Unmanned system cluster formation control method based on aggregation game under directed network

Abstract

The invention particularly relates to an unmanned system cluster formation control method based on an aggregation game under a directed network. The method comprises the following steps: acquiring initial position information and a dynamic model of a multi-cluster unmanned system; determining an expected formation configuration of a leader and a follower in each cluster in the multi-cluster unmanned system; determining an optimization target and a constraint condition of a leader in each cluster, determining aggregation information and a target function structure in a target function according to the optimization target of the leader, and calculating a Nash equilibrium point of the leader; estimating the aggregation information in a distributed manner by adopting a surplus variable compensator and an estimator based on proportion-integration according to the required aggregation information; updating the position information of the leader in real time according to the estimated aggregation information; according to the initial position information of the intelligent agent, the real-time position information of the leader and the expected formation configuration of the leader and the follower, constructing a distributed formation layout control rate under the directed network; and according to the control rate, controlling the leaders and followers in each cluster to form an expected formation configuration and optimizing the goals of the leaders.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned systems, and in particular to an unmanned system cluster formation control method based on aggregate game in a directed network. Background Art

[0002] Swarm formation control of unmanned systems has received extensive attention in the past few years and can be used in many areas such as unmanned vehicles, drones, and unmanned ships. In practical engineering applications, due to environmental or other factors, intelligent agents are usually divided into multiple clusters to perform tasks. Swarm formation control is mainly divided into leaderless swarm formation control and leader-follower tracking formation control. A leader can better control the formation task of the entire swarm, and the followers only need to maintain the formation. In addition, in the formation layout control problem, for each intelligent agent in the swarm, its goals include formation task requirements, distance optimization goals, etc. Therefore, the study of the formation layout problem has great practical significance. It can be applied to practical mission requirements such as multi-cluster formation escort, multi-aircraft coordinated combat, coordinated reconnaissance, and multi-target monitoring.

[0003] In practical applications, the formation deployment control problem of intelligent agents often requires balancing multiple metrics. For example, when multiple drone swarms are scouting different target locations, they need to balance the three metrics of detection mission, support, and supply. Not only must each drone swarm be kept within a certain distance from the detection location, but also the distances between each drone swarm and the supply point. In this process of balancing multiple metrics, each swarm often requires global information, and how to obtain this global information requires further research. Furthermore, in practical applications, due to factors such as environmental influences, the communication network between swarms is not undirected. Therefore, designing a directed network distributed formation control system that can balance multiple metrics is of great significance.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0005] The present invention provides an unmanned system cluster formation control method based on aggregate game in a directed network, which can realize directed network distributed formation control that weighs multiple indicators, and thus can overcome the defects existing in the existing technology to a certain extent.

[0006] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0007] According to a first aspect of the present invention, a method for controlling an unmanned system cluster formation based on an aggregation game in a directed network is provided, the method comprising: The method comprises: S1. Obtain the initial location information of the multi-cluster unmanned system; S2. Determine the dynamic model and system communication topology model of the multi-cluster unmanned system; S3. Determine the desired formation configuration of the leader and followers in each swarm of the multi-swarm unmanned system; S4. Determine the optimization objectives and constraints of the leader in each cluster, determine the aggregate information and objective function structure in the objective function based on the leader's optimization objectives, and calculate the Nash equilibrium point of the leader; S5. Distributively estimating the aggregate information using a surplus variable compensator and a proportional-integral based estimator according to the required aggregate information; S6. Update the leader's location information in real time based on the estimated aggregate information; S7. Construct a distributed formation layout control rate in a directed network based on the initial position information of the agents, the real-time position information of the leader, and the desired formation configuration of the leader and followers; S8. Control the leader and followers in each cluster to form a desired formation configuration and optimize the leader's goal based on the constructed distributed formation layout control rate.

[0008] In some exemplary embodiments, The dynamic model of the multi-cluster unmanned system determined in step S2 is specifically:

[0009] in, Indicates leader Real-time location, Indicates leader The control input quantity, Express Ask about time The first derivative of represents the total number of leaders, Indicates followers Real-time location, Indicates followers The control input quantity, Express Ask about time The first derivative of Indicates the total number of followers, is the system's operating time.

[0010] In some exemplary embodiments, the desired formation configuration of the leader and followers determined in step S3 is specifically:

[0011] in, Indicates leader The position of the followers when forming a formation configuration, Indicates leader No. The position of the followers when forming a formation configuration, is the matrix transpose symbol.

[0012] In some exemplary embodiments, the optimization objectives and constraints of the leader determined in step S4 are:

[0013] in, represents minimization, Indicates the The objective function of a leader is Represents a global aggregation item, represents the first target weight, Indicates leader No. The location of the target point, Indicates leader The number of reconnaissance target points, represents the second target weight, Indicates the A leadership position, Indicates the A leadership position, represents the third target weight, represents the two-norm represents the constraints, represents a closed convex set, Indicates the The gradient of the objective function of each leader.

[0014] In some exemplary embodiments, the surplus variable compensator and the proportional-integral based estimator in step S5 are specifically:

[0015] in, Indicates the The leader's surplus variable, represents a positive parameter, Indicates the The out-degree information of each leader, Indicates the The set of inner neighbors of a leader, The out-degree-based adjacency matrix of the directed topology consisting of all leaders is Rank Elements of the column, Indicates the estimator of leaders, Indicates the estimator of leaders, represents a positive parameter, represents a positive parameter, The in-degree-based adjacency matrix of the directed topology consisting of all leaders is Rank Elements of the column, Indicates the The leader's integral item, represents the perturbation parameter, Express Ask about time The first derivative of Express Ask about time The first derivative of Express Ask about time The first derivative of .

[0016] In some exemplary embodiments, the real-time updating of the leader's location information in step S6 is specifically as follows:

[0017] in, represents the control gain, represents the projection function, Indicates the The gradient of the objective function of each leader.

[0018] In some exemplary embodiments, the distributed formation control rate in the directed network in step S7 is:

[0019] in, represents the control gain, Indicates the The set of followers of a leader, Indicates followers With followers The weight of the links that make up Indicates followers location, Indicates followers The position when the formation configuration is formed, Indicates followers location, Indicates followers With leaders The weight of the links that make up Indicates followers The position when the formation configuration is formed, Indicates followers control rate.

[0020] According to a second aspect of the present invention, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the unmanned system cluster formation control method based on aggregation game in a directed network described in the first aspect is implemented.

[0021] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored. When the computer program is executed by a processor, the unmanned system cluster formation control method based on aggregation game in a directed network described in the first aspect is implemented.

[0022] According to a fourth aspect of the present invention, there is provided an electronic device, comprising: processor; and a memory for storing executable instructions of the processor; Among them, the processor is configured to implement the unmanned system cluster formation control method based on aggregate game in a directed network as described in the first aspect above by executing the executable instructions.

[0023] The present invention provides a cluster formation control method for unmanned systems based on an aggregation game in a directed network. By designing a surplus variable compensator and a proportional-integral-based estimator, the leader can distribute and estimate aggregate information in the directed network. This method is more suitable for controlling intelligent systems in complex networks, more consistent with the communication topology of practical multi-cluster systems, and effectively avoids the waste of communication resources in global interactions. Based on the designed game model, the leader can simultaneously balance multiple indicators to achieve the optimal position, namely the Nash equilibrium (NE) of the game, while minimizing its own objective function. The leader and followers reach the optimal position according to the desired formation configuration based on the designed formation layout control rate. This method is more suitable for practical multi-cluster system tasks such as multi-indicator formation escort and multi-target detection.

[0024] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are incorporated into and constitute a part of this specification, illustrate embodiments consistent with the present invention, and together with the description, serve to explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and it is clear that those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0026] Figure 1 Schematic diagram of a flow chart of a method for controlling an unmanned system cluster formation based on an aggregation game in a directed network according to an embodiment of the present invention; Figure 2 Schematic diagram of the communication topology of a multi-cluster intelligent system according to an embodiment of the present invention; Figure 3 Schematic diagram of the cluster leader estimating global aggregation information and actual aggregation information in an embodiment of the present invention; Figure 4 This is a schematic diagram of the actual location information of the cluster leader in an embodiment of the present invention; Figure 5 This is a schematic diagram of an intelligent agent completing a formation task and reaching a Nash equilibrium point (NE) in an embodiment of the present invention; Figure 6 Schematic diagram of an intelligent agent completing a formation task and reaching a Nash equilibrium point (NE) in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0028] like Figure 1 As shown, an embodiment of the present invention provides an unmanned system cluster formation control method based on an aggregation game in a directed network, comprising the following steps S1 to S8: S1. Obtaining initial information of the multi-cluster intelligent system; In an optional embodiment of the present invention, the cluster unmanned system established in this embodiment includes at least clusters, including leaders and Followers, where a cluster includes a leader and several followers, obtain the initial information of all agents in the cluster unmanned system, the initial information specifically includes: the initial X-axis position information and Y-axis position information of all agents, the initial running time, and the initial estimation information of the leader agent.

[0029] S2. Determine the dynamic model and system communication topology model of the multi-cluster unmanned system; In an optional embodiment of the present invention, the multi-cluster unmanned system established in this embodiment includes leaders and The dynamic model of the follower is as follows:

[0030] in, Indicates leader Real-time location, Indicates leader The control input quantity, Express Ask about time The first derivative of represents the total number of leaders, Indicates followers Real-time location, Indicates followers The control input quantity, Express Ask about time The first derivative of Indicates the total number of followers, is the system's operating time.

[0031] In addition, use Describe the communication topology model corresponding to all leaders, where Represents all leader agent nodes in the leader communication topology model, Represents the set of edges in the leader communication topology, edge Represents a slave node arrive A directed edge of Represents the adjacency matrix of the leader communication topology model, where Represents the adjacency matrix No. Row, No. The elements of the column represent the edges The weight of represents the in-degree matrix of the leader communication topology model, Laplacian matrix representation of the leader communication topology express dimensional Euclidean space, It is strongly connected, and there is a directed edge between any two nodes. Describe the communication topology model of the leader and followers of each cluster, where Represents all agent nodes in each cluster communication topology model. The leader agent in each cluster represents the root node. There is at least one directed path to each other follower node. Represents the set of edges in each cluster communication topology, edge Represents a slave node arrive A directed edge of Represents the adjacency matrix of each cluster communication topology model, where Represents the adjacency matrix No. Row, No. Elements of the column and represent the followers With followers The weight of the links that make up represents the in-degree matrix of the leader communication topology model, The Laplacian matrix representing the communication topology of each cluster.

[0032] In the multi-cluster unmanned system established in this embodiment, each intelligent agent can communicate with its corresponding neighbor, thereby avoiding global communication.

[0033] S3. Determine the desired formation configuration of the leader and followers in each swarm of the multi-swarm unmanned system; In an optional embodiment of the present invention, the desired formation configuration of the leader and followers in this embodiment is specifically:

[0034] in, Indicates leader The position of the followers when forming a formation configuration, Indicates leader No. The position of the followers when forming a formation configuration, is the system's running time, is the matrix transpose symbol.

[0035] S4. Determine the optimization objectives and constraints of the leader in each cluster, determine the aggregate information and objective function structure in the objective function based on the leader's optimization objectives, and determine the Nash equilibrium point (NE) of the leader game; In an optional embodiment of the present invention, the optimization goal of the leader is:

[0036] in, represents minimization, Indicates the The objective function of a leader is Represents a global aggregation item, represents the weight of target 1, Indicates the The location of the target point, Indicates leader The number of reconnaissance target points, represents the second target weight, Indicates the A leadership position, Indicates the A leadership position, represents the third target weight, represents the two-norm represents the constraints, represents a closed convex set, Indicates the The gradient of the objective function of the leader. Driving gradient:

[0037] Obtain the unique Nash equilibrium point (NE) of the game.

[0038] S5. Distributively estimating the aggregate information using a surplus variable compensator and a proportional-integral based estimator according to the required aggregate information; In an optional embodiment of the present invention, the surplus variable compensator and the proportional-integral based estimator are specifically:

[0039] in, Indicates the The leader's surplus variable, represents a positive parameter, Indicates the The out-degree information of each leader, Indicates the The set of inner neighbors of a leader, The out-degree-based adjacency matrix of the directed topology consisting of all leaders is Rank Elements of the column, Indicates the estimator of leaders, represents a positive parameter, represents a positive parameter, The in-degree-based adjacency matrix of the directed topology consisting of all leaders is Rank Elements of the column, Indicates the The leader's integral item, represents the perturbation parameter, Express Ask about time The first derivative of Express Ask about time The first derivative of Express Ask about time The first derivative of is the system's operating time.

[0040] S6. Update the leader's location information in real time based on the estimated aggregate information; In an optional embodiment of the present invention, the real-time updating of the leader's location information is specifically as follows:

[0041] in, represents the control gain, represents the projection function, Indicates the The gradient of the objective function of the leader, is the system's operating time.

[0042] S7. Construct a distributed formation layout control rate in a directed network based on the initial position information of the agents, the real-time position information of the leader, and the desired formation configuration of the leader and followers; In an optional embodiment of the present invention, the distributed formation control rate in the directed network described in this embodiment is:

[0043] in, represents the control gain, Indicates the The set of followers of a leader, Indicates followers With followers The weight of the links that make up Indicates followers location, Indicates followers The position when the formation configuration is formed, Indicates followers location, Indicates followers With leaders The weight of the links that make up Indicates followers The position when the formation configuration is formed, is the system's operating time.

[0044] S8. Control the leader and followers in each cluster to form a desired formation configuration and optimize the leader's goal according to the constructed formation layout control rate.

[0045] In an optional embodiment of the present invention, the leader and followers form a formation according to the formation layout control rate, and together reach the Nash equilibrium point (NE) obtained by the leader, optimizing the objective function of the leader to a minimum.

[0046] The following is a detailed analysis and explanation of the unmanned system cluster formation control method based on aggregation game in a directed network provided by this embodiment with reference to specific examples.

[0047] Four groups of drones stopped at a flat surface The initial position of each drone group has one leader and several followers. All leaders and followers have their own first-order dynamic models. All leaders communicate through directed topology. The leader in each cluster is the root node. There is a directed spanning tree between the leader and followers in each cluster. Each cluster leader is responsible for the following tasks: Each cluster leader is responsible for scouting different locations, ensuring support and information exchange with other clusters, and ensuring timely resupply from the initial location. Each cluster leader uses a surplus variable compensator and a proportional-integral estimator to estimate global information in a distributed manner. By using an aggregated game model, each cluster leader balances three metrics to determine the Nash equilibrium (NE) of the game and reaches it.

[0048] The tasks of the followers of each cluster are as follows: complete the formation task according to the formation configuration expected by the leader and follow the leader to the Nash equilibrium point (NE).

[0049] In a two-dimensional plane, given four groups of drones, the first group of drones has 1 leader and 2 followers, the second group of drones has 1 leader and 2 followers, the third group of drones has 1 leader and 4 followers, and the fourth group of drones has 1 leader and 3 followers. The cluster has a directed communication topology as follows: Figure 2 As shown in the figure, leaders communicate with each other through a directed network. In each cluster, the only leader is the root node, and there is a directed spanning tree between the leader and followers. Given the initial position information of the leader and followers:

[0050] Given the initial running time of all agents , given the initial estimate information of all leaders .

[0051] The dynamic equation for 4 leaders and 11 followers is:

[0052] The expected formation configuration of followers in each cluster is as follows:

[0053] The objective functions of the four leaders are:

[0054] Indicator 1 is reconnaissance mission with a weight of 0.19, indicator 2 is timely supply with a weight of 0.14, and indicator 3 is timely support with a weight of 0.15.

[0055] The reconnaissance locations that each cluster is responsible for are:

[0056] Drive gradient:

[0057] The Nash equilibrium point (NE) of the 4-leader game is:

[0058] The proportional-integral based estimator and surplus variable compensator are:

[0059] Distributed estimation of global aggregate information such as Figure 3 As shown in the figure, the dotted line is the real aggregation information that cannot be obtained directly, and the solid line is the aggregation information obtained by the distributed estimation of each leader. It can be seen from the figure that the aggregation information estimated by all leaders finally reaches consistency, which is very close to the real aggregation information, indicating that the effectiveness of the estimated aggregation information based on proportional-integral estimator and surplus variable compensator designed in the present invention can be well guaranteed.

[0060] The real-time update of the leader's location information is as follows:

[0061] Numerical simulation is performed through MATLAB and SIMULINK, and the initial time is given , set the simulation time , Figure 4 It shows that the four leaders update their real-time position information based on the estimated aggregate information. From the figure, it can be seen that the positions of the four leaders finally reach the obtained Nash equilibrium point.

[0062] The distributed formation control rate in the directed network described in this embodiment is:

[0063] Based on the known leader position information and the distributed formation control rate, the follower position information can be updated in real time.

[0064] This embodiment simulates a four-UAV swarm system under the formation control rate based on the directed network aggregation game. The simulation time ,like Figure 5 and Figure 6 As shown, Indicates the reconnaissance locations that each drone group is responsible for. Represents the Nash equilibrium point of 4 leaders, the hollow circle represents the leader, and the square represents the follower.

[0065] Figure 5 The figure shows that four cluster leaders and followers complete the desired formation configuration under the influence of the formation control rate and move together from the initial starting point to the Nash equilibrium point. Figure 6 The results demonstrate that four swarm leaders and followers can achieve the desired formation configuration and reach a Nash equilibrium under the influence of the formation control rate. These results demonstrate that the four-drone swarm system, under the influence of the formation control rate designed in this invention, can accurately complete the formation task and simultaneously reach a Nash equilibrium point with the leader that optimizes the objective function.

[0066] It should be noted that, as another aspect, the present application also provides a storage medium, which may be included in an electronic device or may exist independently without being incorporated into the electronic device. The storage medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device implements the method described in the following embodiments.

[0067] In one embodiment, the present application provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0068] Furthermore, the above-described figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above-described figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0069] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the claims.

[0070] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings and that various modifications and variations can be made without departing from the scope thereof, which is limited only by the appended claims.

Claims

1. A method for controlling unmanned system cluster formation based on aggregation game in a directed network, characterized in that: The method comprises: S1. Obtain the initial location information of the multi-cluster unmanned system; S2. Determine the dynamic model and system communication topology model of the multi-cluster unmanned system; S3. Determine the desired formation configuration of the leader and followers in each swarm of the multi-swarm unmanned system; S4. Determine the optimization objectives and constraints of the leader in each cluster, determine the aggregate information and objective function structure in the objective function based on the leader's optimization objectives, and calculate the Nash equilibrium point of the leader; S5. Distributively estimating the aggregate information using a surplus variable compensator and a proportional-integral based estimator according to the required aggregate information; S6. Update the leader's location information in real time based on the estimated aggregate information; S7. Construct a distributed formation layout control rate in a directed network based on the initial position information of the agents, the real-time position information of the leader, and the desired formation configuration of the leader and followers; S8. Control the leader and followers in each cluster to form a desired formation configuration and optimize the leader's goal based on the constructed distributed formation layout control rate.

2. The unmanned system cluster formation control method based on the aggregation game in a directed network according to claim 1 is characterized in that: The dynamic model of the multi-cluster unmanned system determined in step S2 is specifically: in, Indicates leader Real-time location, Indicates leader The control input quantity, Express Ask about time The first derivative of represents the total number of leaders, Indicates followers Real-time location, Indicates followers The control input quantity, Express Ask about time The first derivative of Indicates the total number of followers, is the system's operating time.

3. The unmanned system cluster formation control method based on the aggregation game in a directed network according to claim 1 is characterized in that: The expected formation configuration of the leader and followers determined in step S3 is specifically: in, Indicates leader The position of the followers when forming a formation configuration, Indicates leader No. The position of the followers when forming a formation configuration, is the matrix transpose symbol.

4. The unmanned system cluster formation control method based on the aggregation game in a directed network according to claim 1 is characterized in that: The optimization objectives and constraints of the leader determined in step S4 are: in, represents minimization, Indicates the The objective function of a leader is Represents a global aggregation item, represents the first target weight, Indicates leader No. The location of the target point, Indicates leader The number of reconnaissance target points, represents the second target weight, Indicates the A leadership position, Indicates the A leadership position, represents the third target weight, represents the two-norm represents the constraints, represents a closed convex set, Indicates the The gradient of the objective function of each leader.

5. The unmanned system cluster formation control method based on the aggregation game in a directed network according to claim 1 is characterized in that: The surplus variable compensator and the proportional-integral based estimator in step S5 are specifically: in, Indicates the The leader's surplus variable, represents a positive parameter, Indicates the The out-degree information of each leader, Indicates the The set of inner neighbors of a leader, The out-degree-based adjacency matrix of the directed topology consisting of all leaders is Rank Elements of the column, Indicates the estimator of leaders, Indicates the estimator of leaders, represents a positive parameter, represents a positive parameter, The in-degree-based adjacency matrix of the directed topology consisting of all leaders is Rank Elements of the column, Indicates the The leader's integral item, represents the perturbation parameter, Express Ask about time The first derivative of Express Ask about time The first derivative of Express Ask about time The first derivative of .

6. The unmanned system cluster formation control method based on the aggregation game in a directed network according to claim 1 is characterized in that: The real-time updating of the leader's location information in step S6 is specifically as follows: in, represents the control gain, represents the projection function, Indicates the The gradient of the objective function of each leader.

7. The unmanned system cluster formation control method based on the aggregation game in a directed network according to claim 6 is characterized in that: The distributed formation control rate under the directed network in step S7 is: in, represents the control gain, Indicates the The set of followers of a leader, Indicates followers With followers The weight of the links that make up Indicates followers location, Indicates followers The position when the formation configuration is formed, Indicates followers location, Indicates followers With leaders The weight of the links that make up Indicates followers The position when the formation configuration is formed, Indicates followers control rate.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the unmanned system cluster formation control method based on aggregate game in a directed network as described in any one of claims 1 to 7 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the unmanned system cluster formation control method based on aggregation game in a directed network according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to execute the unmanned system cluster formation control method based on aggregate game in a directed network according to any one of claims 1 to 7 by executing the executable instructions.

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