Unmanned aerial vehicle cluster consistency control method based on Lanner Jones potential

Abstract

The invention discloses an unmanned aerial vehicle cluster consistency control method based on the Lanner-Jones potential, and the method comprises the steps: obtaining a mass point dynamics model of an unmanned aerial vehicle cluster, and designing an unmanned aerial vehicle individual control law on the basis; according to the initial state data of the unmanned aerial vehicle cluster, carrying out stress analysis on the unmanned aerial vehicles by using an unmanned aerial vehicle individual control law, and updating the unmanned aerial vehicle individual control law to obtain an updated unmanned aerial vehicle individual control law; performing control movement on the unmanned aerial vehicles based on the updated unmanned aerial vehicle individual control law to obtain state data of the unmanned aerial vehicle cluster after movement; judging whether the unmanned aerial vehicle cluster has consistency or not according to the moved state data; if the unmanned aerial vehicle cluster does not have consistency, taking the moved state data as initial state data, and repeating the above steps until the unmanned aerial vehicle cluster has consistency; according to the invention, consistency control of the unmanned aerial vehicle cluster can be effectively realized.

Description

Technical Field

[0001] The invention relates to a consistency control method of an unmanned aerial vehicle cluster based on Lanner-Jones potential, and belongs to the field of autonomous consistency control of unmanned aerial vehicle clusters. Background Art

[0002] As an important milestone in the development of drone technology, drone swarms not only herald a major trend in the future aviation field, but also demonstrate incomparable advantages in the execution of specific flight missions. Especially when faced with complex and changing mission requirements, traditional large-scale equipment is often bulky and costly. At this time, the use of a large number of cheap drones to form drone swarms has become an ideal alternative. This strategy not only greatly improves the flexibility and economy of mission execution, but also enables drone swarms to complete missions more efficiently than single large equipment in many scenarios.

[0003] More importantly, through the precise coordination and collaborative operation between drones, the cluster system can break through the limitations of a single drone's capabilities and complete tasks that a single drone cannot complete, thus effectively solving the limitations of a single drone device; however, to achieve efficient collaborative operation of drone clusters, the consistency control of drone clusters is particularly important. The so-called consistency control means that in a drone cluster, by designing appropriate control strategies, the states of all drones (such as position, speed, heading, etc.) can gradually converge to the same, or achieve synchronization within a certain error range. The consistency control strategy is an effective way to achieve multi-agent control. This control mechanism is the key to ensuring that drone clusters can maintain overall coordination and stability when performing complex tasks.

[0004] However, existing consistency control methods largely rely solely on mathematical theories and engineering techniques to study swarm dynamics problems. Although this research path can provide some quantitative analysis and design tools, it fails to fully capture the complexity and diversity of the collaborative behavior of UAV swarms and cannot effectively achieve consistency control of UAV swarms. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a consistency control method for a drone cluster based on the Lanner-Jones potential, which can utilize the individual force and potential energy of the drones, update the individual control rate of the drones, and stimulate the complex group emergent behavior of the drone cluster, thereby effectively realizing the consistency control of the drone cluster in a complex environment.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] The present invention provides a consistency control method for a drone cluster based on Lanner-Jones potential, comprising:

[0008] S1: Obtain the particle dynamics model of the UAV cluster;

[0009] S2: Based on the particle dynamics model of the UAV cluster, design the control law of individual UAVs;

[0010] S3: Based on the initial state data of the drone cluster, the drone individual control law is used to perform force analysis on the drones to obtain the individual force and potential energy of the current drones;

[0011] S4: Based on the individual force and potential energy of the current UAV, the individual control law of the UAV is updated to obtain the updated individual control law of the UAV;

[0012] S5: Control the movement of the drones based on the updated individual drone control law to obtain the state data of the drone cluster after movement;

[0013] S6: judging whether the drone cluster is consistent according to the state data after the movement;

[0014] S7: If the drone cluster is not consistent, the state data after the movement is used as the initial state data, and the above steps S3-S6 are repeated until the drone cluster is consistent.

[0015] Optionally, the process of acquiring the UAV cluster mass dynamics model includes:

[0016] Ignoring the shape and dynamic characteristics of the drone itself, the drone is abstracted as a three-dimensional moving mass point, and assuming that the mass of each mass point is 1, the mass point dynamics model of a drone is expressed as follows:

[0017] , ;

[0018] in, , , They represent the position vector, velocity vector and acceleration vector of the i-th UAV respectively; i represents the sequence number; N is the total number of UAVs in the UAV cluster; t represents time;

[0019] Based on the particle dynamics model of N UAVs, the particle dynamics model of the UAV cluster is obtained.

[0020] Optionally, based on the mass point dynamics model of the drone cluster, an individual drone control law is designed, including:

[0021] Set the control input of the UAV individual at time t to:

[0022] ;

[0023] Where: It represents the individual force of the i-th UAV when the individual mass is 1; is the external force on the i-th UAV; is the internal force of the ith UAV.

[0024] Optionally, the external force of the i-th UAV includes a restraining control term and a disturbance, which is expressed as follows:

[0025] ;

[0026] Where: is the traction control item of the i-th UAV at time t; The information received from the virtual leader at time t is calculated based on the state information of the virtual leader; for disturbance; It represents the traction parameter of the i-th UAV at time t. It is 1 when the i-th UAV is characterized as a traction node, otherwise it is 0.

[0027] Optionally, the internal force of the i-th UAV is used to realize the three principles of separation, alignment and aggregation in the bird flock model, including the force and velocity consistency term of the Lanner-Jones potential, and the expression is as follows:

[0028] ;

[0029] Where: represents the Lanner-Jones potential force between the i-th UAV and the j-th UAV; represents the speed consistency term; represents the neighborhood of the i-th UAV; ; indicates that the jth UAV is in the neighborhood of the ith UAV;

[0030] in, It is used to achieve speed matching between individual drones, expressed as follows:

[0031] ;

[0032] Where: represents the Laplace matrix element at time t, only if hour, =1 otherwise =0; , Represent the velocity vectors of the i-th UAV and the j-th UAV respectively;

[0033] in, It is expressed as follows:

[0034] ;

[0035] Where: represents the potential energy function; represents the derivation; represents the distance from the i-th UAV to the j-th UAV; represents the distance vector from the i-th UAV to the j-th UAV; It indicates the depth of the potential energy well, which is the absolute value between the lowest point and zero point of the potential energy; represents the equilibrium distance; n represents the power of the Lanner-Jones potential.

[0036] Optionally, the initial state data of the drone cluster is set according to the drone cluster flight mission, including the initial position vector, velocity vector and acceleration vector of each drone in the drone cluster.

[0037] Optionally, judging whether the drone cluster is consistent based on the state data after the movement includes:

[0038] By calculating the order parameters of the drone cluster after it moves, it is determined whether the drone cluster is consistent;

[0039] If the order parameter of the drone cluster after moving reaches the set threshold, the drone cluster is consistent;

[0040] If the order parameter of the drone cluster after moving does not reach the set threshold, the drone cluster is not consistent.

[0041] Optional, order parameter calculation formula, expressed as follows:

[0042]

[0043] Where: Represents an order parameter.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention proposes a consistency control method for a drone cluster based on the Lanner-Jones potential. On the basis of a particle dynamics model of the drone cluster, an individual drone control law is designed; force analysis is performed on individual drones based on the individual drone control law, and the individual drone control law is updated based on the force analysis result, so as to control the movement of the drone cluster; it is determined whether the drone cluster is consistent after movement, and if not, force analysis is continued on individual drones and the individual drone control law is updated until the drone cluster is consistent; this method can fully capture the complexity and diversity of the coordinated behavior of the drone cluster, and effectively realize the consistency control of the drone cluster in a complex environment.

[0046] The present invention proposes a consistency control method for a drone cluster based on the Lanner-Jones potential. During the internal interaction of the drone cluster, the method uses the Lanner-Jones potential function equation in the field of molecular dynamics to compare the drones to molecules and analyze their working states to update the individual control laws of the drones. Each molecule in the drone cluster will update its own speed direction according to the speed direction of its neighboring molecules, and move at a constant speed in the updated direction, thereby achieving consistency control of the drone cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor, among which:

[0048] Figure 1 The flowchart of the consistency control method of the drone cluster in the embodiment of the present invention is shown;

[0049] Figure 2 Schematic diagram of potential energy function curves of different powers in an embodiment of the present invention;

[0050] FIG3 (a) is a schematic diagram showing the states of the drone at different times of the 12-6 potential energy function in an embodiment of the present invention;

[0051] FIG3 (b) is a schematic diagram showing the states of the drone at different times of the 8-4 potential energy function in an embodiment of the present invention;

[0052] FIG3 (c) is a schematic diagram showing the states of the drone at different times of the 4-2 potential energy function according to an embodiment of the present invention;

[0053] Figure 4 The schematic diagram of the flight trajectory of a drone cluster based on potential energy functions of different powers in an embodiment of the present invention is shown;

[0054] Figure 5 The diagram shows a curve diagram of a change in order parameter of a drone cluster based on potential energy functions of different powers in an embodiment of the present invention;

[0055] FIG6 (a) is a schematic diagram showing the working status of 20 drones at different times in an embodiment of the present invention;

[0056] FIG6 ( b ) is a schematic diagram showing the working status of 40 drones at different times in an embodiment of the present invention;

[0057] FIG6 (c) is a schematic diagram showing the working status of 100 UAVs at different times in an embodiment of the present invention;

[0058] Figure 7 Schematic diagram of flight trajectories of drone clusters of different sizes according to an embodiment of the present invention;

[0059] Figure 8 The figure shows a schematic diagram of the order parameter variation curve of drone clusters of different sizes in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0061] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0062] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0063] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0064] The application principle of the present invention is described in detail below in conjunction with the accompanying drawings.

[0065] Example 1

[0066] like Figure 1As shown, an embodiment of the present invention provides a method for controlling the consistency of a drone cluster based on Lanner-Jones potential, comprising the following steps:

[0067] S1: Obtain the particle dynamics model of the UAV cluster;

[0068] S2: Based on the particle dynamics model of the UAV cluster, design the control law of individual UAVs;

[0069] S3: Based on the initial state data of the drone cluster, the drone individual control law is used to perform force analysis on the drones to obtain the individual force and potential energy of the current drones;

[0070] S4: Based on the individual force and potential energy of the current UAV, the individual control law of the UAV is updated to obtain the updated individual control law of the UAV;

[0071] S5: Control the movement of the drones based on the updated individual drone control law to obtain the state data of the drone cluster after movement;

[0072] S6: judging whether the drone cluster is consistent according to the state data after the movement;

[0073] S7: If the drone cluster is not consistent, the state data after the movement is used as the initial state data, and the above steps S3-S6 are repeated until the drone cluster is consistent.

[0074] The consistency control method of the drone cluster proposed in this embodiment can design the individual drone control law based on the particle dynamics model of the drone cluster; perform force analysis on the individual drones based on the individual drone control law, and update the individual drone control law based on the force analysis result, so as to control the movement of the drone cluster; judge whether the drone cluster is consistent after movement, and if not, continue to perform force analysis on the individual drones and update the individual drone control law until the drone cluster is consistent; this method can effectively realize the consistency control of the drone cluster in a complex environment.

[0075] In this embodiment, the process of obtaining the particle dynamics model of the drone cluster in step S1 includes:

[0076] Ignoring the shape and dynamic characteristics of the drone itself, the drone is abstracted as a three-dimensional moving mass point, and assuming that the mass of each mass point is 1, the mass point dynamics model of a drone is expressed as follows:

[0077] The point dynamics model is expressed as follows:

[0078] ,

[0079] in, , , They represent the position vector, velocity vector and acceleration vector of the i-th UAV respectively; i represents the sequence number; N is the total number of UAVs in the UAV cluster; t represents time;

[0080] Based on the particle dynamics model of N UAVs, the particle dynamics model of the UAV cluster is obtained.

[0081] In this embodiment, in step S2, based on the particle dynamics model of the drone cluster, the control law of individual drones is designed, including:

[0082] Set the control input of the UAV individual at time t to:

[0083]

[0084] Where: It represents the individual force of the i-th UAV when the individual mass is 1; is the external force on the i-th UAV; is the internal force of the ith UAV.

[0085] in, Including the control term and disturbance, it is expressed as follows:

[0086]

[0087] Where: is the traction control item of the i-th UAV at time t; The information received from the virtual leader at time t is calculated based on the state information of the virtual leader; for disturbance; It represents the traction parameter of the i-th UAV at time t. It is 1 when the i-th UAV is characterized as a traction node, otherwise it is 0.

[0088] in, The mathematical expression is:

[0089]

[0090] In the formula, , are the position vectors of the virtual leader and the i-th UAV at time t, respectively; and All are positive coefficients; , are the velocity vectors of the virtual leader and the i-th UAV at time t respectively.

[0091] in, The mathematical expression is:

[0092] ;

[0093] in, It is used to realize the three principles of separation, alignment and aggregation in the bird flock model, including the force and velocity consistency terms of the Lanner-Jones potential. The expression is as follows:

[0094]

[0095] Where: represents the Lanner-Jones potential force between the i-th UAV and the j-th UAV; represents the speed consistency term; represents the neighborhood of the i-th UAV; ; indicates that the jth UAV is in the neighborhood of the ith UAV;

[0096] in, It is used to achieve speed matching between individual drones, expressed as follows:

[0097]

[0098] Where: represents the Laplace matrix element at time t, only if hour, =1 otherwise =0; , Represent the velocity vectors of the i-th UAV and the j-th UAV respectively;

[0099] in, It is expressed as follows:

[0100]

[0101] Where: represents the potential energy function; represents the derivation; represents the distance from the i-th UAV to the j-th UAV; represents the distance vector from the i-th UAV to the j-th UAV; It indicates the depth of the potential energy well, which is the absolute value between the lowest point and zero point of the potential energy; represents the equilibrium distance; n represents the power of the Lanner-Jones potential.

[0102] Specifically, in this embodiment, the force of the 8-4 Lennard-Jones potential with a power of 8 is used, and the two-body force corresponding to the 8-4 potential in the Lennard-Jones potential is:

[0103]

[0104] In this embodiment, step S3 specifically includes:

[0105] According to the UAV swarm flight mission, determine the initial position, speed and acceleration of each UAV in the UAV swarm;

[0106] Based on the initial state data of the drone cluster and the individual drone control law designed in step S2, a force analysis is performed on each drone in the drone cluster to obtain the force and potential energy of each drone in the current state.

[0107] Specifically, the initial state data of the drone cluster in step S3 includes the initial position vector, velocity vector and acceleration vector of each drone in the drone cluster according to the drone cluster flight mission setting. Similarly, the state data of the drone cluster after movement in step S5 also includes the position vector, velocity vector and acceleration vector of each drone in the drone cluster after movement; specifically, in this embodiment, the time interval of each movement is set to 0.1s;

[0108] In this embodiment, in step S6, judging whether the drone cluster is consistent based on the state data after the movement includes:

[0109] By calculating the order parameters of the drone cluster after it moves, it is determined whether the drone cluster is consistent;

[0110] The order parameter calculation formula is as follows:

[0111]

[0112] Where: Represents an order parameter.

[0113] If the order parameter of the drone cluster after moving reaches the set threshold, the drone cluster is consistent;

[0114] If the order parameter of the drone cluster after moving does not reach the set threshold, the drone cluster is not consistent.

[0115] Specifically, by applying the consistency control method described in this embodiment, the drone cluster can move in coordination like an organic whole. The interaction between individual drones makes the movement of the entire cluster consistent and coordinated. This coordinated movement is very important in mission scenarios such as formation flight performances. At the same time, the robustness of the cluster can be enhanced, and even if some drones fail, the whole can still maintain certain functions and performance. For example, when the power system of a drone fails, the speed decreases or even fails, the surrounding drones can adjust their positions and speeds according to the rules to avoid collisions with the faulty drones and continue to maintain the overall motion state of the cluster.

[0116] Example 2

[0117] Based on the consistency control method of the drone cluster proposed in Example 1, this example conducts an experiment on the influence of the power of the potential energy function on the consistency of the drone cluster, as follows:

[0118] The number of drones in the fixed drone cluster is 40, the drone speed is 0.3m / s, and the noise is 0.005m / s. Since the Lanner-Jones potential is used in the consistency control method, the power n is set to 12, 8, and 4 respectively to perform consistency control of the drone cluster.

[0119] Thus, we can obtain three kinds of power potential energy function curves as follows: Figure 2 As shown in the figure, U (r) represents potential energy, and r is the distance between the two bodies, that is, the distance between the two drones.

[0120] On the basis of the above, the balance distance of the UAV is set to 1m. It is assumed that the UAV cluster is fully connected and all the UAVs are in exactly the same initial state. The states of the 12-6, 8-4, and 4-2 potential energy functions at different times are shown in Figures 3(a), 3(b), and 3(c), respectively. In the figure, the x-axis and y-axis represent the horizontal distance and vertical distance from the determined ground base point, respectively. The time points of the UAVs from left to right in the three sub-figures are 0.01 s, 1 s, and 5 s, respectively. The direction of the arrow is the speed direction of the UAV.

[0121] Figure 4 The schematic diagram of the flight trajectory of the drone cluster with different powers of potential energy functions. The three sub-graphs in the figure are 12-6, 8-4, and 4-2 potentials from left to right; Figure 5 Schematic diagram of the change curve of the order parameter of the drone cluster under different powers of potential energy functions. Table 1 shows the time required for the drone cluster to reach consistency under different powers of potential energy functions, as shown below:

[0122]

[0123] From the above experimental data, it can be seen that the consistency control method of the drone cluster proposed in the present invention uses the Lanner-Jones potential as the interaction method within the cluster to effectively solve the drone consistency control problem, and the use of Lanner-Jones potentials of different powers can effectively achieve consistency control of the drone cluster.

[0124] Example 3

[0125] Based on the consistency control method of the drone cluster proposed in Example 1, this example conducts an experiment on the influence of different cluster sizes on the consistency of the drone cluster, as follows:

[0126] The number of drones in the drone cluster is set to 20, 40, and 100 respectively; the drone speed is 0.3m / s, and the noise is 0.005m / s; the power n of the Lanner-Jones potential is set to 8 to perform consistency control of the drone cluster;

[0127] As a result, the working status of different scales of UAV clusters at different times under the 8-4 potential energy function is obtained as shown in Figure 6, where Figures 6 (a), 6 (b), and 6 (c) are schematic diagrams of the working status of 20, 40, and 100 UAVs at different times, respectively. In the figure, the x-axis and y-axis represent the horizontal distance and vertical distance from the determined ground base point, respectively. The time points of the UAVs from left to right in the three sub-figures are 0.01 s, 1 s, and 5 s, respectively, and the direction of the arrow is the speed direction of the UAV;

[0128] Figure 7 The flight trajectory diagram of drone swarms of different sizes. The three sub-graphs in the figure represent the number of drones in the drone swarm from left to right: 20, 40, and 100. Figure 8 The diagram is a schematic diagram of the order parameter change curve of drone clusters of different sizes. Table 2 shows the time required for drone clusters of different sizes to reach consistency, as shown below:

[0129]

[0130] From the above experimental data, it can be seen that by using the consistency control method of the UAV cluster proposed in the present invention, the scale of the UAV cluster is proportional to the convergence speed of the UAVs.

[0131] Example 4

[0132] The embodiment of the present invention provides a consistency control system for a drone cluster based on Lanner Jones potential, including a storage medium and a processor;

[0133] The storage medium is used to store instructions;

[0134] The processor is used to operate according to the instruction to execute the consistency control method according to embodiment 1.

[0135] Example 5

[0136] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the consistency control method described in Example 1 are implemented.

[0137] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0138] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0139] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0141] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A consistency control method for drone clusters based on Lanner-Jones potential, characterized in that: include: S1: Obtain the particle dynamics model of the UAV cluster; S2: Based on the particle dynamics model of the UAV cluster, design the control law of individual UAVs; S3: Based on the initial state data of the drone cluster, the drone individual control law is used to perform force analysis on the drones to obtain the individual force and potential energy of the current drones; S4: Based on the individual force and potential energy of the current UAV, the individual control law of the UAV is updated to obtain the updated individual control law of the UAV; S5: Control the movement of the drones based on the updated individual drone control law to obtain the state data of the drone cluster after movement; S6: judging whether the drone cluster is consistent according to the state data after the movement; S7: If the drone cluster is not consistent, the state data after the movement is used as the initial state data, and the above steps S3-S6 are repeated until the drone cluster is consistent.

2. The consistency control method of the drone cluster based on Lanner-Jones potential according to claim 1 is characterized in that: The process of obtaining the UAV cluster mass point dynamics model includes: Ignoring the shape and dynamic characteristics of the drone itself, the drone is abstracted as a three-dimensional moving mass point, and assuming that the mass of each mass point is 1, the mass point dynamics model of a drone is expressed as follows: ， ； in, , , They represent the position vector, velocity vector and acceleration vector of the i-th UAV respectively; i represents the sequence number; N is the total number of UAVs in the UAV cluster; t represents time; Based on the particle dynamics model of N UAVs, the particle dynamics model of the UAV cluster is obtained.

3. The consistency control method of drone cluster based on Lanner-Jones potential according to claim 2 is characterized in that: Based on the particle dynamics model of the UAV cluster, the individual control law of the UAV is designed, including: Set the control input of the UAV individual at time t to: ； Where: It represents the individual force of the i-th UAV when the individual mass is 1; is the external force on the i-th UAV; is the internal force of the ith UAV.

4. The consistency control method of drone cluster based on Lanner-Jones potential according to claim 3 is characterized in that: The external force of the i-th UAV includes the restraining control term and the disturbance, which is expressed as follows: ； Where: is the traction control item of the i-th UAV at time t; The information received from the virtual leader at time t is calculated based on the state information of the virtual leader; for disturbance; It represents the traction parameter of the i-th UAV at time t. It is 1 when the i-th UAV is characterized as a traction node, otherwise it is 0.

5. The consistency control method of drone cluster based on Lanner-Jones potential according to claim 4 is characterized in that: The internal force of the i-th drone is used to realize the three principles of separation, alignment and aggregation in the bird flock model, including the force and velocity consistency term of the Lanner-Jones potential, and the expression is as follows: ； Where: represents the Lanner-Jones potential force between the i-th UAV and the j-th UAV; represents the speed consistency term; represents the neighborhood of the i-th UAV; ; indicates that the jth UAV is in the neighborhood of the ith UAV; in, It is used to achieve speed matching between individual drones, expressed as follows: ； Where: represents the Laplace matrix element at time t, only if hour, =1 otherwise =0; , Represent the velocity vectors of the i-th UAV and the j-th UAV respectively; in, It is expressed as follows: ； Where: represents the potential energy function; represents the derivation; represents the distance from the i-th UAV to the j-th UAV; represents the distance vector from the i-th UAV to the j-th UAV; It indicates the depth of the potential energy well, which is the absolute value between the lowest point and zero point of the potential energy; represents the equilibrium distance; n represents the power of the Lanner-Jones potential.

6. The consistency control method of drone cluster based on Lanner-Jones potential according to claim 5 is characterized in that: The initial state data of the drone cluster is set according to the drone cluster flight mission, including the initial position vector, velocity vector and acceleration vector of each drone in the drone cluster.

7. The consistency control method of a drone cluster based on Lanner-Jones potential according to claim 6 is characterized in that: Judging whether the drone cluster is consistent based on the state data after the movement includes: By calculating the order parameters of the drone cluster after it moves, it is determined whether the drone cluster is consistent; If the order parameter of the drone cluster after moving reaches the set threshold, the drone cluster is consistent; If the order parameter of the drone cluster after moving does not reach the set threshold, the drone cluster is not consistent.

8. The consistency control method of a drone cluster based on Lanner-Jones potential according to claim 7 is characterized in that: The order parameter calculation formula is as follows: ； Where: Represents an order parameter.

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