Multi-cluster aircraft target allocation fault-tolerant decision-making method based on clustering negotiation

Abstract

The invention discloses a clustering negotiation-based multi-cluster aircraft target allocation fault-tolerant decision-making method, which comprises the following steps of: considering aircraft, target and no-fly zone information and task constraint conditions, establishing a multi-cluster aircraft cluster communication topology, constructing a revenue function of an aircraft cluster on the basis, designing an optimization performance index of the aircraft cluster, and determining the target allocation fault-tolerant decision-making of the aircraft cluster. The problem description of multi-cluster aircraft target allocation is realized; based on the cluster optimization performance index, establishing a target selection rule, a target transfer rule and a distributed clustering negotiation model of the aircraft cluster; and based on an aircraft cluster target selection rule and a target transfer rule, in combination with a heuristic local optimization algorithm, designing a multi-cluster cluster distributed target allocation fault-tolerant decision algorithm to obtain a target allocation result. The method can solve the cluster target distribution problem of the multi-cluster aircraft, and has the advantages of small communication load, strong fault-tolerant capability and the like.

Description

Technical Field

[0001] The invention relates to a fault-tolerant decision method for allocating targets of multiple clusters of aircraft, and belongs to the field of multi-agent task allocation. Background Art

[0002] Swarm aircraft is a system composed of a large number of aircraft of different types and simple structures. Through the functional complementarity and collaboration between aircraft, swarm aircraft can adapt to complex and diverse mission scenarios such as emergency rescue, reconnaissance and detection, improve the efficiency and success rate of mission execution, and have broad application prospects.

[0003] Target allocation is one of the core modules of the decision-making layer of swarm unmanned aerial vehicles. It achieves the optimal match between the aircraft and the target by comprehensively analyzing the performance of the aircraft, the characteristics of the mission target, and environmental factors, providing an important basis for subsequent trajectory planning and individual control of the aircraft. However, due to the large scale of swarm aircraft, individual failures of aircraft often occur, and it is difficult to rely on operators to directly allocate and adjust the targets of each aircraft. Therefore, the study of the fault-tolerant decision algorithm for dynamic target allocation of multi-cluster aircraft clusters is of great significance to expanding the scale of clusters and improving cluster reliability.

[0004] The cluster-distributed dynamic target allocation fault-tolerant decision algorithm needs to meet the following requirements: (1) Information is locally known. That is, the individual aircraft of each aircraft cluster can only obtain information about some related aircraft and cannot grasp the information of the entire cluster. (2) Independent decision-making by the central node of the aircraft cluster. That is, the aircraft cluster needs to select targets based on the local information obtained and locally allocate targets that match each aircraft in the cluster. (3) Convergence of the algorithm. That is, through the target selection and target transfer of the aircraft cluster, the target allocation result of the cluster can tend to remain unchanged. (4) Fault tolerance of the algorithm. That is, when some individual aircraft fail, can the cluster autonomously optimize and adjust the target allocation result of the cluster through the independent decision-making of each aircraft cluster. The existing target allocation algorithms are difficult to meet the above requirements. Therefore, in view of the above requirements, the present invention designs a multi-cluster aircraft target allocation fault-tolerant decision-making method based on cluster negotiation. Summary of the invention

[0005] The present invention discloses a fault-tolerant decision method for target allocation of multiple clusters of aircraft based on cluster negotiation, which relates to the field of multi-agent task allocation and can solve the real-time dynamic target allocation problem of multiple clusters of aircraft, reduce cluster communication load and improve cluster reliability.

[0006] To achieve the above object, the embodiments of the present invention adopt the following technical solutions:

[0007] (1) Based on the information of aircraft and targets, the target conflict constraints and target matching constraints of cluster aircraft are clarified, and the communication topology of multiple cluster aircraft is established;

[0008] (2) Based on the information of aircraft, target, and no-fly zone, construct the mission execution efficiency function of the aircraft on the target, then design the aircraft cluster benefit function and establish the aircraft cluster optimization performance index;

[0009] (3) Based on the aircraft cluster benefit function and cluster optimization performance index, the target selection rule and target transfer rule of the aircraft cluster are designed, and a centralized and decentralized cluster negotiation model is established;

[0010] (4) Based on the target selection rules and target transfer rules of aircraft clusters and combined with the heuristic local optimization algorithm, a fault-tolerant decision-making algorithm for distributed dynamic target allocation of multi-cluster aircraft clusters is designed.

[0011] The present invention relates to the technical field of cluster aircraft cooperative control, and specifically is a method for combining a chaotic simulated annealing particle swarm algorithm with a contract network protocol and applying it to a distributed dynamic target allocation method for aircraft clusters with multi-formation layouts. First, by comprehensively considering aircraft, targets, no-fly zone information and task constraints, a reasonable multi-cluster aircraft communication topology is constructed to lay the foundation for the subsequent fault-tolerant decision-making process. Secondly, on the basis of the communication topology, a task execution efficiency function for the target is constructed for each individual aircraft, and the aircraft cluster benefit function is designed accordingly, and cluster optimization performance indicators are formulated. These indicators are intended to reflect the benefits of cluster aircraft in the process of executing tasks and provide a quantitative basis for cluster target allocation. Then, based on the cluster optimization performance indicators, the present invention further establishes the target selection rules and target transfer rules of aircraft sub-formations, and introduces a distributed cluster negotiation model, so that the aircraft cluster, under the premise of following certain rules, determines the ownership of each target to be allocated through inter-cluster communication negotiation. Finally, by combining the aircraft cluster target selection rules, target transfer rules and heuristic local optimization algorithm, the present invention designs a cluster distributed dynamic target allocation fault-tolerant decision algorithm, which realizes dynamic adjustment and optimization of the target allocation strategy while reducing the communication load.

[0012] The present invention can not only solve the problem of target allocation for multiple clusters of aircraft, but also has the advantages of low communication load and strong fault tolerance. Through the clustering negotiation mechanism of aircraft clusters for targets and efficient algorithm design, it significantly improves the efficiency of collaborative task execution of cluster aircraft, and has high application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A schematic diagram of a method for calculating the performance of an aircraft on a target mission according to an embodiment of the present invention;

[0015] Figure 2 A flow chart of a cluster-distributed dynamic target allocation algorithm according to an embodiment of the present invention;

[0016] Figure 3 It is a flow chart of the heuristic local optimization algorithm of aircraft cluster according to an embodiment of the present invention;

[0017] Figure 4 To simulate the distribution of aircraft, targets and no-fly zones in each sub-formation of the cluster on the experimental platform;

[0018] Figure 5 To simulate the target allocation results of the cluster in a fault-free situation on the experimental platform;

[0019] Figure 6 This is the curve of cluster revenue change when simulating a fault-free situation on the experimental platform;

[0020] Figure 7 To simulate the target allocation results of the cluster under the condition of aircraft failure on the experimental platform;

[0021] Figure 8 This is the curve showing the change of cluster benefits when simulating aircraft failure on the experimental platform;

[0022] Table 1 shows the maneuverability of the aircraft in each sub-formation simulated on the experimental platform;

[0023] Table 2 shows the importance coefficients of each target simulated on the experimental platform;

[0024] Table 3 shows the center coordinates, radius and risk level information of each no-fly zone simulated on the experimental platform. DETAILED DESCRIPTION

[0025] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be construed as a limitation of the present invention. Those skilled in the art of the present technology can understand that unless specifically stated, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. Those skilled in the art of the present technology can understand that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless defined as herein.

[0026] The target assignment scenarios considered in the present invention are as Figure 1 , including aircraft, targets, and no-fly zones, where:

[0027] Aircraft: There are m aircraft. The coordinate information of aircraft i ∈ {1,..., m} is denoted as The maneuverability of aircraft i is denoted as ρ i , ρ i is a positive constant less than 1. All aircraft form p clusters, where p < min{m, n}. Each aircraft cluster is regarded as a sub-formation. Sub-formation k ∈ {1,..., p} consists of aircraft. The set of aircraft within sub-formation k

[0028] Targets: There are n targets. The coordinate information of target j ∈ {1,..., n} is denoted as The target importance coefficient is denoted as w j , w j is a positive constant less than 1, which characterizes the importance of executing the target task.

[0029] No-fly zones: There are q no-fly zones. The central coordinate of no-fly zone l ∈ {1,..., q} is denoted as The coverage radius is denoted as R l , and the risk level is denoted as β l , β l is a positive constant less than 1.

[0030] The embodiment of the present invention provides a fault-tolerant decision-making method for dynamic target assignment of multi-cluster aircraft based on clustering negotiation. The specific implementation is as follows:

[0031] Step 1: clarify the mission constraints of cluster aircraft and establish multi-cluster communication topology.

[0032] Step 1.1, decision variable matrix.

[0033] The present invention considers assigning n targets to m aircrafts. In order to characterize the matching relationship between targets and aircrafts, the decision variable matrix X is defined as [x ij ] m×n , where x ij ∈{0,1} represents the matching relationship between aircraft i∈{1,…,m} and target j∈{1,…,n}, x ij =1 indicates that the aircraft i and target j match successfully, otherwise the aircraft i and target j match unsuccessfully.

[0034] Step 1.2, task constraints.

[0035] Considering that each aircraft matches at most one target, the following target conflict constraints are established:

[0036]

[0037] Multiple sub-teams independently select one or more targets, and the target allocation benefit function of each sub-team needs to be calculated based on the target task execution efficiency. In order to facilitate the calculation of the task execution efficiency of each sub-team, it is necessary to limit each target to be selected by at most one sub-team, and establish the following target matching constraints:

[0038]

[0039] in, is the target set selected by the aircraft in the sub-formation k∈{1,…,p}.

[0040] Step 1.3, cluster communication topology.

[0041] Considering that the aircraft cluster is composed of multiple sub-formations, an undirected graph G = (V, E) is established to represent the communication topology of multiple clusters of aircraft, where the node set V = {v i |i=1,…,m} represents the set of all aircraft, and the edge set E represents the set of communication links between aircraft. In order to quickly and accurately calculate the target allocation benefits of aircraft clusters within each sub-formation, each sub-formation needs to use a star topology for internal communication, and designate the central node of each sub-formation to collect information about other aircraft in the sub-formation and calculate the target allocation benefits. Therefore, the communication topology of sub-formation k∈{1,…,p} is represented as a subgraph G of graph G k =(V k ,E k ),in, Characterize the set of aircraft in the sub-formation, Characterization G k The central node of Represents the set of communication links connected to the central node within the sub-formation.

[0042] Considering that the communication node set of the entire cluster is composed of each sub-team node, we get:

[0043]

[0044] In order to save communication resources and reduce communication interference, it is required that aircraft clusters communicate only through the central node. Therefore, the communication link set of the entire cluster consists of the communication link within each sub-formation and the communication link between the formation center node, and we get:

[0045]

[0046] Step 2: construct the aircraft cluster benefit function and establish the aircraft cluster optimization performance index.

[0047] Step 2.1, aircraft mission execution performance function.

[0048] The mission execution efficiency of aircraft i∈{1,…,m} against target j∈{1,…,n} is related to the penetration performance of the aircraft against each no-fly zone, while the penetration performance of aircraft i against no-fly zone l∈{1,…,q} is related to the maneuverability of the aircraft ρ i ∈(0,1), risk level of no-fly zone lβ l ∈(0,1) and the distance d where the straight flight path of the aircraft overlaps with the no-fly zone l il Based on the above relationship, the penetration performance function of aircraft i against no-fly zone l is established as follows:

[0049]

[0050] In order to obtain the mission execution efficiency of aircraft i to target j, it is necessary to calculate the distance between the line connecting aircraft i and target j and the no-fly zone l (see Figure 1 )as follows:

[0051]

[0052] in, is the vertical distance from the center of no-fly zone l to the line connecting aircraft i and target j, is the straight-line distance from aircraft i to the center of no-fly zone l, is the straight-line distance from target j to the center of no-fly zone l, and ε is a small positive number. The above formula shows that when R l <h ijlWhen , the distance between the line connecting aircraft i and target j and the no-fly zone l is 0, that is, the aircraft does not pass through the no-fly zone l, otherwise

[0053] According to the penetration performance of aircraft i against no-fly zone l and the distance at which the line connecting the aircraft and target j overlaps with no-fly zone l, the mission execution effectiveness function of aircraft i against target j is established, that is, the probability function of aircraft i successfully executing the mission against target j is as follows:

[0054]

[0055] Step 2.2, aircraft cluster profit function.

[0056] Since multiple aircraft in a sub-formation k∈{1,…,p} need to work together to perform one or more target tasks, the benefit of the sub-formation performing the target task is related to the allocation of targets to each aircraft in the sub-formation. represents the target allocation of each aircraft in sub-formation k, where X k is X = [x ij ] m×n According to the aircraft i∈M k Task execution efficiency function and decision matrix X for target j k , and get the target j∈N k The probability of a task being successfully executed is as follows:

[0057]

[0058] According to the target importance coefficient w j ∈(0,1), the profit function is as follows:

[0059]

[0060] Step 2.3, cluster optimization performance indicators.

[0061] The optimization goal of the entire aircraft cluster is to maximize the sum of the benefits of all sub-formations in the cluster, the combination of the cluster optimization performance index and the selection goal of each sub-formation aircraft {N k |k=1,…,p}, therefore, the optimization goal of establishing a cluster is as follows:

[0062]

[0063] Among them, u ij Characterizes the mission execution effectiveness of aircraft i on target j, w j Characterizes the importance coefficient of target j.

[0064] Step 3: Determine the target selection rules and target transfer rules of the aircraft cluster and establish a centralized and decentralized cluster negotiation model.

[0065] Step 3.1, aircraft cluster target selection rules.

[0066] definition Characterizes the target set selected by the aircraft in the sub-formation k∈{1,…,p} at time t, target Represents the center of the sub-formation target set at time t. Definition Represents the set of targets that are not selected by any sub-formation at time t, is the decision matrix of sub-formation k at time t, Represents the payoff of sub-formation k at time t.

[0067] Based on the above relationship and the aircraft cluster benefit function, the aircraft cluster target selection rule can be established as follows: If the target Satisfies the following formula, then the target That is, the target at time t+1 Quilt formation choose:

[0068]

[0069] in, Characterize the target selected by sub-formation k at time t+1 The maximum value of the profit function after selecting the target is the gain of the maximum profit of the aircraft cluster before selecting the target. Heng established; Characterization target Relative to the target center j of sub-formation k at time t 0,k The distance dominance of (t), Target center 0,k The coordinates of (t) are the target coordinates selected by the aircraft cluster k at the initial moment; λ1,λ2∈[0,1] represent the weights of the maximum benefit gain and distance advantage of the sub-formation, respectively. * , the values ​​of λ1 and λ2 are also different.

[0070] Step 3.2, aircraft cluster target transfer rules.

[0071] According to the aircraft cluster target selection rule, if That is, the maximum benefit gain of all sub-teams is 0, or Then, the target at time t+1 is will not be selected by any sub-formation. At this time, it is necessary to reallocate the target that has been selected by a sub-formation at time t to another aircraft cluster. Therefore, the aircraft cluster target transfer rule can be established as follows: Satisfies the following formula

[0072]

[0073] So, the goal That is, the target at time t+1 Transfer from sub-group k' to sub-group At this time, the target center j 0,k The coordinate calculation method of (t) is as follows: take the mean of the horizontal and vertical coordinates of all selected targets in aircraft cluster k at time t to obtain the reference coordinate point, and select the target point closest to the reference coordinate point among all selected targets in aircraft cluster k at that time as the target center j at time t. 0,k (t).

[0074] Step 3.3, centralized and decentralized clustering negotiation model.

[0075] Based on the above aircraft cluster target selection rules and target transfer rules, a centralized and decentralized cluster negotiation model can be established. Accordingly, the target allocation problem can be transformed into a target clustering problem, that is, through negotiation and communication between aircraft clusters, each target can be dynamically divided into different sub-formations according to the target selection rules and target transfer rules.

[0076] definition is the sub-formation k at time t for the target The clustering confidence of the aircraft cluster is as follows: at each time step, any aircraft cluster independently pre-selects a new target or pre-transfers a selected target to other sub-formations, and independently calculates the expected change in the sub-formation's benefit. Other sub-formations receive the target information pre-selected or transferred by the aircraft cluster, independently calculate the expected gain and clustering confidence of the benefit, and send the above information to the center node of the aircraft cluster. The aircraft cluster specifies the ownership of this target at this moment based on the target selection rule and the target transfer rule. At the same time, since each sub-formation independently calculates the benefit of allocating the selected target to the aircraft in the cluster in a locally centralized manner, the entire cluster completes the dynamic target allocation in a locally centralized and overall decentralized manner. Therefore, the above target allocation model is called a centralized and decentralized clustering negotiation model.

[0077] In the above dynamic process, if the status of the aircraft, target and no-fly zone remains unchanged, the sum of the maximum gains of all sub-formations will gradually increase until the gain is 0, which is consistent with the optimization goal of the cluster.

[0078] Step 4: Design a multi-cluster distributed target allocation fault-tolerant decision algorithm.

[0079] In order to enable clustered aircraft to realize the division of targets by aircraft clusters through local information interaction between aircraft clusters under the communication topology, and to enable the cluster to spontaneously adjust the target allocation results when the status of some aircraft changes, the following method is adopted: Figure 2 The distributed target allocation fault-tolerant decision-making idea shown in the figure, in which the benefit calculation of each aircraft cluster is as follows: Figure 3 The heuristic local optimization algorithm shown.

[0080] In step 4.1, the heuristic local optimization algorithm is used to calculate the aircraft cluster benefits.

[0081] The purpose of this step is to maximize the set N of targets selected by all aircraft in aircraft cluster k at a certain moment. k Profit when distributing J k (X k ,N k ).

[0082] Based on the particle swarm algorithm, a solution to the optimization problem, namely an X k , is regarded as a particle. There are a number of particles. The properties of particles include speed, position and fitness. The fitness is the particle X k The corresponding income J k (X k ,N k ), the fitness of the α∈{1,…,a}th particle at the sth iteration is recorded as Position vector Indicates that χ (i) ∈N k (i=1,…,m k ) represents the target number selected by aircraft i in sub-formation k. The velocity of the αth particle at the sth iteration Location The update formula is as follows:

[0083]

[0084] in, is the individual extreme value, G s is the global extreme value, c1 and c2 are learning factors, r1 s and is a random number between (0,1), ω s is the inertia factor.

[0085] At the same time, based on the simulated annealing algorithm, local optimization is performed on each particle, and the position of the particle is updated with a certain acceptance probability in each iteration.

[0086] The algorithm steps are as follows:

[0087] Step 1. Randomly generate an initial population of a particles, initialize the speed and position of each particle, and give the initial inertia factor ω 0 , learning factors c1 and c2, initial acceptance probability p r ∈(0,1) and temperature attenuation coefficient ξ∈(0,1);

[0088] Step 2. Calculate the fitness of each particle α and get the initial individual extreme value Global extremum G 0 , and initialize the annealing temperature to

[0089] Step 3. If the algorithm reaches the termination condition, output the result, otherwise proceed to the following loop part from s 0 to S, where S is the maximum number of iterations;

[0090] Step 4. Calculate the fitness of each particle Update individual extreme values ​​and global extreme values;

[0091] Step 5. According to the formula ω s =ω 0 -(ω 0 -ω S )s / S updates the inertia factor and based on the chaos optimization formula r ι s+1 =ur ι s (1-r ι s ),ι=1,2 to get r1 s and Thus the speed and position of the new particle are calculated;

[0092] Step 6. Calculate the fitness of each new particle make like or Then accept the new position, otherwise keep the original position;

[0093] Step 7. Annealing temperature T s+1 =ξT s , s=s+1, go to Step 4.

[0094] When executing the above steps, the central node in the aircraft cluster outputs the global extreme value and its corresponding target allocation solution based on the status information of the aircraft and the targets to be matched in the cluster and the no-fly zone information after the algorithm reaches the termination condition, thereby realizing heuristic local optimization within the cluster.

[0095] Step 4.2, centralized and distributed dynamic target allocation fault-tolerant decision-making ideas.

[0096] On the basis of the heuristic local optimization algorithm, according to the aircraft cluster target selection rules and target transfer rules, the following distributed dynamic target allocation fault-tolerant decision algorithm is designed:

[0097] Step 1. Each aircraft cluster center node initializes the aircraft status information, all target status information to be assigned and no-fly zone information in the cluster. For an aircraft cluster k∈{1,…,p}, the following steps are performed in parallel.

[0098] Step 2. Initialize the unassigned target set of aircraft cluster k The cluster has selected the target set Assume that the initial distance dominance is 0.5, and randomly select the initial target as the target center j of each cluster 0,k (0), and initialize the maximum benefit gain based on the heuristic local optimization algorithm And initialize the clustering confidence parameters λ1, λ2;

[0099] Step 3. If Or if the maximum benefit of all sub-formations is 0, the target selection phase is completed and the target center j of each aircraft cluster is updated. 0,k (t) and go to Step 8, otherwise proceed as follows: half The loop part, where t half is the maximum number of iterations in the target selection phase;

[0100] Step 4. Aircraft cluster k pre-randomly selects targets And based on the heuristic local optimization algorithm, the maximum allocation benefit within the cluster after selecting the target is calculated

[0101] Step 5. Dynamically adjust λ1,λ2 and update the aircraft cluster k for the target at time t The clustering confidence According to the cluster communication topology, the central node Send the target number and cluster confidence information pre-selected by aircraft cluster k at that moment to the central nodes of other sub-formations;

[0102] Step 6. The center node of aircraft cluster k receives the information of other sub-formations about the target. The cluster confidence and the maximum benefit gain information within the cluster are used to select the target based on the target selection rule. The sub-formation with the highest confidence that satisfies the condition that the maximum benefit gain is greater than 0 is designated to formally select the target.

[0103] Step 7. Update the unassigned target set and the target set selected by the aircraft cluster k t=t+1, go to Step 4;

[0104] Step 8. When t = t fault When the status of the aircraft in aircraft cluster k changes, for example, some aircraft fail and cannot continue to perform the mission, the maximum allocation benefit of the current aircraft cluster k is calculated based on the heuristic local optimization algorithm. Get the change in revenue At this point, the total revenue of the cluster decreases;

[0105] Step 9. If the algorithm meets the termination condition, output the result, otherwise proceed as follows: fault to max The loop part, where t max is the maximum number of iterations;

[0106] Step 10. Let k' = k, and the aircraft cluster k' pre-assigns a target To be transferred, calculate the change in the intra-cluster benefit before and after the target transfer The central node assigns the target number The information on the change in revenue before and after the transfer is sent to the central nodes of other sub-formations;

[0107] Step 11. The center node of aircraft cluster k' receives the information of other sub-formations about the target. The cluster confidence and the maximum benefit gain information within the cluster are based on the target transfer rule. If the maximum benefit gain of all sub-formations except the aircraft cluster k' is not greater than the target Change in revenue within aircraft cluster k' before and after the transfer The target is considered not to satisfy the target transfer rule, and the aircraft cluster k' reassigns a target to be transferred and goes to Step 10. Otherwise, the target is reassigned based on the target transfer rule. Attribution, update the target center j of aircraft cluster k' 0,k' (t) Go to Step 10.

[0108] Step 5: Simulation verification.

[0109] Step 5.1, no-fault scenario simulation.

[0110] The simulation is based on the Matlab simulation platform, and considers a target allocation scenario consisting of 30 aircraft, 24 targets, and 4 no-fly zones. The 30 aircraft are divided into 5 sub-formations, and each sub-formation consists of 6 aircraft. The aircraft maneuverability of each sub-formation, the importance coefficient of each target, the center coordinates, radius, and risk level of each no-fly zone are shown in Tables 1, 2, and 3. At the initial moment, none of the aircraft in all sub-formations have selected a target. like Figure 4 shown.

[0111] Table 1

[0112]

[0113] Table 2

[0114]

[0115] Table 3

[0116] No-fly zone number Center horizontal coordinate Center ordinate Coverage Radius Risk level 1 1643 881 500 0.4627 2 1786 1331 500 0.4150 3 1506 1817 500 0.4744 4 1802 2250 500 0.3523

[0117] In the fault-free case, based on the target selection rule, the clustering confidence parameters λ1 and λ2 are set to change dynamically, λ1 = 1-exp(-0.08×t), λ2 = 1-λ1. In the early stage of iteration, the distance advantage index is emphasized, while in the later stage of iteration, the sub-formation benefit gain index is emphasized. Figure 5 The target allocation results of each sub-formation aircraft when there is no fault are given, where the connection lines between each aircraft and each target represent the matching relationship between the aircraft and the target.

[0118] Figure 6 The curve of the total benefit of the cluster changing with the number of iterations is given. It can be seen that under the designed decentralized target allocation algorithm, the total benefit of the cluster continues to increase and finally converges at the 19th iteration.

[0119] Step 5.2, fault scenario simulation.

[0120] The distributed task allocation algorithm proposed in this invention can adapt to failures. Figure 2 It can be seen that for any aircraft i∈{1,…,m}, after removing the failed aircraft, the currently selected target may not be the one that optimizes the performance index To maximize the optimal goal, it is necessary to make local adjustments to the aircraft target allocation based on the target transfer rule. The cluster will continue to iterate until the total benefit of the cluster converges.

[0121] In order to compare with the normal situation, the coordinates of each sub-formation aircraft and the target coordinates are the same as those in the normal situation, such as Figure 4 As shown in Table 1, Table 2, and Table 3, the maneuverability of each sub-formation's aircraft, the importance coefficient of each target, the center coordinates, radius, and risk level of each no-fly zone are the same as those in Table 1, Table 2, and Table 3. The cluster is adjusted based on the target allocation results in the absence of a fault. Considering that all aircraft with complete failures belong to sub-formation 1, after the failure, the cluster confidence parameter λ1=λ2=0.5 is set, and the target allocation results of the cluster are as follows: Figure 7 As shown, the connecting line between each aircraft and each target represents the matching relationship between the aircraft and the target.

[0122] Figure 8The curve of the total revenue of the cluster changing with the number of iterations is given. It can be seen that the total revenue of the cluster decreases in the 25th iteration due to the occurrence of complete failure. After that, under the designed decentralized target allocation fault-tolerant decision-making algorithm, the total revenue of the cluster continues to increase and finally converges at the 30th iteration.

Claims

1. A fault-tolerant decision method for multi-cluster aircraft target allocation based on cluster negotiation, characterized in that: The decision-making method includes the following: (1) Define a decision variable matrix to characterize the matching relationship between aircraft and targets, determine the aircraft target conflict constraints and target matching constraints, and establish a multi-cluster aircraft communication topology; (2) constructing an aircraft performance function for target mission execution based on the aircraft's penetration performance against a no-fly zone, designing a revenue function for an aircraft cluster based on the performance function, and establishing an optimized performance index for the aircraft cluster; (3) Design the target selection rule and target transfer rule of the aircraft cluster based on the revenue function of the aircraft cluster and the optimization performance index of the aircraft cluster, and establish a centralized and decentralized cluster negotiation model; (4) Based on the target selection rules and target transfer rules of the aircraft cluster and combined with the heuristic local optimization algorithm, a multi-cluster cluster distributed target allocation fault-tolerant decision algorithm is designed to realize cluster dynamic fault-tolerant target allocation under the multi-cluster aircraft communication topology.

2. The method according to claim 1, characterized in that The above (1) specifically includes: Suppose there are m aircraft and n targets, and define the decision variable matrix X = [x ij ] m×n , where x ij ∈{0,1} represents the matching relationship between aircraft i∈{1,…,m} and target j∈{1,…,n}. Each aircraft cluster is regarded as a sub-formation. Given p sub-formations, sub-formation k∈{1,…,p} consists of m k The aircraft in the sub-formation is defined as k and the target set N selected by the aircraft in sub-formation k k ; Create a target conflict constraint: Create target matching constraints: An undirected graph G = (V, E) is established to represent the multi-cluster aircraft cluster communication topology, where the node set V = {v i |i=1,…,m} represents the set of all aircraft, and the edge set E represents the set of communication links between aircraft; the communication topology of the sub-formation k∈{1,…,p} is represented as a subgraph G of G k =(V k ,E k ),in, Characterize the set of aircraft in the sub-formation, Characterization G k The central node of Characterize the set of communication links connected to the central node within the sub-formation; get:

3. The method according to claim 2, characterized in that: The above (2) specifically includes: According to the coordinates of the i-th aircraft The center coordinates of the no-fly zone l∈{1,…,q} And coverage radius R l , the penetration performance function of aircraft i against no-fly zone l is established as follows: Among them, ρ i ,β l is a positive constant less than 1, ρ i is a parameter that characterizes the maneuverability of aircraft i, β l is a parameter that characterizes the risk level of the no-fly zone l, d il is the distance where the straight-line flight path of aircraft i overlaps with the no-fly zone l; According to the penetration performance of aircraft i into the no-fly zone l and the coordinates of target j∈{1,…,n} The mission execution efficiency function of aircraft i on target j is established as follows: in, is the distance between the line connecting aircraft i and target j and the no-fly zone l, ε is a small positive number, r il and r jl are the straight-line distances from aircraft i and target j to the center of no-fly zone l, h ijl is the vertical distance from the center of the no-fly zone l to the line connecting the aircraft i and the target j; Define the aircraft cluster, that is, the decision matrix of each aircraft in sub-formation k X k is X = [x ij ] m×n The profit function of each aircraft in the sub-array and sub-formation k selecting the target is as follows: Among them, w j ∈(0,1) is a parameter that represents the importance of target j; The optimization goals for establishing the entire aircraft cluster are as follows: Among them, u ij Characterizes the mission execution effectiveness of aircraft i on target j, w j Characterizes the importance coefficient of target j.

4. The method according to claim 3, characterized in that: The above (3) specifically includes: definition Characterizes the target set selected by the aircraft in the sub-formation k at time t, target Represents the center of the sub-formation target set at time t, Represents the set of targets that are not selected by any sub-formation at time t, is the decision matrix of sub-formation k at time t, Represents the payoff of sub-formation k at time t; The aircraft cluster target selection rules are as follows: If for the target Satisfies the following formula, then the target That is, the target at time t+1 Quilt formation choose: in, Characterization target Relative to the target center j of sub-formation k at time t 0,k The distance advantage of (t), λ1,λ2∈[0,1] represent the weights of the maximum benefit gain and distance advantage of the sub-formation respectively; The design of the aircraft cluster target transfer rule is as follows: Satisfies the following formula, then the target That is, the target at time t+1 Transfer from sub-group k' to sub-group A centralized and decentralized clustering negotiation model is established based on the aircraft cluster target selection rule and target transfer rule.

5. The method according to claim 4, characterized in that: The above (4) specifically includes: Design a multi-cluster cluster distributed target allocation fault-tolerant decision algorithm to achieve target division through local information interaction between aircraft clusters, and automatically adjust target allocation when the aircraft status changes; In the process of cluster dynamic target allocation, the aircraft cluster uses a heuristic local optimization algorithm to calculate the allocation benefit of the cluster, and uses particle swarm optimization and simulated annealing strategies to perform local optimization, so as to maximize the allocation benefit of all aircraft in the cluster to the selected target set N at a certain moment. k Profit when distributing J k (X k ,N k ); According to the aircraft cluster target selection rules and target transfer rules, define is the sub-formation k at time t for the target The clustering confidence of Output the cluster target allocation results to realize dynamic target allocation and fault-tolerant decision-making of cluster aircraft.

6. The method according to claim 5, characterized in that: Based on the heuristic local optimization algorithm, in one iteration, the aircraft cluster pre-selects a target, recalculates the cluster's distribution benefit to the target, and communicates and negotiates with the central nodes of other aircraft clusters based on the above clustering confidence to determine the ownership of the target.

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