A Multi-Agent Dynamic Event-Triggered Fixed-Time Binary Consistency Control Method

Abstract

This invention relates to the field of multi-agent control technology and discloses a multi-agent dynamic event-triggered fixed-time binary consensus control method. The method includes constructing a signed undirected topological graph, Laplace matrix, and mathematical model of the multi-agent system to determine the binary consensus control objective; establishing a binary consensus controller, sampling the event triggering speed of each agent and inputting it into the controller, further obtaining measurement error, event triggering function, and dynamic event triggering threshold to determine the dynamic triggering conditions; and calculating the fixed convergence time upper limit of the multi-agent dynamic event-triggered binary consensus controller, ensuring that the multi-agent system can achieve binary consensus within a fixed time under any initial state. The controller designed in this invention can achieve binary consensus among multiple agents, making it applicable not only to multi-agent cooperative scenarios but also to multi-agent competitive scenarios, thus having a wider range of applications.

Description

Technical Field

[0001] This invention belongs to the field of multi-agent control technology, specifically relating to a multi-agent dynamic event-triggered fixed-time binary consensus control method. Background Technology

[0002] With the rapid development of artificial intelligence technology, multi-agent control has received widespread attention. For example, the existing patent CN101853006A, "Multi-Agent Cooperative Control System," discloses the modular structure and working process of a multi-agent cooperative control system. Currently, multi-agent systems have been widely applied in fields such as multi-drone collaboration, intelligent transportation systems, smart homes, and multi-robot collaborative work. Consistency in multi-agent systems refers to the fact that the agents in the system, through mutual cooperation and communication, make their states or behaviors tend to be consistent to a certain extent. Consistency is a key characteristic of the stability and cooperation of multi-agent systems and is crucial for achieving system cooperative control.

[0003] Past research on the consistency problem has primarily focused on achieving global consistency across multiple agents. However, in real-world scenarios, agents may not only cooperate but also compete. For example, in industrial robot systems, robots can be divided into different groups. We only need consistency within each group, not necessarily across all robots. Research on binary consistency has addressed the challenge of achieving global consistency to some extent, making the system more flexible and adaptable to different needs.

[0004] In multi-agent research, convergence speed is a key performance indicator, reflecting the time required for the system to reach a consistent state from its initial state. Traditional asymptotic time control achieves convergence when the multi-agent system reaches infinity, resulting in a slow convergence speed. Therefore, researchers have proposed finite-time control to improve the convergence speed. However, finite-time control methods rely on the initial state of the system; if the initial state is large, the convergence time will be excessively long. Fixed-time control, on the other hand, improves the convergence speed while remaining unaffected by the initial state, ensuring that the system reaches the set state within a fixed time regardless of the initial state. Fixed-time control is particularly suitable for applications with high real-time requirements and strict time constraints, such as autonomous driving and collaborative robot operations.

[0005] Since the bandwidth and resources of a system's network communication system are limited, a well-designed control scheme can effectively solve problems such as limited transmission resources, communication limitations, network latency, and quantization. Compared with traditional time-triggered control, event-triggered control requires very little information transmission and control task execution. An event is triggered only when the state measurement error exceeds a threshold, causing the controller to update the data. Because control actions are only executed when necessary, event-triggered control can reduce system energy consumption and improve energy efficiency. However, traditional event-triggered control, due to its fixed trigger threshold, still suffers from excessive event triggering. A more flexible event-triggered scheme needs to be introduced to further reduce the number of system triggers and save communication resources. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a multi-agent dynamic event-triggered fixed-time binary consensus control method. Based on the dynamic event triggering mechanism, the method introduces a dynamic threshold to further reduce the number of event triggers and save the system's communication resources. At the same time, the fixed-time control improves the convergence speed of the system, thereby realizing multi-agent binary consensus control.

[0007] To address the aforementioned technical problems, this invention provides a multi-agent dynamic event-triggered fixed-time binary consensus control method, comprising the following process:

[0008] Step S1: Construct a signed undirected topological graph and Laplace matrix for the multi-agent system, establish a mathematical model of the multi-agent system, and determine the binary consistency control objective;

[0009] Step S2: Establish a binary consensus controller with fixed-time dynamic event triggering for multiple agents. Sample the event triggering speed of each agent and input it into the binary consensus controller to obtain the output of the binary consensus controller for each agent. Further obtain the measurement error, event triggering function and dynamic event triggering threshold to determine the dynamic triggering conditions. When the dynamic triggering conditions are met, update the sampled event triggering speed in the multiple agents and use it as the input of the binary consensus controller to update the output value of the binary consensus controller. Otherwise, the binary consensus controller remains unchanged.

[0010] The output value of the binary consensus controller is transmitted to the motor driver of each agent, driving the motor to rotate and realize the movement of each agent; the binary consensus is achieved by iterating through the states of multiple agents.

[0011] Step S3: Calculate the fixed upper limit of the convergence time of the multi-agent dynamic event-triggered binary consensus controller, so that the multi-agent can achieve binary consensus within a fixed time under any initial state.

[0012] As an improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of the present invention:

[0013] The process of establishing the signed undirected topological graph and the Laplace matrix is ​​as follows:

[0014] Collect the position x of each agent separately i (t) and velocity v i (t), where the initial position and initial velocity signals are denoted as x and x, respectively. i (0) and v i (0), by sampling the information in each agent, if agent i contains the information of agent j, it means that agent i and agent j can exchange information with each other, and at this time a ij ≠0, otherwise a ij =0, and the adjacency matrix A = [a] is obtained through this method. ij ]∈R n×n Since it is an undirected graph, a ij =a ji The communication topology diagram of the multi-agent system is as follows: in It is a set of non-empty nodes. Let it be the set of edges;

[0015] The Laplace matrix L = [l] of the multi-sense agent ij ] N×N satisfy:

[0016]

[0017] Determining if a communication topology graph is a structured balance graph: If there is a symbol G, it can be divided into... and Two parts, satisfying and For all There exists a ij ≥0; if Then a ij If the value is ≤0, then graph G is a structural equilibrium graph; otherwise, graph G is a structural disequilibrium graph.

[0018] As a further improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of the present invention:

[0019] The mathematical model for the multi-agent system is as follows:

[0020]

[0021] Where, x i (t) represents the real-time location information of the i-th agent, v i(t) represents the velocity information v of the i-th agent. i (t), u i (t)∈R N This is the output of the binary consensus controller for the i-th agent.

[0022] As a further improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of the present invention:

[0023] The objective of the binary search consistency control is:

[0024] (1) The speed of multiple agents satisfies: When i,j∈V r When r = {1, 2}, k ij =1, when i∈V r ,j∈V 3-r k ij =-1;

[0025] (2) The positions of the multi-agents satisfy: there exists a positive value C such that sup t≥T ||x i (t)-x j (t)||≤C,i,j∈V r r = {1, 2};

[0026] And there exists a constant value T. max , satisfying T≤T max ;

[0027] Lemma 1: For a multi-intelligent agent, if there exists a Lyapunov function V satisfying:

[0028]

[0029] Where α1, α1, p∈(1,∞), q∈(0,1), then the system satisfies fixed-time stability, and the stability time is independent of the initial state; the fixed time is:

[0030]

[0031] Lemma 2: If there exist constants k1, k2…k N If ≥0, then it exists.

[0032]

[0033] As a further improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of the present invention: the binary consensus controller is:

[0034]

[0035] Where p∈(1,∞), q∈(0,1), c1,c2,c3∈(0,∞), It is the latest event trigger time for agent i.

[0036] As a further improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of the present invention: the measurement error is:

[0037]

[0038] As a further improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of the present invention: the event triggering function is:

[0039]

[0040] Where θ∈(0,∞) and ε∈(0,1) are the parameters of the trigger function.

[0041] As a further improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of the present invention: the dynamic event triggering threshold is:

[0042]

[0043] Where η i (0)∈(0,∞) represents the initial value of the dynamic threshold, γ∈(0,1), and c4, c5, c6∈(0,∞) are the system design parameters. As a further improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of this invention:

[0044] The dynamic triggering condition is:

[0045]

[0046] When condition (11) is met, the binary consensus controller is updated; otherwise, the binary consensus controller is not updated. As a further improvement to the multi-agent dynamic event-triggered fixed-time binary consensus control method of the present invention:

[0047] The upper limit of the fixed convergence time is:

[0048] (1) The parameters c2 in equation (7), θ and ε in equation (9), and c6 and γ in equation (10) must satisfy the following conditions:

[0049]

[0050] (2) The upper limit of the fixed convergence time is:

[0051]

[0052] in

[0053] The beneficial effects of this invention are mainly reflected in:

[0054] 1. The controller designed in this invention can achieve convergence in any initial state within a fixed time, and the system convergence speed is faster;

[0055] 2. The controller designed in this invention adopts dynamic event triggering, adjusts the event triggering threshold based on the real-time status of the system, reduces the number of event triggers, improves system efficiency, reduces energy consumption, and extends the life of the controller.

[0056] 3. The controller designed in this invention can achieve binary consensus among multiple agents, making it applicable not only to cooperative multi-agent scenarios but also to competitive multi-agent scenarios. Therefore, this invention has a wider range of applications. Attached Figure Description

[0057] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0058] Figure 1 This is a flowchart illustrating a multi-agent dynamic event-triggered fixed-time binary consensus control method according to the present invention.

[0059] Figure 2 This is a topological diagram of the multi-agent instance of the present invention;

[0060] Figure 3 This is a velocity trajectory diagram of the multi-agent system of the present invention;

[0061] Figure 4 The position trajectory diagram of the multi-agent in this invention;

[0062] Figure 5 This is the dynamic threshold diagram of the multi-agent system in this invention;

[0063] Figure 6 This is a timeline of event triggering for the multi-agent system in this invention. Detailed Implementation

[0064] The present invention will be further described below with reference to specific embodiments, but the scope of protection of the present invention is not limited thereto:

[0065] Example 1: A multi-agent dynamic event-triggered fixed-time binary consensus control method. Each agent is equipped with a sensor, a motor controller, a motor driver, and a motor. Each agent collects speed and position information through its own sensors, constructing a signed undirected topological graph and Laplace matrix for the multi-agent system. A corresponding mathematical model and binary consensus control objective are established for the multi-agent system. Then, a multi-agent dynamic event-triggered fixed-time binary consensus controller is established, introducing dynamic thresholds and dynamic event triggering conditions. The upper limit of the fixed convergence time for the multi-agent dynamic event-triggered binary consensus control is calculated, such as... Figure 1 As shown, the specific process is as follows:

[0066] Multiple agents, under the control of a designed controller, achieve binary consensus. Specifically:

[0067] 1. Construct a binary consistency control objective

[0068] By collecting the relevant velocity and position information of each agent through sensors set up in a multi-agent system, a signed undirected topological graph of the multi-agent system is constructed, a corresponding mathematical model of the multi-agent system is established, and the binary consensus control objective is determined. The process includes the following steps:

[0069] 1.1 The position and velocity of each agent are collected using position and velocity sensors installed on each agent, and the collected position and velocity are converted into digital signals x. i (t) and v i (t), where the initial position and initial velocity signals are denoted as x and x, respectively. i (0) and v i (0). Each agent will transmit its own velocity information v at the moment the event is triggered. i (t) The information is broadcast to its neighboring agents via a sensor network, so each agent will contain its own information and neighbor information. By sampling the information in each agent, if agent i contains the velocity information of agent j, it means that agent i and agent j can exchange information with each other, and in this case, a ij ≠0, otherwise a ij =0. This method yields the adjacency matrix A = [a...]. ij ]∈R n×n And establish a multi-agent communication topology. in It is a set of non-empty nodes. Let L be the edge set. Determine the Laplace matrix L = [l ij ] N×N satisfy:

[0070]

[0071] 1.2 Determine if the communication topology diagram is a balanced structure diagram. If there is a symbol G, it can be divided into... and Two parts, satisfying and For all There exists a ij ≥0. If Then a ij If the value is ≤0, then Figure G is a structural equilibrium diagram. Otherwise, Figure G is a structural disequilibrium diagram.

[0072] 1.3 Real-time position information of each intelligent agent is detected by sensors inside each intelligent agent. i (t) and velocity information v i (t). And establish a mathematical model of multiple intelligent entities:

[0073]

[0074] Among them, u i (t)∈R N The controller output of the i-th agent will be transmitted as an input signal to the motor drivers installed on each agent to drive the agent to move.

[0075] 1.4 Define the control objective: The goal is for the multi-agent system to achieve binary consistency within a fixed time frame, given any initial state. Specifically, the following conditions must be met:

[0076] (1) The speed of multiple agents satisfies: When i,j∈V r When r = {1, 2}, k ij =1, when i∈V r ,j∈V 3-r k ij =-1.

[0077] (2) The positions of the multi-agents satisfy: there exists a positive value C such that sup t≥T ||x i (t)-x j (t)||≤C,i,j∈V r r = {1, 2}.

[0078] And there exists a constant value T. max , satisfying T≤T max .

[0079] Lemma 1: For a multi-intelligent agent, if there exists a Lyapunov function V satisfying:

[0080]

[0081] Where α1, α1, p ∈ (1, ∞), q ∈ (0, 1), then the system satisfies fixed-time stability, and the stability time is independent of the initial state. The fixed time is:

[0082]

[0083] Where T is the upper limit of the fixed convergence time for multiple agents to achieve binary consensus.

[0084] Lemma 2: If there exist constants k1, k2 … k N If ≥0, then it exists.

[0085]

[0086] 2. Design a binary consensus controller with fixed-time dynamic event triggering for multiple agents, introducing a dynamic threshold to establish the dynamic event triggering conditions. The system only updates the binary consensus controller when the dynamic event triggering conditions are met; otherwise, it does not update the controller.

[0087] 2.1 Design of a binary consensus controller:

[0088]

[0089] Where p∈(1,∞), q∈(0,1), c1,c2,c3∈(0,∞), It is the latest event trigger time for agent i.

[0090] 2.2 Definition of measurement error:

[0091]

[0092] 2.3 Determine the event triggering function:

[0093]

[0094] Where θ∈(0,∞) and ε∈(0,1) are the parameters of the trigger function.

[0095] 2.4 Introduce dynamic event trigger thresholds:

[0096]

[0097] Where η i (0)∈(0,∞) is the initial value of the dynamic threshold, γ∈(0,1), and c4,c5,c6∈(0,∞) are the design parameters of the system.

[0098] 2.5 Determine the triggering time of dynamic events (i.e., the triggering conditions of dynamic events):

[0099]

[0100] When the condition of equation (11) is met, the binary consensus controller (i.e., equation (7)) is updated; otherwise, the binary consensus controller is not updated.

[0101] 3. Calculate the upper limit of the fixed convergence time for binary consistency control with fixed trigger times for multi-agent dynamic events.

[0102] 3.1 The parameters c2 in the binary consistency controller (i.e., Equation (7)), θ and ε in the event triggering function (i.e., Equation (9)), and c6 and γ in the dynamic event triggering threshold function (i.e., Equation (10)) must satisfy the following conditions:

[0103]

[0104] 3.2 The upper limit of the fixed convergence time for multi-agent systems is determined as follows:

[0105]

[0106] in

[0107] The fixed time limit for multi-agent systems is given by equation (13), which is a fixed time value and is independent of the initial state of the system.

[0108] 4. Multi-agent binary consistency control

[0109] First, the real-time position and velocity of each agent are collected using position and velocity sensors installed on each agent. Each agent broadcasts its own event-triggered velocity to its neighboring agents via a sensor network at the moment an event is triggered. By sampling whether each agent has neighboring agent velocity information, a signed undirected topological graph and Laplace matrix of the multi-agent system are constructed to establish a mathematical model of the multi-agent system and determine the binary consensus control objective.

[0110] A binary consensus controller with fixed-time dynamic event triggering for multiple agents is established. The event triggering velocity at the moment of event triggering in each agent is sampled and input into the binary consensus controller to obtain the controller output of each agent. Further, the measurement error, event triggering function, and dynamic event triggering threshold are obtained to determine the dynamic triggering conditions. Only when the dynamic triggering conditions are met are the sampled event triggering velocities in the multiple agents updated and used as input to the binary consensus controller, causing the controller's output value to update; otherwise, the binary consensus controller remains unchanged. The controller's output value is transmitted to the motor drivers of each agent, driving the motors to rotate and realize the movement of each agent. Iterative operation is performed until the state of the multiple agents is reached to achieve binary consensus. Finally, the upper limit of the fixed convergence time of the multi-agent dynamic event-triggered binary consensus control is calculated. Based on the controller parameter values ​​and the eigenvalues ​​of the system's Laplace matrix, the upper limit of the fixed convergence time of the system is calculated, ensuring that the multiple agents can achieve binary consensus within a fixed time in any initial state.

[0111] 5. Verify that multi-agent systems achieve fixed-time binary consensus under the action of the binary consensus controller of the present invention.

[0112] 5.1 Determine the velocity convergence characteristics:

[0113] Consider the Lyapunov function:

[0114] V(t)=V1(t)+V2(t) (14)

[0115]

[0116] Differentiating the Lyapunov function V(t):

[0117]

[0118] because Therefore, it exists:

[0119]

[0120] And because it exists According to Lemma 1 and Lemma 2, we obtain:

[0121]

[0122] in,

[0123] Therefore, it can be achieved. When i,j∈V r ,r={1,2},sign(a ij ) = 1, when i ∈ V r ,j∈V 3-r ,sign(aij = -1.

[0124] Therefore, the velocity of the multi-agent system satisfies the condition (1) of the desired control objective.

[0125] According to Lemma 1, the fixed time is:

[0126]

[0127] 5.2 Determining the location of multiple agents

[0128] Choose the Lyapunov function:

[0129]

[0130] From step 4.1, we know that for any i, j ∈ V r For all r∈{1,2}, there exist so and Both are bounded. Due to the continuity of functions, there must exist constants M1 and M2 such that... and

[0131] right Taking the derivative, we get:

[0132]

[0133] Integrating equation (15) from 0 to t, we get:

[0134]

[0135] If t < T, then:

[0136]

[0137] If t≥T, then:

[0138]

[0139] Therefore, there exists a constant C1 such that Similarly, it can be proven that there exists a constant C2 such that Let constant C = max{C1} 2 C2 2},so:

[0140]

[0141] Therefore, the positions of the multi-agents satisfy the condition (2) of the desired control objective.

[0142] In summary, under the action of the binary consensus controller of the present invention, multiple agents can achieve binary consensus control within a fixed time.

[0143] experiment

[0144] Simulation: The effectiveness of a multi-agent dynamic event-triggered fixed-time binary consensus control method is verified through numerical simulation. The topology of the multi-agent system is determined as follows: Figure 2 As shown, it includes six multi-agent agents, which can be divided into six groups. and Two parts. Agents 1, 2, and 3 belong to... Agents 4, 5, and 6 belong to and The agents are in a competitive relationship, therefore the multi-agent topology graph is a structurally balanced graph. The corresponding adjacency matrix A and Laplace matrix L are as follows:

[0145]

[0146] The initial position x(0) = [-63-1821015] and initial velocity v(0) = [-63-1821015] are obtained from the sensors. The other parameters are γ = 0.01, c1 = 1.6, c2 = 0.09735, c3 = 0.04, c4 = 0.9, c5 = 0.4, c6 = 0.3, θ = 3.3, ε = 0.2, c2 = 0.09735, p = 21 / 19, q = 5 / 21. Figure 3 and Figure 4 The figure shows the velocity and position simulation of multiple agents. It can be seen from the figure that multiple agents can achieve binary consensus. Figure 5 The graph shows the dynamic threshold of a multi-agent system. As can be seen from the graph, the dynamic threshold gradually decreases, which helps to reduce the number of triggers. Figure 6 This invention presents a multi-agent event triggering time diagram. It employs dynamic event triggering, which further reduces the number of system triggers compared to static event triggering. Because the agents only exchange information at the triggering moment, this invention saves communication resources and extends the controller's lifespan.

[0147] Finally, it should be noted that the above examples are merely some specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. All variations that can be directly derived or conceived by those skilled in the art from the disclosure of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A multi-agent dynamic event-triggered fixed-time binary consensus control method, characterized in that, The process includes the following: Step S1: Construct a signed undirected topological graph and Laplace matrix for the multi-agent system, establish a mathematical model of the multi-agent system, and determine the binary consistency control objective; Step S2: Establish a binary consensus controller with fixed time for dynamic event triggering in multiple agents, sample the event triggering speed in each agent and input it to the binary consensus controller to obtain the output of the binary consensus controller for each agent, and further obtain the measurement error, event triggering function and dynamic event triggering threshold to determine the dynamic triggering conditions; The threshold for triggering dynamic events is: Where η i (0)∈(0,∞) are the initial values ​​of the dynamic threshold, γ∈(0,1), c4,c5,c6∈(0,∞) are the system design parameters, p∈(1,∞), q∈(0,1), c1,c2,c3∈(0,∞), ε∈(0,1), a ij Denotes the elements in the adjacency matrix A, e i (t) represents the measurement error; When the dynamic triggering condition is met, the event triggering speed sampled in the multi-agent system is updated and used as the input of the binary consensus controller, so that the output value of the binary consensus controller is updated; otherwise, the binary consensus controller remains unchanged. The output value of the binary consensus controller is transmitted to the motor driver of each agent, driving the motor to rotate and realize the movement of each agent; the binary consensus is achieved by iterating through the state of multiple agents. Step S3: Calculate the fixed upper limit of the convergence time of the multi-agent dynamic event-triggered binary consensus controller, so that the multi-agent can achieve binary consensus within a fixed time under any initial state.

2. The multi-agent dynamic event-triggered fixed-time binary consensus control method according to claim 1, characterized in that: The process of establishing the signed undirected topological graph and the Laplace matrix is ​​as follows: Collect the position x of each agent separately i (t) and velocity v i (t), where the initial position and initial velocity signals are denoted as x and x, respectively. i (0) and v i (0), by sampling the information in each agent, if agent i contains the information of agent j, it means that agent i and agent j can exchange information with each other, and at this time a ij ≠0, otherwise a ij =0, and the adjacency matrix A = [a] is obtained through this method. ij ]∈R n×n Since it is an undirected graph, a ij =a ji The communication topology diagram of the multi-agent system is as follows: in It is a set of non-empty nodes. Let it be the set of edges; The Laplace matrix L = [l] of the multi-species intelligence ij ] N×N satisfy: Determine if a communication topology graph is a structural balance graph: If the communication topology graph G can be divided into and Two parts, satisfying and For all There exists a ij ≥0; if v j ∈V n If m ≠ n, (m, n ∈ {1, 2}), then a ij If the value is ≤0, then the communication topology graph G is a structurally balanced graph; otherwise, the communication topology graph G is a structurally unbalanced graph.

3. The multi-agent dynamic event-triggered fixed-time binary consensus control method according to claim 2, characterized in that: The mathematical model for the multi-agent system is as follows: Where, x i (t) represents the real-time location information of the i-th agent, v i (t) represents the velocity information of the i-th agent, u i (t)∈R N This is the output of the binary consensus controller for the i-th agent.

4. The multi-agent dynamic event-triggered fixed-time binary consensus control method according to claim 3, characterized in that: The objective of the binary search consistency control is: (1) The speed of multiple agents satisfies: When i,j∈V r When r = {1, 2}, k ij =1, when i∈V r ,j∈V 3-r k ij =-1; (2) The positions of the multi-agents satisfy: there exists a positive value C such that sup t≥T ||x i (t)-x j (t)||≤C,i,j∈V r r = {1, 2}; Where T is the upper limit of the fixed convergence time; And there exists a constant value T. max , satisfying T≤T max ; For a multi-agent system, if there exists a Lyapunov function V that satisfies: Where α1, α2, p∈(1,∞), q∈(0,1), then the system satisfies fixed-time stability, and the stability time is independent of the initial state; the fixed time is: If there exist constants k1, k2 …k N If ≥0, then it exists.

5. The multi-agent dynamic event-triggered fixed-time binary consensus control method according to claim 4, characterized in that: The binary consensus controller is: in, It is the latest event trigger time for agent i.

6. The multi-agent dynamic event-triggered fixed-time binary consensus control method according to claim 5, characterized in that: The measurement error is:

7. The multi-agent dynamic event-triggered fixed-time binary consensus control method according to claim 6, characterized in that: The event triggering function is: Where θ∈(0,∞) are the parameters of the trigger function.

8. The multi-agent dynamic event-triggered fixed-time binary consensus control method according to claim 7, characterized in that: The dynamic triggering condition is: When the condition of equation (11) is met, the binary consensus controller is updated; otherwise, the binary consensus controller is not updated.

9. The multi-agent dynamic event-triggered fixed-time binary consensus control method according to claim 8, characterized in that: The upper limit of the fixed convergence time is: The parameters c2 in equation (7), θ and ε in equation (9), and c6 and γ in equation (10) must satisfy the following conditions: The upper limit of the fixed convergence time is: in

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