A Distributed Resource Scheduling Method for Unmanned Aerial Vehicle Swarms Based on Dynamic Event Triggers

Abstract

This invention discloses a distributed resource scheduling method for UAV swarms based on dynamic event triggering, belonging to the field of distributed optimization technology. First, a UAV swarm resource scheduling model and communication topology model with global equality constraints are constructed based on a multi-agent system. Second, a distributed resource scheduling algorithm is designed for each UAV swarm based on the dynamic event triggering method. Finally, a distributed event triggering controller is designed using dynamic error and its parameters are given to achieve distributed resource scheduling. This invention provides a distributed resource scheduling method for UAV swarms based on dynamic event triggering, which addresses the distributed resource scheduling problem of UAV swarms with global equality constraints. It can exponentially converge to the optimal solution under discrete communication, significantly reducing bandwidth consumption, decreasing latency and packet loss probability, and improving system efficiency.

Description

Technical Field

[0001] This invention belongs to the field of distributed optimization technology, and in particular relates to a distributed resource scheduling method for unmanned aerial vehicle (UAV) swarms based on dynamic event triggering. Background Technology

[0002] Large-scale unmanned aerial vehicle (UAV) swarms are characterized by distributed communication. The goal of distributed optimization for UAV swarms is to design distributed algorithms to find the optimal solution to the global optimization problem through communication and cooperation among UAV swarms. Compared to traditional centralized optimization algorithms, distributed optimization algorithms have the following significant advantages: each UAV swarm only needs to communicate with its neighbors, which not only improves the security of the network system but also reduces communication costs; the information and objective function of each UAV swarm can be effectively protected. Therefore, researching the distributed optimization problem of UAV swarm systems is of great significance.

[0003] However, previous research relied on continuous information exchange between UAV swarms. In practical engineering, system bandwidth and energy resources are limited, and continuous communication is often unreliable. Moreover, excessively high communication frequencies can increase system workload and energy consumption. Event-triggered mechanisms, by designing trigger functions, only activate when trigger conditions are met. Upon triggering, the system performs data sampling, information transmission, and controller updates, effectively reducing communication overhead. Therefore, it is necessary to conduct relevant research on the event-triggered mechanism of distributed resource scheduling.

[0004] Currently, research findings on the distributed event triggering mechanism of network systems can be divided into static event triggering methods and dynamic event triggering methods. The static event triggering mechanism was first applied to distributed control. While traditional static event triggering can utilize dynamic errors to design triggering conditions, this method cannot eliminate the Zeno phenomenon (see Dimargoonas, Dimos V., Emilio Frazzoli, and Karl H. Johansson. "Distributed event-triggered control for multi-agent systems." IEEE Transactions on Automatic Control 57.5(2011):1291-1297.). Building upon the former research, the dynamic event triggering mechanism was used instead of the static event triggering mechanism in distributed control, successfully eliminating the Zeno phenomenon (see Yi X, Liu K, Dimargoonas DV, et al. "Dynamic event-triggered and self-triggered control for multi-agent systems." IEEE Transactions on Automatic Control 64.8(2018):3300-3307.). However, existing research has not applied the dynamic event triggering mechanism to distributed resource scheduling with equality constraints under directed graphs. Summary of the Invention

[0005] The purpose of this invention is to provide a distributed resource scheduling method for UAV swarms based on dynamic event triggering, which solves the problem of distributed resource scheduling of UAV swarms under equality constraints in the existing technology and reduces the system communication pressure.

[0006] To achieve the above objectives, this invention provides a distributed resource scheduling method for unmanned aerial vehicle (UAV) swarms based on dynamic event triggering, comprising the following steps:

[0007] Step 1: Construct a resource scheduling model and communication topology model for unmanned aerial vehicle (UAV) swarms with global equality constraints based on the multi-agent system.

[0008] Step 2: Design a distributed resource scheduling algorithm for each UAV swarm based on the dynamic event triggering method;

[0009] Step 3: Design a distributed event-triggered controller using dynamic error and provide controller parameters to achieve distributed resource scheduling.

[0010] Preferably, the expression for the UAV swarm resource scheduling model with global equality constraints constructed in step 1 is as follows:

[0011]

[0012] Where N is the number of drone swarms, x i The state of drone swarm i is represented by x = (x1, x2, ..., xn), which represents the proportion of drones allocated to drone swarm i to the total number of drones. N ) T This represents the resource allocation of each drone swarm, where x satisfies the equality constraint. The sum of resource allocations for all drone swarms is 1, f i (x i Let be the cost function of the UAV swarm i, satisfying the strong convexity condition. And f i (x i The derivative is Lipschitz continuous. Both l0 and l are positive numbers. It represents the set of real numbers.

[0013] Preferably, the specific description of the UAV swarm communication topology model in step 1 is as follows:

[0014] The communication topology model of a drone swarm is regarded as a multi-agent network system consisting of N nodes, and the communication topology is denoted as... in Represents a set of nodes and a set of edges. (i,j)∈ε indicates that node j receives information from node i, from node i1 to node i l The path is an ordered sequence of edges (i k i k+1 ), k = 1, ..., l-1; the set of neighbors within node i is picture adjacency matrix Defined as a ii =0, if (j,i)∈ε, a ij =1, otherwise 0; Figure Laplace matrix Defined as When i≠j, l ij =-a ij ε represents an edge, a ij Representation matrix The element in the i-th row and j-th column, l ij Representation matrix The element in the i-th row and j-th column, l ii Representation matrix The element in the i-th row and i-th column.

[0015] Preferably, the communication topology model for UAV swarms needs to meet the following requirements: Communication Topology It is a strongly connected equilibrium graph. And satisfy Representing the communication topology The Laplace matrix, express Multiply by 1 on the left, 1 T This represents the transpose of a vector whose elements are all 1.

[0016] Preferably, the specific process of designing a distributed resource scheduling algorithm for each UAV swarm based on the dynamic event triggering method in step 2 is as follows:

[0017] Step 201: Update the status of the drone swarm using the derivative information of each drone swarm;

[0018] Step 202: Design an update method for auxiliary variables based on dynamic error.

[0019] Preferably, the expression for updating the drone swarm state in step 201 using the derivative information of each drone swarm is as follows:

[0020]

[0021] in, Let β represent the derivative of the state of drone swarm i, and let β be the feedback gain satisfying β > 0. yes Time to Real-time state prediction of the drone swarm j It is the time when the drone swarm i is triggered for the kth time. yes Time to Predict the state of drone swarm i at any time.

[0022] Preferably, the expression for the update method of the auxiliary variable based on dynamic error design in step 202 is as follows:

[0023]

[0024] in, η represents the derivative of the auxiliary variables in the design. i (t) represents the auxiliary variable of the design. Let k represent the set of neighbors within drone swarm i. i It is a feedback gain and satisfies k i >0, δ i ∈[0,1] is an arbitrarily chosen parameter, and m is the parameter to be designed.

[0025] Preferably, the specific expression for designing the distributed event-triggered controller using dynamic error in step 3 is as follows:

[0026]

[0027] in, Let m and θ represent the (k+1)th trigger time of the drone swarm i, where inf represents the maximum lower bound, and m and θ represent the time of trigger time. i These are the parameters of the controller to be designed.

[0028] Preferably, the controller parameters in step 3 are as follows:

[0029]

[0030] in yes The 2-norm of a matrix, yes The smallest non-zero eigenvalue, σ∈(0,1).

[0031] Therefore, the above-mentioned distributed resource scheduling method for UAV swarms based on dynamic event triggering, as described in this invention, has the following beneficial effects:

[0032] (1) This invention utilizes dynamic error to design a distributed algorithm and auxiliary variables. In the algorithm iteration, only the information of itself and its neighbors needs to be used.

[0033] (2) The present invention designs an event triggering function, which generates a triggering time instead of continuous time, thereby reducing the system communication cost;

[0034] (3) This invention proposes a distributed resource scheduling method for UAV swarms based on dynamic event triggering, and provides the parameter conditions that enable the algorithm to converge exponentially.

[0035] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0036] Figure 1 This is an overall flowchart of a distributed resource scheduling method for unmanned aerial vehicle swarms based on dynamic event triggering according to the present invention;

[0037] Figure 2 This is a schematic diagram of the UAV swarm communication topology model used in the simulation experiment of this invention;

[0038] Figure 3 This is a schematic diagram of the resource allocation ratios for each UAV swarm in the simulation experiment of this invention;

[0039] Figure 4 This is a schematic diagram of the triggering times of each UAV swarm in the simulation experiment of this invention;

[0040] Figure 5 This is a schematic diagram illustrating the total changes in UAV resources during the simulation experiment of this invention;

[0041] Figure 6 This is a schematic diagram illustrating the change in the total system cost during the simulation experiment of this invention. Detailed Implementation

[0042] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0043] Please see Figure 1-6 A method for distributed resource scheduling of unmanned aerial vehicle (UAV) swarms based on dynamic event triggering includes the following steps:

[0044] Step 1: Construct a UAV swarm resource scheduling model and communication topology model with global equality constraints based on the multi-agent system; the expression of the constructed UAV swarm resource scheduling model with global equality constraints is as follows:

[0045]

[0046] Where N is the number of drone swarms, x i The state of drone swarm i is represented by x = (x1, x2, ..., xn), which represents the proportion of drones allocated to drone swarm i to the total number of drones. N ) T This represents the resource allocation of each drone swarm, where x satisfies the equality constraint. The sum of resource allocations for all drone swarms is 1, f i (x i Let be the cost function of the UAV swarm i, satisfying the strong convexity condition. And f i (x i The derivative is Lipschitz continuous. Both l0 and l are positive numbers. Let represent the set of real numbers. The significance of this model is to minimize the sum of the cost functions of each drone swarm while keeping the total amount of resources allocated to each drone swarm constant.

[0047] The specific description of the UAV swarm communication topology model is as follows: The UAV swarm communication topology model is regarded as a multi-agent network system composed of N nodes, and the communication topology is denoted as... in Represents a set of nodes and a set of edges. (i,j)∈ε indicates that node j receives information from node i, from node i1 to node i l The path is an ordered sequence of edges (i k i k+1 ), k = 1, ..., l-1; the set of neighbors within node i is picture adjacency matrix Defined as a ii =0, if (j,i)∈ε, a ij =1, otherwise 0; Figure Laplace matrix Defined as When i≠j, l ij =-a ij ε represents an edge, a ij Representation matrix The element in the i-th row and j-th column, l ij Representation matrix The element in the i-th row and j-th column, l ii Representation matrix The element in the i-th row and i-th column. Additionally, the UAV swarm communication topology model needs to meet the following requirements: Communication Topology It is a strongly connected equilibrium graph. And satisfy Representing the communication topology The Laplace matrix, express Multiply by 1 on the left, 1 T This represents the transpose of a vector whose elements are all 1.

[0048] Step 2: Design a distributed resource scheduling algorithm for each UAV swarm based on the dynamic event triggering method; the specific process is as follows:

[0049] Step 201: Update the drone swarm status using the derivative information of each drone swarm; the specific expression is as follows:

[0050]

[0051] in, Let β represent the derivative of the state of drone swarm i, and let β be the feedback gain satisfying β > 0. yes Time to Real-time state prediction of the drone swarm j It is the time when the drone swarm i is triggered for the kth time. yes Time to Predict the state of drone swarm i at any time.

[0052] Step 202: Design an update method for auxiliary variables based on dynamic error; the specific expression is as follows:

[0053]

[0054] in, η represents the derivative of the auxiliary variables in the design. i (t) represents the auxiliary variable of the design. Let k represent the set of neighbors within drone swarm i. i It is a feedback gain and satisfies k i >0, δ i ∈[0,1] is an arbitrarily chosen parameter, and m is the parameter to be designed.

[0055] Step 3: Design a distributed event-triggered controller using dynamic errors and provide the controller parameters to implement distributed resource scheduling. The specific expression for designing the distributed event-triggered controller using dynamic errors is as follows:

[0056]

[0057] in, Let m and θ represent the (k+1)th trigger time of the drone swarm i, where inf represents the maximum lower bound, and m and θ represent the time of trigger time. i These are the parameters of the controller to be designed.

[0058] The controller parameters are as follows:

[0059]

[0060] in yes The 2-norm of a matrix, yes The smallest non-zero eigenvalue, σ∈(0,1).

[0061] Example:

[0062] The method of the present invention is verified through simulation experiments, specifically as follows: Consider three UAV swarms that need to perform their respective target tasks. The cost function of UAV swarm 1 is... The cost function of drone swarm 2 is The cost function of drone swarm 3 is The communication topology of the simulated drone swarm is as follows Figure 2 As shown, the topology graph satisfies the properties of a balanced graph and is strongly connected. The initial values ​​for each UAV swarm are set to x1(0) = 0.33, x2(0) = 0.33, and x3(0) = 0.34. Each UAV swarm is updated according to the algorithm given in this invention, and the results are as follows. Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, Figure 3 This indicates that the state indices of each UAV swarm converge to the optimal solution of the distributed resource scheduling model under equality constraints; Figure 4 This indicates that the triggering time of each drone swarm is discrete, which reduces the communication frequency of the system; Figure 5 This shows that the total number of drone resources remains unchanged during the algorithm iteration process, meaning that the equality constraint holds in real time. Figure 6 This indicates that the total system cost converges exponentially to the optimal value under the equality constraint. Overall, the experimental results show that each UAV swarm achieves resource allocation under the conditions of constant total resources and discrete communication, and the algorithm converges exponentially. This proves that the UAV swarm distributed resource scheduling method based on dynamic event triggering proposed in this invention can reduce the consumption of communication bandwidth and improve system efficiency.

[0063] Therefore, the present invention adopts the above-mentioned distributed resource scheduling method for UAV swarms based on dynamic event triggering. For the distributed resource scheduling problem of UAV swarms with global equality constraints, it can exponentially converge to the optimal solution of the problem under discrete communication, greatly reducing the bandwidth occupation of communication, reducing the probability of time delay and packet loss, and improving the efficiency of the system.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A distributed resource scheduling method for unmanned aerial vehicle (UAV) swarms based on dynamic event triggering, characterized in that, Includes the following steps: Step 1: Construct a resource scheduling model and communication topology model for UAV swarms with global equality constraints based on the multi-agent system; Step 2: Design a distributed resource scheduling algorithm for each UAV swarm based on the dynamic event triggering method; Step 3: Design a distributed event-triggered controller using dynamic error and provide controller parameters to achieve distributed resource scheduling; The specific process of designing a distributed resource scheduling algorithm for each UAV swarm based on the dynamic event triggering method in step 2 is as follows: Step 201: Update the status of the drone swarm using the derivative information of each drone swarm; Step 202: Design an update method for auxiliary variables based on dynamic error; The expression for the update method of the auxiliary variable based on dynamic error design in step 202 is as follows: in, The derivative of the auxiliary variable in the design. Indicates auxiliary variables in the design. Indicating drone swarms The set of inner neighbors, It is the feedback gain used to update the auxiliary variable and satisfies , It is an arbitrarily chosen parameter. These are the parameters to be designed. Representation matrix The element in the i-th row and j-th column, Indicating drone swarms The state represents the drone swarm. The proportion of allocated drones to the total number of drones. yes Time to Constantly monitoring drone swarms State prediction, yes Time to Constantly monitoring drone swarms State prediction, It is a swarm of drones The cost function.

2. The method for distributed resource scheduling of unmanned aerial vehicle (UAV) swarms based on dynamic event triggering according to claim 1, characterized in that, The expression for the UAV swarm resource scheduling model with global equality constraints constructed in step 1 is as follows: in, It is the number of drone swarms. Indicating drone swarms The state represents the drone swarm. The proportion of allocated drones to the total number of drones. This indicates the resource allocation of each drone swarm. Satisfying equality constraints The sum of resource allocation for all drone swarms is , It is a swarm of drones The cost function satisfies the strong convexity condition. ,and The derivative is Lipschitz continuous: , , All are positive numbers. It represents the set of real numbers.

3. The method for distributed resource scheduling of unmanned aerial vehicle (UAV) swarms based on dynamic event triggering according to claim 2, characterized in that, The specific description of the UAV swarm communication topology model in step 1 is as follows: The drone swarm communication topology model is considered to be composed of A multi-agent network system consisting of nodes has a communication topology denoted as . ,in Represents a set of nodes and a set of edges. , Represents a node Receiver node Information from nodes To the node The path is an ordered sequence of edges. , ;node The set of inner neighbors is ,picture adjacency matrix Defined as ,if , Otherwise ;picture Laplace matrix Defined as ,when , ; Represents an edge. Representation matrix The element in the i-th row and j-th column, Representation matrix The element in the i-th row and j-th column Representation matrix The element in the i-th row and i-th column.

4. The method for distributed resource scheduling of unmanned aerial vehicle (UAV) swarms based on dynamic event triggering according to claim 3, characterized in that, The communication topology model for unmanned aerial vehicle (UAV) swarms needs to meet the following requirements: Communication Topology It is a strongly connected equilibrium graph. , And satisfy , Representing the communication topology The Laplace matrix, express Left multiplication , This represents the transpose of a vector whose elements are all 1.

5. A distributed resource scheduling method for unmanned aerial vehicle (UAV) swarms based on dynamic event triggering according to claim 4, characterized in that, The expression for updating the drone swarm state in step 201 using the derivative information of each drone swarm is as follows: in, Indicating drone swarms The derivative of the state, It is a feedback gain and satisfies , yes Time to Constantly monitoring drone swarms State prediction, It is a swarm of drones No. The moment of the next trigger yes Time to Constantly monitoring drone swarms State prediction.

6. The method for distributed resource scheduling of unmanned aerial vehicle (UAV) swarms based on dynamic event triggering according to claim 5, characterized in that, The specific expression for designing the distributed event-triggered controller using dynamic error in step 3 is as follows: in, Indicating drone swarms No. The moment of the next trigger Indicates the maximum lower bound. and These are the parameters of the controller to be designed.

7. A method for distributed resource scheduling of unmanned aerial vehicle (UAV) swarms based on dynamic event triggering according to claim 6, characterized in that, The controller parameters in step 3 are as follows: in yes The 2-norm of a matrix, yes The smallest non-zero eigenvalue, .

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