Multi-agent system cooperative control method based on dynamic trigger mechanism

Abstract

The invention discloses a multi-agent system cooperative control method based on a dynamic trigger mechanism, and relates to the field of control theory and computer science, and the method comprises the following steps: 1, simulating information transmission and receiving between agents, and constructing a different fading channel model; 2, dynamic adjustment triggering conditions are designed based on a distributed dynamic event triggering mechanism, unnecessary information updating is reduced, a control signal of an intelligent agent is generated to achieve a consensus target, and generation of the control signal is based on a leader-follower model; step 3, continuously evaluating the state of the multi-agent system based on a preset stability condition, and making a specific action decision for each agent according to the state information and the control signal received by each agent; and 4, evaluating the methods in the steps 1-3 through a simulation experiment, and verifying the performance of the method under different network conditions and system parameters.

Description

Technical Field

[0001] The present invention relates to the fields of control theory and computer science, and in particular to a multi-agent system collaborative control method based on a dynamic trigger mechanism. Background Art

[0002] In the study of multi-agent systems (MASs), how to achieve a collaborative state among the agents in the system under a dynamically changing network environment is a core issue. Especially in wireless communication networks, the reliability and real-time performance of information transmission are seriously affected by the fading characteristics of signals. Most of the existing leader-following algorithms are based on the assumption of continuous or periodic communication, which is difficult to achieve in a resource-constrained wireless network environment. In addition, the static event triggering mechanism (SETM) cannot flexibly adapt to changes in network conditions due to its fixed threshold parameters, resulting in a waste of communication resources. Therefore, designing a control strategy that can effectively reduce the communication burden, adapt to dynamic changes in the network, and maintain system performance in a non-ideal communication environment is a technical problem that needs to be solved in this field. Summary of the invention

[0003] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to design a control strategy that can effectively reduce the communication burden, adapt to dynamic changes in the network and maintain system performance in a non-ideal communication environment.

[0004] To achieve the above object, the present invention provides a multi-agent system collaborative control method based on a dynamic trigger mechanism, comprising the following steps: Step 1: Simulate the information transmission and reception between agents and construct a non-identical fading channel model; Step 2: Based on the distributed dynamic event trigger mechanism, the trigger conditions are dynamically adjusted to reduce unnecessary information updates, and the control signals of the intelligent agents are generated to achieve the consensus goal, wherein the generation of the control signals is based on the leader-follower model; Step 3: continuously evaluating the state of the multi-agent system based on a preset stability condition, and making specific action decisions for each agent according to the state information and control signals received by each agent; Step 4: Evaluate the method in steps 1 to 3 through simulation experiments to verify the performance of the method under different network conditions and system parameters.

[0005] Furthermore, the step 1 includes the following sub-steps: Step 1.1, collecting the state information of each intelligent agent and sending it to the neighboring intelligent agent through the wireless communication channel; wherein the communication process of the wireless communication channel is designed based on the non-identical fading channel model; Step 1.2, each agent receives state feedback information from other agents and stores it in its own state cache for subsequent control decisions, wherein the received state feedback information is demodulated and denoised; Step 1.3: The connection relationship between intelligent agents will change over time. Repeat steps 1.1 to 1.2 to dynamically update the communication relationship between intelligent agents.

[0006] Furthermore, the step 2 includes the following sub-steps: Step 2.1: Based on the distributed dynamic event triggering mechanism, the event triggering conditions are dynamically adjusted in combination with the current state of each agent, the state of neighboring agents, and the communication delay information to reduce unnecessary information updates; Step 2.2: Generate the control signal of the intelligent agent to achieve the consensus goal, wherein the generation of the control signal is based on a leader-follower model, and the state of the leader will affect the behavior of the follower through the communication network.

[0007] Furthermore, the triggering condition in step 2.1 is expressed as: Among them, the trigger event generation function Defined by the following formula: in, , Indicates the most recent triggering moment; is an auxiliary dynamic variable that satisfies the following conditions: in, is the initial condition of the auxiliary system, and is a scalar that satisfies the following conditions: .

[0008] Furthermore, the control law for generating the control signal of the intelligent agent in step 2.2 is: in, is the control input, The state of being a leader, To control the gain.

[0009] Furthermore, step 3 includes: continuously evaluating the state of the multi-agent system based on a preset stability condition, and if the stability condition is not met, issuing an adjustment signal to optimize the parameters of the control law; the adjustment signal is based on the disturbance attenuation level , ensuring satisfaction Performance constraints; Lyapunov stability analysis is introduced to ensure the global stability and asymptotic convergence of the multi-agent system. The necessary and sufficient conditions for the stability of the multi-agent system are expressed in the form of linear matrix inequality (LMI): in, and is the dynamic matrix parameter of the multi-agent system, is a positive definite matrix.

[0010] Furthermore, the step 3 further includes: making specific action decisions for each agent according to the state information and control signals received by each agent to achieve the leader-follower collaborative control goal; the collaborative control goal is described by the state error of the multi-agent system: in, Indicates The state of an agent, Represents the state of the leader; by analyzing the state error, the actions of each agent are adjusted to ensure ultimate coordination.

[0011] Furthermore, step 3 also includes: fault handling and adaptive adjustment. When the communication or hardware of the multi-agent system fails, the multi-agent system continues to operate stably by adjusting control parameters or changing event triggering mechanisms. The adaptive adjustment is based on the analysis of the Lyapunov function, which is specifically expressed as follows: By derivation The derivative of , judge the convergence and stability of the multi-agent system; if , it indicates that the multi-agent system gradually converges and remains stable under the control law.

[0012] Furthermore, step 4 includes the following sub-steps: Step 4.1, build a simulation environment, simulate the actual multi-agent working scenario by building a virtual environment, test the performance of the multi-agent system under different channel fading conditions, and more realistically evaluate the robustness of the control algorithm by introducing random noise and delay factors; Step 4.2: Verify the performance of the multi-agent system by conducting multiple experiments with different trigger thresholds, control gains, and communication topologies to verify the stability, convergence speed, and communication efficiency of the multi-agent system under different network conditions. Step 4.3: Parameter optimization and algorithm debugging are performed, and the control parameters and trigger thresholds are further optimized by analyzing the evaluation results obtained in step 4.2.

[0013] Furthermore, the collaborative error is continuously calculated in step 4.3, and the calculation formula is as follows: in, The state of being a leader, The status of the follower.

[0014] The present invention provides a multi-agent system collaborative control method based on a dynamic trigger mechanism, which has at least the following technical effects: In addition, this system also has the following beneficial effects: 1. The technical solution provided by the present invention proposes a dynamic event triggering mechanism that can dynamically adjust the event triggering threshold according to the state of the intelligent agent and the network conditions, thereby reducing the number of communications between intelligent agents and reducing the network burden.

[0015] 2. The technical solution provided by the present invention takes into account the influence of non-identical fading channels, thereby improving the robustness of the system in non-ideal communication environments.

[0016] 3. The technical solution provided by the present invention provides a theoretical basis for the design of controller parameters and ensures the stability of the system through random analysis methods and matrix analysis technology.

[0017] 4. The technical solution provided by the present invention verifies the effectiveness of the proposed method through simulation experiments, especially in a wireless network environment with limited bandwidth or unstable channel conditions, showing better adaptability and robustness.

[0018] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a collaborative control method of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0021] Example 1 like Figure 1 As shown, a multi-agent system collaborative control method based on a dynamic trigger mechanism provided by an embodiment of the present invention reduces the number of communications between agents by dynamically adjusting the event trigger threshold, while ensuring the stability and performance of the system in a non-ideal communication environment. Specifically, the following steps are included: Step 1: simulate the information transmission and reception between intelligent agents, build a non-identical fading channel model, and simulate the signal fading characteristics in the actual wireless communication environment; Step 2: Based on the distributed dynamic event trigger mechanism, the trigger conditions are dynamically adjusted to reduce unnecessary information updates, and the control signals of the intelligent agents are generated to achieve the consensus goal. The generation of the control signals is based on the leader-follower model. Step 3: Continuously evaluate the state of the multi-agent system based on the preset stability conditions, derive the controller parameters that ensure the stability of the system using stochastic analysis methods and matrix analysis techniques, and make specific action decisions for each agent based on the state information and control signals received by each agent; Step 4: Evaluate the method in steps 1 to 3 through simulation experiments to verify the performance of the method under different network conditions and system parameters.

[0022] Example 2 Based on Example 1, step 1 includes the following sub-steps: Step 1.1, collect the state information of each agent (such as position, speed, etc.) and send it to the neighboring agents through the wireless communication channel; the communication process of the wireless communication channel is designed based on the non-identical fading channel model, taking into account the signal fading, noise and delay that may exist in the actual wireless communication environment; Step 1.2, each agent receives the state feedback information of other agents and stores it in its own state cache for subsequent control decisions, wherein the received state feedback information is demodulated and denoised to ensure the accuracy of the data; Step 1.3: Since the connection relationship between agents may change over time (such as changes in channel conditions or movement of agents), the multi-agent system has the function of dynamically adjusting the network topology to cope with changes in information links. The connection relationship between agents will change over time. Repeat steps 1.1 to 1.2 to dynamically update the communication relationship between agents.

[0023] Example 3 Based on Example 1 or 2, step 2 includes the following sub-steps: Step 2.1: Based on the distributed dynamic event triggering mechanism, the event triggering conditions are dynamically adjusted in combination with the current state of each agent, the state of neighboring agents, and the communication delay information to reduce unnecessary information updates; Step 2.2: Generate control signals for the intelligent agents to achieve the consensus goal, where the generation of control signals is based on the leader-follower model, and the state of the leader will affect the behavior of the followers through the communication network.

[0024] In particular, the trigger condition in step 2.1 is expressed as: Among them, the trigger event generation function Defined by the following formula: in, , Indicates the most recent triggering moment; is an auxiliary dynamic variable that satisfies the following conditions: in, is the initial condition of the auxiliary system, and is a scalar that satisfies the following conditions: .

[0025] In particular, the control law for generating the control signal of the agent in step 2.2 is: in, is the control input, The state of being a leader, To control the gain.

[0026] Example 4 Based on the embodiment 1, 2 or 3, step 3 includes: continuously evaluating the state of the multi-agent system based on the preset stability condition, and if the stability condition is not met, issuing an adjustment signal to optimize the parameters of the control law; the adjustment signal is based on the disturbance attenuation level , ensuring satisfaction Performance constraints; Lyapunov stability analysis is introduced to ensure the global stability and asymptotic convergence of the multi-agent system. The necessary and sufficient conditions for the stability of the multi-agent system are expressed in the form of linear matrix inequality (LMI): in, and is the dynamic matrix parameter of the multi-agent system, is a positive definite matrix.

[0027] In particular, step 3 also includes: making specific action decisions for each agent based on the state information and control signals received by each agent to achieve the leader-follower collaborative control goal; the collaborative control goal is described by the state error of the multi-agent system: in, Indicates The state of an agent, Represents the state of the leader; by analyzing the state error, the actions of each agent are adjusted to ensure ultimate coordination.

[0028] In particular, step 3 also includes: fault handling and adaptive adjustment. When the communication or hardware of the multi-agent system fails, the multi-agent system can continue to operate stably by adjusting the control parameters or changing the event trigger mechanism. Based on the discrete time control scheme, the event-triggered control mechanism (ETC) will significantly reduce the frequency of communication, thereby improving the resource utilization efficiency of the system. By designing a suitable trigger mechanism, the consistency of the follower and leader states is ensured, while the attenuation level of the disturbance is guaranteed.

[0029] Adaptive adjustment is based on the analysis of the Lyapunov function, which is specifically expressed as: By derivation The derivative of , judge the convergence and stability of the multi-agent system; if , it indicates that the multi-agent system gradually converges and remains stable under the control law.

[0030] Example 5 Based on Embodiment 1, 2, 3 or 4, step 4 is used to simulate and verify the entire multi-agent system, aiming to evaluate the performance of the proposed control scheme under different network conditions and system parameters through simulation experiments, including the following sub-steps: Step 4.1: Build a simulation environment. By building a virtual environment, simulate the actual multi-agent working scenario, test the performance of the multi-agent system under different channel fading conditions, and introduce random noise and delay factors to more realistically evaluate the robustness of the control algorithm. Step 4.2, verify the performance of the multi-agent system, conduct multiple groups of experiments for different trigger thresholds, control gains and communication topologies, verify the stability, convergence speed and communication efficiency of the multi-agent system under different network conditions, and especially verify the superiority of the present invention by comparing the performance difference between the dynamic event trigger mechanism and the traditional periodic trigger mechanism; Step 4.3: Optimize parameters and debug the algorithm. By analyzing the simulation results obtained in step 4.1, further optimize the control parameters and trigger thresholds.

[0031] In particular, the collaborative error is continuously calculated in step 4.2, and the calculation formula is as follows: in, The state of being a leader, The status of the follower.

[0032] Example 6 The embodiment of the present invention also provides a multi-agent system leader-follower collaborative control system based on a dynamic event trigger mechanism, comprising: 1. Communication module, used to simulate information transmission and reception between intelligent agents; 2. Control module, used to generate control input according to dynamic event trigger mechanism; 3. Decision module, used to make decisions based on system status and communication information; 4. Simulation module, used to verify system performance.

[0033] In particular, the communication module is one of the cores of this system, mainly used to realize the information transmission and reception between multiple agents. Each agent exchanges state information with each other through this module so as to perform calculations and decisions based on the current network topology. The specific functions of the communication module include: 1) Information collection and transmission: This module collects the state information of each agent (such as position, speed, etc.) and sends it to neighboring agents through wireless communication channels. The communication process is designed based on the non-identical fading channel model, taking into account the signal fading, noise and delay that may exist in the actual wireless communication environment.

[0034] 2) Information reception and feedback: The agent receives state feedback information from other agents through the communication module and stores it in the state cache for subsequent control decisions. The received information is demodulated and denoised to ensure data accuracy.

[0035] 3) Dynamic network topology management: Since the connection relationship between agents may change over time (such as changes in channel conditions or movement of agents), the communication module has the function of dynamically adjusting the network topology to cope with changes in information links. Under the trigger mechanism, this module will update the communication relationship between agents according to the network status when necessary.

[0036] In particular, the control module is the core component of the system to generate control inputs and is designed based on a distributed dynamic event triggering mechanism. Its main function is to generate control signals for the agent and ensure the coordinated convergence of the system through the control algorithm. The specific design is as follows: 1) Event trigger mechanism design: This module dynamically adjusts the trigger conditions based on the current state of the agent, the state of neighboring agents, and communication delay information to reduce unnecessary information updates and optimize communication resource utilization. The trigger conditions can be expressed as: Among them, the trigger event generation function Defined by the following formula: in , Indicates the most recent triggering time.

[0037] also, is an auxiliary dynamic variable that satisfies the following conditions: in, is the initial condition of the auxiliary system, and is a scalar that satisfies the following conditions: 2) Distributed control law: Through this module, the agent generates control inputs to achieve the consensus goal. The generation of control inputs is based on the leader-follower model, and the state of the leader affects the behavior of the followers through the communication network. The form of the control law is: in, is the control input, The state of being a leader, To control the gain.

[0038] In particular, the decision module is used to make decisions and adjust the system's working mode based on the input information of the control module and the current state feedback. Its main functions include: 1) State evaluation and judgment: The decision module continuously evaluates the state of the system and determines whether the system meets these conditions based on the preset stability conditions. If the system fails to meet the stability requirements, the decision module will send an adjustment signal to optimize the parameters of the control law. This adjustment is based on the disturbance attenuation level. , thereby ensuring satisfaction Performance constraints. By introducing Lyapunov stability analysis, the global stability and asymptotic convergence of the system can be guaranteed. The necessary and sufficient conditions for system stability can be expressed in the form of linear matrix inequality (LMI): in, and is the dynamic matrix parameter of the system, is a positive definite matrix, which ensures that the system is stable under the regulation of the control law.

[0039] 2) Agent action decision: Based on the state information and control signals received by each agent, the decision module makes specific action decisions (such as position adjustment, speed change, etc.) for each agent to achieve the leader-follower collaborative control goal. This collaborative control goal can be described by the state error of the system: in, Indicates The state of an agent, Represents the state of the leader. By analyzing the state error, the actions of each agent are adjusted to ensure the final coordination.

[0040] 3) Fault handling and adaptive adjustment: When the system communication or hardware fails, the decision module has a certain degree of adaptive ability and can adjust the control parameters or change the event trigger mechanism to keep the system running stably. Based on the discrete time control scheme, the event triggered control mechanism (ETC) will significantly reduce the frequency of communication, thereby improving the resource utilization efficiency of the system. By designing a suitable trigger mechanism, the consistency of the follower and leader states is ensured, while ensuring the attenuation level of disturbances.

[0041] The adjustments in the decision module are based on the analysis of the Lyapunov function, which can be expressed as: By deriving its derivative , we can judge the convergence and stability of the system. , it indicates that the system gradually converges under the control law and remains stable.

[0042] In particular, the simulation module is used to simulate and verify the entire multi-agent system, aiming to evaluate the performance of the proposed control scheme under different network conditions and system parameters through simulation experiments. Its main functions are as follows: 1) Simulation environment construction: The simulation module builds a virtual environment to simulate the actual multi-agent working scenario and test the performance of the system under different channel fading conditions. By introducing random noise and delay factors, the robustness of the control algorithm can be evaluated more realistically.

[0043] 2) System performance verification: This module can conduct multiple sets of experiments for different trigger thresholds, control gains and communication topologies to verify the stability, convergence speed and communication efficiency of the system under different network conditions. In particular, the superiority of the present invention is verified by comparing the performance differences between the dynamic event trigger mechanism and the traditional periodic trigger mechanism.

[0044] 3) Parameter optimization and algorithm debugging: Through simulation results, developers can further optimize control parameters and trigger thresholds. The simulation module has algorithm debugging functions, supports comparative analysis of different algorithms, and helps determine the best control strategy.

[0045] The formula for calculating the coordination error in the simulation module is as follows: in, The state of being a leader, The status of the follower.

[0046] The above modules together constitute a multi-agent system leader-follower collaborative control system based on a dynamic event trigger mechanism. Through the state information exchange of the communication module, the dynamic control input generation of the control module, the real-time state evaluation and adjustment of the decision module, and the experimental verification and optimization of the simulation module, the present invention provides an efficient, stable, and resource-efficient control system suitable for consensus control tasks in complex multi-agent networks.

[0047] Traditional multi-agent systems usually rely on continuous or periodic communication strategies to achieve leader-follower collaborative control, which will lead to waste of communication resources and degradation of system performance in wireless network environments with limited bandwidth or unstable channel conditions. Although the static event trigger mechanism (SETM) can reduce the number of communications, it cannot adapt to the dynamic changes of network conditions due to its fixed threshold parameters, thus affecting the stability and consensus performance of the system. In addition, signal fading in wireless communications will further aggravate the uncertainty of information transmission and affect the robustness of the system.

[0048] The leader-follower collaborative control method and system of a multi-agent system based on a dynamic event trigger mechanism (DETM) proposed in the embodiment of the present invention can more flexibly adapt to changes in network conditions by dynamically adjusting the event trigger threshold, significantly reducing unnecessary communication burdens. At the same time, considering the influence of non-identical fading channels, the system uses random analysis methods and matrix analysis techniques to derive controller parameters that ensure system stability, thereby improving the robustness of the system in non-ideal communication environments.

[0049] The simulation results show that this system can significantly reduce the number of communications between agents and reduce the network burden while maintaining system performance. Compared with traditional continuous or periodic communication strategies and static event triggering mechanisms, this system exhibits better adaptability and robustness, especially in wireless network environments with limited bandwidth or unstable channel conditions, and can achieve more efficient and reliable leader-following consensus control.

[0050] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A multi-agent system collaborative control method based on a dynamic trigger mechanism, characterized in that: The method comprises the following steps: Step 1: Simulate the information transmission and reception between agents and construct a non-identical fading channel model; Step 2: Based on the distributed dynamic event trigger mechanism, the trigger conditions are dynamically adjusted to reduce unnecessary information updates, and the control signals of the intelligent agents are generated to achieve the consensus goal, wherein the generation of the control signals is based on the leader-follower model; Step 3: continuously evaluating the state of the multi-agent system based on a preset stability condition, and making specific action decisions for each agent according to the state information and control signals received by each agent; Step 4: Evaluate the method in steps 1 to 3 through simulation experiments to verify the performance of the method under different network conditions and system parameters.

2. The multi-agent system collaborative control method based on dynamic trigger mechanism as claimed in claim 1, characterized in that: The step 1 comprises the following sub-steps: Step 1.1, collecting the state information of each intelligent agent and sending it to the neighboring intelligent agent through the wireless communication channel; wherein the communication process of the wireless communication channel is designed based on the non-identical fading channel model; Step 1.2, each agent receives state feedback information from other agents and stores it in its own state cache for subsequent control decisions, wherein the received state feedback information is demodulated and denoised; Step 1.3: The connection relationship between intelligent agents will change over time. Repeat steps 1.1 to 1.2 to dynamically update the communication relationship between intelligent agents.

3. The multi-agent system collaborative control method based on dynamic trigger mechanism as claimed in claim 1, characterized in that: The step 2 includes the following sub-steps: Step 2.1, based on the distributed dynamic event triggering mechanism, dynamically adjust the event triggering conditions in combination with the current state of each agent, the state of neighboring agents, and communication delay information to reduce unnecessary information updates; Step 2.2: Generate the control signal of the intelligent agent to achieve the consensus goal, wherein the generation of the control signal is based on a leader-follower model, and the state of the leader will affect the behavior of the follower through the communication network.

4. The multi-agent system collaborative control method based on dynamic trigger mechanism as claimed in claim 3, characterized in that: The triggering condition in step 2.1 is expressed as: Among them, the trigger event generation function Defined by the following formula: in, , Indicates the most recent triggering moment; is an auxiliary dynamic variable that satisfies the following conditions: in, is the initial condition of the auxiliary system, and is a scalar that satisfies the following conditions: 。 5. The multi-agent system collaborative control method based on dynamic trigger mechanism as claimed in claim 4, characterized in that: The control law for generating the control signal of the intelligent agent in step 2.2 is: in, is the control input, The state of being a leader, To control the gain.

6. The multi-agent system collaborative control method based on dynamic trigger mechanism as claimed in claim 5, characterized in that: The step 3 comprises: continuously evaluating the state of the multi-agent system based on a preset stability condition, and if the stability condition is not met, issuing an adjustment signal to optimize the parameters of the control law; the adjustment signal is based on the disturbance attenuation level , ensuring satisfaction Performance constraints; Lyapunov stability analysis is introduced to ensure the global stability and asymptotic convergence of the multi-agent system. The necessary and sufficient conditions for the stability of the multi-agent system are expressed in the form of linear matrix inequality (LMI): in, and is the dynamic matrix parameter of the multi-agent system, is a positive definite matrix.

7. The multi-agent system collaborative control method based on dynamic trigger mechanism as claimed in claim 6, characterized in that: The step 3 further includes: making specific action decisions for each agent according to the state information and control signals received by each agent to achieve the leader-follower collaborative control goal; the collaborative control goal is described by the state error of the multi-agent system: in, Indicates The state of an agent, Represents the state of the leader; by analyzing the state error, the actions of each agent are adjusted to ensure ultimate coordination.

8. The multi-agent system collaborative control method based on dynamic trigger mechanism as claimed in claim 7, characterized in that: The step 3 also includes: fault handling and adaptive adjustment. When the communication or hardware of the multi-agent system fails, the multi-agent system continues to operate stably by adjusting control parameters or changing event triggering mechanisms. The adaptive adjustment is based on the analysis of the Lyapunov function, which is specifically expressed as: By derivation The derivative of , judge the convergence and stability of the multi-agent system; if , it indicates that the multi-agent system gradually converges and remains stable under the control law.

9. The multi-agent system collaborative control method based on dynamic trigger mechanism as claimed in claim 1, characterized in that: The step 4 includes the following sub-steps: Step 4.1, build a simulation environment, simulate the actual multi-agent working scenario by building a virtual environment, test the performance of the multi-agent system under different channel fading conditions, and more realistically evaluate the robustness of the control algorithm by introducing random noise and delay factors; Step 4.2: Verify the performance of the multi-agent system by conducting multiple experiments with different trigger thresholds, control gains, and communication topologies to verify the stability, convergence speed, and communication efficiency of the multi-agent system under different network conditions. Step 4.3: Parameter optimization and algorithm debugging are performed, and the control parameters and trigger thresholds are further optimized by analyzing the evaluation results obtained in step 4.

2.

10. The multi-agent system collaborative control method based on dynamic trigger mechanism according to claim 9, characterized in that: In step 4.3, the collaborative error is continuously calculated, and the calculation formula is as follows: in, The state of being a leader, The status of the follower.

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