A formation control method and system based on cross-species bionics

Abstract

The present invention discloses a formation control method and system based on cross-species bionics, which belongs to the field of multi-agent cluster control, including: establishing a heterogeneous multi-agent system model of a control system to be controlled, designing a communication protocol, obtaining an ODE tracking error system model of each sparsely distributed agent corresponding to a predator population model and a PDE tracking error system model of a human disease transmission model shared by densely distributed agents; at any time, calculating a control signal according to the position error of each sparsely distributed agent, the tracking error of the virtual agent itself, and the communication time lag, and substituting it into the boundary of the human disease transmission model; if the tracking errors of each agent converge, the formation control ends; otherwise, continuing to update the control signal and causing the physical object to perform the corresponding action. The present invention can realize the coordinated control of a multi-agent system of any scale and any distribution based on the self-stability and adaptability of the biological model, and improve the reliability of the control strategy.

Description

Technical Field

[0001] The present invention belongs to the field of multi-agent cluster control, and more specifically, relates to a formation control method and system based on cross-species bionics. Background Art

[0002] Swarm intelligence is an important development direction of the new generation of artificial intelligence. Multi-agent collaboration is the main way for unmanned systems to emerge with swarm intelligence. Through mutual collaboration, agents can efficiently complete complex tasks and enhance system robustness. They can show greater adaptability and responsiveness in the face of uncertainty and dynamic environments. At the same time, they also provide a new research perspective for understanding and simulating group behavior in nature.

[0003] In recent years, with the rapid development of communication technology and artificial intelligence technology, the cooperative control of multi-agent systems has attracted widespread attention due to its application prospects in the fields of drone swarms, intelligent transportation, microgrid groups, etc. Formation control refers to the movement of each entity object in the control system from the initial position to the specified target position or to form a specified formation. It is a typical cooperative control problem of multi-agent systems. The increasing complexity of actual needs has led to the inevitable growth of the scale of multi-agent systems. In order to apply multi-agent systems to solve the control problems of actual cluster systems, it is necessary to first establish a multi-agent system model corresponding to the cluster system. In the multi-agent system model, each agent corresponds to an entity object in the cluster system. For example, in the multi-agent system corresponding to the drone swarm, each agent corresponds to a drone; in the multi-agent system corresponding to the intelligent transportation system, each agent corresponds to an unmanned vehicle or a traffic light; in the multi-agent system corresponding to the microgrid group, each agent corresponds to a microgrid. After establishing the corresponding multi-agent system model, the subsequent control can be carried out with the help of relevant theoretical methods of the multi-agent system.

[0004] In an open environment, as the complexity of tasks and system functional requirements increase, a single agent or a small-scale multi-agent system cannot effectively cope with diverse and dynamic challenges, and more agents are needed to work together. Due to its outstanding ability to cope with complex tasks and dynamic environments, large-scale multi-agent systems have become an inevitable trend in the development of multi-agent systems. Compared with small-scale multi-agent systems, large-scale multi-agent systems have significant advantages in robustness and flexibility, which not only broadens the application field of multi-agents, but also improves the overall performance of the system, making it an effective way to solve modern complex problems.

[0005] However, large-scale operation will also bring a series of challenges to multi-agent collaborative control. First, the difficulty of system analysis increases. At present, most of the research on multi-agent collaborative control is directly carried out on the multi-agent ordinary differential equation (ODE) model, and the collaborative goal is also achieved based on the control method of ODE (hereinafter referred to as the ODE method). When the scale of multi-agents increases, the dimension and complexity of the ODE model will increase accordingly, resulting in an exponential increase in the difficulty of system analysis, and it is difficult to achieve precise control of each agent, thereby weakening the control effect of the ODE method. Secondly, the collaborative efficiency decreases. As the number of agents increases, the complexity of communication and calculation in the system also increases accordingly. Each agent needs to process more communication data and participate in more complex coordination and decision-making processes, resulting in network congestion and increased computing load, which affects the overall collaborative efficiency.

[0006] In order to cope with the challenge of increasing difficulty in analyzing multi-agent systems due to large-scale development, some scholars have taken a different approach and proposed a method based on partial differential equations (PDEs) (hereinafter referred to as PDE methods) that is different from the ODE method. Compared with the ODE method, the PDE method based on the discrete system continuation strategy has the advantage of characterizing the dynamic evolution of large-scale multi-agent systems in a compact form, which can reduce the difficulty of theoretical analysis and improve the control effect. Regardless of the increase in the number of agents, the analysis difficulty and control effect remain constant. However, there is a big pain point in the current research on large-scale multi-agent cooperative control based on the PDE method: it must be assumed that all agents are uniformly and densely distributed, otherwise the PDE method will no longer be applicable. However, due to the complex and diverse actual needs and the dynamic and changing operating environment, it is inevitable that the agents will be non-uniformly or non-densely distributed, especially when the number of agents is large.

[0007] In general, the existing ODE and PDE methods are difficult to fully cope with the challenges brought by large-scale multi-agent collaborative control. Therefore, how to effectively realize the collaborative control of multi-agent systems of arbitrary scale and distribution is an urgent problem to be solved. Summary of the invention

[0008] In view of the defects of the prior art and the need for improvement, the present invention provides a formation control method and system based on cross-species bionics, which aims to effectively utilize the collaborative control of multi-agent systems to achieve formation control of unmanned systems of any scale and distribution.

[0009] To achieve the above object, according to one aspect of the present invention, a formation control method based on cross-species bionics is provided, comprising:

[0010] Establish a heterogeneous multi-agent system model corresponding to the control system to be controlled; in the heterogeneous multi-agent system model, each agent corresponds to an entity object in the control system to be controlled;

[0011] Based on the ODE model, the dynamic equations of each agent in the heterogeneous multi-agent system model are established to describe the relationship between the speed of the agent, the communication protocol between agents, and the nonlinear function value of the agent's position; the communication protocol is related to the distribution characteristics of the agent, and in the heterogeneous multi-agent system model, the nonlinear functions corresponding to sparsely distributed agents and densely distributed agents are different;

[0012] Based on the dynamic equation of each agent, the ODE tracking error system model of the actual position of each agent relative to the target position is obtained; the ODE tracking error system model corresponding to each sparsely distributed agent is used as the predator population model corresponding to each sparsely distributed agent, and the predator population model uses the position error of the sparsely distributed agent as the number of predators; the ODE tracking error system model of each densely distributed agent is processed continuously to obtain the PDE tracking error system model shared by all densely distributed agents as the human disease transmission model corresponding to the densely distributed agent, and the human disease transmission model uses the position error of the densely distributed agent as the spatiotemporal distribution state of the infected population;

[0013] In any t At this moment, follow the steps below to control the formation:

[0014] (S1) The position error of each sparsely distributed agent X i ( t ) is transmitted to the virtual agent so that the virtual agent can Calculates its own control signal with respect to the rate of change of space u ( t ); i represents the agent number, and represents the number of sparsely distributed agents, represents the number of densely distributed agents, n and v Represent the number of sparsely distributed agents and densely distributed agents respectively; Represents the boundary of the space where densely distributed agents are located; k 1 and k 2 represents the controller gain; the virtual agent is an additional agent located at Computational agents at Indicates that the virtual agent is t Tracking error at any moment; Indicates communication delay, ,and Rate of change satisfy , represents the maximum communication delay, Indicates the maximum rate of change of communication delay;

[0015] (S2) will control the signal u ( t ) is substituted into the boundary of the human disease transmission model to calculate the tracking error of the densely distributed agent Y ( z , t ); Represents the relative positions of densely distributed agents;

[0016] (S3) Determine whether the tracking errors corresponding to all intelligent agents converge within a finite time. If so, the formation control ends; if not, continue to update the control signal to each intelligent agent so that the tracking errors corresponding to all intelligent agents converge within a finite time and the physical objects perform corresponding actions.

[0017] Furthermore, in the heterogeneous multi-agent system model, the communication protocol between sparsely distributed agents and other agents c i ( t )for:

[0018]

[0019] in, s i ( t ) indicates the i Sparsely distributed agents in t The location at the moment, Indicates i The target location of sparsely distributed agents; g i ( ) indicates the i A nonlinear function of the positions of sparsely distributed agents; Represents the topological weight.

[0020] Furthermore, in the heterogeneous multi-agent system model, the communication protocol between densely distributed agents and other agents for:

[0021]

[0022] in, represents the distribution interval of densely distributed agents; m i ( t ) indicates the iDensely distributed agents in t The location at the moment, Indicates i The target location of densely distributed agents; f i ( ) indicates the i A nonlinear function of the positions of densely distributed agents; and are all topological weights.

[0023] Furthermore, the ODE tracking error system model of sparsely distributed agents corresponds to the predator population model, and the expression is as follows:

[0024]

[0025] in, , , represents the natural mortality rate of predators.

[0026] Furthermore, the PDE tracking error system model shared by densely distributed agents corresponds to the human disease propagation model, which is expressed as follows:

[0027]

[0028] Moreover, in the human disease transmission model, the boundary conditions of the spatiotemporal distribution of the infected population are:

[0029]

[0030]

[0031] in, yes The result of the continuity, and , .

[0032] Furthermore, the nonlinear function of the position of the sparsely distributed agent is g i ( ) and a nonlinear function of the positions of densely distributed agents f i ( )satisfy:

[0033]

[0034]

[0035] a and b is a known constant; x 1. x 2. y 1.y 2 represents any constant;

[0036] And, the controller gain k 1 and k 2 can be determined by:

[0037] Construct the following linear matrix inequality that satisfies finite-time stability:

[0038]

[0039] is a symmetric matrix, where the expressions of each element are:

[0040] , , , , , , , , , , , , , , ;

[0041] If there is a positive scalar , , and there exists a positive definite constant p , q , and any scalar T 1. T 2. T 3. T 4 and T 5, so that the above linear matrix inequality holds true, then k 1 and k The value of 2 is taken as the corresponding controller gain.

[0042] According to another aspect of the present invention, there is provided a formation control system based on cross-species bionics, comprising:

[0043] An initialization module is used to establish a heterogeneous multi-agent system model corresponding to the control system to be controlled; in the heterogeneous multi-agent system model, each agent corresponds to an entity object in the control system to be controlled;

[0044] The ODE model building module is used to build the dynamic equations of each agent in the heterogeneous multi-agent system model based on the ODE model; the dynamic equation is used to describe the relationship between the speed of the agent, the communication protocol between agents, and the nonlinear function value of the agent's position; the communication protocol is related to the distribution characteristics of the agent, and in the heterogeneous multi-agent system model, the nonlinear functions corresponding to sparsely distributed agents and densely distributed agents are different;

[0045] The cross-species ODE-PDE error model building module is used to obtain the ODE tracking error system model of the actual position of each agent relative to the target position based on the dynamic equation of each agent; the ODE tracking error system model corresponding to each sparsely distributed agent is used as the predator population model corresponding to each sparsely distributed agent, and the predator population model uses the position error of the sparsely distributed agent as the number of predators; and the ODE tracking error system model of each densely distributed agent is processed continuously to obtain the PDE tracking error system model shared by all densely distributed agents as the human disease transmission model corresponding to the densely distributed agent, and the human disease transmission model uses the position error of the densely distributed agent as the spatiotemporal distribution state of the infected population;

[0046] The first error tracking module is used to t At this moment, the position error of each sparsely distributed agent is X i ( t ) is transmitted to the virtual agent so that the virtual agent can Calculates its own control signal with respect to the rate of change of space u ( t ); i represents the agent number, and represents the number of sparsely distributed agents, represents the number of densely distributed agents, n and v Represent the number of sparsely distributed agents and densely distributed agents respectively; Represents the boundary of the space where densely distributed agents are located; k 1 and k 2 represents the controller gain; the virtual agent is an additional agent located at Computational agents at Indicates that the virtual agent is t Tracking error at any moment; Indicates communication delay, ,and Rate of change satisfy , represents the maximum communication delay, Indicates the maximum rate of change of communication delay;

[0047] The second error tracking module is used to t At this moment, the control signal u ( t ) is substituted into the boundary of the human disease transmission model to calculate the tracking error of the densely distributed agent Y ( z , t ); Represents the relative positions of densely distributed agents;

[0048] And a control module is used to determine whether the tracking errors corresponding to all intelligent agents converge within a finite time. If so, the formation control ends; if not, the control signal continues to be updated to each intelligent agent so that the tracking errors corresponding to all intelligent agents converge within a finite time and the physical objects perform corresponding actions.

[0049] According to another aspect of the present invention, a computer program product is provided, including a stored computer program; when the computer program is executed by a processor, the above-mentioned formation control method based on cross-species bionics provided by the present invention is implemented.

[0050] According to another aspect of the present invention, a computer-readable storage medium is provided, comprising a stored computer program; when the computer program is executed by a processor, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned formation control method based on cross-species bionics provided by the present invention.

[0051] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0052] A computer-readable storage medium for storing a computer program;

[0053] and a processor, for reading a computer program stored in a computer-readable storage medium, and executing the above-mentioned formation control method based on cross-species bionics provided by the present invention.

[0054] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0055] (1) The present invention provides a new idea for multi-agent collaborative control: cross-species bionic collaborative control. Specifically, the collaborative errors of sparsely distributed agents and densely distributed agents in the heterogeneous multi-agent system model used to describe the control system are modeled as a predator population model and a human disease transmission model, respectively, and cross-species bionics is achieved by applying control at the boundary. Based on this cross-species bionic collaborative control strategy, the present invention can achieve the convergence of the error system based on the self-stabilizing and adaptive intrinsic characteristics of the biological model, that is, to achieve collaborative control of multi-agent systems of arbitrary scale and arbitrary distribution. Moreover, compared with existing multi-agent collaborative control methods, cross-species bionic collaborative control has obvious biological interpretability, which improves the reliability of the control strategy.

[0056] (2) The present invention establishes an ODE error system model for each sparsely distributed agent and a shared PDE error system model for all densely distributed agents, thereby proposing a new ODE-PDE analysis method. On the one hand, compared with the commonly used ODE method, since the number of sparsely distributed agents in a multi-agent system is often small, the present invention can accurately characterize the collective dynamics of a large-scale multi-agent system with one PDE error system model and a few ODE error system models, which has obvious advantages in terms of simplicity of analysis and can also perform more effective control on large-scale systems. On the other hand, compared with the existing PDE method, the present invention can be applied to the situation where the agents are sparsely / densely distributed, so the present invention has a wider applicability.

[0057] (3) Based on Lyapunov stability, the present invention constructs a linear matrix inequality that can achieve the finite-time stability of the error system, and determines the control signal at the boundary by solving the linear matrix inequality. u ( t ) in the controller gain k 1 and k The specific value of 2 can achieve the actual finite-time stability of the error system, better meet the timeliness requirements of the actual system, and further improve the practicability of formation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A logical block diagram of a formation control method based on cross-species bionics provided in an embodiment of the present invention.

[0059] Figure 2 The motion trajectory of an intelligent agent in a three-dimensional space provided by an embodiment of the present invention.

[0060] Figure 3 The embodiment of the present invention provides a position tracking error trajectory of a sparsely distributed agent in a single spatial dimension.

[0061] Figure 4 The embodiment of the present invention provides a position tracking error trajectory of a densely distributed agent in the first spatial dimension.

[0062] Figure 5 The embodiment of the present invention provides a position tracking error trajectory of a densely distributed intelligent agent in a second spatial dimension.

[0063] Figure 6 The embodiment of the present invention provides a position tracking error trajectory of a densely distributed agent in the third spatial dimension.

[0064] Figure 7 The control signal provided by the embodiment of the present invention u ( t )’s state trajectory. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0066] In the present invention, the terms "first", "second", etc. (if any) in the present invention and the drawings are used to distinguish similar objects but not necessarily to describe a specific order or sequence.

[0067] In order to solve the problems that the existing ODE-based control methods are high-dimensional and complex when used for large-scale multi-agent systems, and the stability analysis and controller design are difficult, while the existing PDE-based control methods are only applicable to densely distributed multi-agent systems and have low universality, the present invention provides a formation control method and system based on cross-species bionics. The overall concept is to fully consider the actual situation that the agents in the multi-agent system corresponding to the actual system often conform to the sparse / dense mixed distribution situation, and the number of sparsely distributed agents is small, and an ODE error system model is established for each sparsely distributed agent, and for all densely distributed agents A shared PDE error system model is established, so that the collective dynamics of large-scale multi-agent systems can be accurately characterized by using one PDE error system model and a few ODE error system models, reducing the difficulty of analysis while improving universality. The ODE error system model of each sparsely distributed agent and the PDE error system model shared by densely distributed agents are further mapped to the corresponding biological model. By applying control at the boundary, cross-species bionics is achieved. On the one hand, the collaborative control of the multi-agent system is realized based on the self-stabilizing and adaptive intrinsic characteristics of the biological model. On the other hand, the collaborative control has obvious biological interpretability, which improves the reliability of control.

[0068] The ODE-PDE-based formation control method and system provided by the present invention can be applied to the formation control of any entity system such as drone swarms, intelligent transportation, microgrid groups, etc. Without loss of generality, in the following embodiments, the formation control of drone swarms is taken as an example for explanation. It is easy to understand that in order to realize the formation control of drone swarms, it is necessary to establish a heterogeneous multi-agent system model corresponding to the actual drone swarm to be controlled, in which each agent corresponds to a drone entity object, and the number of sparsely distributed agents and the number of densely distributed agents are respectively recorded as n and v .

[0069] by i represents the agent number, then the initial positions of the sparsely distributed agents are expressed as s i (0), the target positions are expressed as , ; The initial positions of densely distributed agents are expressed as m i (0), the target positions are expressed as , , Represents the distribution interval of densely distributed agents.

[0070] Based on the above information, the dynamic equations of each agent in each spatial dimension in the heterogeneous multi-agent system model can be established based on the ODE model.

[0071] For sparsely distributed agents, the initial dynamics equation is as follows:

[0072]

[0073] in, ; s i ( t )express t Moment i The locations of sparsely distributed agents, express s i ( t )’s rate of change, i.e., velocity information; c i ( t ) indicates the i The communication protocol between a sparsely distributed agent and other agents; g i ( ) indicates the i A nonlinear function of the positions of sparsely distributed agents; in the above notation, the superscript represents the spatial dimension, .

[0074] For densely distributed agents, the initial dynamics equation is as follows:

[0075]

[0076] in, ; m i ( t )express t Moment i The locations of densely distributed agents, express m i ( t )’s rate of change, i.e., velocity information; Indicates i A communication protocol between densely distributed agents and other agents; f i ( ) indicates the i is a nonlinear function of the positions of densely distributed agents; similarly, in the above notation, the superscript represents the spatial dimension, .

[0077] Since the dynamics in each dimension are expressed in the same way, we can only study the dynamics in one dimension, and then obtain the dynamic equation of the entire intelligent agent as follows:

[0078]

[0079] The above dynamic equations based on the ODE model describe the relationship between the speed of the agent, the communication protocol between the agents, and the nonlinear function value of the agent's position.

[0080] The following is Example 1.

[0081] A formation control method based on cross-species bionics, such as Figure 1 Said, including:

[0082] A heterogeneous multi-agent system model corresponding to the control system to be controlled is established; in the heterogeneous multi-agent system model, each agent corresponds to an entity object in the control system to be controlled; in this embodiment, the control system to be controlled is an unmanned aerial vehicle system, and accordingly, each entity object is a unmanned aerial vehicle;

[0083] Based on the ODE model, the dynamic equations of each agent in the heterogeneous multi-agent system model are established to describe the relationship between the speed of the agent, the communication protocol between agents, and the nonlinear function value of the agent's position; the communication protocol is related to the distribution characteristics of the agent, and in the heterogeneous multi-agent system model, the nonlinear functions corresponding to sparsely distributed agents and densely distributed agents are different;

[0084] Based on the dynamic equation of each agent, the ODE tracking error system model of the actual position of each agent relative to the target position is obtained; the ODE tracking error system model corresponding to each sparsely distributed agent is used as the predator population model corresponding to each sparsely distributed agent, and the predator population model uses the position error of the sparsely distributed agent as the number of predators; the ODE tracking error system model of each densely distributed agent is processed continuously to obtain the PDE tracking error system model shared by all densely distributed agents as the human disease transmission model corresponding to the densely distributed agent, and the human disease transmission model uses the position error of the densely distributed agent as the spatiotemporal distribution state of the infected population;

[0085] In any t At this moment, follow the steps below to control the formation:

[0086] (S1) The position error of each sparsely distributed agent X i ( t ) is transmitted to the virtual agent so that the virtual agent can Calculates its own control signal with respect to the rate of change of space u (t ); i represents the agent number, and represents the number of sparsely distributed agents, represents the number of densely distributed agents, n and v Represent the number of sparsely distributed agents and densely distributed agents respectively; Represents the boundary of the space where densely distributed agents are located; k 1 and k 2 represents the controller gain; the virtual agent is an additional agent located at Computational agents at Indicates that the virtual agent is t Tracking error at any moment; Indicates communication delay, ,and Rate of change satisfy , represents the maximum communication delay, Indicates the maximum rate of change of communication delay;

[0087] (S2) will control the signal u ( t ) is substituted into the boundary of the human disease transmission model to calculate the tracking error of the densely distributed agent Y ( z , t ); Represents the relative positions of densely distributed agents;

[0088] (S3) Determine whether the tracking errors corresponding to all intelligent agents converge within a finite time. If so, the formation control ends; if not, continue to update the control signal to each intelligent agent so that the tracking errors corresponding to all intelligent agents converge within a finite time and the physical objects perform corresponding actions.

[0089] In this embodiment, the control signal calculated by the virtual agent introduced at the boundary of the space where the densely distributed agents are located is u ( t ) is used to control its rate of change in space. The calculation of this control signal comprehensively considers the tracking error and communication delay of each sparsely distributed agent. , and the virtual agent's own tracking error By introducing a virtual intelligent agent, this embodiment can exert control at the boundary, realize cross-species bionics, facilitate calculation, facilitate actual operation, and be more in line with actual control situations.

[0090] In order to facilitate the realization of cross-species bionics, in the heterogeneous multi-agent system model established in this embodiment, the communication protocol between the sparsely distributed agents and other agents c i ( t ) Specifically:

[0091]

[0092] in, s i ( t ) indicates the i Sparsely distributed agents in t The location at the moment, Indicates i The target location of sparsely distributed agents; g i ( ) indicates the i A nonlinear function of the positions of sparsely distributed agents; represents the topological weight;

[0093] Communication protocol between densely distributed agents and other agents for:

[0094]

[0095] in, represents the distribution interval of densely distributed agents; m i ( t ) indicates the i Densely distributed agents in t The location at the moment, Indicates i The target location of densely distributed agents; f i ( ) indicates the i A nonlinear function of the positions of densely distributed agents; and are all topological weights.

[0096] The communication protocol designed for densely distributed agents in this embodiment fully considers the impact of the tracking errors of adjacent densely distributed agents on the tracking error of the current densely distributed agent, which is consistent with the communication situation between physical objects in an actual system.

[0097] Based on the designed communication protocol, the sparse distribution agent tracking error is defined as , then the ODE tracking error system model corresponding to the sparsely distributed agent is:

[0098]

[0099] in, , express X i ( t ), ;

[0100] From a biological point of view, X i ( t ) represents the number of predators, that is, the number of species such as wolves, tigers, lions, etc. represents the natural mortality rate of predators, represents a nonlinear function determined by the number of food and predators.

[0101] Based on the designed communication protocol, the tracking error of densely distributed agents is defined as , then the ODE tracking error system model corresponding to the densely distributed intelligent agent is:

[0102]

[0103] in, , express Y i ( t ), ;

[0104] After the ODE tracking error system model corresponding to all densely distributed agents is processed continuously, the PDE tracking error system model corresponding to all densely distributed agents is obtained, which is used as a human disease propagation model for describing the tracking error of densely distributed agents. The expression of this model is:

[0105]

[0106] Y ( z , t )express t Time and location are z Tracking error of densely distributed agents; yes From a biological point of view, Y ( z , t ) corresponds to the spatiotemporal distribution of infected people in the human disease transmission model; Represents a nonlinear function determined by the number of susceptible people and the number of infected people.

[0107] This embodiment applies control at the boundary to achieve cross-species biomimetic. Specifically, in the human disease transmission model, the boundary conditions of the spatiotemporal distribution state of the infected population are:

[0108]

[0109]

[0110] In the above boundary conditions, the boundary Boundary conditions at and control signals of virtual agents u ( t ) calculation is consistent.

[0111] This embodiment models the collaborative error of heterogeneous agents as a predator population model and a human disease transmission model, and realizes cross-species bionics by applying control at the boundary, and realizes the convergence of the error system based on the self-stabilizing and adaptive intrinsic characteristics of the biological model, that is, the collaboration of the multi-agent system is realized. Compared with the existing multi-agent collaborative control methods, cross-species bionic collaborative control has obvious biological interpretability and improves the reliability of the control strategy. The biological model selected in this embodiment, on the one hand, matches the corresponding tracking error system model, and on the other hand, is relatively simple, which effectively reduces the complexity of collaborative control while ensuring accurate characterization of the tracking error of the agent.

[0112] Furthermore, in order to achieve finite-time formation control, this embodiment limits the rate of change of the nonlinear function according to the Lipschitz condition to adapt to the nonlinear conditions of the system. Specifically, the nonlinear function of the position of the sparsely distributed agent g i ( ) and a nonlinear function of the positions of densely distributed agents f i ( )satisfy:

[0113]

[0114]

[0115] a and b is a known constant; x 1. x 2. y 1. y 2 represents any constant;

[0116] And, the controller gain k 1 and k 2 is determined by constructing the following linear matrix inequality that satisfies finite-time stability based on Lyapunov stability:

[0117]

[0118] is a symmetric matrix, where the expressions of each element are: , , , , , , , , , , , , , , ;

[0119] If there is a positive scalar , , and there exists a positive definite constant p , q , and any scalar T 1. T 2. T 3. T 4 and T 5, so that the above linear matrix inequality holds true, then k 1 and k The value of 2 is taken as the corresponding controller gain.

[0120] In this embodiment, the controller gain is determined by the above method. k 1 and k 2. It can realize the actual finite time stability of the error system, better meet the timeliness requirements of the actual system, and further improve the practicality of formation control.

[0121] The following is Example 2.

[0122] A formation control system based on cross-species bionics, comprising:

[0123] An initialization module is used to establish a heterogeneous multi-agent system model corresponding to the control system to be controlled; in the heterogeneous multi-agent system model, each agent corresponds to an entity object in the control system to be controlled;

[0124] The ODE model building module is used to build the dynamic equations of each agent in the heterogeneous multi-agent system model based on the ODE model; the dynamic equation is used to describe the relationship between the speed of the agent, the communication protocol between agents, and the nonlinear function value of the agent's position; the communication protocol is related to the distribution characteristics of the agent, and in the heterogeneous multi-agent system model, the nonlinear functions corresponding to sparsely distributed agents and densely distributed agents are different;

[0125] The cross-species ODE-PDE error model building module is used to obtain the ODE tracking error system model of the actual position of each agent relative to the target position based on the dynamic equation of each agent; the ODE tracking error system model corresponding to each sparsely distributed agent is used as the predator population model corresponding to each sparsely distributed agent, and the predator population model uses the position error of the sparsely distributed agent as the number of predators; and the ODE tracking error system model of each densely distributed agent is processed continuously to obtain the PDE tracking error system model shared by all densely distributed agents as the human disease transmission model corresponding to the densely distributed agent, and the human disease transmission model uses the position error of the densely distributed agent as the spatiotemporal distribution state of the infected population;

[0126] The first error tracking module is used to t At this moment, the position error of each sparsely distributed agent is X i ( t ) is transmitted to the virtual agent so that the virtual agent can Calculates its own control signal with respect to the rate of change of space u ( t ); i represents the agent number, and represents the number of sparsely distributed agents, represents the number of densely distributed agents, n and v Represent the number of sparsely distributed agents and densely distributed agents respectively; Represents the boundary of the space where densely distributed agents are located; k 1 and k 2 represents the controller gain; the virtual agent is an additional agent located at Computational agents at Indicates that the virtual agent is t Tracking error at any moment; Indicates communication delay, ,and Rate of change satisfy , represents the maximum communication delay, Indicates the maximum rate of change of communication delay;

[0127] The second error tracking module is used to t At this moment, the control signal u ( t ) is substituted into the boundary of the human disease transmission model to calculate the tracking error of the densely distributed agent Y ( z , t ); Represents the relative positions of densely distributed agents;

[0128] And a control module is used to determine whether the tracking errors corresponding to all intelligent agents converge within a finite time. If so, the formation control ends; if not, the control signal continues to be updated to each intelligent agent so that the tracking errors corresponding to all intelligent agents converge within a finite time and the physical objects perform corresponding actions.

[0129] In this embodiment, the specific implementation of each module can refer to the description in the above embodiment 1 and will not be repeated here.

[0130] The following is Example 3.

[0131] A computer program product includes a stored computer program; when the computer program is executed by a processor, the above-mentioned embodiment 1 provides a formation control method based on cross-species bionics.

[0132] The following is Example 4.

[0133] A computer-readable storage medium includes a stored computer program; when the computer program is executed by a processor, the device where the computer-readable storage medium is located is controlled to execute the cross-species bionics-based formation control method provided in the above-mentioned embodiment 1.

[0134] The following is Example 5.

[0135] An electronic device, comprising:

[0136] A computer-readable storage medium for storing a computer program;

[0137] and a processor for reading a computer program stored in a computer-readable storage medium, the formation control method based on cross-species bionics provided in the above-mentioned embodiment 1.

[0138] The following is an example of cooperative control of a drone cluster in three-dimensional space to further explain the beneficial effects that can be achieved by the present invention. The drone cluster includes 44 drones, and a global XYZ rectangular coordinate system is established in the space where the 44 drones are active. Each drone is regarded as a mass point, and its kinematic model is established according to the system model considered by the present invention. The five drones are sparsely distributed, and the other 39 agents are densely distributed.

[0139] First, for sparsely distributed agents, the nonlinear function on each spatial dimension is set to g i ( s i ( t ), t )=0.2cos( si ( t ))+0.1sin( t ), for densely distributed agents, the nonlinear function on each spatial dimension is set to f i ( m i ( t ), t )=0.5sin( m i ( z , t ))+0.1cos( t ). The topological weights in the designed communication protocol are set as , , , .

[0140] In addition, for the obtained linear matrix inequality, set , , then, using the LMI toolbox in MATLAB, a set of feasible controller gains can be obtained as k 1=-7.5254, k 2=-0.1275, accordingly, the other unknown parameters in LMI are calculated as follows: p =0.0987, q =0.5087, T 1=0, T 2 = -1.3981 × 10 -5 , T 3=0.3417, T 4=0.4096, T 5=0.01.

[0141] In addition, the target positions of the sparsely distributed agents in each spatial dimension are set as:

[0142]

[0143]

[0144]

[0145] The target positions of densely distributed agents in each spatial dimension are set as:

[0146]

[0147]

[0148]

[0149] The initial positions of the sparsely distributed agents in each spatial dimension are set as:

[0150]

[0151]

[0152]

[0153] The initial positions of densely distributed agents in each spatial dimension are set as:

[0154]

[0155]

[0156]

[0157] Under the above parameters and initial conditions, the running trajectory of multiple agents in 3D space can be obtained through MATLAB simulation, as shown in Figure 2 As shown in the figure, the connecting line between the initial position and the target position of each agent represents the running trajectory of the agent. In addition, in order to better demonstrate the control effect, Figure 3 The state trajectory of the sparsely distributed agent tracking error in a single spatial dimension is given. Figure 4 , Figure 5 and Figure 6 The state trajectories of the tracking errors of densely distributed agents in each spatial dimension are given respectively. Figure 7 The designed control signal is given u ( t )’s state trajectory.

[0158] according to Figure 3~Figure 7 It can be seen from the state trajectory shown that the communication protocol and control strategy designed in the present invention are effective and can realize the limited-time formation task of large-scale heterogeneous multi-agent systems under the condition of communication delay.

[0159] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A formation control method based on cross-species bionics, characterized in that: include: Establishing a heterogeneous multi-agent system model corresponding to the control system to be controlled; in the heterogeneous multi-agent system model, each agent corresponds to an entity object in the control system to be controlled; Based on the ODE model, a dynamic equation of each agent in the heterogeneous multi-agent system model is established to describe the relationship between the speed of the agent, the communication protocol between the agents, and the nonlinear function value of the agent position; the communication protocol is related to the distribution characteristics of the agent, and in the heterogeneous multi-agent system model, the nonlinear function corresponding to the sparsely distributed agents and the densely distributed agents is different; Based on the dynamic equations of each agent, the ODE tracking error system model of the actual position of each agent relative to the target position is obtained; The ODE tracking error system model corresponding to each sparsely distributed intelligent agent is used as the predator population model corresponding to each sparsely distributed intelligent agent, and the predator population model uses the position error of the sparsely distributed intelligent agent as the number of predators; The ODE tracking error system model of each densely distributed agent is processed continuously to obtain the PDE tracking error system model shared by all densely distributed agents as the human disease propagation model corresponding to the densely distributed agents, and the human disease propagation model uses the position error of the densely distributed agents as the spatiotemporal distribution state of the infected population; At any time t, the formation control is performed according to the following steps: (S1) The position error X of each sparsely distributed agent i (t) is transmitted to the virtual agent so that the virtual agent can Calculate its own control signal u(t) of the rate of change of space; i represents the number of the agent, and i∈{1,2,…,n} represents the number of the sparsely distributed agent, i∈{n+1,n+2,…,n+v} represents the number of the densely distributed agent, n and v represent the number of sparsely distributed agents and densely distributed agents respectively; l represents the boundary of the space where the densely distributed agent is located; k1 and k2 represent controller gains; the virtual agent is an additional computing agent located at l; Y(l,t) represents the tracking error of the virtual agent at time t; σ(t) represents the communication delay, And the rate of change of σ(t) satisfy represents the maximum communication delay, Indicates the maximum rate of change of communication delay; (S2) Substituting the control signal u(t) into the boundary of the human disease transmission model to calculate the tracking error Y(z,t) of the densely distributed intelligent agent; z∈(0,l) represents the relative position of the densely distributed intelligent agent; (S3) Determine whether the tracking errors corresponding to all intelligent agents converge within a finite time. If so, the formation control ends; if not, continue to update the control signal to each intelligent agent so that the tracking errors corresponding to all intelligent agents converge within a finite time and the entity objects perform corresponding actions.

2. The formation control method based on cross-species bionics as claimed in claim 1, characterized in that: In the heterogeneous multi-agent system model, the communication protocol c between sparsely distributed agents and other agents i (t) is: c i (t)=-β[s i (t)-C i ]-g i (C i ,t) Among them, s i (t) represents the position of the i-th sparsely distributed agent at time t, Γ i represents the target position of the i-th sparsely distributed agent; g i () represents the nonlinear function of the position of the i-th sparsely distributed agent; β represents the topological weight.

3. The formation control method based on cross-species bionics as claimed in claim 2, characterized in that: In the heterogeneous multi-agent system model, the communication protocol between densely distributed agents and other agents for: Where h = l / v represents the distribution interval of densely distributed agents; m i (t) represents the position of the ith densely distributed agent at time t, Ξ(ih) represents the target position of the ith densely distributed agent; f i () represents the nonlinear function of the position of the i-th densely distributed agent; α and μ are both topological weights.

4. The formation control method based on cross-species bionics as claimed in claim 3, characterized in that: The ODE tracking error system model of sparsely distributed agents corresponds to the predator population model and is expressed as follows: in, β' represents the natural mortality rate of predators.

5. The formation control method based on cross-species bionics as claimed in claim 4, characterized in that: The PDE tracking error system model shared by densely distributed agents corresponds to the human disease transmission model and is expressed as follows: Furthermore, in the human disease transmission model, the boundary conditions of the spatiotemporal distribution of the infected population are: in, yes The result of the continuous i (t) represents the tracking error of densely distributed agents, and 6. The formation control method based on cross-species bionics according to any one of claims 1 to 5, characterized in that: Nonlinear function g of the positions of sparsely distributed agents i () and the nonlinear function f of the positions of densely distributed agents i ()satisfy: a and b are known constants; x1, x2, y1, y2 are all arbitrary constants; Furthermore, the controller gains k1 and k2 are determined by: Based on Lyapunov stability, the following linear matrix inequality that satisfies finite-time stability is constructed: is a symmetric matrix, where the expressions of each element are: If there is a positive scalar And there exist positive definite constants p, q, and arbitrary scalars T1, T2, T3, T4 and T5, so that the above linear matrix inequality holds, then the values ​​of k1 and k2 are taken as the corresponding controller gains.

7. A formation control system based on cross-species bionics, characterized in that: include: An initialization module is used to establish a heterogeneous multi-agent system model corresponding to the control system to be controlled; in the heterogeneous multi-agent system model, each agent corresponds to an entity object in the control system to be controlled; An ODE model building module is used to build the dynamic equations of each agent in the heterogeneous multi-agent system model based on the ODE model; the dynamic equation is used to describe the relationship between the speed of the agent, the communication protocol between the agents, and the nonlinear function value of the agent position; the communication protocol is related to the distribution characteristics of the agent, and in the heterogeneous multi-agent system model, the nonlinear functions corresponding to the sparsely distributed agents and the densely distributed agents are different; A cross-species ODE-PDE error model building module is used to obtain the ODE tracking error system model of each agent's actual position relative to the target position based on the dynamic equations of each agent; The ODE tracking error system model corresponding to each sparsely distributed agent is used as the predator population model corresponding to each sparsely distributed agent, and the predator population model uses the position error of the sparsely distributed agent as the number of predators; and the ODE tracking error system model of each densely distributed agent is processed continuously to obtain the PDE tracking error system model shared by all densely distributed agents as the human disease transmission model corresponding to the densely distributed agent, and the human disease transmission model uses the position error of the densely distributed agent as the spatiotemporal distribution state of the infected population; The first error tracking module is used to track the position error X of each sparsely distributed agent at time t. i (t) is transmitted to the virtual agent so that the virtual agent can Calculate its own control signal u(t) of the rate of change of space; i represents the number of the agent, and i∈{1,2,…,n} represents the number of the sparsely distributed agent, i∈{n+1,n+2,…,n+v} represents the number of the densely distributed agent, n and v represent the number of sparsely distributed agents and densely distributed agents respectively; l represents the boundary of the space where the densely distributed agent is located; k1 and k2 represent controller gains; the virtual agent is an additional computing agent located at l; Y(l,t) represents the tracking error of the virtual agent at time t; σ(t) represents the communication delay, And the rate of change of σ(t) satisfy represents the maximum communication delay, Indicates the maximum rate of change of communication delay; A second error tracking module is used to substitute the control signal u(t) into the boundary of the human disease transmission model at time t to calculate the tracking error Y(z,t) of the densely distributed intelligent agent; z∈(0,l) represents the relative position of the densely distributed intelligent agent; And a control module is used to determine whether the tracking errors corresponding to all intelligent agents converge within a finite time. If so, the formation control ends; if not, the control signal continues to be updated to each intelligent agent so that the tracking errors corresponding to all intelligent agents converge within a finite time and the physical objects perform corresponding actions.

8. A computer program product, characterized in that It comprises a stored computer program; when the computer program is executed by a processor, it implements the formation control method based on cross-species bionics as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: It comprises a stored computer program; when the computer program is executed by a processor, it controls the device where the computer-readable storage medium is located to execute the formation control method based on cross-species bionics as described in any one of claims 1 to 6.

10. An electronic device, characterized in that: include: A computer-readable storage medium for storing a computer program; and a processor, configured to read the computer program stored in the computer-readable storage medium and execute the cross-species bionics-based formation control method according to any one of claims 1 to 6.

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