A collaborative formation control method for air-sea heterogeneous unmanned swarms under complex confrontation conditions

Abstract

The present invention discloses a method for controlling an air-sea heterogeneous unmanned cluster collaborative formation under complex confrontation conditions. The method comprises the following steps: constructing a kinematic model of an air-sea heterogeneous unmanned cluster, setting a virtual navigator, and constructing a leader-follower model; constructing a communication interference model of an air-sea heterogeneous unmanned cluster; establishing an adaptive fault observer, and then establishing a control law for an air-sea heterogeneous unmanned cluster collaborative formation, and controlling the air-sea heterogeneous unmanned cluster collaborative formation under complex confrontation conditions according to the communication interference model of the air-sea heterogeneous unmanned cluster and the control law for the air-sea heterogeneous unmanned cluster collaborative formation. The present invention simultaneously considers the impact of countering network attacks on communications, as well as the impact of actuator failures in air-sea heterogeneous unmanned cluster formation members on formation control, thereby realizing the control of an air-sea heterogeneous unmanned cluster collaborative formation under complex confrontation conditions.

Description

Technical Field

[0001] The present invention relates to the field of air-sea heterogeneous unmanned swarm collaborative formation control, and in particular to a method for air-sea heterogeneous unmanned swarm collaborative formation control under complex confrontation conditions. Background Art

[0002] Most of the unmanned swarm projects currently in practical use are based on a single platform. However, for diverse missions and complex environments, a single-platform swarm cannot meet the requirements. Compared to a single platform, a system composed of multi-platform heterogeneous unmanned swarms offers comprehensive functionality, strong adaptability, and robustness, enabling them to perform more challenging missions in more complex environments. Therefore, research on collaborative formation control methods for heterogeneous unmanned swarms is of great practical significance.

[0003] An existing method discloses a heterogeneous cluster formation tracking control method of drones and unmanned vehicles under topology switching. The method enables a cluster system composed of multiple drones and unmanned vehicles to form a desired time-varying formation configuration under topology switching conditions while tracking the desired reference trajectory, thereby realizing distributed time-varying formation tracking motion of the heterogeneous cluster system. However, the method cannot give the exact time of formation convergence, and cannot complete the formation transformation within a certain time limit. In addition, the method does not take into account the complex confrontation conditions faced in actual situations.

[0004] Under complex adversarial conditions, unmanned swarms face a variety of attacks and interference. These factors require consideration: attacks from adversarial networks, the impact of the environment on the swarm, the impact of adversarial network attacks on communications, and the impact of actuator failures within the swarm's formation members on formation control. Actuator failures primarily refer to issues with actuators such as motors and servos on the agents, which can prevent them from executing their intended actions and impact the performance and safety of the entire formation system. Actuator failures typically include, but are not limited to, stuck faults, bias faults, damage faults, and loss of control faults. In summary, unmanned swarm systems in real-world environments are inevitably subject to various network attacks and interference, posing threats to the security of their formation control. Therefore, research on unmanned swarm formation control methods under complex adversarial conditions is of great significance for the safe control of swarm formations. Summary of the Invention

[0005] In response to the above-mentioned deficiencies in the prior art, the present invention provides a method for controlling air-sea heterogeneous unmanned cluster collaborative formations under complex confrontation conditions. This method takes into account the impact of countermeasures against network attacks on communications, as well as the impact of actuator failures in the members of the air-sea heterogeneous unmanned cluster formation on formation control, thereby realizing the control of air-sea heterogeneous unmanned cluster collaborative formations under complex confrontation conditions.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0007] A method for controlling air-sea heterogeneous unmanned swarm collaborative formations under complex confrontation conditions includes the following steps:

[0008] S1. Construct a kinematic model of an air-sea heterogeneous unmanned swarm, set up a virtual navigator, and construct a leader-follower model based on the kinematic model of the air-sea heterogeneous unmanned swarm and the virtual navigator.

[0009] S2. Based on the leader-follower model, a communication interference model for heterogeneous unmanned swarms in the air and sea is constructed;

[0010] S3. Establish an adaptive fault observer, establish the control law of air-sea heterogeneous unmanned swarm collaborative formation based on the leader-follower model and the adaptive fault observer, and control the air-sea heterogeneous unmanned swarm collaborative formation under complex confrontation conditions according to the communication interference model of the air-sea heterogeneous unmanned swarm and the control law of the air-sea heterogeneous unmanned swarm collaborative formation.

[0011] Furthermore, in step S1, a kinematic model of an air-sea heterogeneous unmanned swarm is constructed, including the following steps:

[0012] A1. Construct a three-degree-of-freedom point mass model of the UAV;

[0013] A2. Construct a point mass model of the unmanned ship;

[0014] A3. Construct a three-degree-of-freedom particle kinematic model for an unmanned underwater vehicle;

[0015] A4. Based on the three-degree-of-freedom mass model of the UAV, the mass model of the unmanned ship, and the three-degree-of-freedom mass kinematic model of the unmanned submarine, a kinematic model of heterogeneous unmanned swarms in the air and sea is constructed.

[0016] Furthermore, in step A4, based on the three-degree-of-freedom mass model of the UAV, the mass model of the unmanned ship, and the three-degree-of-freedom mass kinematic model of the unmanned underwater vehicle, a kinematic model of the air-sea heterogeneous unmanned swarm is constructed, which is expressed as:

[0017]

[0018] Where: p i (t) is the three-dimensional spatial coordinate of agent i at time t, v i (t) is the speed of agent i at time t, u i (t) is the virtual control input of agent i at time t.

[0019] Furthermore, in the kinematic model of heterogeneous unmanned swarm in air and sea, the conversion relationship between the virtual control input and the actual control input in the kinematic model of the UAV is specifically as follows:

[0020]

[0021] Where: V cn (t) is the actual speed control input of UAV n at time t, χ cn (t) is the actual control input of the heading angle of UAV n at time t, γ cn (t) is the actual control input of the pitch angle of UAV n at time t, τ V is the time constant corresponding to the speed, τ χ is the time constant corresponding to the heading angle, τ γ is the time constant corresponding to the pitch angle, u xn (t) is the virtual control input of UAV n in the x direction at time t, u yn (t) is the virtual control input of UAV n in the y direction at time t, u zn (t) is the virtual control input of UAV n in the z direction at time t, V n (t) is the speed of UAV n at time t, χ n (t) is the heading angle of UAV n at time t, γ n (t) is the pitch angle of UAV n at time t;

[0022] The conversion relationship between virtual control input and actual control input in the kinematic model of the unmanned ship is as follows:

[0023]

[0024] Where: V cl (t) is the actual control input of the speed of the unmanned ship l at time t, ψ cl (t) is the actual control input of the heading angle of the unmanned ship l at time t, τ V is the time constant corresponding to the speed, τ χ is the time constant corresponding to the heading angle, u xl (t) is the virtual control input of the unmanned ship l in the x direction at time t, u yl (t) is the virtual control input of the UAV l in the y direction at time t, V l (t) is the speed of the unmanned ship l at time t, ψ l (t) is the heading angle of the unmanned ship l at time t;

[0025] The conversion relationship between virtual control input and actual control input in the kinematic model of the unmanned underwater vehicle is as follows:

[0026]

[0027] Where: V ck(t) is the actual control input of the speed of the unmanned underwater vehicle k at time t, ψ ck (t) is the actual control input of the heading angle of the unmanned underwater vehicle k at time t, θ ck (t) is the actual control input of the longitudinal inclination angle of the unmanned underwater vehicle k at time t, τ V is the time constant corresponding to the speed, τ χ is the time constant corresponding to the heading angle, τ γ is the time constant corresponding to the longitudinal inclination angle, u xk (t) is the virtual control input of the unmanned underwater vehicle k in the x direction at time t, u yk (t) is the virtual control input of the UUV k in the y direction at time t, u zk (t) is the virtual control input of the unmanned underwater vehicle k in the z direction at time t, V k (t) is the speed of the unmanned underwater vehicle k at time t, ψ k (t) is the heading angle of the unmanned underwater vehicle k at time t, θ k (t) is the longitudinal inclination angle of the unmanned underwater vehicle k at time t.

[0028] Furthermore, in step S2, based on the leader-follower model, a communication interference model of the air-sea heterogeneous unmanned swarm is constructed, which is expressed as:

[0029] G δ(t) =(V, E δ(t) , A δ(t) )

[0030] Among them: G δ(t) is an undirected graph with switching topology associated with time t, V is the velocity, E δ(t) and A δ(t) are the edge set and adjacency matrix associated with time t, respectively, and δ(t) is the switching signal of the communication topology.

[0031] Furthermore, in step S3, an adaptive fault observer is established, including the following steps:

[0032] B1. Set the adaptive gain of the adaptive fault observer;

[0033] B2. Establish an adaptive fault observer based on the adaptive gain of the adaptive fault observer.

[0034] Furthermore, in step B1, the adaptive gain of the adaptive fault observer is set, which is expressed as:

[0035]

[0036] Where: k d1 and k d2All are adaptive gains, s d is the auxiliary sliding variable, σ d is an arbitrary threshold greater than zero, and ω1, γ1, and ε1 are all constants with positive signs.

[0037] Furthermore, in step B2, an adaptive fault observer is established according to the adaptive gain of the adaptive fault observer, which is expressed as:

[0038]

[0039] in: and Both are sliding variables, u i is the control output of agent i, k d1 and k d2 All are adaptive gains, s d is the auxiliary sliding variable.

[0040] Furthermore, based on the leader-follower model and the adaptive fault observer, the control law for the air-sea heterogeneous unmanned swarm cooperative formation is established, which is expressed as:

[0041]

[0042] Where: u i is the control output of agent i, l1 and l2 are the arrival law parameters, sig is the control law function, p2 and g2 are the control law parameters, s i is the fixed time sliding surface, Ψ i is the control compensation term, f si (e pi ) is the position tracking error e pi is the sliding mode function of the variable, is the observation value of the adaptive fault observer.

[0043] The present invention has the following beneficial effects:

[0044] (1) The present invention constructs a kinematic model of an air-sea heterogeneous unmanned cluster, sets a virtual navigator, and constructs a leader-follower model based on the kinematic model of the air-sea heterogeneous unmanned cluster and the virtual navigator. Then, based on the leader-follower model, a communication interference model of the air-sea heterogeneous unmanned cluster is constructed. Finally, an adaptive fault observer is established. Based on the leader-follower model and the adaptive fault observer, a control law for the collaborative formation of the air-sea heterogeneous unmanned cluster is established. The collaborative formation of the air-sea heterogeneous unmanned cluster under complex confrontation conditions is controlled according to the communication interference model of the air-sea heterogeneous unmanned cluster and the control law for the collaborative formation of the air-sea heterogeneous unmanned cluster. This process simultaneously considers the impact of the counter-network attack on the communication and the impact of the actuator failure in the formation members of the air-sea heterogeneous unmanned cluster on the formation control, thereby realizing the control of the collaborative formation of the air-sea heterogeneous unmanned cluster under complex confrontation conditions.

[0045] (2) The present invention establishes a collaborative formation control law for heterogeneous unmanned swarms in the air and sea, which can complete the desired formation configuration task of the heterogeneous unmanned swarm within a fixed time, and can control the position tracking error and velocity tracking error of the formation members to converge to a certain range within a fixed time. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a method for controlling air-sea heterogeneous unmanned swarm collaborative formation under complex confrontation conditions.

[0047] Figure 2 Schematic diagram of the three-dimensional motion trajectory of heterogeneous unmanned swarms in the air and sea;

[0048] Figure 3 It is the actual control instruction change curve;

[0049] Figure 4a It is the position tracking error map of UAV formation members;

[0050] Figure 4b It is the velocity tracking error diagram of UAV formation members;

[0051] Figure 5a This is the position tracking error map of USV formation members;

[0052] Figure 5b is the USV formation member speed tracking error map;

[0053] Figure 6a It is the position tracking error map of UUV formation members;

[0054] Figure 6b Velocity tracking error diagram of UUV formation members. DETAILED DESCRIPTION

[0055] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0056] like Figure 1 As shown, a method for controlling air-sea heterogeneous unmanned swarm collaborative formation under complex confrontation conditions includes steps S1-S3, which are specifically as follows:

[0057] S1. Construct a kinematic model of an air-sea heterogeneous unmanned swarm, set up a virtual navigator, and construct a leader-follower model based on the kinematic model of the air-sea heterogeneous unmanned swarm and the virtual navigator.

[0058] In an optional embodiment of the present invention, the present invention constructs a kinematic model of an air-sea heterogeneous unmanned swarm, including the following steps:

[0059] A1. Construct a three-degree-of-freedom point mass model of the UAV, expressed as:

[0060]

[0061] Among them: n∈{i|i=1, 2,..., N}, x n (t), y n (t), z n (t) are the x-direction coordinate, y-direction coordinate, and z-direction coordinate of the n-th UAV at time t, respectively. V n (t), χ n (t), γ n (t) are speed, heading angle, and pitch angle, respectively, V cn (t), χ cn (t), γ cn (t) are their control inputs, τ V , τ χ , τ γ are their corresponding time constants respectively.

[0062] A2. Construct a point mass model of the unmanned ship, expressed as:

[0063]

[0064] Among them: l∈{i|i=1, 2,..., N}, x l (t) and y l (t) are the x-direction coordinate and y-direction coordinate of the lth unmanned ship at time t, V l (t) and ψ l(t) are the speed and heading angle of the lth unmanned ship at time t, V cl (t), ψ cl (t) are their control inputs, τ V , τ ψ are their corresponding time constants respectively.

[0065] A3. Construct a three-degree-of-freedom particle kinematic model for the unmanned underwater vehicle, expressed as:

[0066]

[0067] Among them: k∈{i|i=1, 2, ..., N}, x k (t), y k (t), z k (t) are the x-direction coordinate, y-direction coordinate, and z-direction coordinate of the kth unmanned underwater vehicle at time t, V k (t), ψ k (t), θ k (t) are the speed, heading angle, and longitudinal inclination angle of the kth unmanned underwater vehicle at time t, V cl (t), ψ cl (t), θ ck (t) are their control inputs, τ V , τx, τ θ are their corresponding time constants respectively.

[0068] A4. Based on the three-degree-of-freedom mass model of the UAV, the mass model of the unmanned ship, and the three-degree-of-freedom mass kinematic model of the unmanned submarine, a kinematic model of heterogeneous unmanned swarms in the air and sea is constructed.

[0069] To facilitate the design of subsequent control algorithms, the present invention simplifies the three-degree-of-freedom mass point model of the UAV, the mass point model of the unmanned ship, and the three-degree-of-freedom mass point kinematic model of the unmanned underwater vehicle into a second-order integrator to construct a kinematic model of the air-sea heterogeneous unmanned swarm, which can be expressed as:

[0070]

[0071] Where: p i (t) is the three-dimensional spatial coordinate of agent i at time t, p i (t)=[p xi (t),p yi (t),p zi (t)] T , v i (t) is the speed of agent i at time t, v i (t)=[v xi (t),v yi (t),vzi (t)] T ,u i (t) is the virtual control input of agent i at time t, u i (t)=[u xi (t),u yi (t),u zi (t)] T .

[0072] In the kinematic model of the air-sea heterogeneous unmanned swarm, the conversion relationship between the virtual control input and the actual control input in the kinematic model of the UAV is specifically as follows:

[0073]

[0074] Where: V cn (t) is the actual speed control input of UAV n at time t, χ cn (t) is the actual control input of the heading angle of UAV n at time t, γ cn (t) is the actual control input of the pitch angle of UAV n at time t, τ V is the time constant corresponding to the speed, τ χ is the time constant corresponding to the heading angle, τ γ is the time constant corresponding to the pitch angle, u xn (t) is the virtual control input of UAV n in the x direction at time t, u yn (t) is the virtual control input of UAV n in the y direction at time t, u zn (t) is the virtual control input of UAV n in the z direction at time t, V n (t) is the speed of UAV n at time t, χ n (t) is the heading angle of UAV n at time t, γ n (t) is the pitch angle of UAV n at time t.

[0075] The conversion relationship between virtual control input and actual control input in the kinematic model of the unmanned ship is as follows:

[0076]

[0077] Where: V cl (t) is the actual control input of the speed of the unmanned ship l at time t, ψ cl (t) is the actual control input of the heading angle of the unmanned ship l at time t, τ V is the time constant corresponding to the speed, τ χ is the time constant corresponding to the heading angle, u xl (t) is the virtual control input of the unmanned ship l in the x direction at time t, u yl(t) is the virtual control input of the UAV l in the y direction at time t, V l (t) is the speed of the unmanned ship l at time t, ψ l (t) is the heading angle of the unmanned ship l at time t.

[0078] The conversion relationship between virtual control input and actual control input in the kinematic model of the unmanned underwater vehicle is as follows:

[0079]

[0080] Where: V ck (t) is the actual control input of the speed of the unmanned underwater vehicle k at time t, ψ ck (t) is the actual control input of the heading angle of the unmanned underwater vehicle k at time t, θ ck (t) is the actual control input of the longitudinal inclination angle of the unmanned underwater vehicle k at time t, τ V is the time constant corresponding to the speed, τ χ is the time constant corresponding to the heading angle, τ γ is the time constant corresponding to the longitudinal inclination angle, u xk (t) is the virtual control input of the unmanned underwater vehicle k in the x direction at time t, u yk (t) is the virtual control input of the UUV k in the y direction at time t, u zk (t) is the virtual control input of the unmanned underwater vehicle k in the z direction at time t, V k (t) is the speed of the unmanned underwater vehicle k at time t, ψ k (t) is the heading angle of the unmanned underwater vehicle k at time t, θ k (t) is the longitudinal inclination angle of the unmanned underwater vehicle k at time t.

[0081] The present invention sets up a virtual navigator and establishes a virtual navigator and follower model, and completes the task of tracking the reference trajectory and forming the formation by controlling the movement of the virtual navigator.

[0082] The present invention designs the model of the virtual navigator into the following form:

[0083]

[0084] Where: p0(t) is the three-dimensional spatial position coordinate of the virtual navigator at time t, v0(t) is the speed of the virtual navigator at time t, and u0(t) is the virtual control input of the virtual navigator at time t.

[0085] S2. Based on the leader-follower model, a communication interference model for heterogeneous unmanned swarms in the air and sea is constructed.

[0086] In an optional embodiment of the present invention, the present invention constructs a communication interference model of an air-sea heterogeneous unmanned swarm based on a leader-follower model, which is expressed as:

[0087] G δ(t) =(V, E δ(t) , A δ(t) )

[0088] Among them: G δ(t) is an undirected graph with switching topology associated with time t, V is the velocity, E δ(t) and A δ(t) are the edge set and adjacency matrix associated with time t, respectively, and δ(t) is the switching signal of the communication topology.

[0089] The present invention considers the impact of countering network attacks on communications. When the communication link between two formation members is attacked, the communication link is disconnected. If the communication link between formation members is also destroyed, the system can activate the communication plan. That is, the attacked formation member can reconnect the communication link with the unattacked formation, and then re-establish communication with the system, ensuring the connectivity of the topology graph. The present invention describes the impact of countering network attacks on communications as switching the communication topology, and uses the above undirected graph with switching topology to represent the communication topology structure of the air-sea heterogeneous unmanned cluster.

[0090] S3. Establish an adaptive fault observer, establish the control law of air-sea heterogeneous unmanned swarm collaborative formation based on the leader-follower model and the adaptive fault observer, and control the air-sea heterogeneous unmanned swarm collaborative formation under complex confrontation conditions according to the communication interference model of the air-sea heterogeneous unmanned swarm and the control law of the air-sea heterogeneous unmanned swarm collaborative formation.

[0091] In an optional embodiment of the present invention, the present invention establishes an adaptive fault observer, comprising the following steps:

[0092] B1. Set the adaptive gain of the adaptive fault observer, expressed as:

[0093]

[0094] Where: k d1 and k d2 All are adaptive gains, s d is the auxiliary sliding variable, σ d is an arbitrary threshold greater than zero, and ω1, γ1, and ε1 are all constants with positive signs.

[0095] Specifically, the actuator fault model in this paper can be described as follows:

[0096]

[0097] Where: u ic (t) is the desired control input instruction, is the control instruction actually received, d i =[d xi ,d yi ,d zi ] T For unknown paranoid failure.

[0098] When considering the impact of actuator failure, the present invention constructs a second-order integrator model of the intelligent agent based on the air-sea heterogeneous unmanned swarm kinematic model:

[0099]

[0100] B2. According to the adaptive gain of the adaptive fault observer, an adaptive fault observer is established, which is expressed as:

[0101]

[0102] in: and Both are sliding variables, u i is the control output of agent i, k d1 and k d2 All are adaptive gains, s d is the auxiliary sliding variable.

[0103] Auxiliary sliding variables have the following form:

[0104]

[0105] Where: v i is the speed of agent i.

[0106] The present invention defines the observation error of the fault as:

[0107]

[0108] The auxiliary sliding variable s d The observation error e of the fault d Satisfies the following state equation:

[0109]

[0110] The present invention sets the derivative of the actuator fault is bounded but the upper bound is unknown, so consider the auxiliary sliding variable s d The observation error e of the fault dSatisfying the above state equation, when the adaptive fault observer is designed as the above form, the adaptive gain of the adaptive fault observer is set to satisfy the above form, then there are positive constants ω1, γ1 and ε1 so that the observation error e of the fault d Converges to zero in finite time.

[0111] The present invention establishes a control law for the air-sea heterogeneous unmanned swarm collaborative formation based on the leader-follower model and the adaptive fault observer, which is:

[0112]

[0113] Where: u i is the control output of agent i, l1 and l2 are the parameters of the arrival law, l1>0, l2>0, sig is the control law function, p2 and g2 are the control law parameters, 0 <p2<1,g2> 1,s ii is the fixed time sliding surface, Ψ i is the control compensation term, f si (e pi ) is the position tracking error e pi is the sliding mode function of the variable, is the observation value of the adaptive fault observer.

[0114] The control compensation term in the present invention is:

[0115] Ψ i =[Ψ i1 ,Ψ i2 , Ψxx] T ∈R 3 ,

[0116]

[0117] Among them: i1 ,Ψ i2 , Ψ i3 are the control compensation terms of UAV, UAV and UUV respectively, T is the transpose symbol, m=1,2,3, R,κ i 、k u , ζ are constants, κ i 、k u and ζ satisfy κ i ≥||u di ||, k u >0,ζ>0,u di is a constant with a known upper bound.

[0118] Control compensation term Ψ i With sliding surface s i Satisfies the following relationship:

[0119]

[0120] In the present invention, the position tracking error e pi The sliding mode function of the variable is expressed as:

[0121]

[0122] in: They are UAV, UAV, UAV and UUV respectively. pi is the first-order derivative of the design function of the variable, a1, b1, a2, b2, m are all pre-designed constants, e vim is the component of velocity tracking error.

[0123] The present invention adopts the communication interference model of the air-sea heterogeneous unmanned cluster to always maintain communication connectivity. Under the condition of actuator failure, the above-mentioned air-sea heterogeneous unmanned cluster collaborative formation control law is adopted, and all variables in the closed-loop system are consistently and ultimately bounded.

[0124] To better demonstrate the effectiveness of the proposed method in controlling heterogeneous unmanned air and sea swarms to perform formation missions under complex adversarial conditions, a set of control experiments was conducted to compare the control results. Under identical conditions, the formation was first controlled using a sliding mode consistency-based formation control method. This controller did not consider the effects of external interference and only considered the formation control task under ideal conditions.

[0125] Under the same conditions, the present invention uses the communication interference model of the air-sea heterogeneous unmanned swarm and the air-sea heterogeneous unmanned swarm cooperative formation control law designed by the present invention to control the air-sea heterogeneous unmanned swarm formation. The results of the simulation experiment are as follows: Figures 2 to 6b As shown. Among them, Figure 2 This is a schematic diagram of the three-dimensional motion trajectory of a heterogeneous unmanned swarm in the air and sea. From the figure, we can see that the unmanned swarm quickly converges from the randomly distributed initial position to the desired formation configuration, and continues to maintain a stable formation forward. Figure 3 This is a curve diagram of the actual control command changes. From the figure, we can see that under the action of the controller, the speed, yaw angle, and pitch angle of each member in the formation quickly converge to the expected values, which means that the unmanned cluster completed the desired formation configuration in a short time. Figures 4a to 6b is the state tracking error of each member of the air-sea heterogeneous unmanned swarm, Figure 4a is the position tracking error of the UAV formation members, Figure 4b is the velocity tracking error of the UAV formation members, Figure 5a is the position tracking error of USV formation members, Figure 5b is the USV formation member speed tracking error, Figure 6a is the position tracking error of the UUV formation members, Figure 6b is the velocity tracking error of the UUV formation members. As can be seen from these figures, the state tracking error of each formation member converges to zero within 15 seconds. This means that under the action of the controller, the formation control task of the air-sea heterogeneous unmanned swarm completed the desired formation configuration within 15 seconds under complex confrontation conditions.

[0126] Comparative analysis of the two simulation experiments above demonstrates that, under complex adversarial conditions involving denial-of-service attacks and actuator failures, the controller designed under ideal conditions using the sliding mode consistency method performs poorly in controlling the formation of a heterogeneous air-sea unmanned swarm. The state tracking errors of each swarm member fluctuate significantly, meaning the swarm cannot effectively achieve the desired formation configuration. However, under the control of the controller designed in this paper, the heterogeneous air-sea unmanned swarm is able to achieve the desired formation configuration under complex adversarial conditions. Therefore, the simulation experiments designed in this section validate the effectiveness of the control algorithm designed in this chapter.

[0127] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, 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 flowcharts and / or block diagrams. 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.

[0128] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0130] Specific embodiments are used in the present invention 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 ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0131] 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 it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for controlling air-sea heterogeneous unmanned swarm collaborative formation under complex confrontation conditions, characterized in that: The following steps are involved: S1. Construct a kinematic model of an air-sea heterogeneous unmanned swarm, set up a virtual navigator, and construct a leader-follower model based on the kinematic model of an air-sea heterogeneous unmanned swarm and the virtual navigator; S2. Based on the leader-follower model, a communication interference model of heterogeneous unmanned swarms in the air and sea is constructed; S3. Establish an adaptive fault observer, establish the air-sea heterogeneous unmanned swarm collaborative formation control law based on the leader-follower model and the adaptive fault observer, and control the air-sea heterogeneous unmanned swarm collaborative formation under complex confrontation conditions according to the communication interference model of the air-sea heterogeneous unmanned swarm and the air-sea heterogeneous unmanned swarm collaborative formation control law; Based on the leader-follower model and adaptive fault observer, the control law of air-sea heterogeneous unmanned swarm cooperative formation is established, which is expressed as: ， ， in: For intelligent agents i The control output, and is the arrival law parameter, is the control law function, and are control law parameters, is the fixed time sliding surface, To control the compensation term, The position tracking error is the sliding mode function of the variable, is the observed value of the adaptive fault observer, , , are the control compensation items for UAV, unmanned ship and unmanned submarine respectively. is the transpose symbol, , , , , are constants, , and Satisfy , , , is a constant with a known upper bound.

2. According to the method for controlling air-sea heterogeneous unmanned swarm collaborative formation under complex confrontation conditions described in claim 1, it is characterized in that: In step S1, a kinematic model of an air-sea heterogeneous unmanned swarm is constructed, including the following steps: A1. Construct a three-degree-of-freedom point model of the UAV; A2. Construct a particle model of the unmanned ship; A3. Construct a three-degree-of-freedom particle kinematic model for an unmanned underwater vehicle; A4. Based on the three-degree-of-freedom particle model of the UAV, the particle model of the unmanned ship and the three-degree-of-freedom particle kinematic model of the unmanned submarine, a kinematic model of heterogeneous unmanned clusters in the air and sea is constructed.

3. According to the method for controlling the air-sea heterogeneous unmanned swarm coordinated formation under complex confrontation conditions as described in claim 2, it is characterized in that: In step A4, the kinematic model of the air-sea heterogeneous unmanned swarm is constructed based on the three-degree-of-freedom mass model of the UAV, the mass model of the unmanned ship, and the three-degree-of-freedom mass kinematic model of the unmanned underwater vehicle, which is expressed as: in: For intelligent agents i exist t The three-dimensional spatial position coordinates at the moment, For intelligent agents i exist t The speed of time, For intelligent agents i exist t The virtual control input at the moment.

4. According to the method for controlling air-sea heterogeneous unmanned swarm coordinated formation under complex confrontation conditions as described in claim 3, it is characterized in that: In the kinematic model of heterogeneous unmanned swarm in air and sea, the conversion relationship between virtual control input and actual control input in the kinematic model of UAV is as follows: in: For drones n exist t The actual control input of the speed at the moment, For drones n exist t The actual control input of the heading angle at the moment, For drones n exist t The actual control input of the pitch angle at the moment, is the time constant corresponding to the speed, is the time constant corresponding to the heading angle, is the time constant corresponding to the pitch angle, For drones n exist t Moment x Direction virtual control input, For drones n exist t Moment y Direction virtual control input, For drones n exist t Moment z Direction virtual control input, For drones n exist t The speed of time, For drones n exist t The heading angle at the moment, For drones n exist t The pitch angle at the moment; The conversion relationship between the virtual control input and the actual control input in the kinematic model of the unmanned ship is as follows: in: For unmanned ships l exist t The actual control input of the speed at the moment, For unmanned ships l exist t The actual control input of the heading angle at the moment, is the time constant corresponding to the speed, is the time constant corresponding to the heading angle, For unmanned ships l exist t Moment x Direction virtual control input, For unmanned ships l exist t Moment y Direction virtual control input, For unmanned ships l exist t The speed of time, For unmanned ships l exist t The heading angle at the moment; The conversion relationship between virtual control input and actual control input in the kinematic model of the unmanned underwater vehicle is as follows: in: Unmanned underwater vehicle k exist t The actual control input of the speed at the moment, Unmanned underwater vehicle k exist t The actual control input of the heading angle at the moment, Unmanned underwater vehicle k exist t The actual control input of the longitudinal inclination angle at the moment, is the time constant corresponding to the speed, is the time constant corresponding to the heading angle, is the time constant corresponding to the longitudinal inclination, Unmanned underwater vehicle k exist t Moment x Direction virtual control input, Unmanned underwater vehicle k exist t Moment y Direction virtual control input, Unmanned underwater vehicle k exist t Moment z Direction virtual control input, Unmanned underwater vehicle k exist t The speed of time, Unmanned underwater vehicle k exist t The heading angle at the moment, Unmanned underwater vehicle k exist t The longitudinal inclination at the moment.

5. According to the method for controlling air-sea heterogeneous unmanned swarm coordinated formation under complex confrontation conditions as described in claim 1, it is characterized in that: In step S2, based on the leader-follower model, a communication interference model of the air-sea heterogeneous unmanned cluster is constructed, which is expressed as: in: For time The related undirected graph with switching topology, For speed, and Respectively with time The associated edge set and adjacency matrix, It is the switching signal of the communication topology.

6. According to the method for controlling air-sea heterogeneous unmanned swarm coordinated formation under complex confrontation conditions as described in claim 1, it is characterized in that: In step S3, an adaptive fault observer is established, including the following steps: B1. Setting the adaptive gain of the adaptive fault observer; B2. Establish an adaptive fault observer according to the adaptive gain of the adaptive fault observer.

7. The method for controlling air-sea heterogeneous unmanned swarm coordinated formation under complex confrontation conditions according to claim 6 is characterized in that: In step B1, the adaptive gain of the adaptive fault observer is set, which is expressed as: in: and All are adaptive gains. is the auxiliary sliding variable, is any threshold greater than zero, , , are all constants with positive signs.

8. The method for controlling air-sea heterogeneous unmanned swarm coordinated formation under complex confrontation conditions according to claim 6 is characterized in that: In step B2, an adaptive fault observer is established according to the adaptive gain of the adaptive fault observer, which is expressed as: in: and All are sliding variables. For intelligent agents i The control output, and All are adaptive gains. Auxiliary sliding variable.

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