A method, system and device for formation tracking control of a swarm system

Abstract

The application provides a kind of cluster system formation tracking control method, system and equipment, it is related to cluster system formation control field, this method includes: constructing single agent related model and multi-agent communication related model;For any follower in cluster system, define the formation function of the follower;Using hierarchical control algorithm, the DDoS attack constraint of network layer and the partial failure fault of actuator of physical layer are decoupled and handled in layers, based on multi-agent communication related model, update the neighbor target estimator state of follower;According to neighbor target estimator state and formation function, determine the estimator state of follower;According to the estimator state of follower, determine the control input of follower;Based on single agent related model, update the current state of follower using control input, so that the updated state meets formation condition, realize cluster system formation tracking.The application can weaken the influence brought by DDoS attack and actuator partial failure fault.

Description

Technical Field

[0001] This invention relates to the field of cluster system formation control, and in particular to a cluster system formation tracking control method, system, and device. Background Technology

[0002] Formation tracking and control in swarm systems is a current hot research topic. It refers to the process by which a team of multiple intelligent agents maintains a predetermined relative state while following a reference trajectory or a leader agent. Formation control in swarm systems can be applied to moving vehicles such as unmanned vehicles, drones, ships, and satellites, and plays a significant role in various fields including industry, aerospace, and the military. In industrial production, multi-robot systems can collaboratively transport large objects, which requires certain formation positioning of the robots to ensure stability and load balancing during transport. In the aerospace field, satellite formations can significantly reduce system costs, improve system reliability and survivability, and extend and surpass the functions of traditional single satellites, accomplishing many tasks that are impossible for a single spacecraft. In the military field, multiple mobile robots in a reasonable formation can replace soldiers in performing military tasks such as reconnaissance, search and rescue, mine clearance, and patrol in harsh and dangerous environments. Taking reconnaissance missions as an example, the ability of a single robot to acquire environmental information is usually limited. However, if multiple robots maintain a reasonable formation and divide the tasks of acquiring surrounding environmental information, it is possible to quickly and accurately perceive the environmental information of the group's area, making the group's resource utilization rate higher than when members are randomly distributed.

[0003] Several mature control methods exist for swarm system formation tracking control. Compared with leader-follower, behavior-based, and virtual structure-based methods, consensus-based formation tracking control methods offer better robustness and scalability, and are easier to design. Therefore, they have attracted widespread attention from researchers both domestically and internationally, and have achieved some applications. In this method, agents acquire state information from neighboring agents through communication, and then generate their own control commands based on this information, ultimately enabling the swarm system to form a formation. However, in general research on this method, it is usually assumed that the communication network between multiple agents and the actuators of the agents themselves can maintain normal operation. However, in practical applications, especially in extreme adversarial environments such as combat scenarios, swarm systems are likely to be threatened by network and physical layers, thus affecting formation implementation. Multi-UAV systems are a typical example of this problem. On the one hand, abundant theoretical analysis and attack experiments have demonstrated the vulnerability of multi-UAV systems to network attacks; on the other hand, since 2001, at least hundreds of military and commercial UAVs have crashed due to component failures, particularly actuator failures, during flight. These attacks and failures affect the coordination between agents, posing a significant challenge to the realization of formation tracking.

[0004] DDoS attacks, or Distributed Denial-of-Service attacks, are a typical type of network attack that can cause varying degrees of congestion on different links in a cluster system's communication network, disrupting the availability of neighboring agent information and the connectivity of the network topology, making it difficult for agents to maintain a predetermined relative state through sufficient information. Partial failure is a typical type of actuator failure that can reduce the output efficiency of actuators, leaving agents without sufficient maneuverability to achieve the desired control effect. Current consensus-based formation tracking control methods cannot effectively handle the combined constraints of DDoS attacks and partial actuator failures. Summary of the Invention

[0005] The purpose of this invention is to provide a cluster system formation tracking control method, system, and device to mitigate the impact of DDoS attacks and actuator failures on the system.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A cluster system formation tracking and control method, comprising:

[0008] Construct single-agent related models and multi-agent communication related models; the single-agent related models include a single-agent model and an actuator partial failure model; the multi-agent communication related models include a communication topology model, a periodic communication mechanism, and a DDoS attack model;

[0009] For any follower in the cluster system, define the grouping function for the follower; the cluster system includes multiple agents, each including a leader and multiple followers;

[0010] A hierarchical control algorithm is adopted to decouple and process the DDoS attack constraints of the network layer and the failure of the actuator part of the physical layer in a hierarchical manner. Based on the multi-agent communication correlation model, the neighbor target estimator state of the follower is updated.

[0011] The estimator state of the follower is determined based on the neighbor target estimator state and the formation function;

[0012] The control input of the follower is determined based on the estimator state of the follower;

[0013] Based on the single-agent correlation model, the current state of the follower is updated using the control input, so that the updated state satisfies the formation conditions, thereby realizing the formation tracking of the cluster system.

[0014] Optionally, the update law for the neighbor target estimator state is:

[0015]

[0016] in, Let A be the updated state of the neighbor target estimator; A is the first system parameter matrix. B is the state of the neighbor target estimator; B is the second system parameter matrix; v j (t) represents the formation compensation signal for the j-th agent; t is the time; t i,j,k The time when the k-th information transmission is successfully completed on the directed channel (j,i); t i,j,k+1 The time when the (k+1)th information transmission is successful on the directed channel (j,i) is recorded.

[0017] Optionally, the update law for the follower's estimator state is:

[0018]

[0019] Among them, u ei (t) represents the estimator input of the i-th agent; K is the control feedback parameter matrix; N i Let a be the neighbor set of the i-th agent; ij Let x represent the communication relationship between agent j and agent i. ei (t) represents the estimator state of the i-th agent; h i (t) is the grouping function for the i-th agent; h represents the estimate of the estimator state of the i-th agent for the j-th agent, i.e., the state of the neighbor target estimator; j (t) is the grouping function for the j-th agent; v i (t) is the formation compensation signal for the i-th agent.

[0020] Optionally, the follower's control input u i (t) is:

[0021]

[0022] in, This is the first adaptive parameter matrix; For target state tracking error; This is the second adaptive parameter matrix; u ei (t) is the estimator input for the i-th agent.

[0023] Optionally, the formation conditions are:

[0024]

[0025] Where, x i (t) represents the state of the i-th agent; hi (t) is the formation function of agent i; x0(t) is the state of the leader; ∈ h t represents the upper bound of the formation error; N is the total number of followers; and t is the time.

[0026] A cluster system swarm tracking and control system, comprising:

[0027] The model building module is used to build single-agent related models and multi-agent communication related models; the single-agent related models include a single-agent model and an actuator partial failure model; the multi-agent communication related models include a communication topology model, a periodic communication mechanism, and a DDoS attack model.

[0028] The grouping function definition module is used to define the grouping function of any follower in the cluster system; the cluster system includes multiple agents, each including a leader and multiple followers;

[0029] The neighbor target estimator state update module is used to decouple and process the DDoS attack constraints of the network layer and the actuator failure of the physical layer in a hierarchical control algorithm, and update the neighbor target estimator state of the follower based on the multi-agent communication correlation model.

[0030] The follower estimator state update module is used to determine the follower's estimator state based on the neighbor target estimator state and the formation function;

[0031] A control input determination module is used to determine the control input of the follower based on the estimator state of the follower;

[0032] The formation tracking control module is used to update the current state of the follower based on the single agent correlation model and the control input, so that the updated state satisfies the formation conditions, thereby realizing the formation tracking of the cluster system.

[0033] Optionally, the update law for the neighbor target estimator state is:

[0034]

[0035] in, Let A be the updated state of the neighbor target estimator; A is the first system parameter matrix. B is the state of the neighbor target estimator; B is the second system parameter matrix; v j (t) represents the formation compensation signal for the j-th agent; t is the time; t i,j,k The time when the k-th information transmission is successfully completed on the directed channel (j,i); t i,j,k+1The time when the (k+1)th information transmission is successful on the directed channel (j,i) is recorded.

[0036] Optionally, the update law for the follower's estimator state is:

[0037]

[0038] Among them, u ei (t) represents the estimator input of the i-th agent; K is the control feedback parameter matrix; N i Let a be the neighbor set of the i-th agent; ij Let x represent the communication relationship between agent j and agent i. ei (t) represents the estimator state of the i-th agent; h i (t) is the grouping function for the i-th agent; h represents the estimate of the estimator state of the i-th agent for the j-th agent, i.e., the state of the neighbor target estimator; j (t) is the grouping function for the j-th agent; v i (t) is the formation compensation signal for the i-th agent.

[0039] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to enable the electronic device to perform the above-described cluster system formation tracking control method.

[0040] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described cluster system formation tracking control method.

[0041] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention provides a cluster system formation tracking control method, system, and device, which constructs multiple models, and based on the constructed models, adopts a hierarchical control algorithm to decouple and process the DDoS attack constraints of the network layer and the actuator partial failure faults of the physical layer in a hierarchical manner. The neighbor target estimator state and the formation function are used to determine the estimator state of the follower to obtain the control input, so that the updated state satisfies the formation conditions, thereby realizing the formation tracking of the cluster system and reducing the impact of DDoS attacks and actuator partial failure faults on formation. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 The flowchart of the cluster system formation tracking control method provided by the present invention is shown below;

[0044] Figure 2 A simplified diagram of the cluster system formation tracking control method provided by the present invention;

[0045] Figure 3 This is a diagram of the physical experimental platform architecture provided by the present invention;

[0046] Figure 4 This is a diagram of the communication topology of the cluster system provided by the present invention;

[0047] Figure 5 This is a schematic diagram of the DDoS attack range provided by the present invention;

[0048] Figure 6 This is a snapshot of the formation process provided by the present invention;

[0049] Figure 7 This is the cluster system state diagram at time 0s provided by the present invention;

[0050] Figure 8 This is the state diagram of the cluster system at time 80s provided by the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The purpose of this invention is to provide a cluster system formation tracking and control method, system, and device that reduces the impact of DDoS attacks and actuator failures on formation.

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Example 1

[0055] like Figure 1 As shown, the present invention provides a cluster system formation tracking control method, comprising:

[0056] Step 101: Construct a single-agent related model and a multi-agent communication related model; the single-agent related model includes a single-agent model and an actuator partial failure model; the multi-agent communication related model includes a communication topology model, a periodic communication mechanism, and a DDoS attack model.

[0057] 1) Construct a single-agent model and an actuator partial failure model:

[0058] The cluster system consists of N+1 agents, where the leader is numbered 0 and the followers are numbered 1,...,N. The model of the i-th agent is:

[0059]

[0060] Where t represents time. This represents the state of the i-th agent. This represents the control input of the i-th agent. This represents the first system parameter matrix of the agent. The second system parameter matrix representing the agent is a column full-rank matrix. This is a diagonal matrix representing the failure coefficients of the actuator portion of the i-th agent, where each element on the diagonal takes a value within (0,1]. The leader's control input must satisfy boundedness, i.e., there exists... Make ||u0(t)||≤M.

[0061] Represents the set of n-dimensional real vectors; Represents the set of m-dimensional real vectors; Represents the set of n x n dimensional real matrices; Represents the set of n x m dimensional real matrices; Represents the set of m x m dimensional real matrices; It represents the set of all positive real numbers.

[0062] The expression for the actuator partial failure model is Λ i In the absence of actuator failure, the normal model of the agent is as follows: When an actuator malfunctions, the actual control input transmitted to the system changes from the original u. i (t) becomes multiplied by an unknown diagonal matrix Λ i Λ i u i (t), therefore the faulty agent model is

[0063] 2) Constructing the multi-agent communication topology:

[0064] Cluster systems obtain the state variables of other agents through communication between them; this communication relationship can be represented by a directed graph. To indicate, among which It is a set of nodes, where each node represents an intelligent agent. It is an edge set. The communication relationships between nodes are represented by an adjacency matrix. Let represent a directed channel from node j to node i, where node j is called a (predecessor) neighbor of node i and node i is called a (successor) neighbor of node j, (j,i)∈ε and a ij >0, otherwise And a ij =0. Define the neighbor set of node i as... The in-degree of node i is picture The in-degree matrix is ​​D = diag i∈{1,...,N} {d i},picture The Laplace matrix is Let the N×N matrix formed by removing the first row and first column from matrix L be ? A line from node arrive A directed path is of the form The communication topology must satisfy the condition that for any follower agent i, there exists a directed path from the leader to agent i.

[0065] 3) Constructing communication mechanisms and DDoS attack models:

[0066] Communication between agents is asynchronous and periodic across channels. That is, for any agent i and one of its neighbors j, agent j communicates via asynchronous and periodic channels. T sends information to agent i periodically; ij The period during which agent j sends information to agent i.

[0067] This invention considers a DDoS attack model with limited frequency and duration. Specifically, for each directed channel (j,i), the time interval of the m-th attack on that channel is defined as... in, This is the start time of the attack. For the duration of the attack, record and Let be the total number of attacks against edge (j,i) within the time interval [t1,t2) and the total duration, respectively. Then, there exists a normal number. and Make as well as in To describe the constant and proportional parameters that define the upper limit of the number of attacks an attacker can make on channel (j,i) within a certain time period, Let t be a constant parameter and a scaling parameter describing the upper limit of an attacker's attack time on channel (j,i) within a certain period. When channel (j,i) is attacked, information transmission on it will be hindered. Therefore, let t be the time when the k-th information transmission to channel (j,i) is successful. i,j,k .

[0068] Step 102: For any follower in the cluster system, define the grouping function of the follower; the cluster system includes multiple agents, each of which includes a leader and multiple followers.

[0069] In practical applications, the standard definition for cluster system formation construction and implementation is as follows:

[0070] For follower i, its formation function is defined as follows: Let h represent the set of n-dimensional real vectors. i (t) is a piecewise continuously differentiable function, and satisfies 0 n-m Let B be a zero vector of dimension (nm), where B is a vector of all zeros. - Let BB be a (nm) x n dimensional formation feasibility condition matrix, satisfying BB = 0. (n-m)×m 0 (n-m)×m Let be a zero matrix of dimension (nm) x m, and let it be an invertible matrix. Let B be the set of n x n dimensional real matrices such that B + B = I m B + For the formation compensation matrix, I m Let be an m x m dimensional identity matrix. If the following condition holds: there exists a positive number ∈ representing the upper bound of the formation error. h , making This means that the cluster system has achieved the desired formation tracking.

[0071] Step 103: Using a hierarchical control algorithm, the DDoS attack constraints of the network layer and the failure of the actuator part of the physical layer are decoupled and processed in layers. Based on the multi-agent communication correlation model, the neighbor target estimator state of the follower is updated.

[0072] In practical applications, the control method for a single intelligent agent is designed as follows:

[0073] This invention designs a single-agent control protocol to implement the cluster system formation designed in step 102. This invention employs a hierarchical control algorithm to decouple and process DDoS attack constraints at the network layer and actuator failures at the physical layer in a hierarchical manner. It mainly consists of the following two steps:

[0074] 1) Each follower is equipped with a target estimator to estimate the desired state (hereinafter referred to as the target state) that it needs to achieve when forming the group. The agent updates the estimator state through neighbor information so that the estimator state can eventually approximate the target state with bounded error.

[0075] 2) Each follower is designed with control inputs that track its estimator state, thereby achieving formation tracking at the physical layer. To address actuator inefficiencies, the controller needs to adjust gain parameters online to adaptively compensate for the impact of the fault.

[0076] For step 1), the target estimator design is as follows:

[0077] Set the target estimator dynamics for follower i as follows:

[0078] in, This represents the estimator state of the i-th agent. Let $\frac{i}{i}$ represent the estimator input for the $i$-th agent. During formation, the leader and followers transmit their true state and estimator state respectively according to the communication topology. However, under a DDoS attack, followers may be unable to obtain information from their neighbors for a certain period. Therefore, each follower needs to estimate its current state value to be transmitted based on the value most recently transmitted by its neighbor; this process is implemented by the neighbor target estimator. For agents $j ∈ N$ i {0}, N i {0} represents the set of neighbors N of agent i. i Remove agent 0, i.e., the leader, from the middle, and let... Let {i} be the estimator state of agent i for agent j, and if {0}∈N i Then let Let x be the estimate of the leader's state by agent i. For simplicity, let x be... e0 =x0 represents the leader's state and h0 = 0 n Let be an n-dimensional vector of all zeros. The update law for the neighbor target estimator is set as follows:

[0079]

[0080] And when t = t i,j,k At that time, set v j For formation compensation signals, the form is as follows: If the j-th agent is the leader, then its value is 0; otherwise, its form is: h j (t) is the formation function for the j-th agent, B + This is the formation compensation matrix.

[0081] It satisfies the following two properties: (1) B + B = I m I m (2) The existence of a formation feasibility condition matrix B is an m x m dimensional identity matrix, where m is the number of columns in matrix B. - Satisfy B - B=0 (n-m)×m And matrix Reversible, where 0 (n-m)×m Let A be a zero matrix of dimension (nm) x m, where n is the number of rows in matrix A.

[0082] Based on the above information, the estimator update law for agent i can be set as follows:

[0083]

[0084] in, It is a feedback parameter matrix, u ei (t) represents the estimator input of the i-th agent; K is the control feedback parameter matrix; a ij Let a be the communication relationship between agent j and agent i. If a directed communication link exists from agent j to agent i, then a ij >0, otherwise a ij =0; Let x be the set of neighbors of the i-th agent, i.e., the set of agents that can send information to the i-th agent; ei (t) represents the estimator state of the i-th agent; h i (t) is the grouping function for the i-th agent; The estimate of the estimator state of the i-th agent for the j-th agent is the state of the neighbor target estimator; h j (t) is the grouping function for the j-th agent; v i (t) is the formation compensation signal for the i-th agent, and its form is:

[0085] Step 104: Determine the estimator state of the follower based on the neighbor target estimator state and the formation function.

[0086] Step 105: Determine the control input of the follower based on the estimator state of the follower.

[0087] For step 2), the adaptive controller design is based on the target state estimate x of agent i. ei (t) can be set to its control input u i (t) is:

[0088]

[0089] in It is the target state tracking error. and It is an adaptive parameter matrix to be determined.

[0090] Step 106: Based on the single agent correlation model, update the current state of the follower using the control input so that the updated state satisfies the formation conditions, thereby realizing the formation tracking of the cluster system.

[0091] The conditions for the effectiveness of the formation control method of this invention are as follows:

[0092] Under the following conditions:

[0093] For any directed channel (j,i)∈ε, the following condition is satisfied:

[0094] The estimator feedback parameter matrix is ​​given by K = -μB T Given P, where P is a positive definite solution to the following Riccati equation:

[0095]

[0096] Where μ and ρ1 are positive numbers, Q is a positive definite matrix, and Z = diag i∈{1,...,N} {1 / z i}, z i column vector The i-th element.

[0097] σ m (Z) and σ M (Z) represent the minimum and maximum singular values ​​of Z, respectively. n It is an n x n dimensional identity matrix, 0 n×n Let Z be an n x n dimensional all-zero matrix; Z is an intermediate variable in the mathematical description, defined for the sake of simplicity, and has no specific meaning. For example, the above formula can also be written in the following form:

[0098]

[0099] in express The first element, express The Nth element, Omitted The 2nd to N-1th elements. However, writing it this way would take up a lot of space and be unsightly, so z and Z are used instead, which belong to "auxiliary variables for mathematical conciseness".

[0100] There exist positive numbers σ, ∈, γ ij For any i ∈ {1,...,N}, j ∈ {0,...,N}, such that the following linear matrix inequality holds.

[0101]

[0102] in, For I N+1 In the (i+1)th row, R i For I N The i-th row, a iN This represents the communication relationship between the Nth agent and the i-th agent. Π 12 T Represents Π 12 The transpose of .

[0103] Other symbols A d , Γ ∈ , Π 11 , Π 12 and Π 22 It has no explicit meaning; it is only used for the sake of concise expression and belongs to the category of "auxiliary variables for mathematical conciseness".

[0104] The controller's parameter matrix is ​​updated according to the following adaptive law:

[0105]

[0106] in, yes The j-th row, yes The j-th diagonal element, B j It is the j-th column of B, u eij is u ei The j-th element, and They are positive definite matrices and positive numbers, respectively. This is a positive definite solution to the following Riccati equation.

[0107]

[0108] Where r iIt is a positive number and Q i It is a positive definite matrix.

[0109] like Figure 2 As shown, through the control protocol designed above, the cluster system can mitigate the impact of DDoS attacks and actuator failures, thereby achieving the desired formation of the array function.

[0110] Example 2

[0111] like Figure 3 As shown, consider a swarm system consisting of four quadcopter UAVs and study its formation tracking problem in the XY plane. The UAVs in the X and Y directions can be approximated by a second-order system, therefore the state of UAV i is... These represent its position and velocity in the X and Y directions, respectively, and the system matrix of the UAV is:

[0112]

[0113] in, It represents the Kronecker product.

[0114] The system's communication topology is as follows: Figure 4 As shown in the diagram, all edge weights are equal to 1, and the communication cycle between drones, measured in seconds, is marked on the edges. For example, the number 0.1 on edge (0,1) indicates that drone 0 sends a message to drone 1 every 0.1 seconds. The communication link corresponding to the dashed arrow in the diagram is under a DDoS attack, and the attack range is as follows. Figure 5 As shown in the darker areas. Meanwhile, Figure 4 The actuators of the dark-colored agents 1 and 3 have partial failures, with failure parameters Λ1=diag(0.3,0.5) and Λ3=diag(0.4,0.6), respectively.

[0115] Initially, the leader drone's position is [1.09m, 0.01m], while the follower drones 1, 2, and 3 are positioned at [2.61m, -0.39m], [0.95m, 1.23m], and [0.52m, -0.91m], respectively. The leader drone 0 tracks a circular trajectory x via the PD controller. re f = [r0cos(w0t), r0sin(w0t)] T Where r0 is the radius of the circular trajectory, w0 is the angular velocity of the circular trajectory, and the control input is... in Let v be the position error vector of the leader following the circular trajectory and v = [v ix ,v iy ] T K is the velocity vector of the leader. p0 and K d0These are the P control gain and D control gain of the PD controller, respectively, with parameters r0 = 1m, w0 = 0.1 rad / s, and K. p0 =1,K d0 =2. The formation function for follower drone i is:

[0116]

[0117] Where r = 1.2m is the radius of the circular formation function, and w = -0.2 rad / s is the angular velocity of the circular formation function. Based on the parameter design standards of the control protocol, the hierarchical control algorithm parameters are taken as Q = 49I⁴, μ = 0.0143, and γ... ij =10 6 , r i =7, Q i =I4 / 20, Γ ij =0.2I4,c ij =0.2. The motion process of the multi-UAV system within 80 seconds was obtained through experiments. Figure 6 A snapshot of the formation process is given. Figure 7 and Figure 8 The numbers represent the relative positions of the agents in the cluster system at times 0s and 80s, respectively, showing that the system has finally achieved the designed formation.

[0118] Example 3

[0119] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a cluster system formation tracking and control system is provided below.

[0120] A cluster system swarm tracking and control system, comprising:

[0121] The model building module is used to build single-agent related models and multi-agent communication related models; the single-agent related models include a single-agent model and an actuator partial failure model; the multi-agent communication related models include a communication topology model, a periodic communication mechanism, and a DDoS attack model.

[0122] The grouping function definition module is used to define the grouping function of any follower in the cluster system; the cluster system includes multiple agents, including a leader and multiple followers.

[0123] The neighbor target estimator state update module is used to decouple and process the DDoS attack constraints of the network layer and the actuator failure of the physical layer in a hierarchical control algorithm, and update the neighbor target estimator state of the follower based on the multi-agent communication correlation model.

[0124] The follower estimator state update module is used to determine the follower's estimator state based on the neighbor target estimator state and the formation function.

[0125] A control input determination module is used to determine the control input of the follower based on the estimator state of the follower.

[0126] The formation tracking control module is used to update the current state of the follower based on the single agent correlation model and the control input, so that the updated state satisfies the formation conditions, thereby realizing the formation tracking of the cluster system.

[0127] In practical applications, the update law for the neighbor target estimator state is:

[0128]

[0129] in, Let A be the updated state of the neighbor target estimator; A is the first system parameter matrix. B is the state of the neighbor target estimator; B is the second system parameter matrix; v j (t) represents the formation compensation signal for the j-th agent; t is the time; t i,j,k The time when the k-th information transmission is successfully completed on the directed channel (j,i); t i,j,k+1 The time when the (k+1)th information transmission is successful on the directed channel (j,i) is recorded.

[0130] In practical applications, the update law for the follower's estimator state is:

[0131]

[0132] Among them, u ei (t) represents the estimator input of the i-th agent; K is the control feedback parameter matrix; N i Let a be the set of neighbors of the i-th agent, that is, the set of agents that can send information to the i-th agent; ij Let x represent the communication relationship between agent j and agent i. ei (t) represents the estimator state of the i-th agent; h i (t) is the grouping function for the i-th agent; h represents the estimate of the estimator state of the i-th agent for the j-th agent, i.e., the state of the neighbor target estimator; j (t) is the grouping function for the j-th agent; v i (t) is the formation compensation signal for the i-th agent.

[0133] Example 4

[0134] This invention provides an electronic device including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to execute the cluster system formation tracking control method provided in Embodiment 1.

[0135] In practical applications, the aforementioned electronic devices can be servers.

[0136] In practical applications, electronic devices include: at least one processor, memory, bus, and communication interface.

[0137] The processor, communication interface, and memory communicate with each other via a communication bus.

[0138] A communication interface is used to communicate with other devices.

[0139] The processor is used to execute programs, specifically the methods described in the above embodiments.

[0140] Specifically, the program may include program code, which includes computer operation instructions.

[0141] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The electronic device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0142] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0143] Based on the description of the above embodiments, this application provides a storage medium storing computer program instructions thereon, which can be executed by a processor to implement the methods described in any embodiment.

[0144] The cluster system formation tracking and control system provided in this application exists in various forms, including but not limited to:

[0145] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0146] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access capabilities. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0147] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0148] (4) Other electronic devices with data interaction functions.

[0149] Specific embodiments of the subject matter have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0150] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0151] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components. Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0153] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0154] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0155] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0156] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0157] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, and CD-ROM.

[0158] Digital multifunction optical disc (DVD) or other optical storage, magnetic cassette tape, magnetic tape, disk storage or other magnetic storage devices

[0159] Or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0160] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0161] This application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific transactions or implement specific abstract data types. This application can also be practiced in distributed computing environments where transactions are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0162] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0163] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for formation tracking control in a cluster system, characterized in that, include: Construct single-agent related models and multi-agent communication related models; the single-agent related models include a single-agent model and an actuator partial failure model; the multi-agent communication related models include a communication topology model, a periodic communication mechanism, and a DDoS attack model; For any follower in the cluster system, define the grouping function for the follower; the cluster system includes multiple agents, each including a leader and multiple followers; A hierarchical control algorithm is adopted to decouple and process the DDoS attack constraints of the network layer and the failure of the actuator part of the physical layer in a hierarchical manner. Based on the multi-agent communication correlation model, the neighbor target estimator state of the follower is updated. The estimator state of the follower is determined based on the neighbor target estimator state and the formation function; The control input of the follower is determined based on the estimator state of the follower; Based on the single-agent related model, the current state of the follower is updated using the control input, so that the updated state satisfies the formation conditions, thereby realizing the formation tracking of the cluster system. The update law for the neighbor target estimator state is: ； in, Let A be the updated state of the neighbor target estimator; A is the first system parameter matrix. B represents the state of the neighbor target estimator; B is the second system parameter matrix. For the first j Formation compensation signals for individual agents; t For time; For directed channels Upper The moment when the information transmission is successful; For directed channels Upper The moment when the information transmission is successful; The update law for the estimator state of the follower is: ； in, This is the input to the estimator of the i-th agent; K To control the feedback parameter matrix; Let i be the neighbor set of the i-th agent; This represents the communication relationship between the j-th agent and the i-th agent. The estimator state for the i-th agent; Let i be the grouping function for the i-th agent; This is the estimate of the estimator state of the i-th agent for the j-th agent, i.e., the state of the neighbor target estimator; Let j be the grouping function for the j-th agent; For the first i Formation compensation signals for individual agents; The follower's control input for: ； in, This is the first adaptive parameter matrix; For target state tracking error; This is the second adaptive parameter matrix; This is the input to the estimator of the i-th agent; The formation conditions are as follows: ； in, Let i be the state of the i-th agent; Let i be the grouping function for agent i; The state of a leader; t represents the upper bound of the formation error; N is the total number of followers; and t is the time.

2. A cluster system formation tracking and control system, characterized in that, include: The model building module is used to build single-agent related models and multi-agent communication related models; the single-agent related models include a single-agent model and an actuator partial failure model; the multi-agent communication related models include a communication topology model, a periodic communication mechanism, and a DDoS attack model. The grouping function definition module is used to define the grouping function of any follower in the cluster system; the cluster system includes multiple agents, each including a leader and multiple followers; The neighbor target estimator state update module is used to decouple and process the DDoS attack constraints of the network layer and the actuator failure of the physical layer in a hierarchical control algorithm, and update the neighbor target estimator state of the follower based on the multi-agent communication correlation model. The follower estimator state update module is used to determine the follower's estimator state based on the neighbor target estimator state and the formation function; A control input determination module is used to determine the control input of the follower based on the estimator state of the follower; The formation tracking control module is used to update the current state of the follower based on the single agent correlation model and the control input, so that the updated state satisfies the formation conditions, thereby realizing the formation tracking of the cluster system. The update law for the neighbor target estimator state is: ； in, Let A be the updated state of the neighbor target estimator; A is the first system parameter matrix. B represents the state of the neighbor target estimator; B is the second system parameter matrix. For the first j Formation compensation signals for individual agents; t For time; For directed channels Upper The moment when the information transmission is successful; For directed channels Upper The moment when the information transmission is successful; The update law for the estimator state of the follower is: ； in, This is the input to the estimator of the i-th agent; K To control the feedback parameter matrix; Let i be the neighbor set of the i-th agent; This represents the communication relationship between the j-th agent and the i-th agent. The estimator state for the i-th agent; Let i be the grouping function for the i-th agent; This is the estimate of the estimator state of the i-th agent for the j-th agent, i.e., the state of the neighbor target estimator; Let j be the grouping function for the j-th agent; For the first i Formation compensation signals for individual agents; The follower's control input for: ； in, This is the first adaptive parameter matrix; For target state tracking error; This is the second adaptive parameter matrix; This is the input to the estimator of the i-th agent; The formation conditions are as follows: ； in, Let i be the state of the i-th agent; Let i be the grouping function for agent i; The state of a leader; t represents the upper bound of the formation error; N is the total number of followers; and t is the time.

3. An electronic device, characterized in that, It includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the cluster system formation tracking control method as described in claim 1.

4. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the cluster system formation tracking control method as described in claim 1.

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