m and n are the total number of states and operations corresponding to all the unmanned aerial vehicles.
Further, the temporal Petri network model of the unmanned aerial vehicle group in step S3 is (P, T, F, M)0SI) a five-component temporal Petri net model; wherein the content of the first and second substances,
(1)
a set of pre-conditions or post-conditions for operation transition of each unmanned aerial vehicle is equivalent to a directed arc set, wherein K is the total number of directed arcs;
(2)Mi＝(vol(p1)，vol(p2)，…，vol(pm) Is a state vector before the operation transition of the unmanned aerial vehicle group, wherein the initial time i is 0, vol (p)j) Is the current state pjThe number of unmanned aerial vehicles; when a certain unmanned aerial vehicle is in MiState transition τrIs known as state MiEnable occurs, labeled Mi]＞τr：
(3) SI is the longest time interval relative to the initial time when the operation of the current state is changed, wherein the initial time SI is [0, 0 ]]For arbitrary operation set
SI(τ)＝[max(eft(τ))，min(lft(τ))](eft ≦ lft), eft denotes the earliest transition time of the operation, and lft denotes the latest transition time of the operation.
Further, the step S4 of formulating a consistent collaborative scheme specifically includes:
s401: determining a communication topological graph according to the design requirements of an internal communication protocol of the unmanned aerial vehicle cluster;
s402: determining an adjacency matrix A, a degree matrix D and a Laplace matrix L according to the communication topological graph;
s403: and further formulating a state consistency coordination scheme of each unmanned aerial vehicle according to the communication rule determined by the matrix information.
Further, the communication topology described in step S401 is composed of node information V and side information E, where each set of drone nodes V ═ V j1, …, q }, and a set of connectivity relationships E { (v) between the dronesj，vk)|vj∈V，vkE is V, and q is more than or equal to 2 and is the number of the unmanned aerial vehicles; the adjacency matrix described in step S402
Wherein when (v)j，vk) E is E, ajk1, otherwise ajk0; the degree matrix
Wherein diag (·) is a diagonalization operation; the Laplace matrix
The communication rule in step S403 is a connectivity relationship; the consistency coordination scheme of the jth unmanned aerial vehicle is
Wherein the content of the first and second substances,
is the state data vector of the jth unmanned aerial vehicle with the dimension of M, wherein M is more than or equal to 1 and less than or equal to 3, and NjA set of drone nodes adjacent to the jth drone; the unmanned aerial vehicle consistency coordination scheme can be further written into a compact form
Wherein the content of the first and second substances,
is a tensor product, IMIs an M-dimensional identity matrix.
Further, the step S5 specifically includes:
s501: the unmanned aerial vehicle group calculates expectation according to the state data vectors of the self and the neighbor nodes after transition
Wherein Z is a group satisfying
Is a convolution, H ═ H (H)jk)1≤j≤Mq，1≤k≤MqA channel matrix for the communication;
s502: the unmanned aerial vehicle group calculates an expected steady-state tracking error e as Z-Y according to the state data vectors of the unmanned aerial vehicle group and the neighbor nodes;
s503: designing a performance function index for evaluating the consistency precision of the unmanned aerial vehicle group:
wherein the content of the first and second substances,
is a positive definite weight matrix;
s504: performing performance transformation on the performance function index of the unmanned aerial vehicle group to obtain a quadratic programming problem;
s505: and obtaining discrete time steps, namely transition time, required by the unmanned aerial vehicle cluster when the expected steady-state tracking error is within a design requirement range by utilizing a quadratic programming problem solver, and realizing consistency control by using control data.
Further, the quadratic programming problem described in step S504 is generally in the form of: minimization
The constraint condition is
Further, the transformation equivalent is: minimize (z-y)TS (z-y)/2, with the constraint conditions of Ls ═ 0 and S ═ H × z; further, the compact form is transformed into: minimize (z-y)TS(z-y)/2+β·(H*z)TL · (H ×) z)/2, with the constraint L · (H ·) z ═ 0; wherein the content of the first and second substances,
respectively the vectorization of the matrix Z, Y,
beta > 0 is a scaling coefficient, Γ (-) is a vectorized representation of the corresponding saturation constraint, IqIs a q-dimensional identity matrix.
Common quadratic programming problem solvers include Gurobi, quadprog and the like, and particularly, the scaling parameter β can be obtained by means of deep learning and the like in consideration of calculation efficiency.
Further, the step S6 is to specify the transition time and the response time t of the drone in the Petri network of the transition time to be solved for the drone swarmpCombining, utilizing the inverse transition time of the unmanned plane group and the respective response time t of each unmanned planepAnd pushing the transition initial time interval to finish the assignment of the SI interval.
Further, the unmanned aerial vehicle group transition policy in step S6 is specifically: according to a matrix equation: mj＝Mi+X·(D+-D-) Solving the value of X; (di) if X ═ Mj-Mi)·(D+-D-)-1Without solution, proving that no operation can realize the slave M of the unmanned aerial vehicle groupiState transition to MjA state; (III) if X has a solution, the value of X is MiState reaches MjOperating parameters of a particular transition process of a state; wherein:
(1) input matrix D-Satisfies the following conditions: there is no τ in the TPN network graphaTo pbDirected arc of (1), then-[a，b]0; one tau exists in TPZN network diagramaTo pbIs directed arc of, and τaOperation Enable capable of producing State pbThe number of (2) is s, then D-[a，b]S; wherein, 1 is more than or equal to a≤n，1≤b≤m；
(2) Output matrix D+Satisfies the following conditions: absence of p in TPN network graphbTo tauaDirected arc of (1), then+[a，b]0; there is one p in TPZN network diagrambTo tauaAnd can enable operation τaState p ofbThe number of (2) is s, then D+[a，b]S; wherein a is more than or equal to 1 and less than or equal to n, and b is more than or equal to 1 and less than or equal to m.
The invention has the beneficial effects that: the invention provides a time Petri network-based unmanned aerial vehicle cluster consistency cooperative control method, which macroscopically realizes the overall strategy control of the unmanned aerial vehicle cluster by utilizing the organic combination of the time Petri network and consistency control, microscopically improves the precision of the unmanned aerial vehicle cluster control, achieves the expected steady-state performance and consistency target, and ensures the control safety of the unmanned aerial vehicle cluster in a complex environment.
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For the purpose and technical solution of the present invention, the present invention is illustrated by the following drawings:
FIG. 1 is a flow chart of a method for cooperative control of the consistency of an unmanned aerial vehicle cluster based on a time Petri network;
FIG. 2 is a communication topology diagram of an embodiment of the present invention; wherein, 1, 2, 3, 4 are unmanned aerial vehicle respectively.
Detailed Description
In order to make the purpose and technical solution of the present invention more clearly understood, the present invention will be described in detail with reference to the accompanying drawings and examples.
Example (b): the control scene of an unmanned aerial vehicle cluster is assumed to be the unmanned aerial vehicle cluster consisting of 4 unmanned aerial vehicles, wherein the unmanned aerial vehicle 1 and the unmanned aerial vehicle 2 have two-way communication, and the unmanned aerial vehicle cluster is formed into a team to automatically control the unmanned aerial vehicle to fly towards a destination, and only the situation of plane flying is considered. The presence of communication delays between drones is considered and the corresponding frequency domain is represented as: h(s) exp (- θ · s), where θ is a delay matrix, where θ is 0.05 · I8(ii) a The communication data is one-dimensional data, i.e., M is 2. The embodiment provides a "time Petri network-based unmanned aerial vehicle cluster consistency cooperative control method", which, with reference to FIG. 1, includes the following steps:
the method comprises the following steps: the unmanned aerial vehicles in the unmanned aerial vehicle cluster are used as communication nodes, and p is 4, which is the number of the unmanned aerial vehicles, and as shown in fig. 2, a directed communication connection mode of communication connection of specified objects and a communication topological relation of a fixed topological structure are established.
Step two: and acquiring the state information and the communication topology condition of the unmanned aerial vehicle in real time through communication, and formulating the related constraint rule of unmanned aerial vehicle control information stream transmission.
The unmanned aerial vehicle state information comprises a unique identification name Id-name, Time and attribute At of the unmanned aerial vehicle; the time comprises the response time t of the unmanned aerial vehiclepAnd a system global time tg(ii) a The attributes of the drone include all possible states of the drone sensor, P ═ PiI ═ 1, 2, …, m } and all operations T ═ { τ) for drones j1, 2, …, n }; and m and n are the total number of states and operations corresponding to all the unmanned aerial vehicles. Wherein, the state of the unmanned aerial vehicle is mainly normal environment p1Alarm p for obstacle in front2Left side has obstacle alarm p3Alarm p with barrier on right side4Arrival state p5Left deviation course p6Right deviation course line p7The operation of the drone is mainly take-off tau of the drone1Hovering tau2Straight line τ3Left steering τ4Right steering τ6C, fall tau6。
Further designing flight parameters of the unmanned aerial vehicle, such as acceleration, steering speed and the like, and calculating the numerical value Y of the expected state vector of the position, the speed, the acceleration and the like of the unmanned aerial vehicle cluster through a set algorithm corresponding to each operation application parameter.
Step three: and establishing a time Petri network of the unmanned aerial vehicle cluster based on the unmanned aerial vehicle state information and the unmanned aerial vehicle control information stream transmission related constraint rule, and performing frame formal description of state and transition.
The time Petri network model of the unmanned aerial vehicle cluster is (P, T, F, M)0SI) a five-component temporal Petri net model; wherein the content of the first and second substances,
(1)
a set of pre-conditions or post-conditions for operation transition of each unmanned aerial vehicle is equivalent to a directed arc set, wherein K is the total number of directed arcs; such as: f { (p)1，τ3)，(τ4，p1)，(p5，τ6)，...}
(2)Mi＝(vol(p1)，vol(p2)，…，vol(pm) Is a state vector before the operation transition of the unmanned aerial vehicle group, wherein the initial time i is 0, vol (p)j) Is the current state pjThe number of unmanned aerial vehicles; when a certain unmanned aerial vehicle is in MiState transition τrIs known as state MiEnable occurs, labeled Mi]＞τr；
(3) SI is the longest time interval relative to the initial time when the operation of the current state is changed, wherein the initial time SI is [0, 0 ]]For arbitrary operation set
SI(τ)＝[max(eft(τ))，min(lft(τ))](eft ≦ lft), eft denotes the earliest transition time of the operation, and lft denotes the latest transition time of the operation.
Step four: the specific step of formulating the consistent cooperation scheme is as follows:
s401: determining an adjacency matrix A, a degree matrix D and a Laplace matrix L according to the communication topological graph;
wherein the content of the first and second substances,
D＝diag(3，2，3，2)，
s402: and further formulating a state consistency coordination scheme of each unmanned aerial vehicle according to the communication rule determined by the matrix information.
Further, the communication rule in step S402 is a connectivity relationship; each unmanned aerial vehicle adopts oneDescribing the state of the order differential model; the consistency coordination scheme of the jth unmanned aerial vehicle is
Wherein the content of the first and second substances,
is the state information of the jth unmanned aerial vehicle of 2 dimensions, NjA set of drone nodes adjacent to the jth drone; the unmanned aerial vehicle consistency coordination scheme can be further written into a compact form
Wherein the content of the first and second substances,
is a tensor product, I2Is a 2-dimensional identity matrix.
Step five: and combining the specific working environment of the unmanned aerial vehicle cluster and the self constraint condition limit, calculating an expected steady-state tracking error by each unmanned aerial vehicle according to the state information of the unmanned aerial vehicle and the adjacent nodes, designing a performance function index according to a consistency cooperation scheme, carrying out performance transformation on the tracking error, solving the transition time of the unmanned aerial vehicle cluster, and realizing consistency control.
Further, the fifth step is specifically:
s501: the unmanned aerial vehicle group calculates an expected steady-state tracking error e as Z-Y according to the state data vectors of the unmanned aerial vehicle group and the neighbor nodes;
s502: designing a performance function index for evaluating the consistency precision of the unmanned aerial vehicle group:
wherein the content of the first and second substances,
is a positive definite weight matrix;
s503: performing performance transformation on the performance function index of the unmanned aerial vehicle group to obtain a quadratic programming problem;
s504: and obtaining discrete time steps, namely transition time, required by the unmanned aerial vehicle cluster when the expected steady-state tracking error is within a design requirement range by utilizing a quadratic programming problem solver, and realizing consistency control by using control data.
Further, the quadratic programming problem described in step S504 is generally in the form of: minimization
The constraint condition is
Further, the transformation equivalent is: minimize (z-y)TS (z-y)/2, with the constraint conditions of Ls ═ 0 and S ═ H × z; further, the compact form is transformed into: minimize (z-y)TS(z-y)/2+β·(H*z)TL · (H ×) z)/2, with the constraint L · (H ·) z ═ 0; wherein the content of the first and second substances,
respectively the vectorization of the matrix Z, Y,
beta > 0 is a scaling coefficient, Γ (-) is a vectorized representation of the corresponding saturation constraint, I4Is a 4-dimensional identity matrix.
Step six: and (4) bringing the solved transition time of the unmanned aerial vehicle cluster into a time Petri network, predicting the transition strategies of the unmanned aerial vehicles in different environments, and repeating the fifth step to the sixth step until the flying reaches the destination.
The Petri network for bringing the transition time of the solved unmanned aerial vehicle cluster into time is concretely the transition time and the response time t of the unmanned aerial vehiclepCombining, utilizing the inverse transition time of the unmanned plane group and the respective response time t of each unmanned planepAnd pushing the transition initial time interval to finish the assignment of the SI interval.
Further, the unmanned aerial vehicle group transition policy in step S6 is specifically: according to a matrix equation: mj＝Mi+X·(D+-D-) Solving the value of X; (di) if X ═ Mj-Mi)·(D+-D-)-1Without solution, proving that no operation can realize the slave M of the unmanned aerial vehicle groupiState transition to MjA state; (III) if X has a solution, the value of X is MiState reaches MjOperating parameters of a particular transition process of a state; wherein:
(1) input matrix D-Satisfies the following conditions: there is no τ in the TPN network graphaTo pbDirected arc of (1), then-[a，b]0; one tau exists in TPZN network diagramaTo pbIs directed arc of, and τaOperation Enable capable of producing State pbThe number of (2) is s, then D-[a，b]S; wherein a is more than or equal to 1 and less than or equal to n, b is more than or equal to 1 and less than or equal to m:
(2) output matrix D+Satisfies the following conditions: absence of p in TPN network graphbTo tauaDirected arc of (1), then+[a，b]0; there is one p in TPZN network diagrambTo tauaAnd can enable operation τaState p ofbThe number of (2) is s, then D+[a，b]S; wherein a is more than or equal to 1 and less than or equal to n, and b is more than or equal to 1 and less than or equal to m.
It should be noted that if no operation can realize the transition of the drone swarm from the initial state to the arrival state, the relevant flight parameters of the drone need to be redesigned.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (9)
1. The time Petri network-based unmanned aerial vehicle cluster consistency cooperative control method is characterized by comprising the following steps of:
s1: establishing a communication topology by taking each unmanned aerial vehicle of the unmanned aerial vehicle cluster as a communication node;
s2: acquiring state information and communication topology conditions of the unmanned aerial vehicle in real time through communication, and making a related constraint rule of unmanned aerial vehicle control information stream transmission;
s3: establishing a time Petri network of the unmanned aerial vehicle cluster based on the state information of the unmanned aerial vehicle and the related constraint rule of unmanned aerial vehicle control information stream transmission, and performing frame formal description of state and transition;
s4: establishing a consistent cooperation scheme aiming at the communication relation of each unmanned aerial vehicle in the unmanned aerial vehicle cluster communication topology, wherein the unmanned aerial vehicles communicate with each other according to the communication topology so as to acquire the state information of the neighbor nodes;
s5: combining the specific working environment of the unmanned aerial vehicle cluster and the self constraint condition limit, calculating an expected steady-state tracking error by each unmanned aerial vehicle according to the state information of the unmanned aerial vehicle and the state information of the neighbor nodes, designing a performance function index according to a consistency cooperation scheme, carrying out performance transformation on the tracking error, solving the transition time of the unmanned aerial vehicle cluster, and realizing the consistency control of the unmanned aerial vehicle;
s6: and (4) bringing the solved transition time of the unmanned aerial vehicle cluster into a time Petri network, and predicting the transition strategies of the unmanned aerial vehicles in different environments.
2. The method according to claim 1, wherein the communication topology of step S1 is configured as one of a directed type or a undirected type according to the requirement of information circulation and exchange inside the drone swarm, or one or more of connection modes of leader-follower communication, neighbor communication, and communication of designated objects are configured according to the connection relationship of the drone swarm, or one of a fixed topology and a switched topology is selected according to the information of application scale, optimization control, and power and energy consumption of the drone swarm.
3. The Time Petri Net based unmanned aerial vehicle cluster consistency cooperative control method according to claim 1, wherein the unmanned aerial vehicle state information in step S2 comprises a unique identification name Id-name, a Time Time and an attribute At of the unmanned aerial vehicle; the time comprises the response time t of the unmanned aerial vehiclepAnd a system global time tg(ii) a What is needed isThe attributes of the drone include all possible states P ═ P of the drone sensorsiI ═ 1, 2, …, m } and all operations T ═ { τ) for dronesj1, 2, …, n }; and m and n are the total number of states and operations corresponding to all the unmanned aerial vehicles.
4. The time Petri Net based unmanned aerial vehicle cluster consistency cooperative control method according to claim 1, wherein the time Petri Net model of the unmanned aerial vehicle cluster in the step S3 is (P, T, F, M)0SI) a five-component temporal Petri net model; wherein the content of the first and second substances,
(1)
a set of pre-conditions or post-conditions for operation transition of each unmanned aerial vehicle is equivalent to a directed arc set, wherein K is the total number of directed arcs;
(2)Mi＝(vol(p1)，vol(p2)，…，vol(pm) Is a state vector before the operation transition of the unmanned aerial vehicle group, wherein the initial time i is 0, vol (p)j) Is the current state pjThe number of unmanned aerial vehicles; when a certain unmanned aerial vehicle is in MiState transition τrIs known as state MiEnable occurs, labeled Mi]＞τr；
(3) SI is the longest time interval relative to the initial time when the operation of the current state is changed, wherein the initial time SI is [0, 0 ]]For arbitrary operation set
SI(τ)＝[max(eft(τ))，min(lft(τ))](eft ≦ lft), eft denotes the earliest transition time of the operation, and lft denotes the latest transition time of the operation.
5. The unmanned aerial vehicle fleet consistency cooperative control method based on the time Petri network as claimed in claim 1, wherein the step S4 of formulating the consistency cooperative scheme specifically comprises:
s401: determining a communication topological graph according to the design requirements of an internal communication protocol of the unmanned aerial vehicle cluster;
s402: determining an adjacency matrix A, a degree matrix D and a Laplace matrix L according to the communication topological graph;
s403: and further formulating a state consistency coordination scheme of each unmanned aerial vehicle according to the communication rule determined by the matrix information.
6. The method according to claim 5, wherein the communication topology map of step S401 is composed of node information V and side information E, and wherein each set of nodes V ═ V { V } of unmanned aerial vehicles is defined as a set of unmanned aerial vehicle nodesj1, …, q }, and a set of connectivity relationships E { (v) between the dronesj，vk)|vj∈V，vkE is V, and q is more than or equal to 2 and is the number of the unmanned aerial vehicles; the adjacency matrix described in step S402
Wherein when (v)j，vk) E is E, ajk1, otherwise ajk0; the degree matrix
Wherein diag (·) is a diagonalization operation; the Laplace matrix
The communication rule in step S403 is a connectivity relationship; the consistency coordination scheme of the jth unmanned aerial vehicle is
Wherein the content of the first and second substances,
is the state data vector of the jth unmanned aerial vehicle with the dimension of M, wherein M is more than or equal to 1 and less than or equal to 3, and NjA set of drone nodes adjacent to the jth drone; the identity of the unmanned aerial vehicleThe sexual coordination scheme can be further written as a compact form
Wherein the content of the first and second substances,
is a tensor product, IMIs an M-dimensional identity matrix.
7. The unmanned aerial vehicle fleet consistency cooperative control method based on the time Petri network as claimed in claim 1, wherein the step S5 specifically comprises:
s501: the unmanned aerial vehicle group calculates expectation according to the state data vectors of the self and the neighbor nodes after transition
Wherein Z is a group satisfying
Is a convolution, H ═ H (H)jk)1≤j≤Mq，1≤k≤MqA channel matrix for the communication;
s502: the unmanned aerial vehicle group calculates an expected steady-state tracking error e as Z-Y according to the state data vectors of the unmanned aerial vehicle group and the neighbor nodes;
s503: designing a performance function index for evaluating the consistency precision of the unmanned aerial vehicle group:
wherein the content of the first and second substances,
is a positive definite weight matrix;
s504: performing performance transformation on the performance function index of the unmanned aerial vehicle group to obtain a quadratic programming problem;
s505: and obtaining discrete time steps, namely transition time, required by the unmanned aerial vehicle cluster when the expected steady-state tracking error is within a design requirement range by utilizing a quadratic programming problem solver, and realizing consistency control by using control data.
8. The time Petri Net based unmanned aerial vehicle cluster consistency cooperative control method according to claim 7, wherein the quadratic programming problem in the step S504 is in a general form: minimization
The constraint condition is
Further, the transformation equivalent is: minimize (z-y)TS (z-y)/2, with the constraint conditions of Ls ═ 0 and S ═ H × z; further, the compact form is transformed into: minimize (z-y)TS(z-y)/2+β·(H*z)TL · (H ×) z)/2, with the constraint L · (H ·) z ═ 0; wherein the content of the first and second substances,
respectively the vectorization of the matrix Z, Y,
beta > 0 is a scaling coefficient, Γ (-) is a vectorized representation of the corresponding saturation constraint, IqIs a q-dimensional identity matrix.
9. The time Petri network-based unmanned aerial vehicle cluster consistency cooperative control method according to claim 1, wherein the step S6 is implemented by bringing the transition time of the solved unmanned aerial vehicle cluster into the time Petri network, specifically by bringing the transition time and the unmanned aerial vehicle response time tpAnd finishing the assignment of the SI interval in combination.
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