A distributed optimal space-time cooperative hunting guidance method for a drone cluster

Abstract

The application discloses a kind of distributed optimal space-time cooperative hunting guidance methods of UAV cluster, including the following steps: constructing control instruction form under distributed constraint;Optimal problem is constructed to realize space-time cooperative hunting;Based on optimal problem, optimal control method is used to obtain optimal guidance instruction;The optimal guidance instruction obtained is used to control the flight of UAV.The distributed optimal space-time cooperative hunting guidance method of UAV cluster disclosed by the application can make multiple interceptors meet relative hunting formation constraint and equilibrium overload constraint at the end of hitting target, and improve the cooperative interception efficiency of cluster under small communication constraint.

Description

Technical Field

[0001] This invention relates to an optimal spatiotemporal cooperative encirclement and capture guidance method for distributed unmanned aerial vehicle (UAV) swarms, belonging to the field of flight control technology. Background Technology

[0002] Unauthorized drone flights pose a significant threat to public safety. For instance, unauthorized flights within civil aviation areas can easily lead to major safety incidents. In addition to signal suppression, the general approach to intercepting unauthorized drones involves multi-drone, spatiotemporal coordinated guidance.

[0003] To achieve coordinated guidance in time and space, clusters can use different communication methods to share information and calculate coordinated instructions. Centralized communication refers to the existence of one or more central nodes in the cluster that can exchange information with all other aircraft. For example, the earliest proposal in the literature "Impact-time-control guidance law for anti-ship missiles" suggested selecting the maximum value of the predicted terminal guidance time of multiple aircraft as the desired simultaneous strike time. Then, based on the PN guidance law, an optimal time control guidance law was designed to eliminate the error between the predicted guidance time and the desired time, thus achieving time coordination. Under the condition of simultaneously specifying the coordinated strike time and strike direction, the literature "Dong W, Wang C, Liu J, et al. Three-dimensional vector guidance law with impact time and angle constraints[J]. Journal of the Franklin The literature “Institute, 2023, 360(2): 693-718” and “Tang Yang, Zhu Xiaoping, Zhou Zhou, et al. A cooperative guidance method based on attack time and angle control [J]. Acta Aeronautica Sinica, 2022, 43(1): 466-478” proposes a spatiotemporal cooperative guidance method that can be used to strike stationary targets by designing terminal angle constraint control guidance law and ITCG guidance law in series. The literature “Tao H, Lin D, Song T, et al. Optimalspatial-temporal cooperative guidance against a maneuvering target [J]. Journal of the Franklin Institute, 2023, 360(13): 9886-9903” constructs the guidance time error between two adjacent aircraft into a high-dimensional state vector in the relative coordinate system, and solves the centralized spatiotemporal cooperative guidance law that can simultaneously intercept maneuvering targets in multiple directions at the terminal using the principle of high-dimensional Schwarz inequality.

[0004] However, the above methods all employ centralized collaborative guidance, which places a high burden on system communication and a high degree of dependence on the central node.

[0005] Existing technologies also propose distributed communication cooperative guidance, where a single UAV only needs to coordinate its status with an aircraft that has a communication connection, resulting in a smaller amount of information exchange and no reliance on a central node. For example, in the literature “Li Guofei, Li Bohao, Wu Yunjie, et al. Cooperative guidance method for attack time control of multi-group aircraft [J]. Journal of Astronautics, 2023, 44(1):110-118”, the time cooperative guidance problem is transformed into a nonlinear tracking problem with the leader's state as the expected value based on the leader-follower method. By making the distance between each follower's missile and the leader's distance tend to be consistent, simultaneous attack is achieved, avoiding the estimation of the remaining guidance time. In the literature “Wang Z, Fu W, Fang Y, et al. Prescribed-time cooperative guidance law against maneuvering target based on leader-following strategy [J]. ISA Transactions, 2022, 129:257-270” and “Dong W, Wang C, Wang J, et al. Fixed-time terminal angle-constrained cooperative guidance law against maneuvering target [J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(2): 1352-1366” proposed a distributed spatiotemporal cooperative guidance law with a hybrid single and dual-layer architecture. By combining the time cooperative guidance law based on finite / fixed time nonlinear consistency and the terminal angle constraint guidance law in series, it realizes multi-directional simultaneous attack with automatic coordination of interception time for maneuvering targets. However, the terminal cooperative interception angle of each aircraft still needs to be set in advance.

[0006] Furthermore, existing distributed cooperative guidance methods based on nonlinear control theory only focus on the consistency convergence effect of state variables, without considering the overload consumption cost required for state convergence. For guidance problems that require error convergence under finite time and finite energy conditions, this can easily lead to increased overload consumption and reduced endurance of the interceptor.

[0007] Therefore, it is necessary to conduct more in-depth research on existing spatiotemporal cooperative guidance methods for unmanned aerial vehicle (UAV) swarms in order to solve the above problems. Summary of the Invention

[0008] To overcome the above problems, in-depth research was conducted, and an optimal spatiotemporal cooperative encirclement guidance method for distributed UAV swarms was proposed, including the following steps:

[0009] S1. Construct a control instruction form subject to distributed constraints;

[0010] S2. Construct the optimal problem for achieving spatiotemporal coordinated encirclement and capture;

[0011] S3. Based on the optimal problem, the optimal control method is used to obtain the optimal guidance command;

[0012] S4. Use the obtained optimal guidance commands to control the flight of the UAV.

[0013] In a preferred embodiment, in S1, the control command is decomposed into a normal channel and a tangential channel for relative acceleration, and the normal channel control command is set to the following form:

[0014]

[0015] in, Let N represent the basic guidance law for the i-th intercepting drone, where N is an adjustable guidance parameter. Let σ represent the relative acceleration between the i-th intercepting drone and the target. i This represents the relative line-of-sight angle between the i-th intercepting drone and the target. Let represent the bias term acceleration of the i-th intercepting drone.

[0016] In a preferred embodiment, in S2, the optimal problem is expressed as:

[0017]

[0018] L(X f +X △ ) = 0

[0019] in,

[0020]

[0021] X = [x a ,x t ] T

[0022]

[0023] In the formula, min represents the minimum, st represents the constraint, J represents the energy, and t represents the current time. fLet τ represent the guidance time, B, U, and X be intermediate variables, τ represent the time factor, W represent the weight matrix of the optimization problem, and L represent the Laplace matrix. f Let I represent the terminal state of the intermediate variable X, n represent the total number of intercepting aircraft, and I n This represents an n-dimensional matrix whose elements are all 1s. This represents the remaining guidance time for the i-th intercepting drone. Let represent the approach acceleration of the i-th intercepting drone along the line-of-sight direction with respect to the target. Let X represent the approach speed between the i-th intercepting drone and the target. a X t X Δ As an intermediate variable, This represents the terminal line-of-sight angle of the i-th intercepting drone. This represents the guidance time of the i-th intercepting drone. This represents the terminal attack angle error matrix among the various UAVs. The terminator represents the terminal attack time error matrix among the various UAVs, k. a >0, k t >0 is an adjustable parameter.

[0024] In a preferred embodiment, the dynamics of the end-view angle satisfy:

[0025]

[0026] The dynamics of the terminal guidance time satisfy:

[0027]

[0028] In a preferred embodiment, in S3, the obtained optimal guidance command in the normal direction is:

[0029]

[0030] The optimal guidance command obtained in the line-of-sight direction is:

[0031]

[0032] in As an intermediate variable, it is represented as:

[0033]

[0034] in, L is the pseudo-inverse of the Laplace matrix determined by the communication topology between interceptor clusters. + Element.

[0035] The beneficial effects of this invention include:

[0036] (1) Achieve multi-directional encirclement and simultaneous interception under distributed communication constraints;

[0037] (2) It enables multiple interceptors to simultaneously meet the relative encirclement formation constraint and the equilibrium overload constraint at the end of the target hit, thereby improving the collaborative interception efficiency of the cluster under the constraint of low communication volume. Attached Figure Description

[0038] Figure 1 This diagram illustrates a preferred embodiment of a distributed unmanned aerial vehicle (UAV) swarm optimal spatiotemporal cooperative encirclement and guidance method according to the present invention.

[0039] Figure 2 This illustrates the distributed communication topology among the four interceptors configured in Example 1;

[0040] Figure 3 The diagram shows the trajectory of the interceptor and the target in Example 1;

[0041] Figure 4 The remaining interception time curve of the interceptor in Example 1 is shown;

[0042] Figure 5 The line-of-sight curve of the interceptor in Example 1 is shown;

[0043] Figure 6 The interceptor acceleration control command in Embodiment 1 is shown. Detailed Implementation

[0044] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Through these descriptions, the features and advantages of the present invention will become clearer and more apparent.

[0045] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments. Although various aspects of embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated otherwise.

[0046] The present invention provides an optimal spatiotemporal cooperative encirclement and capture guidance method for distributed unmanned aerial vehicle (UAV) swarms, such as... Figure 1 As shown, it includes the following steps:

[0047] S1. Construct a control instruction form subject to distributed constraints;

[0048] S2. Construct the optimal problem for achieving spatiotemporal coordinated encirclement and capture;

[0049] S3. Based on the optimal problem, the optimal control method is used to obtain the optimal guidance command;

[0050] S4. Use the obtained optimal guidance commands to control the flight of the UAV.

[0051] According to the present invention, the motion control equations for the target and the intercepting UAV are expressed as follows:

[0052]

[0053] in, The relative acceleration of the normal channel, i.e., the normal channel control command, r i V represents the distance between the i-th intercepting drone and the target. Ri Let δ represent the relative acceleration between the i-th intercepting drone and the target. i σ represents the relative leading angle. i γ represents the relative line-of-sight angle. Ri This indicates the relative track angle.

[0054] In S1, the control command is decomposed into a normal channel and a tangential channel for relative acceleration, and the normal channel control command is set to the following form:

[0055]

[0056] in, Let N represent the basic guidance law for the i-th intercepting drone, where N is an adjustable guidance parameter. Let σ represent the relative acceleration between the i-th intercepting drone and the target. i This represents the relative line-of-sight angle between the i-th intercepting drone and the target. Let represent the bias term acceleration of the i-th intercepting drone.

[0057] According to the present invention, This ensures the precise interception of targets by interceptor drones. Used to adjust the end-view line of sight to achieve spatial coordination.

[0058] Preferably, N ≥ 3.

[0059] In S2, the optimal problem is represented as:

[0060]

[0061] L(X f +X △ ) = 0

[0062] in,

[0063]

[0064] X = [x a ,x t ]T

[0065]

[0066] In the formula, min represents the minimum, st represents the constraint, J represents the energy, and t represents the current time. f The guidance time is represented by τ, B, U, and X are intermediate variables, τ represents the time factor, W represents the weight matrix of the optimization problem, and L represents the Laplace matrix, obtained from the communication topology of the UAV swarm. f The final state of the intermediate variable X is represented as X. f =[x a (t f ),x t (t f )],n represents the total number of intercepted aircraft, I n This represents an n-dimensional matrix whose elements are all 1s. This represents the remaining guidance time for the i-th intercepting drone. Let represent the approach acceleration of the i-th intercepting drone along the line-of-sight direction with respect to the target. Let X represent the approach speed between the i-th intercepting drone and the target. a X t X Δ As an intermediate variable, This represents the terminal line-of-sight angle of the i-th intercepting drone. This represents the guidance time of the i-th intercepting drone. The matrix representing the terminal attack angle error between various UAVs can be freely set by those skilled in the art. This represents the terminal attack time error matrix between various UAVs, which can be freely set by those skilled in the art, k a >0, k t >0 is an adjustable parameter.

[0067] Furthermore, the dynamics of the end-view angle satisfy:

[0068]

[0069] The dynamics of the terminal guidance time satisfy:

[0070]

[0071] Unlike traditional optimization problems, the optimization problem constructed in this invention achieves spatial and temporal cooperative guidance in the normal and tangential channels by constraining the bias term acceleration and the approach acceleration along the line of sight, respectively, thus enabling high-dimensional state consistency of control commands under distributed constraints.

[0072] Furthermore, this optimization problem can also enable multiple interceptors to simultaneously satisfy relative encirclement formation constraints and equilibrium overload constraints at the end of the target hit, thereby improving the collaborative interception efficiency of the cluster under low communication constraints.

[0073] In S3, the optimal guidance command obtained in the normal direction is:

[0074]

[0075] The optimal guidance command obtained in the line-of-sight direction is:

[0076]

[0077] in As an intermediate variable, it is represented as:

[0078]

[0079] in, L is the pseudo-inverse of the Laplace matrix determined by the communication topology between interceptor clusters. + Element.

[0080] Preferably, an acceleration command can be obtained based on the guidance command, and the final obtained acceleration command in the normal direction of the intercepting drone is... for:

[0081]

[0082] Intercepting the tangential acceleration command of the drone for:

[0083]

[0084] Among them, a T For the target acceleration, γ Ri Let γ be the relative trajectory angle between the i-th intercepting drone and the target. T The target's track angle, Let δ be the trajectory angle of the i-th intercepting drone. i Let be the relative forward angle between the i-th intercepting drone and the target.

[0085] Example

[0086] Example 1

[0087] A simulation experiment of a coordinated encirclement and capture guidance method was conducted, setting up four intercepting drones to collaboratively intercept a maneuvering target. The initial positions and velocities of the four drones were set as follows:

[0088]

[0089] The target motion model is set as follows:

[0090]

[0091] Among them, a Tx and a Ty These represent the acceleration components in the target velocity reference frame, with units of m / s². 2 .

[0092] The simulation process includes the following steps:

[0093] S1. Construct a control instruction form subject to distributed constraints;

[0094] S2. Construct the optimal problem for achieving spatiotemporal coordinated encirclement and capture;

[0095] S3. Based on the optimal problem, the optimal control method is used to obtain the optimal guidance command;

[0096] S4. Use the obtained optimal guidance commands to control the flight of the UAV.

[0097] In S1, the control command is decomposed into a normal channel and a tangential channel for relative acceleration, and the normal channel control command is set to the following format:

[0098]

[0099] In S2, the optimal problem is represented as:

[0100]

[0101] L(X f +X △ ) = 0

[0102] in,

[0103]

[0104] X = [x a ,x t ] T

[0105]

[0106] The dynamics of the end-view angle satisfy:

[0107]

[0108] The dynamics of the terminal guidance time satisfy:

[0109]

[0110] In S3, the optimal guidance command obtained in the normal direction is:

[0111] The optimal guidance command obtained in the line-of-sight direction is:

[0112]

[0113]

[0114] During the simulation, a distributed communication topology was set up among the four interceptor drones as follows: Figure 2 As shown, the Laplace matrix obtained from the topological graph is:

[0115]

[0116] During the simulation, the guidance parameters were set to N=4, ka=3, and kt=2, and the line-of-sight angle between two adjacent UAVs at the terminal was set to 20°. Therefore, the line-of-sight angle deviation state vector is: To achieve simultaneous interception by four drones, the guidance time deviation state vector is:

[0117] Simulation results are as follows Figure 3-6 As shown, where, Figure 3 The trajectories of four interceptors simultaneously intercepting a maneuvering target from multiple directions were demonstrated. Figure 4 This indicates that all four interceptors completed guidance within 14.54 seconds, achieving time coordination. Figure 5 The terminal line-of-sight angles of each interceptor are shown to be 39.03°, 19.03°, -0.97°, and -19.97°, indicating that the four interceptors can simultaneously intercept maneuvering targets from multiple directions with a line-of-sight angle interval of 20 degrees. Figure 6 The acceleration control commands are shown, from Figure 6 As can be seen, the acceleration command changes with the target's maneuvering and converges to the target's acceleration amplitude at the end, providing sufficient maneuver margin to resist external interference at the end.

[0118] The present invention has been described above with reference to preferred embodiments; however, these embodiments are merely exemplary and illustrative. Various substitutions and modifications can be made to the present invention based on these embodiments, all of which fall within the scope of protection of the present invention.

Claims

1. A method for optimal spatiotemporal cooperative encirclement and capture guidance of distributed unmanned aerial vehicle (UAV) swarms, characterized in that, Includes the following steps: S1. Construct a control instruction form subject to distributed constraints; S2. Construct the optimal problem for achieving spatiotemporal coordinated encirclement and capture; S3. Based on the optimal problem, the optimal control method is used to obtain the optimal guidance command; S4. Use the obtained optimal guidance commands to control the flight of the UAV; In S1, the control command is decomposed into a normal channel and a tangential channel for relative acceleration, and the normal channel control command is set to the following form: ， in, , Indicates the first A basic guidance law for intercepting drones. N It is an adjustable guidance parameter. Indicates the first The relative acceleration between the intercepting drone and the target Indicates the first The relative line-of-sight angle between the intercepting drone and the target. Indicates the first The bias term acceleration for intercepting drones; In S2, the optimal problem is represented as: ， in, ， ， ， ， ， ， ， In the formula, min represents the minimum, and st represents the constraint. J Indicates energy. Indicates the current time. Indicates guidance time. , , As an intermediate variable, τ represents the time factor. The weight matrix represents the optimization problem. Represents the Laplace matrix, This represents the final state of the intermediate variable X. This indicates the total number of intercepted aircraft. This represents an n-dimensional matrix whose elements are all 1s. Indicates the first The remaining guidance time for intercepting the drone. Indicates the first The approach acceleration of the intercepting drone and the target along the line of sight. Indicates the first The approach speed between the intercepting drone and the target. , , As an intermediate variable, Indicates the first The terminal line-of-sight angle of an intercepting drone. Indicates the first The guidance time for intercepting a drone. This represents the terminal attack angle error matrix among the various UAVs. This represents the terminal attack time error matrix among the various drones. , It is an adjustable parameter; In S3, the optimal guidance command obtained in the normal direction is: ， The optimal guidance command obtained in the line-of-sight direction is: ， in As an intermediate variable, it is represented as: ， in, The pseudo-inverse of the Laplace matrix determined by the communication topology between interceptor clusters. Element.

2. The optimal spatiotemporal cooperative encirclement and guidance method for distributed UAV swarms according to claim 1, characterized in that, The dynamics of the end-view angle satisfy: ， The dynamics of the terminal guidance time satisfy: 。

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