Multi-agent time-specified consistency control method based on dynamic event-driven

Abstract

The present invention discloses a multi-agent specified time consistency control method based on dynamic event driving. The control method proposes a new consistency control protocol to coordinate the multi-agent system to reach consistency within a specified time, so that the user can independently select the specified adjustment time according to the specific needs of the task to enhance the convergence performance of the system. At the same time, the present invention introduces different types of gain functions when designing the consistency control protocol, which not only enhances the generalization ability of the controller, but also ensures that the output remains within a stable range, thereby enhancing the robustness and adaptability of the controller. In addition, the dynamic trigger condition proposed by the present invention not only takes into account the state error of adjacent agents at their respective triggering moments, but also introduces the inverse term of the dynamic variable to compensate for the instability of the system during discontinuous communication, solving the problem that the previous dynamic trigger mechanism still has Zeno behavior after the system reaches consistency.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-agent collaborative control, and specifically relates to a multi-agent specified time consistency control method based on dynamic event driving. Background Art

[0002] Inspired by the behavior of swarming animals, distributed tracking control for multi-agent systems has attracted widespread attention due to its scalability and robustness in completing multiple tasks. Convergence performance is a key metric for evaluating the quality of a controller, characterizing the stability and convergence speed of a multi-agent system during task execution. Furthermore, controller parameter optimization can maximize controller performance, ensuring that the system can more quickly and smoothly adjust control inputs to the desired range before reaching steady state.

[0003] Based on the conservatism of the upper bound of convergence time, second-order multi-agent distributed tracking control algorithms can be divided into finite-time tracking control algorithms and fixed-time tracking control algorithms. The convergence time of the finite-time tracking control algorithm is affected by the initial state of the agent, while the upper bound of the convergence time of the fixed-time control algorithm is not only affected by the controller parameters but is also relatively conservative. To achieve smoother control input and save system energy consumption, the specified-time tracking control algorithm has emerged. Compared with the previous two algorithms, a significant advantage of the specified-time tracking control algorithm is that it can preset the system's stabilization time, allowing users to set the convergence time for the multi-agent system according to different task requirements. This algorithm eliminates the need for conservative estimates of the upper bound of the convergence time and ensures that the stabilization time set by the user in advance is independent of the system's initial state and controller parameters.

[0004] Resource allocation is a crucial issue in distributed control, and its necessity can be explained from both the perspective of individual agents and the entire system. The communication resources and computing power of individual agents are limited. Frequent communication between agents can lead to latency and data loss. When the system is stable and a certain level of tracking error is tolerated, the control input errors between adjacent agents must generally be within an acceptable range, and the position errors of the same agent at consecutive time points are typically small. This suggests that continuous communication between adjacent agents is unnecessary. To minimize unnecessary communication, a trigger mechanism has been proposed for multi-agent systems. This mechanism only updates the controller when the measured state error exceeds a certain threshold. Trigger mechanisms can be further categorized as static and dynamic. Dynamic trigger mechanisms incorporate an adaptive threshold variable that adjusts in real time based on the actual state of the agent. Compared to static trigger mechanisms, dynamic trigger mechanisms can reduce the frequency of agent communication while maintaining good control performance. However, existing dynamic trigger mechanisms cannot guarantee the non-negativity of the dynamic variable after execution, resulting in Zeno behavior and an inevitable increase in communication overhead. Therefore, establishing a new form of dynamic triggering specified time control algorithm for second-order multi-agent systems is a promising research topic. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-agent specified time consistency control method based on dynamic event driving to solve the problems raised in the above background technology.

[0006] The present invention proposes a multi-agent designated time consistency control method based on dynamic event driving, which includes the following steps:

[0007] Step 1: Establish the communication network topology diagram of the multi-agent system.

[0008] Step 2: Construct a dynamic model of the multi-agent system.

[0009] Step 3: Design dynamic event triggering conditions.

[0010] Step 4: Establish the consensus tracking controller u for the multi-agent system i (t):

[0011] u i (t) = δ i (t)[γθ i (t)+sig(θ i (t)) α +S(θ i (t))]

[0012] Among them, δ i(t) is the gain function; θ i (t) is the linear combination of the speed error and position error between the ith agent and other agents and the virtual leader; sig(θ i (t)) α =[sign(θ i1 (t)|θ i1 (t)| α ),...,sign(θ im (t)|θ im (t)| α )] T ; m is the dimension of the agent's position coordinates and velocity state; n is the number of agents; γ, α, and q are all preset parameters, q, γ>0, 0<α<1.

[0013] Step 5: The multi-agent system moves with the virtual leader under the action of dynamic event triggering conditions and consistency tracking controller, and reaches consistent position and speed at the specified time.

[0014] As an example, in the step 4, the gain function δ i The expression of (t) is:

[0015]

[0016] Where, χ, ξ are preset parameters, χ, ξ>0; T is the specified stabilization time;

[0017] As a preference, in the step 1, each node of the network topology structure represents an agent, and each edge represents the communication relationship between two agents; the network topology structure diagram of the multi-agent system can be recorded as a directed graph G n (V, E, A); where V is the set of multi-agents; E is the set of communication relationships between agents; and A is the adjacency matrix. The adjacency matrix A of the multi-agent system network topology is expressed as:

[0018]

[0019] Among them, a ij is the weight of the edge between the ith agent and the jth agent; if the ith agent can receive information from the jth agent, then a ij >0(j≠i); if the i-th agent cannot receive information from the j-th agent, then a ij =0; j = 1, 2, ..., n.

[0020] Preferably, the linear combination θ i The expression of (t) is:

[0021]

[0022] in, The moment when the agent triggers the most recent dynamic event The location coordinates of The i-th agent at the time of the most recent dynamic event triggering The location coordinates of The jth agent at the time of the most recent dynamic event triggering Speed ​​state; The i-th agent at the time of the most recent dynamic event triggering speed state; η, σ are preset parameters, σ, η>0.

[0023] Preferably, the dynamic event triggering condition is:

[0024]

[0025] in, The next time a dynamic event is triggered; is the time when the i-th agent last triggered a dynamic event; inf(·) is the set lower bound operation; Λ i The expression of (t) is:

[0026]

[0027] Among them, w xi (t) is the position coupling error of the i-th agent at the current time t; w vi (t) is the velocity coupling error of the i-th agent at the current time t; The i-th agent at the time of the most recent dynamic event triggering Position coupling error; The i-th agent at the time of the most recent dynamic event triggering velocity coupling error; b0, b1, b2 are preset parameters, b0, b1, b2>0; fi(t) is the internal dynamic variable.

[0028] Preferably, the calculation method of the internal dynamic variable fi(t) is as follows:

[0029]

[0030] Among them, b3, b4 are preset parameters, b3, b4>0; f i (0)>0.

[0031] Preferably, the preset parameters η, σ, and b0 satisfy the following relationship:

[0032]

[0033]

[0034] Among them, δ i is the gain function δ i (t); γ is a preset parameter, γ>0; τ min is a matrix The minimum eigenvalue of ; is a matrix The minimum eigenvalue of d t The upper bound of the maximum time interval for all event triggers; is the characteristic matrix, and the expression is M is the definition of the transformation matrix, the expression is For a directed graph G n The Laplace matrix of (V, E, A); diag{a 10 ,...,a n0} is a diagonal matrix; I m is the unit matrix of order m; l1, l2 are preset parameters, l1, l2>0.

[0035] Preferably, the position coupling error w xi (t) and and the velocity coupling error w vi (t) and The expressions are:

[0036]

[0037] Among them, e xi (t) is the position error of the i-th agent at the current time t, e vi (t) is the speed error of the i-th agent at the current time t, and are the i-th agent at the most recent triggering moment position and velocity; is the sum of the position errors between the ith agent and the multi-agent system, is the sum of the speed errors between the ith agent and the multi-agent system, and are the jth agent at the most recent triggering moment position and velocity.

[0038] Preferably, in the step 2, the expression of the constructed kinetic model is:

[0039]

[0040] in, is the position coordinate x of the i-th agent i The derivative of (t); is the velocity state v of the i-th agent i The derivative of (t); u i (t) is the control input of the i-th agent; C i (x i (t), v i (t)) is a nonlinear term that satisfies the following inequality:

[0041] ||C i (x i (t), v i (t))-C0(x0(t),v0(t))||≤l1||x i (t)-x0(t)||+l2||v i (t)-v0(t)||

[0042] Among them, C0(x0(t), v0(t)) is the nonlinear term of the virtual leader; x0(t) is the position coordinate of the virtual leader at time t; v0(t) is the speed state of the virtual leader at time t; l1, l2 are preset parameters, l1, l2>0.

[0043] As a preference, in the step 2, the control input u0(t) of the virtual leader is set to 0; the relative position of the i-th agent and the virtual leader is taken as the position coordinate x of the i-th agent at time t. i (t); The relative speed between the ith agent and the virtual leader is taken as the speed state v of the ith agent at time t i (t); Construct an extended communication topology graph G with the virtual leader as the reference state n+1 ; Extended communication topology graph G n+1 Contains the adjacency matrix B=(a 10 , a 20 ,...,a n0 );a i0 is the weight between the ith agent and the virtual leader; if the ith agent can receive information from the virtual leader, then a i0 >0; if the i-th agent cannot receive information from the virtual leader, then a i0 =0.

[0044] The present invention has the following beneficial effects:

[0045] This paper proposes a new consensus control protocol to coordinate a multi-agent system to reach consensus within a specified time. This allows users to autonomously select specific adjustment times based on task requirements to enhance the system's convergence performance. Furthermore, the present invention introduces different types of gain functions in the design of the consensus control protocol, which not only enhances the controller's generalization capabilities but also ensures that the output remains within a stable range, thereby strengthening the controller's robustness and adaptability.

[0046] 2. The dynamic triggering condition proposed in this invention not only takes into account the state error of adjacent intelligent agents at their respective triggering moments, but also introduces the inverse term of the dynamic variable to compensate for the instability of the system during discontinuous communication, solving the problem that the previous dynamic triggering mechanism still has Zeno behavior after the system reaches consensus. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of the present invention;

[0048] Figure 2 This is the communication topology diagram of the present invention.

[0049] Figure 3 Graph showing the displacement of the four controlled robots over time in the present invention.

[0050] Figure 4 This is a graph showing the speed changes of the four controlled robots over time in the present invention.

[0051] Figure 5 This is a time interval diagram of the first controlled robot event triggering in the present invention.

[0052] Figure 6 This is a time interval diagram of event triggering of the second controlled robot in the present invention.

[0053] Figure 7 This is a time interval diagram of event triggering of the third controlled robot in the present invention.

[0054] Figure 8 This is a time interval diagram of the fourth controlled robot event triggering in the present invention. DETAILED DESCRIPTION

[0055] The present invention will be further described below with reference to the accompanying drawings.

[0056] like Figure 1 As shown, a multi-agent specified time consistency control method based on dynamic event driving includes the following steps:

[0057] Step 1: Establish the communication network topology diagram of the multi-agent system

[0058] Determine the position set x={x1,...,x n} and velocity set v={v1,...,v n}. Where n is the number of agents; x i is the position coordinate of the i-th agent; v i is the velocity state of the i-th agent; i = 1, 2, ..., n.

[0059] For a system containing n agents, each node in the network topology represents an agent, and each edge represents the communication relationship between two agents. The network topology diagram of the multi-agent system can be recorded as a directed graph G n (V, E, A); where V is the set of multi-agents; E is the set of communication relationships between agents; and A is the adjacency matrix. The adjacency matrix A of the multi-agent system network topology is expressed as:

[0060]

[0061] Among them, a ij is the weight of the edge between the ith agent and the jth agent; if the ith agent can receive information from the jth agent, then a ij >0(j≠i); if the i-th agent cannot receive information from the j-th agent, then a ij =0; j = 1, 2, ..., n.

[0062] There is a virtual leader in the multi-agent system. The position coordinates x of the i-th agent at time t i (t) Take the relative position of the ith agent and the virtual leader; the speed state v of the ith agent at time t i (t) Take the relative speed between the i-th agent and the virtual leader. Construct the extended communication topology G with the virtual leader as the reference state n+1 ; Extended communication topology graph G n+1 Contains the adjacency matrix B=(a 10 , a 20 ,...,a n0 );a i0 is the weight between the ith agent and the virtual leader; if the ith agent can receive information from the virtual leader, then a i0 >0; if the i-th agent cannot receive information from the virtual leader, then a i0 = 0. By setting a virtual leader, the multi-agent system can more easily converge to the predetermined ideal position and speed.

[0063] Step 2: Construct a dynamic model of the multi-agent system

[0064] Dynamic model of a second-order nonlinear multi-agent system:

[0065]

[0066] in, is the position coordinate x of the i-th agent i The derivative of (t); is the velocity state v of the i-th agent i The derivative of (t); u i (t) is the control input of the i-th agent; C i (x i (t), v i (t)) is an uncertain nonlinear term.

[0067] In order to facilitate calculation, the nonlinear term C of the present invention is i (x i (t), v i (t)) The following conditions must be met:

[0068] ||C i (x i (t), v i (t))-C0(x0(t), v0(t))||≤l1||x i (t)-x0(t)||+l20v i (t)-v0(t)||

[0069] Where C0(x0(t), v0(t)) is the nonlinear term of the virtual leader; x0(t) is the position coordinate of the virtual leader at time t; v0(t) is the velocity state of the virtual leader at time t; l1 and l2 are preset parameters, l1 and l2>0; ||·|| is the norm symbol.

[0070] When i=0, the dynamic model of the above-mentioned second-order nonlinear multi-agent system is the dynamic model of the virtual leader, and its corresponding control inputs are all set to 0, that is, u0(t)=0.

[0071] Step 3: Design dynamic event trigger conditions

[0072] Based on the observability of the multi-agent system state, a new internal dynamic variable f is introduced i (t), the given dynamic event triggering mechanism is as follows:

[0073]

[0074] in, is the moment when the i-th agent last triggered a dynamic event; is the time when the next dynamic event is triggered; inf(·) is the set lower bound operation; Λ i The expression of (t) is:

[0075]

[0076] Among them, w xi (t) is the position coupling error of the i-th agent at the current time t, w xi (t) = e xi (t)+σe vi (t); e xi (t) is the position error of the i-th agent at the current time t, e vi (t) is the speed error of the i-th agent at the current time t, and are the i-th agent at the most recent triggering moment The position and velocity of w vi (t) is the velocity coupling error of the i-th agent at the current time t, The i-th agent at the time of the most recent dynamic event triggering The position coupling error, is the sum of the position errors between the ith agent and the multi-agent system, is the sum of the speed errors between the ith agent and the multi-agent system, The i-th agent at the time of the most recent dynamic event triggering The velocity coupling error, is the moment when the jth agent last triggered a dynamic event; σ, η, b0, b1, b2, b3, b4 are preset parameters, σ, η, b0, b1, b2, b3, b4> 0; f i (0)>0.

[0077] The preset parameters η, σ, and b0 satisfy the following relationship:

[0078]

[0079] Among them, δ i is the gain function δ i (t); γ is a preset parameter, γ>0; τ min is a matrix The minimum eigenvalue of ; is a matrix The minimum eigenvalue of d t The upper bound of the maximum time interval for all event triggers; is the characteristic matrix, and the expression is M is the definition of the transformation matrix, the expression is For a directed graph G n Laplacian matrix (Laplacian matrix) of (V, E, A); diag{a 10, ...a n0} is a diagonal matrix; I m is the identity matrix of order m; m is the dimension of the agent's position coordinates and velocity state; is the Kronecker product.

[0080] Step 4: Confirm the consistency control protocol of the multi-agent system

[0081] Based on the dynamic event drive in step 3, the gain function δ is designed respectively i (t) and core controller u * (t), establish the consensus tracking controller u for the multi-agent system i (t):

[0082] u i (t) = δ i (t)u * (t) = δ i (t)[γθ i (t)+sig(θ i (t)) α +S(θ i (t))]

[0083] Among them, θ i (t) is the linear combination of the velocity error and position error between the i-th agent and other agents and the virtual leader; α, q are preset parameters, and their value range is: 0<α<1,q>0; sig(θ i (t)) α =[sign(θ i1 (t)|θ i1 (t)| α ),...,sign(θ im (t)|θ im (t)| α )] T ; sign(·) is the sign function; tanh(·) is the hyperbolic tangent function.

[0084] Gain function δ i The expression of (t) is:

[0085]

[0086] Where, χ, ξ are preset parameters, χ, ξ>0; T is the specified stabilization time;

[0087] Linear combination θ i The expression of (t) is:

[0088]

[0089] Step 5: Determine the stability conditions of the multi-agent system

[0090] By using the Lyapunov stability theorem, the stability condition of the above multi-agent system is determined, and the sub-Lyapunov function J(t) is taken as:

[0091]

[0092] Where, ε(t)=[y(t) T z(t) T ] T ; δ i is the gain function δ i The maximum value of (t); I mn is the m×n unit matrix.

[0093] Based on the above sub-Lyapunov function, the following Lyapunov function is constructed:

[0094]

[0095] in, K is obtained by the following equation:

[0096]

[0097] Among them, Ξ is due to The diagonal matrix composed of the eigenvalues ​​of nm}.

[0098] make Can get The time derivative of V(t) The expression is:

[0099]

[0100] Where λ2(Ω) is the second smallest eigenvalue in the matrix Ω; r, r1, r2 are all preset parameters.

[0101] By applying Bernoulli's equation, we can get the following equation:

[0102]

[0103] Where V0 is the initial value of the Lyapunov function V(t).

[0104] when have So Satisfies the following inequality:

[0105]

[0106] Therefore, according to the above formula, it can be seen that V(t) can converge to a radius of V within the specified stable time T. * (χ, r1, ξ), indicating that the system can achieve output consistency.

[0107] Step 6: Consistency Control of Multiple Agents

[0108] The multi-agent consistency control algorithm based on the specified time consistency triggered by dynamic events is simulated through code programming, and the distributed information interaction between the controlled robots is realized through the directed communication topology. According to the multi-agent consistency control protocol determined in step 4, the n agents are collaboratively controlled so that the n agents meet the stability conditions determined in step 5 and the multi-agent consistency requirements of the control performance requirements. The simulation and experiments prove that Zeno behavior does not exist. The communication topology of the multi-agent system is shown in the figure below. Figure 2 As shown, the virtual leader only communicates with the first agent.

[0109] Example 1

[0110] Taking the task of completing tracking consistency within a specified time by a multi-robot system as an example, the multi-agent specified time consistency control method based on dynamic event-driven is as follows:

[0111] like Figure 2 As shown, in this embodiment, the number of controlled robots is four, and the adjacency matrix A corresponding to the communication topology of the robot system is as follows:

[0112]

[0113] Define the nonlinear term C in the dynamic model i (x i(t), v i (t), t) = cos(x i (t))+sin(x i (t)), C0(x0(t), v0(t), t) = cos(x0(t)) + sin(x0(t)), i = 1, 2, ..., n. The initial position and velocity of the virtual leader are set to x0(0) = -2 and v0(0) = 0.8, respectively. In addition, the sampling time of the experiment is 0.001δ, and the total simulation time is 10s. To avoid the randomness of the experimental results, the present invention repeats the experiment 100 times, and finally obtains the output value of the stability of the tested controller based on the mean and standard deviation of the experimental results.

[0114] In order to verify the reliability of the present invention, the initial positions of four controlled robots are randomly generated in the range of [-6,6]. The displacement changes of the four controlled robots over time are shown in the following table: Figure 3 As shown in the figure. The vertical axis represents the distance from the robot to the origin (m), and the horizontal axis represents the running time (s); Figure 3 This figure shows the convergence process of the four robots' displacements. It shows that the multi-robot system can quickly reach consensus. After 100 operations, the average time to reach consensus was 7.79 seconds, demonstrating the high efficiency of the proposed multi-agent time-specific consensus control method.

[0115] In order to verify the reliability of the present invention, the initial speeds of four controlled robots are randomly generated in the range of [-1.4, 1.4]. The changes of the speeds of the four controlled robots over time are shown in the following table: Figure 4 As shown in the figure. The vertical axis represents the robot's moving speed (m / s), and the horizontal axis represents the running time (s); Figure 4 It reflects the convergence process of the speeds of the four robots. It can be clearly seen from the figure that the multi-robot system can reach consistency in a very short time, and the speeds of the four controlled robots can quickly follow the speed changes of the virtual leader, which reflects the robustness of the multi-agent specified time consistency control method proposed in this invention.

[0116] The time interval for the event triggering of the first controlled robot is as follows Figure 5 shown. Figure 5 In the example, the vertical axis represents the time interval (s), and the horizontal axis represents the running time (s). Figure 5 It can be seen that the communication between robots only occurs when the state (displacement and speed) changes, which greatly reduces the computer communication resources compared to the continuous communication system. Since the virtual leader only communicates with the first controlled robot and the speed of the virtual leader is nonlinear, the speed of the virtual leader will still change after the multi-robot system reaches consensus, which prompts Figure 5The number of communications in the second half increases, but compared to continuous communications, the number of communications in the present invention is within an acceptable range, effectively avoiding the Zeno behavior.

[0117] The time intervals for event triggering of the second to fourth controlled robots are as follows: Figures 6 to 8 As shown; Figures 6 to 8 In the example, the vertical axis represents the time interval (s), and the horizontal axis represents the running time (s). Figures 6 to 8 It can be seen that the communication between robots only occurs when the state (displacement and speed) changes, which greatly reduces the computer communication resources compared to the continuous communication system. Figure 5 The difference is that the second robot does not communicate directly with the virtual leader. Therefore, after the multi-robot system reaches consensus, the second robot's communication frequency is significantly reduced compared to the first robot, effectively avoiding Zeno behavior. The comparison of the stabilization time of the method provided by this embodiment and the conventional method is shown in Table 1 below:

[0118] Table 1 Estimated stabilization time

[0119]

[0120] It can be seen from Table 1 that the method provided in this embodiment can greatly improve the speed at which multiple controlled robots reach consistency requirements.

[0121] The comparison of the event-driven rate between the method provided in this embodiment and the existing static event-driven method is shown in Table 2 below:

[0122] Table 2 The average value of event-driven rate after 100 calculations

[0123] method Controlled robot 1 Controlled Robot 2 Controlled Robot 3 Controlled Robot 4 The present invention 13.42 10.00 9.46 10.18 Static event-driven approach 65.34 52.29 17.47 55.17

[0124] It can be seen from Table 2 that the method provided in this embodiment can greatly reduce the amount of calculation required for the collaboration of multiple controlled robots.

[0125] The above embodiments are only preferred implementation schemes of the present invention, and are not intended to limit the same. It should be noted that those skilled in the art can still modify and improve the solutions proposed in the above embodiments, which should also be regarded as the scope of protection of the present invention and will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A multi-agent time-specified consistency control method based on dynamic event-driven, characterized by: The following steps are involved: Step 1: Establish a communication network topology diagram for the multi-agent system; Step 2: Construct a dynamic model of the multi-agent system; Step 3: Design dynamic event triggering conditions; Step 4: Establish the consensus tracking controller u for the multi-agent system i (t): Among them, δ i (t) is the gain function; is the linear combination of the speed error and position error between the ith agent and other agents and the virtual leader; m is the dimension of the agent’s position coordinates and velocity state; i=1,2,...,n; n is the number of agents; γ, α, q are all preset parameters, q, γ>0, 0<α<1; Step 5: The multi-agent system moves with the virtual leader under the action of dynamic event triggering conditions and the consistency tracking controller, and reaches a consensus at the specified time.

2. The multi-agent designated time consistency control method based on dynamic event driving according to claim 1 is characterized by: In the step 4, the gain function δ i The expression of (t) is: Where, χ,ξ are preset parameters, χ,ξ>0; T is the specified stabilization time; 3. The multi-agent designated time consistency control method based on dynamic event driving according to claim 1 is characterized by: In the above step 1, each node of the network topology structure represents an agent, and each edge represents the communication relationship between two agents. The network topology structure diagram of the multi-agent system can be recorded as a directed graph G n (V, E, A); where V is the set of multi-agents; E is the set of communication relationships between agents; A is the adjacency matrix; the expression of the adjacency matrix A of the multi-agent system network topology structure is: Among them, a ij is the weight of the edge between the ith agent and the jth agent; if the ith agent can receive information from the jth agent, then a ij >0(j≠i); if the i-th agent cannot receive information from the j-th agent, then a ij =0; j = 1, 2, ..., n.

4. The multi-agent designated time consistency control method based on dynamic event driving according to claim 3 is characterized by: The linear combination The expression is: in, The jth agent at the time of the most recent dynamic event triggering The location coordinates of The i-th agent at the time of the most recent dynamic event triggering The location coordinates of The jth agent at the time of the most recent dynamic event triggering Speed ​​state; The i-th agent at the time of the most recent dynamic event triggering speed state; η, σ are preset parameters, σ, η>

0.

5. The multi-agent designated time consistency control method based on dynamic event driving according to claim 4 is characterized in that: The dynamic event triggering conditions are: in, The next time a dynamic event is triggered; is the time when the i-th agent last triggered a dynamic event; inf(·) is the set lower bound operation; Λ i The expression of (t) is: Among them, w xi (t) is the position coupling error of the i-th agent at the current time t; w vi (t) is the velocity coupling error of the i-th agent at the current time t; The i-th agent at the time of the most recent dynamic event triggering Position coupling error; The i-th agent at the time of the most recent dynamic event triggering Speed ​​coupling error; b0, b1, b2 are preset parameters, b0, b1, b2>0; f i (t) is an internal dynamic variable.

6. The multi-agent designated time consistency control method based on dynamic event driving according to claim 5 is characterized by: The internal dynamic variable f i (t) is calculated as follows: Among them, b3, b4 are preset parameters, b3, b4>0; f i (0)>0.

7. The multi-agent designated time consistency control method based on dynamic event drive according to claim 5 is characterized by: The preset parameters η, σ, and b0 satisfy the following relationship: Among them, δ i is the gain function δ i (t); γ is a preset parameter, γ>0; τ min is a matrix The minimum eigenvalue of ; is a matrix The minimum eigenvalue of d t The upper bound of the maximum time interval for all event triggers; is the characteristic matrix, and the expression is M is the definition of the transformation matrix, the expression is For a directed graph G n The Laplace matrix of (V,E,A); diag{a 10 ,...,a n0 } is a diagonal matrix; I m is the unit matrix of order m; l1,l2 are preset parameters, l1,l2>0.

8. The multi-agent designated time consistency control method based on dynamic event drive according to claim 5 is characterized by: The position coupling error w xi (t) and and the velocity coupling error w vi (t) and The expressions are: Among them, e xi (t) is the position error of the i-th agent at the current time t, e vi (t) is the speed error of the i-th agent at the current time t, is the sum of the position errors between the ith agent and the multi-agent system, is the sum of the speed errors between the ith agent and the multi-agent system, 9. The multi-agent designated time consistency control method based on dynamic event driving according to claim 1 is characterized by: The control input u0(t) of the virtual leader is set to 0; the relative position of the i-th agent and the virtual leader is used as the position coordinate x of the i-th agent at time t. i (t); The relative speed between the ith agent and the virtual leader is taken as the speed state v of the ith agent at time t i (t); Construct an extended communication topology graph G with the virtual leader as the reference state n+1 ; Extended communication topology graph G n+1 Contains the adjacency matrix B=(a 10 ,a 20 ,...,a n0 );a i0 is the weight between the i-th agent and the virtual leader; If the i-th agent can receive information from the virtual leader, then a i0 >0; if the i-th agent cannot receive information from the virtual leader, then a i0 =0.

10. The multi-agent designated time consistency control method based on dynamic event driving according to claim 9 is characterized in that: In the step 2, the expression of the constructed kinetic model is: in, is the position coordinate x of the i-th agent i The derivative of (t); is the velocity state v of the i-th agent i The derivative of (t); u i (t) is the control input of the i-th agent; C i (x i (t),v i (t)) is a nonlinear term that satisfies the following inequality: ||C i (x i (t),v i (t))-C0(x0(t),v0(t))||≤l1||x i (t)-x0(t)||+l2||v i (t)-v0(t)|| Among them, C0(x0(t),v0(t)) is the nonlinear term of the virtual leader; x0(t) is the position coordinate of the virtual leader at time t; v0(t) is the speed state of the virtual leader at time t; l1,l2 are preset parameters, l1,l2>0.

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