A time-varying formation control method for heterogeneous unmanned swarms in network confrontation environment

Abstract

The present invention relates to the technical field of time-varying formation control of unmanned clusters, and discloses a method for controlling a time-varying formation of a heterogeneous unmanned cluster in a network confrontation environment, including constructing a heterogeneous unmanned cluster system model; establishing a communication topology directed graph with a switching topology in a network confrontation environment according to the heterogeneous unmanned cluster system model; establishing a time-varying formation task target and a time-varying formation controller of the heterogeneous unmanned cluster system according to the heterogeneous unmanned cluster system model and the communication topology directed graph with a switching topology in the network confrontation environment; and performing time-varying formation control according to the time-varying formation task target and the time-varying formation controller of the heterogeneous unmanned cluster system. The present invention enables a heterogeneous unmanned cluster to realize a time-varying formation task under switching topology and communication delay, and the designed controller can improve the control accuracy and steady-state performance of the heterogeneous unmanned cluster time-varying formation.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned swarm time-varying formation control, and in particular to a heterogeneous unmanned swarm time-varying formation control method in a network confrontation environment. Background Art

[0002] Existing unmanned swarm formation control is mainly aimed at homogeneous systems. However, the traditional single unmanned platform can no longer meet today's multi-dimensional mission requirements and needs to work in collaboration with different swarm systems. Multi-platform heterogeneous unmanned swarm system formations have emerged.

[0003] Unmanned swarms are not simply a sum of numbers. They must be organized in a certain configuration so that they can interact and feedback with each other, and realize the overall emergence of system capabilities. Different formation configurations will produce different capabilities. Studying the formation control of unmanned swarms is of great significance to the development of unmanned swarms. The focus of formation control is that when multiple systems move toward a specific target, they must not only adapt to the environment (such as communication delays, obstacle avoidance, etc.), but also solve the problem of geometric stability control of the entire system. As an important topic, unmanned swarm formation control has become a research hotspot in the field of control.

[0004] In practical applications, multi-agent systems are often in various complex network environments. Each agent in the system is often attacked and interfered during the information exchange process, which may cause communication delays in cluster collaboration and constant switching of network topology, seriously undermining the stability of the multi-agent system and significantly reducing the control accuracy. Therefore, it is extremely important to study multi-agent collaborative control in complex network environments.

[0005] Prior art 1 provides a research scheme for the mean square consistency of a leader-follower multi-agent system under a denial of service attack. This technology targets a leader-follower multi-agent system with process noise and measurement noise, and studies the consistency problem of the multi-agent system under a denial of service attack. First, a state observer based on Kalman filtering is designed to effectively and accurately estimate the state of the agent; then, based on the predictive control theory, a distributed predictive control algorithm based on state estimation information is proposed to achieve mean square consistency control of the leader-follower multi-agent system, and the necessary and sufficient conditions for achieving mean square consistency of the leader-follower multi-agent system under a denial of service attack environment are given; finally, numerical simulation is performed to verify the correctness and effectiveness of the proposed method. However, this scheme considers a homogeneous multi-agent system, which is difficult to meet today's multi-dimensional task requirements.

[0006] The second prior art provides a heterogeneous unmanned cluster formation-encirclement tracking control scheme under switching topology. This technology proposes the definition and framework of "formation-encirclement tracking" control for the control problems of heterogeneous unmanned clusters with different collaborative control objectives within the layer and collaborative coupling between layers. The tracking-leader with time-varying input is used to generate the overall reference trajectory of the cluster system, which overcomes the defect that the overall macroscopic motion of the cluster cannot be effectively controlled in the existing formation-encirclement control. For high-order heterogeneous cluster systems with switching topology, based on distributed robust adaptive estimation technology and pre-defined encirclement control strategy, a distributed formation-encirclement tracking controller is constructed by using neighbor local information interaction, and a multi-step design method of the controller under hierarchical coupling is given. The common Lyapunov stability theory is used to prove the sufficient conditions for heterogeneous clusters to achieve output formation-encirclement tracking under switching topology conditions. The effectiveness of the proposed formation-encirclement tracking control method is verified through the simulation example of a heterogeneous cluster of drones and unmanned vehicles. However, this scheme does not consider the interference problems that may exist in actual situations, such as communication delays, network attacks, etc., and its practical value is insufficient. Summary of the invention

[0007] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for controlling a time-varying formation of a heterogeneous unmanned cluster in a network confrontation environment.

[0008] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0009] A method for controlling a heterogeneous unmanned swarm in a network confrontation environment comprises the following steps:

[0010] Build a heterogeneous unmanned swarm system model;

[0011] According to the heterogeneous unmanned swarm system model, a communication topology directed graph with switching topology in a network confrontation environment is established;

[0012] According to the heterogeneous unmanned swarm system model and the communication topology directed graph with switching topology in the network confrontation environment, the time-varying formation task objectives and time-varying formation controller of the heterogeneous unmanned swarm system are established;

[0013] Time-varying formation control is performed according to the time-varying formation mission objectives and time-varying formation controller of the heterogeneous unmanned swarm system.

[0014] Furthermore, the heterogeneous unmanned swarm system model includes unmanned aerial vehicles, surface unmanned boats, and unmanned underwater vehicles, and its movement space covers underwater, surface, and air.

[0015] Furthermore, the dynamic model of the heterogeneous unmanned cluster system model is specifically:

[0016]

[0017] Among them, A0, C0 are both constant real number matrices of the leader, x0(t) is the state quantity of the leader at time t, is the derivative of the state x0(t), y0(t) is the measured output of the leader at time t, A i ,B i ,C i are constant real matrices of followers, x i (t) is the state of the ith follower at time t, u i (t) is the control input of the ith follower at time t, y i (t) is the measured output of the ith follower at time t, For the state x i (t) Derivative.

[0018] Furthermore, according to the heterogeneous unmanned swarm system model and the communication topology directed graph with switching topology in the network confrontation environment, the time-varying formation mission goal of the heterogeneous unmanned swarm system is established, including the following steps:

[0019] According to the expected formation configuration formed by the unmanned swarm system, set the expected configuration parameters of the time-varying formation;

[0020] The time-varying formation mission objectives of the heterogeneous unmanned swarm system are established according to the expected configuration parameters of the time-varying formation.

[0021] Furthermore, the expected configuration parameters of the time-varying formation are as follows:

[0022]

[0023] h x (t) = [h x1 (t),h x2 (t),…,h xN (t)] T

[0024] h v (t) = [h v1 (t),h v2 (t),…,h vN (t)] T

[0025] Among them, h i (t) is the formation reference vector of the ith follower at time t, h x (t) is the position component of the ith follower at time t, h v (t) is the velocity component of the ith follower at time t, T is the transposed sign, and h xN(t) is the expected position of the Nth follower with respect to the virtual leader at time t, h vN (t) is the velocity vector difference between the Nth follower and the virtual leader at time t.

[0026] Furthermore, the specific objectives of the time-varying formation mission are:

[0027]

[0028] Among them, y i (t) is the measured output of the ith follower at time t, h i (t) is the formation reference vector of the ith follower at time t, and y0(t) is the measured output of the leader at time t.

[0029] Furthermore, the time-varying formation controller is specifically:

[0030] u i (t) = K xi x i (t)+K qi (q i (t)+δ i (t))+a i (t)

[0031] Among them, u i (t) is the time-varying formation control quantity, K xi , K qi are all control gain parameter matrices, x i (t) is the state of the ith follower at time t, q i (t) is the observation of the leader by the ith follower at time t, δ i (t) is the expected time-varying formation state, a i (t) is the time-varying formation tracking compensation input.

[0032] The present invention has the following beneficial effects:

[0033] The present invention establishes a time-varying formation task target and a time-varying formation controller for a heterogeneous unmanned cluster system based on a heterogeneous unmanned cluster system model and a communication topology directed graph with a switching topology in a network confrontation environment, so that the heterogeneous unmanned cluster can achieve the time-varying formation task under the switching topology and communication delay. The designed controller can improve the control accuracy and steady-state performance of the heterogeneous unmanned cluster time-varying formation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of a method for controlling a heterogeneous unmanned swarm in a network confrontation environment;

[0035] Figure 2It is a schematic diagram of the inner / outer ring layered formation control structure;

[0036] Figure 3 It is a directed communication topology graph for heterogeneous formations;

[0037] Figure 4 This is a schematic diagram of the expected formation configuration of the unmanned swarm;

[0038] Figure 5 Schematic diagram of tracking error of virtual leader position state;

[0039] Figure 6 Output error diagram for the formation;

[0040] Figure 7 It is a side view of the motion trajectory of a heterogeneous unmanned swarm in time-varying formation;

[0041] Figure 8(a) is a top view of the configuration of the heterogeneous unmanned swarm time-varying formation at different times at t = 0s;

[0042] Figure 8(b) is a top view of the configuration of the heterogeneous unmanned swarm time-varying formation at different times at time t = 1s;

[0043] Figure 8(c) is a top view of the configuration of the heterogeneous unmanned swarm time-varying formation at different times at time t = 2s;

[0044] Figure 8(d) is a top view of the configuration of the heterogeneous unmanned swarm time-varying formation at different times at t = 5s;

[0045] Figure 8(e) is a top view of the configuration of the heterogeneous unmanned swarm time-varying formation at different times at t = 7s;

[0046] Figure 8(f) is a top view of the configuration of the heterogeneous unmanned swarm time-varying formation at different times at t=10s. DETAILED DESCRIPTION

[0047] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a heterogeneous unmanned swarm time-varying formation control method in a network confrontation environment, comprising the following steps S1 to S4:

[0049] S1. Build a heterogeneous unmanned swarm system model;

[0050] In an optional embodiment of the present invention, Figure 2 As shown, this embodiment considers the inner / outer loop hierarchical formation control structure. In the inner / outer loop structure, the outer loop is the formation configuration control layer, which can calculate the control input of the machine according to the desired configuration, neighbor status, reference track and other information, so that the motion platform can reach the desired position at the desired speed; the inner loop control controls the attitude / rudder, throttle / thrust according to the output of the formation control layer and the dynamic characteristics. In other words, each drone / unmanned boat / unmanned submarine performs attitude control independently in the inner loop, and each drone / unmanned boat / unmanned submarine obtains the position, speed and other status information of itself and its neighbors when performing formation control in the outer loop.

[0051] This embodiment considers a heterogeneous unmanned cluster system consisting of unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs), whose motion space covers underwater, surface, and air. For actual motion platforms, due to differences in motion environments and platform characteristics, unmanned aerial vehicles, unmanned boats, and unmanned underwater vehicles have different forms of dynamic models. Based on the low-speed small maneuver assumption, the convergence time of attitude control is much shorter than the convergence time of formation control, so formation control can only consider the control of the outer loop position speed.

[0052] At the level of formation control, the dynamic response process of the attitude loop is ignored, and the outer loop control is mainly studied and analyzed, and the UAV, UAV and UUV are all regarded as particle systems. For the second-order multi-agent system, in order to facilitate the description and analysis of the time-varying formation problem, the UAV-UAV-UUV system model is uniformly expressed using a linear state space model.

[0053] Consider a multi-agent system with 1 leader and N followers. The leader model is:

[0054]

[0055] Among them, A0, C0 are both constant real number matrices of the leader, x0(t) is the state quantity of the leader at time t, is the derivative of the state x0(t), and y0(t) is the measured output of the leader at time t.

[0056] In the system, the dynamic model of the i-th follower is:

[0057]

[0058] Among them, A i ,B i ,C i are constant real matrices of followers, x i (t) is the state of the ith follower at time t, u i(t) is the control input of the ith follower at time t, y i (t) is the measured output of the ith follower at time t, For the state x i (t) Derivative. For heterogeneous systems, the A of different individuals i ,B i ,C i Vary.

[0059] In summary, the complete dynamic model of the heterogeneous unmanned swarm system is expressed as:

[0060]

[0061] S2. Establish a communication topology directed graph with switching topology in a network confrontation environment based on a heterogeneous unmanned cluster system model;

[0062] In an optional embodiment of the present invention, in a complex communication environment, the unmanned cluster system may be subject to a network attack in a hostile environment during its movement. This embodiment considers DoS attacks in network attacks. DoS attacks will hinder communication and damage the connectivity of the communication network, thereby causing changes in the communication topology. In this case, the information exchange between agents will be affected. In other words, once an attacker launches a DoS attack on a multi-agent system, the system will lose its normal communication capabilities, and information between agents cannot be transmitted normally to each other. When the attack stops, the communication interaction between the agent and the neighboring agents returns to normal.

[0063] DoS attackers can launch attacks on unmanned cluster systems at any time, and the attackers have limited energy, that is, after a period of attack, the attackers need to stop attacking to obtain energy. Due to the limited attack energy, the entire DoS attack time period is divided into two parts: the DoS attack dormant time period and the DoS attack active time period, that is, the system can be in the attack time period or not in the attack time period.

[0064] This embodiment considers the situation that the system is in the activation time period of the DoS attack, and assumes that the DoS attack suffered by the system is a continuous DoS attack that destroys part of the communication link each time, that is, the system communication topology changes continuously over time. When an original communication link in the system is destroyed, the system starts the emergency communication device, re-establishes a new communication link between the intelligent agents that have lost information exchange, and the communication link directed graph has a spanning tree again, so that when the communication topology switches, the system can still update the status information in time to achieve the control target.

[0065] This embodiment assumes that the communication topology between all agents is a directed topology, the communication delay between all agents is equal, the communication delay parameter is τ, and τ>0, and the maximum communication delay is τm This embodiment describes the switching topology by transforming the adjacency matrix. Consider a directed graph G with a switching topology. σ(t) , σ(t) is the topology change signal, σ(t):R + →Γ, the time series of its changes is t0=0,t1,t2,…,t s ,….use represents the communication weight between agent i and agent j at time t, represents the weighted relationship between agent i and the leader at time t. If there is no directed edge from the leader to agent i in the communication topology graph, then

[0066] The adjacency matrix of a directed graph G is defined as A = [a ij ]∈R N×N , used to represent the relationship between nodes and their interactions. Matrix A = [a ij ]∈R N×N The elements in are defined as:

[0067]

[0068] Among them, the provisions of a ii =0.

[0069] For a directed graph G, its Laplacian matrix is ​​L = DA, where D is the degree matrix, which is a diagonal matrix with the elements The Laplace matrix L is used to represent the relationship between nodes and edges in the graph. The element l in L ij (i,j=1,2,…,N) is expressed as:

[0070]

[0071] S3. According to the heterogeneous unmanned swarm system model and the communication topology directed graph with switching topology in the network confrontation environment, the time-varying formation task objectives and time-varying formation controller of the heterogeneous unmanned swarm system are established;

[0072] In an optional embodiment of the present invention, step S3 establishes a time-varying formation mission target of a heterogeneous unmanned swarm system according to a heterogeneous unmanned swarm system model and a communication topology directed graph with a switching topology in a network confrontation environment, including the following steps:

[0073] According to the expected formation configuration formed by the unmanned swarm system, set the expected configuration parameters of the time-varying formation;

[0074] The time-varying formation mission objectives of the heterogeneous unmanned swarm system are established according to the expected configuration parameters of the time-varying formation.

[0075] In this embodiment, the time-varying formation problem requires the unmanned swarm to form a desired formation configuration and be able to continue to maintain the desired formation and sail stably. Considering the applicability of practical applications, the formation controller designed in this embodiment can be applied to both fixed formation and time-varying formation control.

[0076] This embodiment firstly mathematically describes the time-varying formation mission objectives. For the formation generation problem, the expected configuration parameters of the time-varying formation are defined as follows:

[0077]

[0078] h x (t) = [h x1 (t),h x2 (t),…,h xN (t)] T

[0079] h v (t) = [h v1 (t),h v2 (t),…,h vN (t)] T

[0080] Among them, h i (t) is the formation reference vector of the ith follower at time t, h x (t) is the position component of the ith follower at time t, h v (t) is the velocity component of the ith follower at time t, T is the transposed sign, and h xN (t) is the expected position of the Nth follower with respect to the virtual leader at time t, h vN (t) is the velocity vector difference between the Nth follower and the virtual leader at time t. According to the above definition, for the heterogeneous unmanned cluster system with a leader targeted by this embodiment, since the leader is an autonomous system without control input, its formation reference vector is h x (t) = h v (t)=0.

[0081] The time-varying formation mission objectives of heterogeneous unmanned swarms can be described by the following definition:

[0082] For heterogeneous unmanned swarm systems with leaders, their topological structures are all directed communication topologies. For leaders and follower agents with arbitrary initial states, if the designed formation controller can make:

[0083]

[0084] Among them, y i (t) is the measured output of the ith follower at time t, hi (t) is the formation reference vector of the ith follower at time t, and y0(t) is the measured output of the leader at time t.

[0085] This embodiment designs a time-varying formation controller in the following form:

[0086] u i (t) = K xi x i (t)+K qi (q i (t)+δ i (t))+a i (t)

[0087] Among them, u i (t) is the time-varying formation control quantity, K xi , K qi are all control gain parameter matrices, x i (t) is the state of the ith follower at time t, q i (t) is the observation of the leader by the ith follower at time t, δ i (t) is the expected time-varying formation state, a i (t) is the time-varying formation tracking compensation input.

[0088] Considering the time-varying communication topology and the communication delay between agents, based on the delay state error information of relative position and the delay state error information of relative speed, a leader distributed state observer is designed in the following form:

[0089]

[0090] in, is the control gain parameter matrix to be designed, q i (t) is the observation value of the leader’s state at time t by the ith agent, x0 is the state quantity of the leader, τ is the communication delay between agents, τ>0. is the communication weight between agent i and agent j at time t, is the weight between agent i and the leader at time t. If there is no directed edge from the leader to agent i in the communication topology graph, agent i only updates the observation information through neighboring agents and its own state information, that is, At this time, the distributed state observer only contains the first two items. Correspondingly, using L σ(t) and To represent the Laplace matrix and the constraint relationship matrix of the time system. Define the matrix There exists a reversible matrix T such that T -1 M σ(t)T=Λ=diag(0,λ2,…,λ N-1 ,λ N ) holds. Among them, 0<λ2≤…≤λ N is the matrix M σ(t) The characteristic value of .

[0091] In summary, the complete heterogeneous unmanned swarm closed-loop system can be expressed as:

[0092]

[0093] S4. Perform time-varying formation control according to the time-varying formation mission objectives and the time-varying formation controller of the heterogeneous unmanned swarm system.

[0094] The present invention is now verified in conjunction with a specific example on the above-mentioned heterogeneous unmanned cluster time-varying formation control method in a network confrontation environment.

[0095] Consider a heterogeneous unmanned swarm consisting of 1 leader and 8 followers, where the followers are 2 drones, 2 unmanned submersibles, and 3 surface unmanned boats. Considering only the movement of each platform in the horizontal plane, the directed communication topology of the formation changes as follows: Figure 3 shown.

[0096] Assume that the switching time t changes the topology graph every 0.5s starting from time 0, and the topology change order is G1→G2→G3→G4→G1→… Obviously, the formation communication topology contains a directed spanning tree with the virtual navigator as the root node. σ(t) The topological structure is strongly connected.

[0097] The Laplace matrix L = DA and the pinning matrix L0 corresponding to the directed graph are:

[0098] L 01 =L 02 =L 03 =L 04 =diag(1,0,1,0,0,0,1,0)

[0099] Among them, L 01 , L 02 , L 03 , L 04 is the pinning matrix corresponding to each directed graph. The parameters of the leader state space model are designed as:

[0100]

[0101] The state space model parameters and control gain parameters of each follower are as follows:

[0102]

[0103] C1=C2=C3=C4=C5=C6=C7=C8=(10)

[0104] K=(-8-4)

[0105] K x1 =K x2 =K x3 =K x4 =(-10-10)

[0106] K q1 =(1110),K q2 =(910),K q3 =(1112),K q4 =(1111)

[0107] The units of position and velocity involved in the simulation are m and m / s respectively. The initial position of the virtual leader is set to (20,20), the initial velocity in the x direction is 0, and the initial velocity in the y direction is 5. The initial position and initial velocity in the x and y directions of each follower are generated by random numbers (-50,50).

[0108] The expected formation configuration of heterogeneous unmanned swarm is as follows: Figure 4 As shown, the virtual leader moves at a constant speed of 10m / s on the water surface, four unmanned boats and the virtual leader form a wedge formation, two drones sail side by side in the air 10 meters above the unmanned boat formation, and two unmanned submarines sail side by side in the sea 10 meters deep below the unmanned boat formation.

[0109] The expected position information of the time-varying formation state is:

[0110]

[0111] Set the delay parameter to 0.5τ m First, the simulation results of the consistency control of the closed-loop system are given. The position state tracking error of each follower to the virtual leader is like Figure 5 shown.

[0112] Depend on Figure 5 It can be seen that the position state tracking error of the eight followers to the virtual leader converges to zero in the x-direction within 7 seconds, and the error component in the y-direction converges to zero within 6 seconds, indicating that under the conditions of switching topology and communication delay, the distributed state tracker designed above has good observation effect and can update the leader status information in real time.

[0113] Next, we analyze the system’s formation output error, that is, the simulation result of the deviation between the position state of each follower and the expected position configuration. The formation output error e i (t) = y i (t)-y0(t)-h i (t) The error component curves in the x-direction and y-direction are as follows Figure 6 shown.

[0114] Depend on Figure 6 It can be seen that the formation output errors of the eight followers in the closed-loop control system converge to zero within 7 seconds, indicating that under the action of the time-varying formation control law designed by the present invention, the system can successfully achieve the desired formation output, and the system has good steady-state performance.

[0115] Next, we give the simulation results of the time-varying formation task. Figure 7 A side view of the trajectory of the heterogeneous unmanned swarm formation in the simulation is given, where agent 0 (the thin red solid line) is the virtual leader, agents 1 and 2 are drones, agents 3 and 4 are underwater unmanned vehicles, and agents 5-8 are surface unmanned boats.

[0116] Depend on Figure 7 It can be seen that the eight follower agents can quickly adjust their respective states to reach the desired position corresponding to the leader, and can continue to maintain the formation and maintain a stable speed state after reaching the desired formation.

[0117] Figure 8(a) to Figure 8(f) The top view of the configuration of the heterogeneous unmanned swarm at 6 different times is given, which can more intuitively show the formation process of its formation. t = 0s is the time of the initial state of the formation. It can be seen that at t = 5s, the UAV formation and the underwater unmanned vehicle formation have gradually formed a formation of sailing side by side. At t = 7s, the surface unmanned boat formation and the virtual leader basically formed a wedge formation. At t = 10s, the expected time-varying formation configuration has been fully formed, and each follower can maintain a stable formation and move forward synchronously with the virtual leader.

[0118] Based on the above simulation results, when the system communication topology is constantly changing and there is communication delay, the consistency control algorithm and time-varying formation control algorithm designed in this chapter can effectively achieve the desired control effect. The state error and output error in the system can tend to stabilize over time, so that all followers in the leader-follower system synchronize their state information with the leader and output the desired formation, with good control accuracy and steady-state performance.

[0119] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0122] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0123] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A time-varying formation control method for heterogeneous unmanned swarms in a network confrontation environment, characterized in that: The following steps are involved: Construct a heterogeneous unmanned swarm system model; the dynamic model of the heterogeneous unmanned swarm system model is specifically: in, are all constant real number matrices of leaders, is the state quantity of the leader at time t, For the status Seeking guidance, is the measured output of the leader at time t, are constant real number matrices of followers, For the i The state quantity of a follower at time t, For the i The control input of a follower at time t is For the i The measured output of a follower at time t, For the status Derivation; According to the heterogeneous unmanned swarm system model, a communication topology directed graph with switching topology in a network confrontation environment is established; According to the heterogeneous unmanned swarm system model and the communication topology directed graph with switching topology in the network confrontation environment, a time-varying formation task target and a time-varying formation controller of the heterogeneous unmanned swarm system are established; the following steps are included: According to the expected formation configuration formed by the unmanned swarm system, set the expected configuration parameters of the time-varying formation, specifically: in, For the i The formation reference vector of followers at time t, For the i The position component of a follower at time t, For the i The velocity component of a follower at time t, T is the transpose symbol, For the N The expected position of a follower with respect to the virtual leader at time t, For the N The velocity vector difference between the followers and the virtual leader at time t; The time-varying formation mission objectives of the heterogeneous unmanned swarm system are established according to the expected configuration parameters of the time-varying formation, specifically: in, For the i The measured output of a follower at time t, For the i The formation reference vector of followers at time t, is the measured output of the leader at time t; The time-varying formation controller is specifically: in, is the time-varying formation control quantity, , are control gain parameter matrices, For the i The state quantity of a follower at time t, For the i The observations of the leader by a follower at time t, is the expected time-varying formation state quantity, Compensate input for time-varying formation tracking; Time-varying formation control is performed according to the time-varying formation mission objectives and time-varying formation controller of the heterogeneous unmanned swarm system.

2. According to the method for controlling a heterogeneous unmanned swarm in a network confrontation environment in claim 1, it is characterized in that: The heterogeneous unmanned swarm system model includes unmanned aerial vehicles, surface unmanned boats, and unmanned underwater vehicles, and its movement space covers underwater, surface, and air.

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