A consensus control method for UAV swarms based on Lanner-Jones potential

Abstract

This invention discloses a consensus control method for UAV swarms based on Lana-Jones potential. The method includes: acquiring a mass dynamics model of the UAV swarm and designing individual control laws for each UAV based on this model; performing force analysis on the UAVs using the individual control laws based on the initial state data of the UAV swarm, and updating the individual control laws to obtain updated individual control laws; controlling the movement of the UAVs based on the updated individual control laws to obtain the state data of the UAV swarm after movement; determining whether the UAV swarm has consensus based on the state data after movement; if the UAV swarm does not have consensus, using the state data after movement as the initial state data, repeating the above steps until the UAV swarm has consensus; this invention can effectively achieve consensus control of UAV swarms.

Description

Technical Field

[0001] This invention relates to a consensus control method for UAV swarms based on Lana Jones potential, belonging to the field of autonomous consensus control for UAV swarms. Background Technology

[0002] Drone swarms, as a significant milestone in the development of drone technology, not only foreshadow a major trend in the future of aviation but also demonstrate unparalleled advantages in specific flight missions. Especially when facing complex and ever-changing mission requirements, traditional large equipment often proves cumbersome and costly. In such cases, using a large number of inexpensive drones to form a drone swarm becomes 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 a single large piece of equipment in many scenarios.

[0003] More importantly, through precise coordination and collaborative operation among drones, swarm systems can overcome the limitations of individual drone capabilities, accomplishing tasks that a single drone cannot, thus effectively addressing the limitations of individual drone devices. However, to achieve efficient collaborative operation of drone swarms, consistent control of the swarm is crucial. Consistent control refers to designing appropriate control strategies within a drone swarm to gradually bring the states (such as position, speed, and heading) of all drones towards consistency, or to achieve synchronization within a certain error range. Consistent control strategies are an effective way to achieve multi-agent control, and this control mechanism is key to ensuring the overall coordination and stability of the drone swarm when performing complex tasks.

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

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a consensus control method for UAV swarms based on Lanner-Jones potential. This method can utilize the individual forces and potential energy of UAVs to update the individual control rates of UAVs, stimulate complex group emergent behaviors of UAV swarms, and thus effectively achieve consensus control of UAV swarms in complex environments.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] This invention provides a consensus control method for UAV swarms based on Lanner-Jones potential, comprising:

[0008] S1: Obtain the mass dynamics model of the drone swarm;

[0009] S2: Based on the particle dynamics model of the aforementioned UAV swarm, design individual control laws for each UAV;

[0010] S3: Based on the initial state data of the drone swarm, the individual control laws of the drones are used to perform force analysis on the drones to obtain the individual forces and potential energy of the drones at present.

[0011] S4: Based on the current individual force and potential energy of the UAV, update the individual control law of the UAV 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 swarm after movement;

[0013] S6: Determine whether the drone cluster has consistency based on the state data after the movement;

[0014] S7: If the drone cluster is not consistent, use the moved state data as the initial state data and repeat steps S3-S6 until the drone cluster is consistent.

[0015] Optionally, the process of obtaining the mass dynamics model of the UAV swarm includes:

[0016] Ignoring the shape and dynamics of the drone itself, and abstracting it as a three-dimensional moving point mass, assuming that each point mass has a mass of 1, the point mass dynamics model of a certain drone is represented as follows:

[0017] , ;

[0018] in, , , Let represent the position vector, velocity vector, and acceleration vector of the i-th drone, respectively; i represents the sequence number; N is the total number of drones in the drone swarm; and t represents time.

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

[0020] Optionally, based on the particle dynamics model of the UAV swarm, individual UAV control laws are designed, including:

[0021] The control input for the individual UAV at time t is set as follows:

[0022] ;

[0023] In the formula: This represents the individual force exerted by the i-th drone when its individual mass is 1. Let be the external force acting on the i-th drone; Let be the internal force of the i-th drone.

[0024] Optionally, the external forces acting on the i-th UAV include a restraint control term and a disturbance, as shown below:

[0025] ;

[0026] In the formula: Let be the traction control term for the i-th UAV at time t; The information received from the virtual leader at time t is calculated using the virtual leader's state information. For disturbance; Let represent the traction parameter of the i-th UAV at time t. It is 1 when the i-th UAV is represented as the traction node, and 0 otherwise.

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

[0028] ;

[0029] In the formula: Let represent the Lanner-Jones potential force between the i-th drone and the j-th drone; Indicates terms with consistent speed; Let represent the neighborhood of the i-th drone; ; This indicates that the j-th drone is in the neighborhood of the i-th drone;

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

[0031] ;

[0032] In the formula: Denotes the Laplace matrix element at time t, only when hour, =1 otherwise =0; , Let represent the velocity vectors of the i-th and j-th drones, respectively;

[0033] in, It is expressed as follows:

[0034] ;

[0035] In the formula: Represents the potential energy function; To express differentiation; This represents the distance from the i-th drone to the j-th drone; This represents the distance vector from the i-th drone to the j-th drone; The depth of the potential energy well is represented by the absolute value between the lowest point of the potential energy and zero. represents the equilibrium distance; n represents the power of the Lanner-Jones potential.

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

[0037] Optionally, determining whether the drone swarm has consistency based on the moved state data includes:

[0038] By calculating the order parameters after the drone swarm moves, it can be determined whether the drone swarm has consistency.

[0039] If the sequence parameters of the drone swarm after it moves reach a set threshold, then the drone swarm is consistent.

[0040] If the sequence parameters of the drone swarm do not reach the set threshold after the drone swarm moves, the drone swarm is not consistent.

[0041] Optionally, the formula for calculating the order parameter is expressed as follows:

[0042]

[0043] In the formula: Indicates the order parameter.

[0044] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0045] This invention proposes a consensus control method for UAV swarms based on Lana-Jones potential. Based on the particle dynamics model of the UAV swarm, individual UAV control laws are designed. Force analysis is performed on the individual UAVs based on these control laws, and the control laws are updated accordingly to control the movement of the UAV swarm. The method then determines whether the UAV swarm exhibits consensus after movement. If not, force analysis is performed on the individual UAVs, and their control laws are updated until consensus is achieved. This method effectively captures the complexity and diversity of UAV swarm cooperative behavior, enabling consensus control of UAV swarms in complex environments.

[0046] This invention proposes a consensus control method for UAV swarms based on Lanner-Jones potential. During the interaction process within the UAV swarm, this method uses the Lanner-Jones potential function equation from the field of molecular dynamics to analogize the UAVs to molecules, analyze their working states, and update the individual control laws of the UAVs accordingly. Each molecule in the UAV swarm updates its own velocity direction based on the velocity direction of its neighboring molecules, and moves at a constant speed in the updated direction, thereby achieving consensus control of the UAV swarm. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

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

[0049] Figure 2 The diagram shows potential energy function curves of different powers in an embodiment of the present invention.

[0050] Figure 3(a) shows a schematic diagram of the UAV state at different times for the potential energy functions 12-6 in the embodiment of the present invention;

[0051] Figure 3(b) shows a schematic diagram of the UAV state at different times for the potential energy function 8-4 in the embodiment of the present invention;

[0052] Figure 3(c) shows a schematic diagram of the UAV state at different times for the potential energy function 4-2 in the embodiment of the present invention;

[0053] Figure 4 The diagram shows the flight trajectories of a drone swarm based on potential energy functions of different powers in an embodiment of the present invention.

[0054] Figure 5 The figure shows a schematic diagram of the change curves of the UAV swarm sequence parameters based on potential energy functions of different powers in an embodiment of the present invention.

[0055] Figure 6(a) shows a schematic diagram of the working status of 20 UAVs at different times in an embodiment of the present invention;

[0056] Figure 6(b) shows a schematic diagram of the working status of 40 UAVs at different times in an embodiment of the present invention;

[0057] Figure 6(c) shows a schematic diagram of the working status of 100 drones at different times in an embodiment of the present invention;

[0058] Figure 7 The diagram shows the flight trajectories of drone swarms of different sizes in this embodiment of the invention.

[0059] Figure 8 The diagram shows the sequence parameter variation curves of different-sized UAV clusters in this embodiment of the invention. Detailed Implementation

[0060] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

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

[0062] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

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

[0064] The application principle of the present invention will be described in detail below with reference to the accompanying drawings.

[0065] Example 1

[0066] like Figure 1As shown, this embodiment of the invention provides a consensus control method for a drone swarm based on Lana Jones potential, comprising the following steps:

[0067] S1: Obtain the mass dynamics model of the drone swarm;

[0068] S2: Based on the particle dynamics model of the aforementioned UAV swarm, design individual control laws for each UAV;

[0069] S3: Based on the initial state data of the drone swarm, the individual control laws of the drones are used to perform force analysis on the drones to obtain the individual forces and potential energy of the drones at present.

[0070] S4: Based on the current individual force and potential energy of the UAV, update the individual control law of the UAV 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 swarm after movement;

[0072] S6: Determine whether the drone cluster has consistency based on the state data after the movement;

[0073] S7: If the drone cluster is not consistent, use the moved state data as the initial state data and repeat steps S3-S6 until the drone cluster is consistent.

[0074] The unmanned aerial vehicle (UAV) swarm consistency control method proposed in this embodiment can design individual UAV control laws based on the mass dynamics model of the UAV swarm; perform force analysis on individual UAVs based on the individual UAV control laws, and update the individual UAV control laws based on the force analysis results, thereby controlling the movement of the UAV swarm; determine whether the UAV swarm has consistency after movement, and if not, continue to perform force analysis on individual UAVs and update the individual UAV control laws until the UAV swarm has consistency; this method can effectively achieve consistency control of UAV swarms in complex environments.

[0075] In this embodiment, the process of obtaining the mass dynamics model of the UAV swarm in step S1 includes:

[0076] Ignoring the shape and dynamics of the drone itself, and abstracting it as a three-dimensional moving point mass, assuming that each point mass has a mass of 1, the point mass dynamics model of a certain drone is represented as follows:

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

[0078] ,

[0079] in, , , Let represent the position vector, velocity vector, and acceleration vector of the i-th drone, respectively; i represents the sequence number; N is the total number of drones in the drone swarm; and t represents time.

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

[0081] In this embodiment, step S2 involves designing individual control laws for each UAV based on the mass dynamics model of the UAV swarm, including:

[0082] The control input for the individual UAV at time t is set as follows:

[0083]

[0084] In the formula: This represents the individual force exerted by the i-th drone when its individual mass is 1. Let be the external force acting on the i-th drone; Let be the internal force of the i-th drone.

[0085] in, Including control terms and disturbances, as shown below:

[0086]

[0087] In the formula: Let be the traction control term for the i-th UAV at time t; The information received from the virtual leader at time t is calculated using the virtual leader's state information. For disturbance; Let represent the traction parameter of the i-th UAV at time t. It is 1 when the i-th UAV is represented as the traction node, and 0 otherwise.

[0088] in, The mathematical expression is:

[0089]

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

[0091] in, The mathematical expression is:

[0092] ;

[0093] in, To implement the three principles of separation, alignment, and clustering in the flocking bird model, including the force and velocity consistency term of the Lanner-Jones potential, the expression is as follows:

[0094]

[0095] In the formula: Let represent the Lanner-Jones potential force between the i-th drone and the j-th drone; Indicates terms with consistent speed; Let represent the neighborhood of the i-th drone; ; This indicates that the j-th drone is in the neighborhood of the i-th drone;

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

[0097]

[0098] In the formula: Denotes the Laplace matrix element at time t, only when hour, =1 otherwise =0; , Let represent the velocity vectors of the i-th and j-th drones, respectively;

[0099] in, It is expressed as follows:

[0100]

[0101] In the formula: Represents the potential energy function; To express differentiation; This represents the distance from the i-th drone to the j-th drone; This represents the distance vector from the i-th drone to the j-th drone; The depth of the potential energy well is represented by the absolute value between the lowest point of the potential energy and zero. 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. The two-body force corresponding to the 8-4 potential in the Lennard-Jones potential is as follows:

[0103]

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

[0105] Based on the drone swarm flight mission, determine the initial position, velocity, and acceleration of each drone in the drone swarm;

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

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

[0108] In this embodiment, step S6, determining whether the drone cluster has consistency based on the moved state data, includes:

[0109] By calculating the order parameters after the drone swarm moves, it can be determined whether the drone swarm has consistency.

[0110] The formula for calculating the order parameter is as follows:

[0111]

[0112] In the formula: Indicates the order parameter.

[0113] If the sequence parameters of the drone swarm after it moves reach a set threshold, then the drone swarm is consistent.

[0114] If the sequence parameters of the drone swarm do not reach the set threshold after the drone swarm moves, the drone swarm is not consistent.

[0115] Specifically, the consistency control method described in this embodiment enables a drone swarm to move collaboratively as an organic whole. The interaction between individual drones ensures the consistency and coordination of the entire swarm's movement. This collaborative movement is crucial in mission scenarios such as formation flying displays. Simultaneously, it enhances the swarm's robustness, allowing the entire swarm to maintain certain functions and performance even if some drones malfunction. For example, if a drone's power system fails, causing a decrease in speed or even complete failure, surrounding drones can adjust their positions and speeds according to rules to avoid collisions and maintain the overall movement of the swarm.

[0116] Example 2

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

[0118] The fixed drone swarm contains 40 drones, with a speed of 0.3 m / s and a noise level of 0.005 m / s. Since the consensus control method uses the Lanner-Jones potential, the power n is set to take values ​​of 12, 8, and 4 to perform consensus control on the drone swarm.

[0119] Thus, the potential energy function curves for the three powers are obtained 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] Based on the above, the balance distance of the UAV is set to 1m. Assuming that the UAV swarm is fully connected and all UAVs have the same initial state, the states of the potential energy functions at different times of 12-6, 8-4, and 4-2 are shown in Figures 3(a), 3(b), and 3(c), respectively. In the figures, the x-axis and y-axis represent the horizontal and vertical distances 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 direction of the UAV velocity.

[0121] Figure 4 The diagram shows the flight trajectories of a drone swarm with potential energy functions of different powers. The three sub-plots in the diagram, from left to right, represent potentials of 12-6, 8-4, and 4-2, respectively. Figure 5 The diagram illustrates the changes in the order parameters of a drone swarm for potential energy functions of different powers. Table 1 shows the time required for the drone swarm to reach consensus under different powers of potential energy functions, as shown below:

[0122]

[0123] The experimental data in summary show that the consensus control method for UAV swarms proposed in this invention can effectively solve the consensus control problem by using the Lanner-Jones potential as the interaction mode within the swarm, and consensus control of UAV swarms can be effectively achieved by using Lanner-Jones potentials of different powers.

[0124] Example 3

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

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

[0127] Thus, Figure 6 shows the working states of different-sized UAV clusters at different times under the 8-4 potential energy function. Figures 6(a), 6(b), and 6(c) are schematic diagrams of the working states of 20, 40, and 100 UAVs at different times, respectively. In the figures, the x-axis and y-axis represent the horizontal and vertical distances from the determined ground base point, respectively. The time points of the UAVs in the three sub-figures from left to right are 0.01 s, 1 s, and 5 s, respectively, and the arrow direction is the direction of the UAV velocity.

[0128] Figure 7 The diagram shows the flight trajectories of drone swarms of different sizes. The three sub-plots in the diagram represent the number of drones in the swarm from left to right as 20, 40, and 100, respectively. Figure 8 The diagram below illustrates the sequence parameter variation curves for drone swarms of different sizes. Table 2 shows the time required for drone swarms of different sizes to reach consistency, as follows:

[0129]

[0130] The experimental data above show that, using the consensus control method for UAV swarms proposed in this invention, the size of the UAV swarm is directly proportional to the convergence speed of the UAVs.

[0131] Example 4

[0132] This invention provides a consensus control system for a drone swarm based on Lana Jones potential, including a storage medium and a processor;

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

[0134] The processor is configured to operate according to the instructions to execute the consistency control method according to Embodiment 1.

[0135] Example 5

[0136] This invention provides a computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the consistency control method as described in Embodiment 1.

[0137] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 One or more processes and / or boxes Figure 1 The 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 operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A consensus control method for UAV swarms based on Lanner-Jones potential, characterized in that, include: S1: Obtain the mass dynamics model of the drone swarm; S2: Based on the particle dynamics model of the aforementioned UAV swarm, design individual control laws for each UAV; S3: Based on the initial state data of the drone swarm, the individual control laws of the drones are used to perform force analysis on the drones to obtain the individual forces and potential energy of the drones at present. S4: Based on the current individual force and potential energy of the UAV, update the individual control law of the UAV 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 swarm after movement; S6: Determine whether the drone cluster has consistency based on the state data after the movement; S7: If the drone cluster is not consistent, use the moved state data as the initial state data and repeat the above steps S3-S6 until the drone cluster is consistent. The process of obtaining the mass dynamics model of the UAV swarm includes: Ignoring the shape and dynamics of the drone itself, and abstracting it as a three-dimensional moving point mass, assuming that each point mass has a mass of 1, the point mass dynamics model of a certain drone is represented as follows: ， ； in, , , Let represent the position vector, velocity vector, and acceleration vector of the i-th drone, respectively; i represents the sequence number; N is the total number of drones in the drone swarm; and t represents time. Based on the particle dynamics model of N UAVs, the particle dynamics model of the UAV swarm is obtained; Based on the particle dynamics model of the aforementioned UAV swarm, individual UAV control laws are designed, including: The control input for the individual UAV at time t is set as follows: ； In the formula: This represents the individual force exerted by the i-th drone when its individual mass is 1. Let be the external force acting on the i-th drone; For the internal forces of the i-th drone; The external forces acting on the i-th UAV include a restraint control term and a disturbance, as shown below: ； In the formula: Let be the traction control term for the i-th UAV at time t; The information received from the virtual leader at time t is calculated using the virtual leader's state information. For disturbance; This represents the traction parameter of the i-th UAV at time t. It is 1 when the i-th UAV is represented as the traction node, and 0 otherwise. The internal forces of the i-th UAV are used to achieve the three principles of separation, alignment, and aggregation in the bird flock model, including the forces of the Lanner-Jones potential and the velocity consistency term, as shown in the following expression: ； In the formula: Let represent the Lanner-Jones potential force between the i-th drone and the j-th drone; Indicates terms with consistent speed; Let represent the neighborhood of the i-th drone; ; This indicates that the j-th drone is in the neighborhood of the i-th drone; in, The method used to achieve speed matching between individual drones is as follows: ； In the formula: Denotes the Laplace matrix element at time t, only when hour, =1 otherwise =0; , Let represent the velocity vectors of the i-th and j-th drones, respectively; in, It is expressed as follows: ； In the formula: Represents the potential energy function; To express differentiation; This represents the distance from the i-th drone to the j-th drone; This represents the distance vector from the i-th drone to the j-th drone; The depth of the potential energy well is represented by the absolute value between the lowest point of the potential energy and zero. represents the equilibrium distance; n represents the power of the Lanner-Jones potential.

2. The consensus control method for UAV swarms based on Lanner-Jones potential according to claim 1, characterized in that, The initial state data of the drone swarm is set according to the drone swarm flight mission, including the initial position vector, velocity vector and acceleration vector of each drone in the drone swarm.

3. The consensus control method for UAV swarms based on Lanner-Jones potential according to claim 2, characterized in that, Determining the consistency of the drone swarm based on the post-movement state data includes: By calculating the order parameters after the drone swarm moves, it can be determined whether the drone swarm has consistency. If the sequence parameters of the drone swarm after it moves reach a set threshold, then the drone swarm is consistent. If the sequence parameters of the drone swarm do not reach the set threshold after the drone swarm moves, the drone swarm is not consistent.

4. The consensus control method for UAV swarms based on Lanner-Jones potential according to claim 3, characterized in that, The formula for calculating the order parameter is as follows: ； In the formula: Indicates the order parameter.

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