Method for controlling bipartite consistency of multi-agent system based on virtual leader
By using a virtual leader and a fixed-time constraint feedback control protocol, the problems of communication interference and resource consumption in multi-agent systems are solved, achieving efficient binary consistency and robustness while saving communication resources.
Patent Information
- Application Number
- CN202511051727.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-29
- Publication Date
- 2025-11-18
AI Technical Summary
Existing leader-follower multi-agent systems are susceptible to interference during communication, have complex interaction relationships, consume huge communication resources, and are difficult to guarantee system convergence speed and communication robustness.
A virtual leader and a fixed-time quid probing feedback control protocol are adopted, and a first-class follower and a second-class follower are set up. The fixed-time quid probing feedback control protocol and binary consistency error are constructed, and the upper bound of the convergence time is determined. Communication resources are saved through quid probing control.
Achieve binary consensus in multi-agent systems within a fixed time, improve convergence speed and communication robustness, and save communication resources.
Smart Images
Figure CN120973058A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of artificial intelligence technology, specifically to a control method for binary consensus of a multi-agent system based on a virtual leader, a control device for binary consensus of a multi-agent system based on a virtual leader, an electronic device, and a computer-readable storage medium. Background Technology
[0002] The binary consistency problem in leader-follower multi-agent systems focuses on how, within a hierarchical architecture, agents within the system can form two camps with opposite or complementary states and achieve internal consistency within each camp. Research on this problem involves several key dimensions, including system stability, information exchange efficiency, and control strategy design.
[0003] From a system architecture perspective, in the leader-follower model, the leader plays a decision-making and guiding role, with its state serving as a reference signal. Followers, on the other hand, need to adjust their own states based on information from the leader and local interactions. For example, in multi-robot collaborative operations, one group of robots is responsible for environmental exploration, while another group is responsible for material transportation. Robots in the exploration group need to maintain consistent actions to efficiently cover the area, while the transportation group needs to adjust its routes based on feedback from the exploration group. The two groups form a dialectical relationship in terms of goals and actions. At the information interaction level, the communication topology between agents affects the efficiency and accuracy of information transmission. Communication delays, packet loss, or interference can prevent binary consensus from being achieved.
[0004] However, leader-follower multi-agent systems suffer from problems in practical applications, including susceptibility to communication interference, complex inter-agent interactions, and enormous communication resource consumption. Therefore, designing a control algorithm that ensures both system convergence speed and communication robustness while conserving communication resources is crucial for solving these problems. Summary of the Invention
[0005] In view of this, it is necessary to provide a control method, a control device, an electronic device, and a computer-readable storage medium for a multi-agent system based on virtual leader-based binary consensus, so as to achieve the technical effect of ensuring both system convergence speed and communication robustness while saving communication resources.
[0006] To address the aforementioned technical problems, in a first aspect, this application provides a control method for binary consensus in a multi-agent system based on a virtual leader, applicable to controlling a multi-agent system comprising several agents. The control method for binary consensus in a multi-agent system based on a virtual leader includes: A virtual leader is set up, and the plurality of intelligent agents include a type I follower and a type II follower with different controllers; Construct a fixed-time constraint feedback control protocol for each of the aforementioned intelligent agents; The binary consistency error of each of the intelligent agents is constructed according to the fixed-time constraint feedback control protocol; Based on the binary consistency error, the upper bound of the convergence time for the multi-agent system to achieve binary consistency within a fixed time period based on the fixed-time constraint feedback control protocol is determined.
[0007] In one optional embodiment, the construction of the fixed-time restraint feedback control protocol for each of the intelligent agents includes: Obtain the feedback control gain of each of the aforementioned intelligent agents; The fixed-time tethered feedback control protocol is constructed based on the feedback control gain.
[0008] In one optional embodiment, constructing the fixed-time tethered feedback control protocol based on the feedback control gain includes: Based on formula
[0009] Construct the fixed-time restraint feedback control protocol; in, Let be the label of the intelligent agent. For the aforementioned type of follower, For the two types of followers, For intelligent agents The corresponding fixed-time restraint feedback control protocol. For intelligent agents and intelligent agents Relationship parameters, if the agent and intelligent agents If a cooperative relationship exists, then... ,otherwise If the intelligent agent and intelligent agents If there is no interaction between them, then , , It is a symbolic function. , , , The feedback control gain is... When the intelligent agent and the virtual leader belong to the same group ,otherwise .
[0010] In an optional embodiment, the step of constructing the binary consistency error of each agent according to the fixed-time tethered feedback control protocol includes: Obtain the agent dynamics system description of the agent; Construct a dynamic system description of the tethered feedback control system for each of the agents based on the fixed-time tethered feedback control protocol and the agent dynamic system description; The binary consistency error of each agent is constructed based on the description of the restraint feedback control dynamics system.
[0011] In an optional embodiment, the step of constructing the restraint feedback control dynamics system description for each of the agents based on the fixed-time restraint feedback control protocol and the agent dynamics system description includes: Based on formula
[0012] Construct a description of the restraint feedback control dynamics system of the aforementioned type of follower; Based on formula
[0013] Construct a description of the restraint feedback control dynamics system for the two types of followers; in, Let be the label of the intelligent agent. For intelligent agents The state function, These are the state parameters of the virtual leader. For intelligent agents and intelligent agents Relationship parameters, if the agent and intelligent agents If a cooperative relationship exists, then... ,otherwise If the intelligent agent and intelligent agents If there is no interaction between them, then , , For symbolic functions, , , , The feedback control gain is... When the intelligent agent and the virtual leader belong to the same group ,otherwise .
[0014] In an optional embodiment, the step of constructing the binary consistency error of each agent based on the description of the restraint feedback control dynamics system includes: Based on formula
[0015] Construct the binary consistency error for each of the aforementioned intelligent agents; in, Let be the label of the intelligent agent. For intelligent agents The state function, These are the state parameters of the virtual leader. For intelligent agents and intelligent agents Relationship parameters, if the agent and intelligent agents If a cooperative relationship exists, then... ,otherwise If the intelligent agent and intelligent agents If there is no interaction between them, then , , For symbolic functions, , , , The feedback control gain is... When the intelligent agent and the virtual leader belong to the same group ,otherwise .
[0016] In an optional embodiment, determining the upper bound of the convergence time for the multi-agent system to achieve binary consensus within a fixed time based on the binary consensus error includes: Based on formula Determine the upper bound of the convergence time; in, This is the upper bound of the convergence time. , , yes The smallest non-zero eigenvalue, It is an undirected symbolic graph subgraph The corresponding Laplace matrix, topology and Same, but The weights of information exchange between intelligent agents in the system are . , , , yes The smallest non-zero eigenvalue, It is an undirected symbolic graph subgraph The corresponding Laplace matrix, topology and Same, but The weights of information exchange between intelligent agents in the system are . , Let be the label of the intelligent agent. The label for the aforementioned type of follower. The labels for the two types of followers, .
[0017] Secondly, this application also provides a control device for binary consensus in a multi-agent system based on a virtual leader, applied to control a multi-agent system comprising several agents. The control device for binary consensus in a multi-agent system based on a virtual leader includes: A virtual leader setting module is used to set a virtual leader. The plurality of intelligent agents include a first type of followers whose state is consistent with the virtual leader and a second type of followers whose state is opposite to the virtual leader. A control protocol construction module is used to construct a fixed-time restraint feedback control protocol for each of the intelligent agents. An error construction module is provided, which is used to construct the binary consistency error of each of the intelligent agents according to the fixed-time constraint feedback control protocol. The convergence time determination module is used to determine the upper bound of the convergence time for the multi-agent system to achieve binary consensus within a fixed time based on the binary consensus error.
[0018] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the control method for binary consensus of a multi-agent system based on a virtual leader as described in any of the above implementations.
[0019] Fourthly, this application also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps in the control method for binary consensus of a multi-agent system based on a virtual leader as described in any of the above implementations.
[0020] The beneficial effects of this application are as follows: The control method for binary consensus in a multi-agent system based on a virtual leader provided in this application first sets up a virtual leader for a multi-agent system comprising several agents, and makes some agents in the multi-agent system subject to the constraints of the virtual leader, i.e., first-class followers, while other agents are not subject to the constraints of the virtual leader, i.e., second-class followers. Then, based on the leader corresponding to each agent, a fixed-time constraint feedback control protocol is constructed for each agent. Finally, the binary consensus error of each agent is constructed according to the fixed-time constraint feedback control protocol, and the upper bound of the convergence time for the multi-agent system to achieve binary consensus within a fixed time is determined based on the binary consensus error. That is, by combining the fixed-time algorithm and constraint control, the binary consensus problem of a multi-agent system is solved. Since the fixed-time algorithm can achieve convergence within a stable time and the communication robustness between agents in the converged multi-agent system is high, the convergence speed and communication robustness of the binary consensus of the multi-agent system are improved. The constraint control algorithm can effectively save communication resources by constraining a portion of the nodes. Therefore, by combining a fixed-time algorithm and a restraint control in this application, we can achieve the technical effect of ensuring both system convergence speed and communication robustness while saving communication resources. Attached Figure Description To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0021] Figure 1 This is a flowchart illustrating a control method for a multi-agent system based on a virtual leader with binary consensus provided in one embodiment of this application. Figure 2 for Figure 1 A flowchart illustrating one embodiment of S103; Figure 3 This is a schematic diagram of the structure of a control device for a multi-agent system based on a virtual leader with binary consensus provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation
[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0023] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.
[0024] The terms "first," "second," etc., used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.
[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0026] This application provides a control method for binary consensus in a multi-agent system based on a virtual leader, a control device for binary consensus in a multi-agent system based on a virtual leader, an electronic device, and a computer-readable storage medium, which are described below.
[0027] The following lemmas and assumptions are followed during the computation of this application. These include: Lemma 1: For any real number ,have ; ; Assumption 1: The topology of a multi-agent system is connected and structurally balanced.
[0028] Assumption 2: Nonlinear function The Lipschitz condition must be satisfied, i.e., the Lipschitz constant must exist. , making the function satisfy ; in , When the agent and the leader belong to the same group ,otherwise .
[0029] Please refer to Figure 1 The binary consensus control method for multi-agent systems based on a virtual leader provided in this application is used to control a multi-agent system with several agents. For a multi-agent system, a symbolic diagram can be used to describe the communication relationships between the agents, i.e., using... Symbolic diagram composed of nodes To describe a containing The information interaction methods among agents in a multi-agent system with one agent. Among them, the set... It is a set of nodes composed of each intelligent agent. Representing the An intelligent agent. .gather Denotes an edge set if and only if the node and nodes When they are able to communicate with each other, there are Symbol diagram Corresponding adjacency matrix , represent The weights on the agent. If the agent... and intelligent agents If a cooperative relationship exists, then... In this embodiment, it can be taken ,otherwise In this embodiment, it can be taken If the intelligent agent and intelligent agents There is no interaction between them, meaning they cannot communicate with each other. ,but Furthermore, if the diagram There are no self-loops in the array, meaning there are no edges pointing from a node to itself. .
[0030] The control method for binary consensus in a multi-agent system based on a virtual leader provided in this embodiment may specifically include the following steps: Step S101: Set up a virtual leader. The intelligent agent includes a type I follower and a type II follower with different controllers.
[0031] In this step, for N A multi-agent system consisting of several agents is used as the basis for constructing a virtual leader independent of the multi-agent system. After constructing the virtual leader, the multi-agent system...N In each intelligent agent l One group of agents is designated as a type I follower, and another group of agents is designated as a type II follower. The controllers for the type I and type II followers are different. For ease of illustration, this embodiment will use... N The first among the intelligent agents l Each agent is set as a follower, and then... N - l Each agent is configured as a type II follower.
[0032] Step S102: Construct a fixed-time constraint feedback control protocol for each agent.
[0033] In this step, the feedback control gain of each agent can be obtained based on its type, and then a fixed-time restraint feedback control protocol can be constructed based on the feedback control gain. The formula is expressed as: ; in, The label for the intelligent agent. For intelligent agents The corresponding fixed-time restraint feedback control protocol. For intelligent agents and intelligent agents Relationship parameters, if the agent and intelligent agents If a cooperative relationship exists, then... ,otherwise If the intelligent agent and intelligent agents If there is no interaction between them, then , , It is a symbolic function. , , , For feedback control gain, That is, for type I followers, the feedback control gain is a constant greater than 0, and for type II followers, the feedback control gain is 0. As a label for a type of follower, This is the label for the second type of follower.
[0034] Step S103: Construct the binary consistency error of each agent according to the fixed-time constraint feedback control protocol.
[0035] Please refer to Figure 2 In this step, the construction of the binary consistency error for each agent based on the fixed-time constraint feedback control protocol can specifically include: Step S201: Obtain the agent dynamics system description of the agent.
[0036] In this step, the description of the intelligent agent dynamics system can specifically be as follows: , For the first The state parameters of each agent For the first Control input for an intelligent agent It is a smooth nonlinear function.
[0037] Step S202: Construct the constraint feedback control dynamics system description for each agent based on the fixed-time constraint feedback control protocol and the agent dynamics system description.
[0038] In this step, by substituting the fixed-time restraint feedback control protocol of each agent into the agent dynamics system description, the restraint feedback control dynamics system description of each agent can be obtained, and the specific formula is expressed as follows: Based on formula
[0039] Construct a description of a follower-based constraint feedback control dynamic system; Based on formula
[0040] Construct a description of the dynamic system of the restraint feedback control of two types of followers.
[0041] Step S203: Construct the binary consistency error of each agent based on the description of the restraint feedback control dynamics system.
[0042] In this step, we first define the general binary consistency error of the agent. ; Substituting the description of the constraint feedback control dynamics system of each agent into the general binary consensus error, we can obtain the binary consensus error formula: .
[0043] Step S104: Based on the binary consensus error, enable the multi-agent system to achieve binary consensus within a fixed time based on the fixed-time constraint feedback control protocol, and calculate the upper bound of the convergence time.
[0044] In this step, the specific steps can be based on the formula. Determine the upper bound of the convergence time.
[0045] Among them, among them, To converge the upper bound of time, , , yes The smallest non-zero eigenvalue, It is an undirected symbolic graph subgraph The corresponding Laplace matrix, topology and Same, but The weights of information exchange between intelligent agents in the system are . , , , yes The smallest non-zero eigenvalue, It is an undirected symbolic graph subgraph The corresponding Laplace matrix, topology and Same, but The weights of information exchange between intelligent agents in the system are . , .
[0046] Furthermore, in this step, after achieving binary consensus, some followers are in the same state as the virtual leader, while others are in the opposite state. It is understood that, in this application, for any agent, whether its state is consistent with the virtual leader after achieving binary consensus is not necessarily related to whether it is classified as a first-class follower or a second-class follower in step S101.
[0047] Below, we will discuss the formula. The proof is as follows: Construct the Lyapunov function for the binary consistency error: ; right Differentiation yields: ; Combining the derivative result with assumption 2, we can obtain: ; Combining the two equations above, we can obtain: .
[0048] First of all, there are
[0049]
[0050] make Further organization yields
[0051] in It is an undirected symbolic graph subgraph The corresponding Laplace matrix, topology and Same, but The weights of information exchange between intelligent agents in the system are . , , yes The smallest non-zero eigenvalue. Therefore, we can derive (1) Secondly, there are
[0052] in It is an undirected symbolic graph subgraph The corresponding Laplace matrix, topology and Same, but The weights of information exchange between intelligent agents in the system are . , , yes The smallest non-zero eigenvalue. Therefore, we can derive (2) Finally, combining equations (1) and (2), we can obtain
[0053] in , That is, the convergence time of the system. satisfy: Complete the formula The proof.
[0054] Compared with related technologies, the binary consistency control method for multi-agent systems based on virtual leaders provided in this application first sets up a virtual leader for a multi-agent system comprising several agents, and makes some agents in the multi-agent system subject to the constraints of the virtual leader, while others are not subject to the constraints of the virtual leader. Then, a fixed-time constraint feedback control protocol is constructed for each agent based on the leader corresponding to each agent. Finally, the binary consistency error of each agent is constructed according to the fixed-time constraint feedback control protocol, and the upper bound of the convergence time for the multi-agent system to achieve binary consistency within a fixed time is determined based on the binary consistency error. That is, the binary consistency problem of multi-agent systems is solved by combining the fixed-time algorithm and constraint control. Since the fixed-time algorithm can achieve convergence within a stable time and the communication robustness between agents in the converged multi-agent system is high, the convergence speed and communication robustness of the binary consistency of the multi-agent system are improved. The constraint control algorithm can effectively save communication resources by constraining a portion of the nodes. Therefore, by combining a fixed-time algorithm and a restraint control in this application, we can achieve the technical effect of ensuring both system convergence speed and communication robustness while saving communication resources.
[0055] To better implement the control method for binary consensus in a multi-agent system based on a virtual leader in the embodiments of this application, based on the control method for binary consensus in a multi-agent system based on a virtual leader, correspondingly, as follows: Figure 3 As shown in the illustration, this application also provides a control device for binary consensus in a multi-agent system based on a virtual leader. The control device for binary consensus in a multi-agent system based on a virtual leader includes: Virtual leader setting module 301 is used to set a virtual leader. The intelligent agent includes a type of follower who is controlled by the virtual leader and a type of follower corresponding to the type of follower. Control protocol construction module 302 is used to construct fixed-time restraint feedback control protocols for each intelligent agent; Error construction module 303 is used to construct the binary consistency error of each agent according to the fixed-time constraint feedback control protocol. The convergence time determination module 304 is used to determine the upper bound of the convergence time of the multi-agent system to achieve binary consistency within a fixed time based on the binary consistency error.
[0056] The control device for binary consensus of a multi-agent system based on a virtual leader provided in the above embodiments can realize the technical solutions described in the above embodiments of the control method for binary consensus of a multi-agent system based on a virtual leader. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the control method for binary consensus of a multi-agent system based on a virtual leader, and will not be repeated here.
[0057] Please refer to Figure 4 This application also provides an electronic device 400. The electronic device 400 includes a processor 401, a memory 402, and a display 403. Figure 4 Only some components of the electronic device 400 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.
[0058] In some embodiments, processor 401 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 402 or process data, such as the binary consensus control method for a multi-agent system based on a virtual leader in this application.
[0059] In some embodiments, processor 401 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.
[0060] In some embodiments, memory 402 may be an internal storage unit of electronic device 400, such as a hard disk or memory of electronic device 400. In other embodiments, memory 402 may also be an external storage device of electronic device 400, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 400.
[0061] Furthermore, the memory 402 may include both internal storage units of the electronic device 400 and external storage devices. The memory 402 is used to store application software and various types of data installed on the electronic device 400.
[0062] In some embodiments, display 403 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 403 is used to display information from electronic device 400 and to display a visual user interface. Components 401-403 of electronic device 400 communicate with each other via a system bus.
[0063] In one embodiment, when processor 401 executes the control program for a binary consensus multi-agent system based on a virtual leader in memory 402, the following steps can be implemented: A virtual leader is set up, and the intelligent agent includes a type of follower who is controlled by the virtual leader and a type of follower corresponding to the type of follower; Construct a fixed-time constraint feedback control protocol for each intelligent agent; The binary consistency error of each agent is constructed based on a fixed-time constraint feedback control protocol; Based on the binary consistency error, determine the upper bound of the convergence time for a multi-agent system to achieve binary consistency within a fixed time using a fixed-time constraint feedback control protocol.
[0064] It should be understood that when the processor 401 executes the control program for the binary consensus of the multi-agent system based on the virtual leader in the memory 402, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.
[0065] Furthermore, this application does not specifically limit the type of electronic device 400 mentioned in the embodiments. Electronic device 400 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of this application, electronic device 400 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).
[0066] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the control method for binary consensus of a multi-agent system based on a virtual leader provided in the above-described method embodiments.
[0067] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.
[0068] The control method, apparatus, electronic device, and storage medium for a multi-agent system based on a virtual leader with binary consensus provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A control method for binary consensus in a multi-agent system based on a virtual leader, characterized in that, The binary consensus control method for multi-agent systems comprising several agents, applied to the control of such systems, includes: A virtual leader is set up, and the plurality of intelligent agents include a type I follower and a type II follower with different controllers; Construct a fixed-time constraint feedback control protocol for each of the aforementioned intelligent agents; The binary consistency error of each of the intelligent agents is constructed according to the fixed-time constraint feedback control protocol; Based on the binary consensus error, the multi-agent system achieves binary consensus within a fixed time based on the fixed-time constraint feedback control protocol, and the upper bound of the convergence time is calculated.
2. The control method for binary consensus in a multi-agent system based on a virtual leader according to claim 1, characterized in that, The fixed-time constraint feedback control protocol for constructing each of the intelligent agents includes: Obtain the feedback control gain of each of the aforementioned intelligent agents; The fixed-time tethered feedback control protocol is constructed based on the feedback control gain.
3. The control method for binary consensus in a multi-agent system based on a virtual leader according to claim 2, characterized in that, The step of constructing the fixed-time tethered feedback control protocol based on the feedback control gain includes: Based on formula Construct the fixed-time restraint feedback control protocol; in, Let be the label of the intelligent agent. For intelligent agents The corresponding fixed-time restraint feedback control protocol. For intelligent agents and intelligent agents Relationship parameters, if the agent and intelligent agents If a cooperative relationship exists, then... ,otherwise If the intelligent agent and intelligent agents If there is no interaction between them, then , , It is a symbolic function. , , , The feedback control gain is... When the intelligent agent and the virtual leader belong to the same group ,otherwise , The label for the aforementioned type of follower. The labels are for the two types of followers.
4. The control method for binary consensus in a multi-agent system based on a virtual leader according to claim 2, characterized in that, The construction of the binary consistency error for each agent based on the fixed-time constraint feedback control protocol includes: Obtain the agent dynamics system description of the agent; Construct a dynamic system description of the tethered feedback control system for each of the agents based on the fixed-time tethered feedback control protocol and the agent dynamic system description; The binary consistency error of each agent is constructed based on the description of the restraint feedback control dynamics system.
5. The control method for binary consensus in a multi-agent system based on a virtual leader according to claim 4, characterized in that, The construction of the constraint feedback control dynamics system description for each agent based on the fixed-time constraint feedback control protocol and the agent dynamics system description includes: Based on formula Construct a description of the restraint feedback control dynamics system of the aforementioned type of follower; Based on formula Construct a description of the restraint feedback control dynamics system for the two types of followers; in, Let be the label of the intelligent agent. For intelligent agents The state function, These are the state parameters of the virtual leader. For intelligent agents and intelligent agents Relationship parameters, if the agent and intelligent agents If a cooperative relationship exists, then... ,otherwise If the intelligent agent and intelligent agents If there is no interaction between them, then , , For symbolic functions, , , , The feedback control gain is... When the intelligent agent and the virtual leader belong to the same group ,otherwise .
6. The control method for binary consensus in a multi-agent system based on a virtual leader according to claim 4, characterized in that, The construction of the binary consistency error for each agent based on the description of the restraint feedback control dynamics system includes: Based on formula Construct the binary consistency error for each of the aforementioned intelligent agents; in, Let be the label of the intelligent agent. For intelligent agents The state function, These are the state parameters of the virtual leader. For intelligent agents and intelligent agents Relationship parameters, if the agent and intelligent agents If a cooperative relationship exists, then... ,otherwise If the intelligent agent and intelligent agents If there is no interaction between them, then , , For symbolic functions, , , , The feedback control gain is... When the intelligent agent and the virtual leader belong to the same group ,otherwise .
7. The control method for binary consensus in a multi-agent system based on a virtual leader according to claim 1, characterized in that, The step of determining the upper bound of the convergence time for the multi-agent system to achieve binary consensus within a fixed time based on the binary consensus error includes: Based on formula Determine the upper bound of the convergence time; in, This is the upper bound of the convergence time. , , yes The smallest non-zero eigenvalue, It is an undirected symbolic graph subgraph The corresponding Laplace matrix, topology and Same, but The weights of information exchange between intelligent agents in the system are , , , , yes The smallest non-zero eigenvalue, It is an undirected symbolic graph subgraph The corresponding Laplace matrix, topology and Same, but The weights of information exchange between intelligent agents in the system are , , Let be the label of the intelligent agent. , The label of the follower that is consistent with the virtual leader's state. The label of the follower is the opposite of the virtual leader state.
8. A control device for a binary consensus mechanism in a multi-agent system based on a virtual leader, characterized in that, The control device for controlling a multi-agent system comprising several agents, wherein the control device for the binary consensus of the multi-agent system based on a virtual leader includes: A virtual leader setting module is used to set a virtual leader. The plurality of intelligent agents include a first type of followers whose state is consistent with the virtual leader and a second type of followers whose state is opposite to the virtual leader. A control protocol construction module is used to construct a fixed-time restraint feedback control protocol for each of the intelligent agents. An error construction module is provided, which is used to construct the binary consistency error of each of the intelligent agents according to the fixed-time constraint feedback control protocol. The convergence time determination module is used to determine the upper bound of the convergence time for the multi-agent system to achieve binary consensus within a fixed time based on the binary consensus error.
9. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the control method for binary consensus of a multi-agent system based on a virtual leader as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the control method for binary consensus of a multi-agent system based on a virtual leader as described in any one of claims 1 to 7.