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

Abstract

The invention discloses a multi-agent specified time consistency control method based on dynamic event driving, and the control method proposes a new consistency control protocol to coordinate a multi-agent system to achieve consistency within specified time, so that a user can achieve the consistency of the multi-agent system according to the specific requirements of a task. A specified adjustment time is autonomously selected to enhance the convergence performance of the system. Meanwhile, different types of gain functions are introduced when a consistency control protocol is designed, the generalization ability of the controller is enhanced, it is guaranteed that output is kept within a stable range, and then the robustness and the adaptivity of the controller are enhanced. Besides, according to the dynamic triggering condition provided by the invention, state errors of adjacent agents at respective triggering moments are considered, and a reciprocal term of a dynamic variable is introduced to compensate instability of the system during discontinuous communication, so that the problem that a Zeno behavior still exists after the system is consistent in the conventional dynamic triggering mechanism is solved.

Description

Technical Field

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

[0002] Inspired by swarming animals, distributed tracking control of multi-agent systems has attracted widespread attention due to its good scalability and robustness in completing multiple tasks. Among them, convergence performance is often an important indicator for evaluating the quality of a controller, which characterizes the stability and convergence speed of the multi-agent system during task execution. In addition, controller parameter optimization can maximize the performance of the controller and ensure that the system can adjust the control input to the desired range more quickly and smoothly before reaching a steady state.

[0003] Based on the conservatism of the upper bound of the convergence time, the second-order multi-agent distributed tracking control algorithm can be divided into a finite-time tracking control algorithm and a fixed-time tracking control algorithm. 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 also relatively conservative. In order to achieve a smoother control input and save the energy consumption of the system, the specified time tracking control algorithm came into being. Compared with the previous two algorithms, a significant advantage of the specified time tracking control algorithm is that the system's stabilization time can be preset, 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 two perspectives: a single agent and the entire system. The communication resources and computing power of a single agent are limited. If agents communicate frequently, delays and data packet loss may occur. When the system reaches stability and allows a certain level of tracking error, the control input error between adjacent agents usually needs to be within the allowable range, and the position error of the same agent at consecutive time points is usually small. This shows that there is no need for continuous communication between adjacent agents. In order to minimize unnecessary communication, a trigger mechanism for multi-agent systems is proposed. This mechanism will only operate by updating the controller when the measured state error exceeds a certain threshold. The trigger mechanism can be further divided into a static trigger mechanism and a dynamic trigger mechanism. Among them, the dynamic trigger mechanism contains an adaptive threshold variable that can be adjusted in real time according to the actual state of the agent. Therefore, compared with the static trigger mechanism, the dynamic trigger mechanism can reduce the frequency of agent communication while maintaining good control performance. However, the existing dynamic trigger mechanism cannot guarantee the non-negativity of the dynamic variable after execution, Zeno behavior will occur, and the communication burden will inevitably increase. Therefore, establishing a new form of dynamically triggered 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 designated 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 comprises 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 trigger conditions.

[0010] Step 4: Establish the consensus tracking controller u of 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, 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] Preferably, in 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] Preferably, in 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 expression of the adjacency matrix A of the network topology of the multi-agent system is:

[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, is the time when the agent triggers the most recent dynamic event The location coordinates of is the time when the i-th agent triggers the most recent dynamic event The location coordinates of is the jth agent at the time of the most recent dynamic event triggering Speed ​​state; is the time when the i-th agent triggers the most recent dynamic event 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 ith agent last triggered a dynamic event; inf(·) is the operation of taking the lower bound of the set; Λ i The expression of (t) is:

[0026]

[0027] Among them, w xi (t) is the position coupling error of the ith agent at the current time t; w vi (t) is the velocity coupling error of the ith agent at the current time t; is the time when the i-th agent triggers the most recent dynamic event Position coupling error; is the time when the i-th agent triggers the most recent dynamic event speed 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 For the matrix The minimum eigenvalue of ; For the matrix The minimum eigenvalue of t The maximum upper bound of the time interval for all event triggers; is the feature 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 speed coupling error w vi (t) and The expressions are:

[0036]

[0037] Among them, e xi (t) is the position error of the ith agent at the current time t, e vi (t) is the speed error of the ith agent at the current time t, and are respectively the i-th agent at the most recent triggering time 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 time position and velocity.

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

[0039]

[0040] in, is the position coordinate x of the ith agent i The derivative of (t); is the velocity state v of the ith agent i The derivative of (t); u i (t) is the control input of the ith agent; C i (x i (t), v i (t)) is a nonlinear term, which 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 preferred embodiment, in the step 2, the control input u0(t) of the virtual leader is set to 0; the relative position of the ith agent and the virtual leader is taken as the position coordinate x of the ith 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] 1. The present invention proposes a new consistency control protocol to coordinate the multi-agent system to reach consistency within a specified time, so that users can autonomously 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.

[0046] 2. The dynamic trigger condition proposed in the present 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, thus solving the problem that the previous dynamic trigger 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 a communication topology diagram of the present invention.

[0049] Figure 3 This is a graph showing the displacement of the four controlled robots over time in the present invention.

[0050] Figure 4 Graph showing the speed variations of the four controlled robots over time in the present invention.

[0051] Figure 5 This is a time interval diagram of the first event triggering of the controlled robot 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 in conjunction with the accompanying drawings.

[0056] like Figure 1 As shown, a multi-agent designated 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 the velocity set v = {v1,...,v n}. Where n is the number of agents; x i is the position coordinate of the ith agent; v i is the speed 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 expression of the adjacency matrix A of the network topology of the multi-agent system is:

[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 ith agent and the virtual leader. 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. 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 second-order nonlinear multi-agent system:

[0065]

[0066] in, is the position coordinate x of the ith agent i The derivative of (t); is the velocity state v of the ith agent i The derivative of (t); u i (t) is the control input of the ith 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] 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; ||·|| is the norm symbol.

[0070] When i=0, the dynamic model of the above 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 state of the multi-agent system, a new internal dynamic variable f is introduced i (t), given the dynamic event triggering mechanism is as follows:

[0073]

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

[0075]

[0076] Among them, w xi (t) is the position coupling error of the ith agent at the current time t, w xi (t) = e xi (t)+σe vi (t); e xi (t) is the position error of the ith agent at the current time t, e vi (t) is the speed error of the ith agent at the current time t, and are respectively the i-th agent at the most recent triggering time The position and velocity of w vi (t) is the velocity coupling error of the ith agent at the current time t, is the time when the i-th agent triggers the most recent dynamic event 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, is the time when the i-th agent triggers the most recent dynamic event The speed 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 For the matrix The minimum eigenvalue of ; For the matrix The minimum eigenvalue of t The maximum upper bound of the time interval for all event triggers; is the feature matrix, and the expression is M is the definition of the transformation matrix, the expression is For a directed graph G n Laplacian matrix of (V, E, A); diag{a 10, ...a n0} is a diagonal matrix; I m is the unit 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 of 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 speed error and position error between the ith 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] Through 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] Among them, 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] Among them, 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 diagram. 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 diagram of the multi-agent system is shown in 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 structure 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 speed of the virtual leader are set to x0(0) = -2, v0(0) = 0.8, respectively. In addition, the sampling time of the experiment is 0.001δ, and the total simulation time is 10s. In order 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 controller under test based on the mean and standard deviation of the experimental results.

[0114] 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 Figure 3 As shown. The vertical axis represents the distance from the robot to the origin (m), and the horizontal axis represents the running time (s); Figure 3 The figure shows the convergence process of the displacement of the four robots. It can be seen from the figure that the multi-robot system can quickly reach consistency. After 100 operations, the average time to reach consistency is 7.79s, which reflects the efficiency of the multi-agent specified time consistency control method proposed by the present invention.

[0115] 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 Figure 4 As shown. 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 the present 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, so after the multi-robot system reaches consensus, the number of communications of the second robot is significantly reduced compared to the first robot, effectively avoiding Zeno behavior. The comparison of the stabilization time of the method provided in 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 present invention. It should be noted that those skilled in the art can still modify and improve the schemes proposed in the aforementioned embodiments, which should also be regarded as the protection scope 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 specified time consistency control method based on dynamic event driving, characterized by: The following steps are involved: Step 1: Establish a communication network topology diagram of the multi-agent system; Step 2: Construct a dynamic model of the multi-agent system; Step 3: Design dynamic event trigger conditions; Step 4: Establish the consensus tracking controller u of 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 drive according to claim 1 is characterized in that: In 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 drive according to claim 1 is characterized in that: 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 network topology of the multi-agent system 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 drive according to claim 3 is characterized in that: The linear combination The expression is: in, is the jth agent at the time of the most recent dynamic event triggering The location coordinates of is the time when the i-th agent triggers the most recent dynamic event The location coordinates of is the jth agent at the time of the most recent dynamic event triggering Speed ​​state; is the time when the i-th agent triggers the most recent dynamic event speed state; η, σ are preset parameters, σ, η>

0.

5. The multi-agent designated time consistency control method based on dynamic event drive 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 ith agent last triggered a dynamic event; inf(·) is the operation of taking the lower bound of the set; Λ i The expression of (t) is: Among them, w xi (t) is the position coupling error of the ith agent at the current time t; w vi (t) is the velocity coupling error of the ith agent at the current time t; is the time when the i-th agent triggers the most recent dynamic event Position coupling error; is the time when the i-th agent triggers the most recent dynamic event 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 drive according to claim 5 is characterized in that: The internal dynamic variable f i The calculation method of (t) is 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 in that: 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 For the matrix The minimum eigenvalue of ; For the matrix The minimum eigenvalue of t The maximum upper bound of the time interval for all event triggers; is the feature 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 speed coupling error w vi (t) and The expressions are: Among them, e xi (t) is the position error of the ith agent at the current time t, e vi (t) is the speed error of the ith 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 drive according to claim 1 is characterized by: The control input u0(t) of the virtual leader is set to 0; the relative position between the ith agent and the virtual leader is taken as the position coordinate x of the ith 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 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 drive 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 ith agent i The derivative of (t); is the velocity state v of the ith agent i The derivative of (t); u i (t) is the control input of the ith agent; C i (x i (t),v i (t)) is a nonlinear term, which 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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