A method for accelerating synchronization of unmanned system clusters for multi-lane network disruptions

Abstract

The application belongs to the field of unmanned system cluster control and autonomous cooperation, and particularly relates to an unmanned system cluster acceleration synchronization method for multi-channel network interruption. A kind of unmanned aerial vehicle cluster system is modeled as a linear system model, and a multi-channel communication topology network and a distributed elastic controller of the unmanned aerial vehicle cluster system are constructed for the model. A Lyapunov function depending on the network interruption mode is constructed, and the exponential stability and gain conditions of the synchronization error system under different network interruption modes are established to cope with the synchronization error. The synchronization rate is characterized as an optimization problem characterized by single-channel attenuation rate and network interruption duration constraints. Then, an iterative optimization algorithm is designed to solve the controller gain under different network interruption modes to achieve accelerated synchronization. Finally, the proposed method is simulated and verified, and the results show that the method can effectively solve the problem of accelerated synchronization of unmanned system cluster under multi-channel network interruption.

Description

Technical Field

[0001] This invention belongs to the field of unmanned system cluster control and autonomous collaboration, and specifically relates to a method for accelerating synchronization of unmanned system clusters in the event of multi-channel network interruption. It is a method for achieving elastic control and accelerated synchronization of unmanned system clusters in the event of multi-channel network interruption. Background Technology

[0002] With the rapid development of sensor, communication, and data processing technologies, swarm systems composed of multiple autonomous individuals have shown great potential in collaborative tasks. In particular, unmanned system swarms, represented by UAVs, unmanned surface vessels, and unmanned vehicles, can achieve wide-area coverage, distributed environmental monitoring, and collaborative operations in complex dynamic environments through close collaboration and task allocation among multiple agents, and have become a research hotspot in the field of unmanned system swarm control and autonomous collaboration.

[0003] In practical applications, unmanned system swarms are typically distributed over a wide spatial area, and their collaborative control and state synchronization heavily rely on dynamically changing wireless communication networks. However, this open communication environment makes the links between agents highly susceptible to various network outages (such as denial-of-service attacks). In high-speed or highly adversarial environments, network outages can lead to the loss of interactive data packets and delayed feedback information, resulting in swarm configuration disintegration, task interruption, and even collisions. Furthermore, due to the spatial differences in the network communication environment and communication channels of each agent, network outages often exhibit multi-channel and asynchronous characteristics, meaning that the timing and duration of damage to different communication links are not consistent. While existing resilient control research can guarantee the asymptotic stability of the system under network outages to a certain extent, it still faces the following severe challenges in handling multi-channel asynchronous network outages: 1) Limited synchronization rate: Due to frequent topology switching caused by interruptions, the system convergence speed drops significantly, making it difficult to meet the stringent timeliness requirements of real-time collaborative tasks.

[0004] 2) Difficulty in handling multi-channel coupling: Traditional single-channel interruption models are difficult to accurately depict the complex network communication environment of multiple links being independently damaged, resulting in overly conservative control gain design.

[0005] Therefore, it is essential to study the problem of accelerated synchronization of unmanned system clusters under multi-channel network interruption environments, and there are currently no inventions in this area. Summary of the Invention

[0006] The purpose of this invention is to provide an accelerated synchronization method for unmanned system clusters facing multi-channel network interruptions. This method addresses the challenges of multi-channel, asynchronous network interruptions (such as denial-of-service attacks) in collaborative tasks, including random switching of communication topologies, limited synchronization rates, and overly conservative control gain design in traditional single-channel models. By constructing a novel Lyapunov function analysis system synchronization method dependent on network interruption modes, and integrating it with a distributed elastic controller, elastic control of the cluster system is achieved in multi-channel independently damaged communication environments. Simultaneously, an iterative optimization algorithm is used to determine the optimal control gain under each interruption mode, thereby significantly shortening the dynamic response time of the cluster system in extreme communication environments and improving the system's survivability and task execution efficiency.

[0007] The technical solution of the present invention: A method for accelerating synchronization of unmanned system clusters in the face of multi-channel network interruptions, comprising the following: First, for a class of drone swarm systems, a linear model of the system is constructed; Secondly, for the communication links between the UAV swarm system, a multi-channel communication topology is constructed; the spatiotemporal characteristics of network interruptions occurring asynchronously on different channels are accurately characterized, and a dynamic topology switching model describing the damage to each communication channel is established.

[0008] Furthermore, for the established linear model and multi-channel communication topology network, a distributed elastic controller is designed and a synchronization error system is constructed by defining state deviation quantities. A distributed control strategy is then developed to ensure the robust consistency of the closed-loop system under multi-channel network interruption.

[0009] Then, a Lyapunov function that depends on the switching of different network interruption modes is designed, and the exponential stability of the synchronization error system under multi-channel network interruption environment is derived. Sufficient conditions for gain performance.

[0010] Finally, the synchronization acceleration problem is transformed into a constrained optimization problem with channel attenuation rate and network interruption constraints as boundary conditions. An iterative optimization algorithm is designed to solve the controller gain under each network interruption mode, so as to achieve the goal of the cluster system achieving synchronization at the optimal rate.

[0011] The beneficial effects of this invention are: This invention significantly improves the robustness and timeliness of clusters in complex network adversarial environments. By constructing a distributed control strategy and designing a Lyapunov function dependent on network interruption modes, it derives the exponential stability of the synchronization error system under multi-channel network interruption environments. Sufficient conditions for gain performance are established to ensure the resilient consistency of the unmanned system swarm in complex network adversarial environments. Furthermore, the designed optimization algorithm can solve for the controller gain that optimizes the convergence rate, ensuring that the unmanned system swarm can quickly achieve stable synchronization even under frequent network interruptions and topology changes. This significantly shortens task response time and enhances the system's survivability and task efficiency in complex network adversarial environments. Attached Figure Description

[0012] Figure 1 This is a flowchart of the present invention.

[0013] Figure 2 The diagram shows the communication topology under different network interruption modes in the embodiments of the present invention; wherein (a) and (b) are the network topologies under two connectivity-preserving interruptions; and (c) is the network topology under a connectivity-disconnection interruption.

[0014] Figure 3 This is a schematic diagram of the on / off sequences of different channels in an embodiment of the present invention.

[0015] Figure 4 This is a 3D trajectory diagram of a drone swarm in an embodiment of the present invention.

[0016] Figure 5 This is a schematic diagram of the trajectory of the drone cluster in the x-direction in an embodiment of the present invention.

[0017] Figure 6 This is a schematic diagram of the trajectory of the drone cluster in the y-direction in an embodiment of the present invention.

[0018] Figure 7 This is a schematic diagram of the synchronization error of the drone cluster in the x-direction in an embodiment of the present invention.

[0019] Figure 8 This is a schematic diagram of the synchronization error of the drone cluster in the y-direction in an embodiment of the present invention.

[0020] Figure 9 This is a schematic diagram comparing the synchronization rates under different feasible solutions and the optimal solution in an embodiment of the present invention. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0022] An accelerated synchronization method for unmanned system swarms is proposed to address multi-channel network interruptions. The target system is a class of unmanned aerial vehicle (UAV) swarm systems, such as... Figure 1 As shown, it includes the following steps: Step 1: Under certain flight altitude and speed conditions, a class of UAV swarm systems is modeled as a continuous-time linear system model, as follows.

[0023] The linear system model of the leader drone is as follows: in, Indicates the leader system status (including angle of attack and pitch rate). Indicates the leader system state The derivative of This is the system matrix.

[0024] The linear system model of the follower drone is as follows: in, and They represent the first i The system state vector (including angle of attack and pitch rate) and output vector (including angle of attack and pitch rate) of the follower UAV. Indicates system state The first derivative, Indicates the first i The control inputs for the follower drone (including elevator deflection angle). No. i An external disturbance of a follower drone and is energy-bounded. , , , , and This is the system matrix.

[0025] Step 2: Establish a multi-channel communication topology network for a type of UAV swarm system; The communication topology between drone swarms is represented by a weighted undirected graph. Indicates; among which, It is an index set, representing the collection of drones in a drone swarm system. For the leader drone in the system, For follower drones. It is an edge set, representing information exchange between drone swarms. Define an adjacency matrix. When the follower drone i Able to receive follower drones j When receiving information, ,otherwise Define the target matrix. When the follower drone i When the leader's drone can receive information, ,otherwise Define the Laplace matrix. ,when hour, ,when hour, Define the communication matrix. .

[0026] Define a set of multi-channel network interruption modes This includes connectivity-preserving network interruptions. and connectivity disconnection type network interruption Two types of network outage modes are identified. It is also assumed that a network outage can independently disrupt a single communication channel. .

[0027] Based on the impact of network interruptions on communication topology connectivity, the following two types of network interruption modes are defined: Connectivity-preserving network interruption In a network interruption that maintains connectivity, some communication links in the network are damaged. This causes changes to the initial topology, but the system can still ensure the connectivity of the network topology during the interruption.

[0028] Disconnection-type network interruption In a network outage where connectivity is disrupted, some or all communication links are destroyed, resulting in a complete disconnection of the network topology.

[0029] Step 3: Design a distributed resilient controller and build a synchronization error system; (3.1) The distributed elastic controller is designed as follows: in, It is the gain matrix of the controller to be designed. Indicates drone j and drones The communication channel between them, and the channel and Equivalent. The summation symbol is used to represent the summation symbol. Represents a node (Each drone is a node) in an undirected graph Neighbor set in This represents a set of multi-channel network interruption modes.

[0030] (3.2) Define the synchronization error variable Then, a synchronization error system can be established as follows: in, Represents the synchronization error variable The derivative of .

[0031] (3.3) Construction depends on network interruption mode Lyapunov function Analysis of synchronization error system Gain performance, specifically in the following form: in, This represents the augmented synchronization error vector. express 3D synchronization error vector, express A dimensional system state vector. Indicates multi-channel network interruption mode The moment the change occurred, It is the set of real numbers. , represents the time-varying Lyapunov function matrix, and and Indicates the first In network interruption mode The starting constant Lyapunov function matrix and the ending constant Lyapunov function matrix. This represents the time interval between the switching times of two adjacent network interrupt modes, and satisfies: . This represents the Kronecker product operator. express An identity matrix of order 1.

[0032] Based on Lyapunov functions Calculate the corresponding derivatives as follows: in, Representing Lyapunov functions The derivative of This represents the communication matrix under different network interruption modes. This represents the Kronecker product operator.

[0033] In this embodiment of the invention, for a given parameter , , If a Lyapunov matrix exists and and the newly defined variables to be solved ( ), satisfying the following inequality: For systems with connectivity-preserving network interruptions (i.e.) ): For systems experiencing network outages due to disconnection (i.e.) ): in, , , , , , Representation function , , , and The independent variable, specifically the matrix and . Indicates the time sequence number at which the network interruption mode changed. This indicates the total number of times the network interruption mode switching occurred. Indicates single channel Network outage intensity. Indicates in edge set In addition to Other than the part, Indicates in edge set In addition to The part other than that. This represents the percentage of multi-channel network interruption time in the total network communication time; other parameters have been defined in step 3.

[0034] The synchronization error system can achieve exponential stability and satisfy the following: Gain performance: in, , . and These represent taking all variables. and The maximum absolute value. For function and The time independent variable represents the continuous time of system operation.

[0035] Step 4: Design an iterative optimization algorithm to solve the controller gain under each network interruption mode, thereby accelerating synchronization.

[0036] The specific process of solving the controller gain using the iterative optimization algorithm is as follows: First, define the variables. The initial value can be obtained by solving step 3 to get the Lyapunov matrix. and Then, the resulting Lyapunov matrix... and Given the linear matrix inequality constraints in step 3 (3.3), minimize the following objective function: in, Let be the objective function, representing the synchronization rate of a class of UAV swarm systems. Other parameters have been defined in steps 1-3. Through continuous iteration of the optimization algorithm, the optimal synchronization rate of a class of UAV swarm systems is determined. and the corresponding controller gain and the variable to be determined It can be computed, that is, to complete the accelerated synchronization of unmanned system clusters.

[0037] The purpose of this embodiment is to design a distributed resilient controller to ensure exponential stability of a type of UAV swarm system under multi-channel network outage conditions, and to meet the following requirements. Gain performance. Furthermore, the proposed iterative optimization algorithm effectively addresses the shortcomings of existing research, significantly improving the synchronization rate of unmanned system swarms.

[0038] The method of the present invention will now be applied to a type of drone swarm system consisting of one leader and four followers to verify its effectiveness.

[0039] The linear system model of leader and follower in a drone swarm system is shown below: The meanings of the corresponding parameters have been given in step 1.

[0040] The corresponding parameter matrix is ​​given below: To present the main results, the external perturbation input is selected as... And set simulation parameters , , .like Figure 2 As shown, three different network outage modes are randomly generated to simulate complex multi-channel network outage scenarios. Among them, Figure 2 In Figures (a) and (b), the network topology is shown under the condition of a connectivity-preserving network interruption. Figure 2 In section (c), the network interruption occurs when connectivity is lost. Based on this topology, each channel... The on / off state follows Figure 3 The asynchronous sequence shown reflects the randomness and asynchronicity of multi-channel network outages. Furthermore, the initial state settings for the leader and four follower drones are as follows: Next, based on the preset system parameters and initial values, the corresponding Lyapunov matrix is ​​calculated by solving step 3. and As shown below: Furthermore, iterative optimization algorithms are used to optimize the performance of the synchronization error system and determine the optimal synchronization rate of the system. Based on this, the corresponding controller gain matrix is ​​solved simultaneously. and the variable to be determined as follows: Finally, the controller gain under different network interrupt modes Substituting these results into the designed distributed resilient controller, the main findings are presented. Figure 4-9 In the event of multi-channel network outages and external disturbances, the status trajectories of all UAVs are as follows: Figure 4-6 As shown. Figure 7 and Figure 8 The synchronization error curves of the UAVs in two different directions are presented. As shown in the figures, the trajectories of all follower UAVs asymptotically converge to the trajectory of the leader UAV, and the corresponding synchronization errors all converge to zero, verifying the robustness of the control system. Furthermore, Figure 9The synchronization rates of different schemes within the feasible solution space were compared. The results show that the optimal solution obtained through the iterative optimization algorithm described in this invention has a significantly higher synchronization rate than the general feasible solution. In conclusion, the simulation results demonstrate that the design scheme disclosed in this invention is effective.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. 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 be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for accelerating synchronization of unmanned system clusters in the face of multi-channel network interruption, characterized in that, The steps are as follows: Step 1: Under certain flight altitude and speed conditions, model a type of UAV swarm system as a continuous-time linear system model, as follows; The linear system model of the leader drone is as follows: in, Indicates the leader system status. Indicates the leader system state The derivative, For the system matrix; The linear system model of the follower drone is as follows: in, and They represent the first i The system state vector and output vector of a follower drone. Indicates system state The first derivative, Indicates the first i The control input for a follower drone No. i An external disturbance of a follower drone and is energy-bounded; , , , , and For the system matrix; Step 2: Establish a multi-channel communication topology network for a type of UAV swarm system; The communication topology between drone swarms uses a weighted undirected graph. Indicates; among which, It is an index set, representing the set of drones in a drone swarm system. For the leader drone in the system, For follower drones; It is an edge set, representing information exchange between drone swarms; define an adjacency matrix. When the follower drone i Able to receive follower drones j When receiving information, ,otherwise Define the target matrix When the follower drone i When the leader's drone can receive information, ,otherwise Define the Laplace matrix. ,when hour, ,when hour, Define the communication matrix ; Define a set of multi-channel network interruption modes This includes connectivity-preserving network interruptions. and connectivity disconnection type network interruption Two types of network outage modes; simultaneously, it is assumed that a network outage can independently disrupt a single communication channel. ; Step 3: Design a distributed resilient controller and build a synchronization error system; (3.1) The distributed elastic controller is designed as follows: in, It is the gain matrix of the controller to be designed. Indicates drone j and drones The communication channel between them, and the channel and Equivalent; The summation symbol is used to represent the summation symbol. Represents a node In undirected graphs Neighbor set in Represents a set of multi-channel network interruption modes; (3.2) Define the synchronization error variable Then, a synchronization error system is established as follows: in, Represents the synchronization error variable The derivative; (3.3) Construction depends on network interruption mode Lyapunov function Analysis of synchronization error system Gain performance, specifically in the following form: in, This represents the augmented synchronization error vector. express 3D synchronization error vector, express 3D system state vector; Indicates multi-channel network interruption mode The moment the change occurred, It is the set of real numbers; , represents the time-varying Lyapunov function matrix, and and Indicates the first In network interruption mode The starting constant Lyapunov function matrix and the ending constant Lyapunov function matrix; This represents the time interval between the switching times of two adjacent network interrupt modes, and satisfies: ; This represents the Kronecker product operator. express An identity matrix of order 1; Based on Lyapunov functions Calculate the corresponding derivatives as follows: in, Representing Lyapunov functions The derivative, This represents the communication matrix under different network interruption modes. This represents the Kronecker product operator; For a given parameter , , If a Lyapunov matrix exists and and the newly defined variables to be solved , It satisfies the following inequality: For systems with connectivity-preserving network interruptions, i.e. : For systems experiencing network outages due to disconnection, i.e. : in, , , , , , Representation function , , , and The independent variable, specifically the matrix and ; Indicates the time sequence number at which the network interruption mode changed. This indicates the total number of times the network interruption mode switching occurred. Indicates single channel The network outage intensity; Indicates in edge set In addition to Other than the part, Indicates in edge set In addition to The part other than; This indicates the percentage of multi-channel network outage time in the total network communication time. The synchronization error system can achieve exponential stability and satisfy the following: Gain performance: in, , ; and These represent taking all variables. and The maximum absolute value; For function and The time-dependent variable represents the continuous time of system operation; Step 4: Design an iterative optimization algorithm to solve the controller gain under each network interruption mode, thereby accelerating synchronization; First, define the variables. The initial value is obtained by solving step 3 to get the Lyapunov matrix. and Then, the resulting Lyapunov matrix... and Given the linear matrix inequality constraints in step 3 (3.3), minimize the following objective function: in, Let be the objective function, representing the synchronization rate of a class of unmanned aerial vehicle (UAV) swarm systems; through continuous iteration of the optimization algorithm, the optimal synchronization rate of a class of UAV swarm systems is determined. and the corresponding controller gain and the variable to be determined It can be computed, that is, to complete the accelerated synchronization of unmanned system clusters.

2. The method for accelerating synchronization of unmanned system clusters in the event of multi-channel network interruption as described in claim 1, characterized in that, The leader's system state includes angle of attack and pitch rate; the follower UAV's system state vector includes angle of attack and pitch rate, and its output vector includes angle of attack and pitch rate; the follower UAV's control input includes elevator deflection angle.

3. The method for accelerating synchronization of unmanned system clusters in the event of multi-channel network interruption as described in claim 1, characterized in that, Based on the impact of network interruptions on communication topology connectivity, the following two types of network interruption modes are defined: Connectivity-preserving network interruption In a network interruption that maintains connectivity, some communication links in the network are damaged; this can lead to changes in the initial topology, but the system can still ensure the connectivity of the network topology during the interruption. Disconnection-type network interruption In a network outage where connectivity is disrupted, some or all communication links are destroyed, resulting in a complete disconnection of the network topology.

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