An integrated method for group tracking and control of unmanned aerial vehicle swarms

Abstract

The present invention relates to an integrated method for group tracking and control of unmanned aerial swarms. First, a virtual cluster leader and a virtual cluster reference are set for the unmanned aerial swarm, and the unmanned aerial swarms are grouped to obtain several unmanned aerial formations. Each unmanned aerial formation includes a formation leader and several formation followers. A virtual formation reference is set for each unmanned aerial formation. A control law for the formation leader at the current moment is derived based on the target state at the next moment and the position error and velocity error of the formation leader at the current moment. Furthermore, a control law for the formation followers at the current moment is derived based on the position error and velocity error of the formation followers at the current moment. This method integrates formation control and target trajectory tracking, overcoming the drawback of traditional target tracking methods, which suffer from long phase adjustment times.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned flight control, and in particular to an integrated method for unmanned flight cluster grouping tracking control. Background Art

[0002] In recent years, unmanned aerial vehicles (UAVs) have been widely used in both military and civilian fields due to their unique advantages. Due to the limitations of their onboard sensors, a single UAV cannot perform complex tasks. However, a formation composed of multiple UAVs combines the performance advantages of each UAV, significantly improving the system's fault tolerance and efficiency, and has become a hot topic of current research. Formation control technology has also made great progress. Classic formation control methods include leader-follower, virtual structure, and behavior-based methods. Among them, the leader-follower method is constrained by the formation structure and has the disadvantage of low formation fault tolerance; the formation described by the virtual structure method is relatively fixed and computationally intensive; and the behavior-based method makes it difficult to mathematically describe the specific behavior of UAVs, which is not conducive to system stability analysis. Summary of the Invention

[0003] In view of this, the present invention provides an integrated method for unmanned aerial vehicle cluster group tracking control, which realizes the integrated design of formation control and target trajectory tracking, and overcomes the defect of long phase adjustment time of traditional target tracking methods.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] An integrated method for group tracking and control of unmanned aerial vehicle swarms, comprising:

[0006] Setting a virtual cluster leader and a virtual cluster benchmark for the unmanned aerial vehicle cluster;

[0007] The unmanned aerial vehicle clusters are grouped to obtain a plurality of unmanned aerial vehicle formations; each of the unmanned aerial vehicle formations includes a formation leader and a plurality of formation followers; and each of the unmanned aerial vehicle formations sets a virtual formation benchmark;

[0008] Obtaining a formation parameter model of each of the unmanned aerial formations at a current moment based on the current dynamic models of each of the virtual formation benchmarks and each of the formation leaders;

[0009] Obtaining an error of each formation leader at a current moment based on each formation parameter model;

[0010] generating a dynamic model of the virtual cluster leader at the next moment based on the target state at the next moment;

[0011] Determining a control law for each of the formation leaders at the current moment based on a dynamic model and error of each of the formation leaders at the current moment, a parameter model of each of the formations at the current moment, a dynamic model of the virtual cluster leader at the next moment, and a dynamic model of the virtual cluster reference at the current moment;

[0012] Obtaining an error of each of the formation followers at the current moment based on a dynamic model of each of the formation followers at the current moment;

[0013] Based on the error and dynamic model of each of the formation followers at the current moment, the dynamic model of each of the virtual formation benchmarks and the control law of each of the formation leaders, the control law of each of the formation followers at the current moment is obtained.

[0014] Preferably, each formation leader only receives messages transmitted by the virtual cluster leader and other formation leaders; each formation follower only receives messages transmitted by the formation leader and other formation followers in its unmanned aerial vehicle formation.

[0015] Preferably, generating the dynamic model of the virtual cluster leader at the next moment based on the target state at the next moment includes:

[0016] The target acceleration at the current moment is obtained based on the target velocities at the previous moment and the current moment;

[0017] The target speed at the next moment is obtained based on the target speed and target acceleration at the current moment;

[0018] The target position at the next moment is obtained based on the target position and target speed at the current moment; the target state includes the target speed and target position.

[0019] The present invention also provides an integrated system for tracking and controlling unmanned aerial vehicle cluster groups, comprising:

[0020] A cluster setting module sets a virtual cluster leader and a virtual cluster benchmark for the unmanned aerial vehicle cluster;

[0021] a grouping module for grouping the unmanned aerial vehicle cluster to obtain a plurality of unmanned aerial vehicle formations; each of the unmanned aerial vehicle formations includes a formation leader and a plurality of formation followers; and each of the unmanned aerial vehicle formations sets a virtual formation benchmark;

[0022] a parameter group module, which obtains a formation parameter model of each of the unmanned aerial vehicle formations at a current moment based on the dynamic models of each of the virtual formation benchmarks and each of the formation leaders at a current moment;

[0023] a formation leader error module, which obtains the error of each formation leader at a current moment based on each formation parameter model;

[0024] A prediction module, which generates a dynamic model of the virtual cluster leader at the next moment based on the target state at the next moment;

[0025] a formation leader control module, which obtains a control law for each formation leader at a current moment based on a dynamic model and error of each formation leader at a current moment, a formation parameter model at a current moment, a dynamic model of the virtual cluster leader at a next moment, and a dynamic model of the virtual cluster reference at a current moment;

[0026] a formation following error module, which obtains an error of each formation follower at a current moment based on a dynamic model of each formation follower at a current moment;

[0027] The formation following control module obtains the control law of each of the formation followers at the current moment based on the error and dynamic model of each of the formation followers at the current moment, the dynamic model of each of the virtual formation benchmarks and the control law of each of the formation leaders.

[0028] Preferably, each formation leader only receives messages transmitted by the virtual cluster leader and other formation leaders; each formation follower only receives messages transmitted by the formation leader and other formation followers in its unmanned aerial vehicle formation.

[0029] Preferably, the prediction module includes:

[0030] An acceleration unit, which obtains a target acceleration at the current moment based on the target velocities at the previous moment and the current moment;

[0031] The speed unit obtains the target speed at the next moment based on the target speed and target acceleration at the current moment;

[0032] The position unit obtains the target position at the next moment based on the target position and target speed at the current moment; the target state includes the target speed and target position.

[0033] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0034] The present invention relates to an integrated method for group tracking and control of unmanned aerial swarms. First, a virtual cluster leader and a virtual cluster reference are set for the unmanned aerial swarm, and the unmanned aerial swarms are grouped to obtain several unmanned aerial formations. Each unmanned aerial formation includes a formation leader and several formation followers. A virtual formation reference is set for each unmanned aerial formation. A control law for the formation leader at the current moment is derived based on the target state at the next moment and the position error and velocity error of the formation leader at the current moment. Furthermore, a control law for the formation followers at the current moment is derived based on the position error and velocity error of the formation followers at the current moment. This method integrates formation control and target trajectory tracking, overcoming the drawback of traditional target tracking methods, which suffer from long phase adjustment times. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 This is a flow chart of the integrated method for group tracking and control of unmanned aerial vehicle clusters of the present invention;

[0037] Figure 2 This is a schematic diagram of the formation parameter model;

[0038] Figure 3 Schematic diagram of target tracking trajectory. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] The purpose of the present invention is to provide an integrated method for group tracking and control of unmanned aerial vehicle clusters, realize the integrated design of formation control and target trajectory tracking, and overcome the defect of long phase adjustment time of traditional target tracking methods.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Figure 1This is a flow chart of the integrated method for group tracking and control of unmanned aerial vehicle clusters of the present invention. Figure 1 As shown, the present invention provides an integrated method for group tracking and control of unmanned aerial vehicle clusters, comprising:

[0043] In step S1, a virtual cluster leader and a virtual cluster benchmark are set for the unmanned aerial vehicle cluster.

[0044] Step S2: grouping the unmanned aerial vehicle clusters to obtain a plurality of unmanned aerial vehicle formations; each of the unmanned aerial vehicle formations includes a formation leader and a plurality of formation followers; and each of the unmanned aerial vehicle formations sets a virtual formation benchmark.

[0045] In this embodiment, each formation leader only receives messages transmitted by the virtual cluster leader and other formation leaders; each formation follower only receives messages transmitted by the formation leader and other formation followers in its unmanned aerial vehicle formation.

[0046] Taking the rotor UAV as the research object, a nonlinear model with Lipschitz terms is used to describe the dynamic model of the UAV, as shown in the following formula:

[0047]

[0048] Among them, j∈{0,1,2,…,N}, N is the total number of aircraft in the unmanned aerial vehicle cluster, p j (t)∈R d represents the position of the jth aircraft at time t, R is the real number domain, v j (t)∈R d represents the speed of the jth aircraft at time t, m j represents the mass of the jth aircraft at time t, d>1 represents the spatial dimension, Q j (t)∈R d represents the attitude rotation matrix of the j-th aircraft at time t, e3 = [0 0 1] T , T is the transpose, · represents the derivative, T j (t)∈R + represents the total lift generated by the j-th aircraft rotor at time t, g represents the acceleration due to gravity; f (v j (t), t) represents a continuously differentiable nonlinear function that describes the inherent dynamic characteristics of the UAV and satisfies the following Lipschitz condition:

[0049] ||f(v1(t),t)-f(v2(t),t)||≤μ||v1(t)-v2(t)||;

[0050] Where μ>0 and is a constant.

[0051] When studying formation group control, we mainly focus on the position and speed changes of UAVs, so we ignore the influence of inner loop attitude control and define the auxiliary control quantity u j (t)∈R d :

[0052]

[0053] Then the dynamic model in formula (1) can be rewritten as:

[0054]

[0055] At the same time, in order to meet the actual conditions, the flight speed of the UAV is set to meet the following constraints:

[0056] v min (t)||≤||v j (t)||≤||v max (t)||;

[0057] Where: v min (t) represents the minimum velocity at time t, v max (t) represents the maximum velocity at time t.

[0058] Cluster leader UAV p The dynamic model can be expressed as follows:

[0059]

[0060] Among them, p p (t) represents the position of the virtual cluster leader at time t, v p (t) represents the speed of the virtual cluster leader at that moment, u p (t) is the control input of the virtual cluster leader at time t, which determines the state trajectory of the entire unmanned aerial vehicle cluster.

[0061] Virtual Cluster Benchmarker UAV b and Virtual Formation Benchmarker UAV b' The phase references are provided between formation leaders and followers respectively. The dynamic models of the two can be described as:

[0062]

[0063] Where p k 、v k and u k Represents UAV k The reference vector, reference variation and reference control quantity of u k is a predetermined parameter, pk and v k receive u k Control, p k and v k Here, the normalized position and velocity are used as formation parameters.

[0064] Step S3: obtaining a formation parameter model of each of the unmanned aerial formations at the current moment based on the dynamic models of each of the virtual formation benchmarks and each of the formation leaders at the current moment.

[0065] Taking a certain unmanned aerial vehicle formation as an example, by constructing a formation parameter model, formation control based on three basic movements: translation, scaling, and rotation is achieved:

[0066] T(N)={p0(t),p b' (t),C1(t),…,C n (t)} (6)

[0067] C j (t) = r j (t)R j (t), j∈{1,2,…,n} (7)

[0068] Where p0(t) is the position of the formation leader at time t, which determines the spatial position of the formation as the formation center without affecting the geometric configuration of the formation. b' (t) is the position of the virtual formation benchmark at time t, r j (t)>0 is a scaling parameter, which represents the distance from the jth formation follower to the formation leader at time t; R j (t) is the rotation parameter, representing the rotational transformation matrix of the jth formation follower from its current position to its desired position at time t. n is the number of formation followers in the unmanned aerial vehicle formation. Both scaling and rotational motion are relative to the virtual formation reference, and together they determine the geometric configuration of the unmanned aerial vehicle formation.

[0069] The formation parameter model T(N) in formula (6) includes the elements of the desired formation shape. The specific definition of the formation follower control problem based on the formation parameter model is given below.

[0070] Definition 1: For the initial state of any follower in an unmanned flight formation, if

[0071]

[0072] The control of the desired formation of the unmanned aerial vehicle fleet is achieved. j (t)=p0(t)+C j (t)p b'(t) represents the desired formation of the unmanned aerial vehicle formation, which can be specifically described as: each formation follower reaches the desired position with the formation leader as the center and the virtual formation benchmark as the benchmark.

[0073] Taking into account:

[0074] p b' (t) = [cos(θ b' (t))sin(θ b' (t))] T (9)

[0075] r=[r1,r2,r3,r4],

[0076] The formation of unmanned aerial vehicle formation based on formation parameter model Figure 2 shown.

[0077] like Figure 2 As shown, if the static coordinate x-axis is used as the azimuth reference, when the unmanned flight formation performs a rotational motion, the angle θ between each formation follower and the reference axis is j All change, that is, θ j '≠θ j , the formation of the unmanned aerial vehicle formation does not match the initial description, and the subsequent motion state calculation is more complicated. Setting a virtual formation benchmark can avoid this problem. The virtual formation benchmark is used as a dynamic orientation benchmark. When the formation rotates as a whole, the angle θ between the virtual formation benchmark and the x-axis is b' (t) changes, when p b' After (t) is determined, all followers in the unmanned flight formation will take p0(t) as the center and b' (t) is the reference, and the corresponding angle is rotated counterclockwise, and then the desired position is reached. At this time, the angle between each formation follower and the dynamic reference is still θ j , that is, θ j '=θ j The formation of the unmanned aerial vehicle formation is consistent with the initially described formation, thereby achieving phase coordination and effectively avoiding collisions between aircraft within the formation. It can meet complex formation tasks such as target tracking and searching that have high requirements on the phase of the formation followers.

[0078] The coordination between UAV formations is similar to the coordination between formation followers within an UAV formation. The difference is that the virtual cluster leader serves as the center for controlling the UAV cluster flight, while each formation leader, as a follower, achieves phase coordination based on the virtual cluster benchmark. The cluster parameter model is as follows:

[0079] T′(N)={p p (t),p b(t),C′1(t),…,C′ m (t)} (11)

[0080] C i ′(t)=r i ′(t)R i ′(t),i∈{1,2,…,m} (12)

[0081] Where p p (t) is the position of the virtual cluster leader at time t, which represents the center of the unmanned aerial vehicle cluster. By guiding the trajectory of each formation leader, the spatial position of the entire unmanned aerial vehicle cluster is controlled. m represents the number of unmanned aerial vehicle formations, r i ′ is the scaling parameter of the unmanned aerial vehicle cluster, which determines the relative distance between the unmanned aerial vehicle formations. i ′ is reasonably set to ensure that there will be no collision between the unmanned flight formations; R i ′ is the rotation parameter of the unmanned aerial vehicle cluster, which can make each unmanned aerial vehicle formation take the virtual cluster leader as the center and the virtual cluster benchmark as the benchmark to achieve phase coordination between unmanned aerial vehicle formations. Therefore, C i ′(t) jointly determine the scale of the aircraft.

[0082] Definition 2: For the initial state of any formation leader in an unmanned aerial vehicle swarm, if it satisfies:

[0083]

[0084] The control of the desired formation of the unmanned aerial vehicle cluster is achieved. The desired formation of the unmanned aerial vehicle formation can be specifically described as follows: each formation leader reaches a desired position with the virtual cluster leader as the center and the virtual cluster benchmark as the benchmark.

[0085] Step S4: obtaining the error of each formation leader at the current moment based on each formation parameter model.

[0086] The calculation formula is as follows:

[0087]

[0088]

[0089] Where: represents the position error of the i-th formation leader at time t, represents the velocity error of the i-th formation leader at time t.

[0090] Definition 3: For an unmanned flight formation, given any initial state, if there exists a bounded time t0 such that when t ≥ t0, the following conditions are satisfied:

[0091]

[0092] Then the aircraft in the cluster system are said to be able to achieve the desired group formation.

[0093] Step S5: generating a dynamic model of the virtual cluster leader at the next moment based on the target state at the next moment.

[0094] For the tracking task of unmanned aerial swarms, the virtual swarm leader only needs to continuously track the target at the sampling time. Each formation leader, with the virtual swarm leader as the center, maintains phase coordination under the action of the virtual swarm reference to complete the tracking of the maneuvering target. Therefore, the target tracking control of the unmanned aerial swarm can be specifically broken down into the following:

[0095] 1) Tracking of maneuvering targets by virtual swarm leaders.

[0096] 2) Collaborative control among formation leaders.

[0097] Assuming that the target's maneuvering form is unknown but its motion state is measurable at each sampling moment, the motion direction of the virtual cluster leader can be adjusted according to the target's motion state at each sampling moment, so that it continuously approaches the target's position until it coincides with the target. Figure 3 shown.

[0098] The tracking process can be described as:

[0099] Process 1: Given the initial position p of the virtual cluster leader p (0), and define the initial velocity as v p (0) = e p (0)·||v p ||, where e p (0) is the virtual cluster leader UAV p The direction of the target at the initial moment.

[0100] Process 2: According to the target's speed v at the previous moment t (t-Δt) and the current velocity v t (t), we can get the acceleration of the target at the current moment Based on this, the speed and position state of the target at the next moment are estimated, v t (t+Δt)=v t (t)+u t Δt,p t(t+Δt)=p t (t)+v t (t)·Δt. Based on the estimation of the target motion state at the current moment, the UAV can be determined p The current direction of motion is Then determine the current UAV p Speed ​​v p (t) = e p (t)·||v p (t)||.

[0101] Process 3: UAV p The auxiliary control input is Based on this, the motion status of the virtual cluster leader is updated: p (t+Δt)=v p (t)+u'(t)·Δt,p t (t+Δt)=p t (t)+v t (t)·Δt.

[0102] Process 4: Repeat process 2-3 until UAV p Tracking the target.

[0103] Step S6, based on the dynamic model and error of each formation leader at the current moment, the formation parameter model at the current moment, the dynamic model of the virtual cluster leader at the next moment, and the dynamic model of the virtual cluster benchmark at the current moment, obtain the control law of each formation leader at the current moment.

[0104] The control law of the formation leader is as follows:

[0105]

[0106]

[0107]

[0108] Where: k is the control gain, μ is the Lipschitz constant, represents the Kronecker product, and 12 represents a 2-dimensional column vector whose elements are all 1.

[0109] Step S7: obtaining an error of each of the formation followers at the current moment based on the dynamic model of each of the formation followers at the current moment.

[0110] For the unmanned flight formation l, let Then V l ={ε l +1,ε l +2,…,εl +n l According to Definition 1, the position error of each follower in the unmanned aerial formation l∈{1,2,…,m} can be obtained: l is the total number of formation followers in the l-1 unmanned flight formations before the lth unmanned flight formation.

[0111]

[0112] The velocity error of each follower in the formation can be obtained by taking the derivative:

[0113]

[0114] Step S8, obtaining the control law of each of the formation followers at the current moment based on the error and dynamic model of each of the formation followers at the current moment, the dynamic model of each of the virtual formation benchmarks and the control law of each of the formation leaders.

[0115] The formation follower control law is as follows:

[0116]

[0117]

[0118]

[0119] Theorem 1 For any unmanned flight formation l, if the control gain k satisfies:

[0120]

[0121] It is said that under the action of the formation parameter model and the formation follower control law, the unmanned aerial formation forms the desired formation.

[0122] Figure 2 This is the structural diagram of the integrated system for group tracking and control of unmanned aerial vehicles of the present invention. Figure 2 As shown, the present invention provides an integrated system for tracking and controlling unmanned aerial vehicle cluster groups, comprising:

[0123] The cluster setting module sets a virtual cluster leader and a virtual cluster benchmark for the unmanned aerial vehicle cluster.

[0124] The grouping module groups the unmanned aerial vehicle cluster to obtain a plurality of unmanned aerial vehicle formations; each of the unmanned aerial vehicle formations includes a formation leader and a plurality of formation followers; and each of the unmanned aerial vehicle formations sets a virtual formation benchmark.

[0125] The parameter group module obtains the formation parameter model of each unmanned aerial formation at the current moment based on the dynamic model of each virtual formation benchmark and each formation leader at the current moment.

[0126] The formation leader error module obtains the error of each formation leader at a current moment based on each formation parameter model.

[0127] The prediction module generates a dynamic model of the virtual cluster leader at the next moment based on the target state at the next moment.

[0128] The formation leader control module obtains the control law of each formation leader at the current moment based on the dynamic model and error of each formation leader at the current moment, the formation parameter model at the current moment, the dynamic model of the virtual cluster leader at the next moment, and the dynamic model of the virtual cluster benchmark at the current moment.

[0129] A formation following error module is configured to obtain an error of each formation follower at the current moment based on a dynamic model of each formation follower at the current moment.

[0130] The formation following control module obtains the control law of each of the formation followers at the current moment based on the error and dynamic model of each of the formation followers at the current moment, the dynamic model of each of the virtual formation benchmarks and the control law of each of the formation leaders.

[0131] Preferably, each formation leader only receives messages transmitted by the virtual cluster leader and other formation leaders; each formation follower only receives messages transmitted by the formation leader and other formation followers in its unmanned aerial vehicle formation.

[0132] Specifically, the prediction module includes:

[0133] The acceleration unit obtains a target acceleration at a current moment based on target velocities at a previous moment and a current moment.

[0134] The speed unit obtains the target speed at the next moment based on the target speed and target acceleration at the current moment.

[0135] The position unit obtains the target position at the next moment based on the target position and target speed at the current moment; the target state includes the target speed and target position.

[0136] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0137] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. An integrated method for group tracking and control of unmanned aerial vehicle swarms, characterized in that: include: Setting a virtual cluster leader and a virtual cluster benchmark for the unmanned aerial vehicle cluster; The unmanned aerial vehicle clusters are grouped to obtain a plurality of unmanned aerial vehicle formations; each of the unmanned aerial vehicle formations includes a formation leader and a plurality of formation followers; and each of the unmanned aerial vehicle formations sets a virtual formation benchmark; Each of the formation leaders only receives messages transmitted by the virtual cluster leader and other formation leaders; each of the formation followers only receives messages transmitted by the formation leader and other formation followers in the unmanned aerial vehicle formation to which it belongs; Taking the rotor UAV as the research object, a nonlinear model with Lipschitz terms is used to describe the dynamic model of the UAV, as shown in the following formula: Among them, j∈{0,1,2,…,N}, N is the total number of aircraft in the unmanned aerial vehicle cluster, p j (t)∈R d represents the position of the jth aircraft at time t, R is the real number domain, v j (t)∈R d represents the speed of the jth aircraft at time t, m j represents the mass of the jth aircraft at time t, d>1 represents the spatial dimension, Q j (t)∈R d represents the attitude rotation matrix of the j-th aircraft at time t, e3 = [001] T , T is the transpose, · represents the derivative, T j (t)∈R + represents the total lift generated by the jth aircraft rotor at time t, g represents the acceleration due to gravity; f(v j (t), t) represents a continuously differentiable nonlinear function that describes the inherent dynamic characteristics of the UAV and satisfies the following Lipschitz condition: ||f(v1(t), t)-f(v2(t), t)||≤μ||v1(t)-v2(t)||; where μ>0 and is a constant; Based on the current dynamic models of each virtual formation benchmark and each formation leader, the current formation parameter model of each unmanned aerial formation is obtained. Specifically, taking a certain unmanned aerial formation as an example, by constructing the formation parameter model, formation control based on three basic movements: translation, scaling, and rotation is achieved: T(N)={p0(t),p b' (t),C1(t),…,C n (t)}; C j (t)=r j (t)R j (t),j∈{1,2,…,n}; Where p0(t) is the position of the formation leader at time t, which determines the spatial position of the formation as the formation center without affecting the geometric configuration of the formation. b' (t) is the position of the virtual formation benchmark at time t, r j (t)>0 is a scaling parameter, which represents the distance from the jth formation follower to the formation leader at time t; R j (t) is the rotation parameter, which represents the rotation transformation matrix of the j-th formation follower from its current position to the desired position at time t. n is the number of formation followers in the unmanned aerial formation. Both scaling and rotational motions are relative to the virtual formation reference, and together they determine the geometric configuration of the unmanned aerial formation. Based on the formation parameter models, the error of each formation leader at the current moment is obtained, and the calculation formula is as follows: Where: represents the position error of the i-th formation leader at time t, represents the velocity error of the i-th formation leader at time t; generating a dynamic model of the virtual cluster leader at the next moment based on the target state at the next moment; Determining a control law for each of the formation leaders at the current moment based on a dynamic model and error of each of the formation leaders at the current moment, a parameter model of each of the formations at the current moment, a dynamic model of the virtual cluster leader at the next moment, and a dynamic model of the virtual cluster reference at the current moment; Obtaining an error of each of the formation followers at the current moment based on a dynamic model of each of the formation followers at the current moment; Based on the error and dynamic model of each of the formation followers at the current moment, the dynamic model of each of the virtual formation benchmarks and the control law of each of the formation leaders, the control law of each of the formation followers at the current moment is obtained.

2. The integrated method for group tracking and control of unmanned aerial vehicle swarms according to claim 1, characterized in that: The generating of the dynamic model of the virtual cluster leader at the next moment based on the target state at the next moment includes: The target acceleration at the current moment is obtained based on the target velocities at the previous moment and the current moment; The target speed at the next moment is obtained based on the target speed and target acceleration at the current moment; The target position at the next moment is obtained based on the target position and target speed at the current moment; the target state includes the target speed and target position.

3. An integrated system for tracking and controlling unmanned aerial vehicle swarm groups, characterized by: include: A cluster setting module sets a virtual cluster leader and a virtual cluster benchmark for the unmanned aerial vehicle cluster; a grouping module for grouping the unmanned aerial vehicle cluster to obtain a plurality of unmanned aerial vehicle formations; each of the unmanned aerial vehicle formations includes a formation leader and a plurality of formation followers; and each of the unmanned aerial vehicle formations sets a virtual formation benchmark; Each of the formation leaders only receives messages transmitted by the virtual cluster leader and other formation leaders; each of the formation followers only receives messages transmitted by the formation leader and other formation followers in the unmanned aerial vehicle formation to which it belongs; Taking the rotor UAV as the research object, a nonlinear model with Lipschitz terms is used to describe the dynamic model of the UAV, as shown in the following formula: Among them, j∈{0,1,2,…,N}, N is the total number of aircraft in the unmanned aerial vehicle cluster, p j (t)∈R d represents the position of the jth aircraft at time t, R is the real number domain, v j (t)∈R d represents the speed of the jth aircraft at time t, m j represents the mass of the jth aircraft at time t, d>1 represents the spatial dimension, Q j (t)∈R d represents the attitude rotation matrix of the j-th aircraft at time t, e3 = [001] T , T is the transpose, · represents the derivative, T j (t)∈R + represents the total lift generated by the jth aircraft rotor at time t, g represents the acceleration due to gravity; f(v j (t), t) represents a continuously differentiable nonlinear function that describes the inherent dynamic characteristics of the UAV and satisfies the following Lipschitz condition: ||f(v1(t), t)-f(v2(t), t)||≤μ||v1(t)-v2(t)||; where μ>0 and is a constant; The parameter group module obtains the current formation parameter model of each unmanned aerial formation based on the current dynamic model of each virtual formation benchmark and each formation leader. Specifically, taking a certain unmanned aerial formation as an example, the formation parameter model is constructed to realize formation control based on three basic movements: translation, scaling, and rotation: T(N)={p0(t),p b' (t),C1(t),…,C n (t)}; C j (t)=r j (t)R j (t),j∈{1,2,…,n}; Where p0(t) is the position of the formation leader at time t, which determines the spatial position of the formation as the formation center without affecting the geometric configuration of the formation. b' (t) is the position of the virtual formation benchmark at time t, r j (t)>0 is a scaling parameter, which represents the distance from the jth formation follower to the formation leader at time t; R j (t) is the rotation parameter, which represents the rotation transformation matrix of the j-th formation follower from its current position to the desired position at time t. n is the number of formation followers in the unmanned aerial formation. Both scaling and rotational motions are relative to the virtual formation reference, and together they determine the geometric configuration of the unmanned aerial formation. The formation leader error module obtains the error of each formation leader at the current moment based on the formation parameter model. The calculation formula is as follows: Where: represents the position error of the i-th formation leader at time t, represents the velocity error of the i-th formation leader at time t; A prediction module, which generates a dynamic model of the virtual cluster leader at the next moment based on the target state at the next moment; a formation leader control module, which obtains a control law for each formation leader at a current moment based on a dynamic model and error of each formation leader at a current moment, a formation parameter model at a current moment, a dynamic model of the virtual cluster leader at a next moment, and a dynamic model of the virtual cluster reference at a current moment; a formation following error module, which obtains an error of each formation follower at a current moment based on a dynamic model of each formation follower at a current moment; The formation following control module obtains the control law of each of the formation followers at the current moment based on the error and dynamic model of each of the formation followers at the current moment, the dynamic model of each of the virtual formation benchmarks and the control law of each of the formation leaders.

4. The integrated system for tracking and controlling unmanned aerial vehicle swarm groups according to claim 3, characterized in that: The prediction module includes: An acceleration unit, which obtains a target acceleration at the current moment based on the target velocities at the previous moment and the current moment; The speed unit obtains the target speed at the next moment based on the target speed and target acceleration at the current moment; The position unit obtains the target position at the next moment based on the target position and target speed at the current moment; the target state includes the target speed and target position.

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