Distributed unmanned aerial vehicle cluster optimal space-time cooperative hunting guidance method

Abstract

The invention discloses a distributed unmanned aerial vehicle cluster optimal space-time cooperative hunting guidance method. The method comprises the following steps: constructing a distributed constrained control instruction form; constructing an optimal problem for realizing space-time collaborative surrounding; based on the optimal problem, an optimal control method is adopted to obtain an optimal guidance instruction; and performing flight control on the unmanned aerial vehicle by adopting the obtained optimal guidance instruction. According to the distributed unmanned aerial vehicle cluster optimal space-time cooperative hunting guidance method disclosed by the invention, multiple interceptors can meet the relative hunting formation constraint and the average overload constraint while hitting the tail end of the target, and the cooperative interception efficiency of the cluster under the small communication traffic constraint is improved.

Description

Technical Field

[0001] The present invention relates to an optimal spatio-temporal cooperative pursuit guidance method for a distributed UAV cluster, belonging to the technical field of flight control. Background Art

[0002] Unmanned aircraft flying illegally will have a huge impact on social security. For example, unmanned aircraft flying illegally within the scope of civil aviation are extremely likely to cause major safety accidents. For illegal unmanned aircraft, in addition to signal suppression, multi-UAV spatio-temporal cooperative guidance interception is generally adopted.

[0003] To achieve cooperative guidance in terms of time and space, different communication methods can be adopted among clusters to share information and calculate cooperative commands. Among them, centralized communication means that there is one or more central nodes in the cluster that can communicate with all other aircraft. For example, the literature "Impact-time-control guidance law for anti-ship missiles" first proposed selecting the maximum value of the predicted terminal guidance times of multiple aircraft as the expected simultaneous strike time, and then designing an optimal time control guidance law based on the PN guidance law to eliminate the error between the predicted guidance time and the expected time, achieving time cooperation; under the conditions of simultaneously specifying the cooperative strike time and the 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 Institute, 2023, 360(2): 693-718" and the literature "Tang Yang, Zhu Xiaoping, Zhou Zhou, et al. A cooperative guidance method based on attack time and angle control[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(1): 466-478" proposed a spatio-temporal cooperative guidance method that can be used to strike stationary targets by serially designing the terminal angle constraint control guidance law and the ITCG guidance law; the literature "Tao H, Lin D, Song T, et al. Optimal spatial-temporal cooperative guidance against a maneuvering target[J]. Journal of the Franklin Institute, 2023, 360(13): 9886-9903." In the relative coordinate system, the guidance time error between two adjacent aircraft is constructed as a high-dimensional state vector, and the centralized spatio-temporal cooperative guidance law for simultaneously multi-directionally intercepting maneuvering targets at the terminal is solved by using the principle of the high-dimensional Schwarz inequality.

[0004] However, the above methods all adopt centralized cooperative guidance, which has a relatively high system communication burden and a high degree of dependence on the central node.

[0005] Distributed communication cooperative guidance has also been proposed in the prior art. Each UAV only needs to coordinate the state with the aircraft with which it has a communication connection, with a small amount of information interaction and no dependence on the 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 non-linear tracking problem with the leader's state as the expected value based on the leader-follower method. By making the missile-to-target distance of each follower tend to be the same as the leader's distance, simultaneous strikes are achieved, avoiding the estimation of the remaining guidance time; 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 the literature "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" propose a distributed spatio-temporal cooperative guidance law with a hybrid single-double layer architecture. By serially combining the time cooperative guidance law based on finite / fixed-time non-linear consensus and the terminal angle constraint guidance law, multi-directional simultaneous attacks with automatic coordination of the interception time of maneuvering targets are achieved. However, the terminal cooperative interception angle of each aircraft still needs to be set in advance.

[0006] Moreover, the existing distributed cooperative guidance methods designed based on non-linear control theory only focus on the convergence effect of state quantity consistency and do not consider the overload consumption cost required for state convergence. For guidance problems that require error convergence under finite time and finite energy conditions, it is easy to cause an increase in the overload consumption of the interceptor and a reduction in the endurance time.

[0007] Therefore, it is necessary to conduct a more in-depth study on the existing spatio-temporal cooperative guidance methods for UAV swarms to solve the above problems. Summary of the Invention

[0008] To overcome the above problems, in-depth research has been carried out, and an optimal spatio-temporal cooperative pursuit guidance method for a distributed UAV swarm is proposed, including the following steps:

[0009] S1. Construct the form of control commands subject to distributed constraints;

[0010] S2. Construct the optimal problem for realizing spatio-temporal cooperative pursuit;

[0011] S3. Based on the optimal problem, use the optimal control method to obtain the optimal guidance commands;

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

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

[0014]

[0015] where, represents the basic guidance law of the i-th intercept UAV, N is an adjustable guidance parameter, represents the relative acceleration between the i-th intercept UAV and the target, σ i represents the relative line-of-sight angle between the i-th intercept UAV and the target, represents the offset acceleration of the i-th intercept UAV.

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

[0017]

[0018] L(X f +X △ )=0

[0019] where,

[0020]

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

[0022]

[0023] In the formula, min represents minimum, s.t. represents constraint, J represents energy, t represents the current time, t f ​Denote the guidance time, B, U, X are intermediate variables, τ represents the time factor, W represents the weight matrix of the optimization problem, L represents the Laplacian matrix, and X f Denote the terminal state of the intermediate variable X, n represents the total number of intercept vehicles, and I n Denote an n-dimensional matrix with all elements being 1, Denote the remaining guidance time of the i-th intercept UAV, Denote the approaching acceleration along the line of sight between the i-th intercept UAV and the target, Denote the approaching speed between the i-th intercept UAV and the target, X a 、X t 、X Δ Are intermediate variables, Denote the terminal line-of-sight angle of the i-th intercept UAV, Denote the guidance time of the i-th intercept UAV, Denote the terminal strike angle error matrix between UAVs, Denote the terminal strike time error matrix between UAVs, k a >0, k t >0 are adjustable parameters.

[0024] In a preferred embodiment, the dynamics of the terminal line-of-sight 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 obtained optimal guidance command in the line-of-sight direction is:

[0031]

[0032] Where Is an intermediate variable, expressed as:

[0033]

[0034] Where, Is the element of the pseudo-inverse matrix L of the Laplacian matrix determined by the communication topology among the interceptor clusters + Of.

[0035] The beneficial effects of the present invention include:

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

[0037] (2) Enable multiple interceptors to simultaneously satisfy the relative encirclement formation constraint and the equal-potential overload constraint at the end of hitting the target, and improve the collaborative interception efficiency of the cluster under small communication volume constraints. Description of the Drawings

[0038] Figure 1 Show a schematic flow diagram of the optimal spatio-temporal collaborative encirclement guidance method for a distributed UAV cluster according to a preferred embodiment of the present invention;

[0039] Figure 2 Show the distributed communication topology among the four interceptors set in Embodiment 1;

[0040] Figure 3 Show the interceptor and target trajectory diagram in Embodiment 1;

[0041] Figure 4 Show the remaining interception time curve of the interceptor in Embodiment 1;

[0042] Figure 5 Show the line-of-sight angle curve of the interceptor in Embodiment 1;

[0043] Figure 6 Show the acceleration control command of the interceptor in Embodiment 1. Detailed Embodiment

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

[0045] The special term "exemplary" here means "serving as an example, embodiment, or illustration". Any embodiment described as "exemplary" here does not have to be construed as superior to or better than other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0046] A distributed UAV cluster optimal spatio-temporal collaborative encirclement guidance method provided by the present invention, as Figure 1 shown, includes the following steps:

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

[0048] S2. Construct an optimal problem for realizing spatio-temporal collaborative encirclement;

[0049] S3. Based on the optimal problem, use the optimal control method to obtain the optimal guidance instruction;

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

[0051] According to the present invention, the motion control equation of the target and the intercept UAV is expressed as:

[0052]

[0053] Wherein, is the relative acceleration of the normal channel, that is, the control command of the normal channel, r i represents the distance between the i-th intercept UAV and the target, V Ri represents the relative acceleration between the i-th intercept UAV and the target, δ i represents the relative lead angle, σ i represents the relative line-of-sight angle, γ Ri represents the relative track angle.

[0054] In S1, the control command is decomposed into the normal channel and the tangential channel of the relative acceleration, and the form of the control command of the normal channel is set as:

[0055]

[0056] Wherein, represents the basic guidance law of the i-th intercept UAV, N is an adjustable guidance parameter, represents the relative acceleration between the i-th intercept UAV and the target, σ i represents the relative line-of-sight angle between the i-th intercept UAV and the target, represents the offset term acceleration of the i-th intercept UAV.

[0057] According to the present invention, ensures the accurate interception of the target by the intercept UAV, and is used to adjust the terminal line-of-sight angle to achieve spatial coordination.

[0058] Preferably, N≥3.

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

[0060]

[0061] L(X f +X △ )=0

[0062] Wherein,

[0063]

[0064] X=[x a ,x t ​T

[0065]

[0066] In the formula, min represents the minimum, s.t. represents the constraint, J represents the energy, t represents the current time, t f represents the guidance time, B, U, X are intermediate variables, τ represents the time factor, W represents the weight matrix of the optimization problem, L represents the Laplacian matrix, obtained from the communication topology of the UAV swarm, X f represents the end state of the intermediate variable X, denoted as X f =[x a (t f ), x t (t f )], n represents the total number of intercept vehicles, I n represents an n-dimensional matrix with all elements being 1, represents the remaining guidance time of the i-th intercept UAV, represents the approaching acceleration along the line of sight between the i-th intercept UAV and the target, represents the approaching speed between the i-th intercept UAV and the target, X a 、X t 、X Δ are intermediate variables, represents the end line-of-sight angle of the i-th intercept UAV, represents the guidance time of the i-th intercept UAV, represents the end strike angle error matrix between UAVs, which can be freely set by those skilled in the art, represents the end strike time error matrix between UAVs, which can be freely set by those skilled in the art, k a >0, k t >0 are adjustable parameters.

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

[0068]

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

[0070]

[0071] Different from the traditional optimal problem, the optimal problem constructed in the present invention realizes spatial cooperation and time cooperation guidance in the normal channel and tangential channel respectively by separately constraining the bias term acceleration and the approaching acceleration along the line of sight, so that the control instruction is subject to the high-dimensional state consistency of distributed constraints.

[0072] Furthermore, for this optimal problem, multiple interceptors can also satisfy the relative encirclement formation constraint and the equal - potential overload constraint when hitting the target end, improving the cooperative interception efficiency of the cluster under the small communication volume constraint.

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

[0074]

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

[0076]

[0077] Where is an intermediate variable, expressed as:

[0078]

[0079] Where is the element of the pseudo - inverse matrix L of the Laplacian matrix determined by the communication topology among the interceptor clusters + of.

[0080] Preferably, the acceleration command can be obtained according to the guidance command, and the normal - direction acceleration command of the interception UAV finally obtained is:

[0081]

[0082] The tangential - direction acceleration command of the interception UAV is:

[0083]

[0084] Where a T is the acceleration of the target, γ Ri is the relative track angle between the i - th interception UAV and the target, γ T is the track angle of the target, is the track angle of the i - th interception UAV, and δ i is the relative lead angle between the i - th interception UAV and the target.

[0085] Embodiment

[0086] Embodiment 1

[0087] A simulation experiment on the cooperative encirclement guidance method is carried out. Four interception UAVs are set to cooperate in intercepting a maneuvering target. The initial positions and speeds of the four UAVs are set as:

[0088]

[0089] The target motion model is set as:

[0090]

[0091] where a Tx and a Ty respectively represent the acceleration components in the target velocity reference frame, with the unit of m / s 2 .

[0092] During the simulation process, the following steps are included:

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

[0094] S2. Construct the optimal problem for realizing spatio-temporal cooperative encirclement;

[0095] S3. Based on the optimal problem, adopt the optimal control method to obtain the optimal guidance instruction;

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

[0097] In S1, the control instruction is decomposed into the normal channel and the tangential channel of the relative acceleration, and the form of the control instruction for the normal channel is set as:

[0098]

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

[0100]

[0101] L(X f +X △ ) = 0

[0102] where

[0103]

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

[0105]

[0106] The dynamics of the terminal line-of-sight angle satisfy:

[0107]

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

[0109]

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

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

[0112]

[0113]

[0114] During the simulation process, the distributed communication topology among four intercept drones is set as Figure 2 shown, and the Laplacian matrix obtained according to the topology diagram is:

[0115]

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

[0117] The simulation results are as Figures 3 - 6 shown, where Figure 3 shows the trajectories of four interceptors simultaneously intercepting a maneuvering target in multiple directions, Figure 4 indicating that all four interceptors complete guidance at 14.54 seconds and achieve time coordination, Figure 5 shows that the end line of sight angles of each interceptor are 39.03°, 19.03°, -0.97°, and -19.97° respectively, indicating that multiple directions with a 20-degree line of sight angle interval are formed among the four interceptors to simultaneously intercept the maneuvering target. Figure 6 shows the acceleration control command. From Figure 6 it can be seen that the acceleration command changes with the target maneuver and can converge to the target acceleration amplitude at the end, providing sufficient maneuvering margin to resist external interference at the end.

[0118] The present invention has been described above in combination with preferred embodiments, but these embodiments are only exemplary and only serve an illustrative role. On this basis, various substitutions and improvements can be made to the present invention, and all of these fall within the protection scope of the present invention.

Claims

1. A distributed UAV cluster optimal spatiotemporal collaborative capture and guidance method, characterized in that: The following steps are involved: S1, construct the control instruction form subject to distribution constraints; S2, construct the optimal problem to achieve spatiotemporal coordinated roundup; S3. Based on the optimal problem, the optimal control method is used to obtain the optimal guidance command; S4. Use the obtained optimal guidance instructions to control the flight of the UAV.

2. The distributed UAV cluster optimal spatiotemporal collaborative encirclement and guidance method according to claim 1 is characterized in that: In S1, the control instruction is decomposed into the normal channel and the tangential channel of the relative acceleration, and the normal channel control instruction is set to the form of: in, represents the basic guidance law of the i-th intercepting UAV, N is the adjustable guidance parameter, represents the relative acceleration between the i-th intercepting UAV and the target, σ i represents the relative sight angle between the i-th intercepting UAV and the target, represents the bias acceleration of the i-th intercepting UAV.

3. The distributed UAV cluster optimal spatiotemporal collaborative encirclement and guidance method according to claim 1 is characterized in that: In S2, the optimal problem is expressed as: L(X f +X Δ )=0 in, X=[x a ,x t ] T X Δ =[x σΔ ,x tΔ ] In the formula, min means minimum, st means constraint, J means energy, t means current time, and t f represents the guidance time, B, U, and X are intermediate variables, τ represents the time factor, W represents the weight matrix of the optimization problem, L represents the Laplace matrix, and X f represents the terminal state of the intermediate variable X, n represents the total number of intercepted aircraft, I n represents an n-dimensional matrix whose elements are all 1, represents the remaining guidance time of the i-th intercepting UAV, represents the approach acceleration between the i-th intercepting UAV and the target along the line of sight, represents the closing speed between the i-th intercepting UAV and the target, X a , X t , X Δ is the intermediate variable, represents the terminal sight angle of the i-th intercepting drone, represents the guidance time of the i-th intercepting UAV, represents the terminal strike angle error matrix between each UAV, represents the terminal strike time error matrix between each UAV, k α >0, k t >0 is an adjustable parameter.

4. The distributed UAV cluster optimal spatiotemporal collaborative encirclement and guidance method according to claim 3 is characterized in that: The dynamics of the terminal sight angle satisfies: The dynamics of the terminal guidance time satisfies:

5. The distributed UAV cluster optimal spatiotemporal collaborative encirclement and guidance method according to claim 1 is characterized in that: In S3, the optimal guidance instruction in the normal direction is obtained as: The optimal guidance instruction obtained in the line of sight direction is: in is an intermediate variable, expressed as: in, is the pseudo-inverse matrix L of the Laplace matrix determined by the communication topology between the interceptor clusters + elements.

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