DoS-attack-oriented layered cooperative control method and device for triggering of unmanned ship edge events

Abstract

The invention discloses an unmanned ship edge event triggering hierarchical cooperative control method and device oriented to DoS attack, and relates to the field of unmanned surface ship control, and the device comprises a periodic DoS attack acquisition module, a distributed edge event construction module, a communication network, an anti-interference position tracking control module, a target ship and a following ship. When the state variable quantity of the unmanned ship exceeds a set threshold value, constructing a distributed edge event triggering ESO according to the current state information and based on an edge event triggering method, obtaining state data of the target unmanned ship, and establishing a relative dynamic equation; and through dimension reduction ESO, predicting an uncertain disturbance function of the target unmanned ship, and based on an anti-interference position tracking law of the dimension reduction ESO, controlling the following unmanned ship to track the position of the target unmanned ship. According to the method and the device, the influence of DoS attacks on system state updating can be effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of surface unmanned boat control, and in particular to a layered collaborative control method and device for unmanned boat edge event triggering under DoS attacks. Background Art

[0002] As the research and development and application of multi-UAV collaborative motion control technology continue to expand, it is showing a trend of integrated development. The collaborative tracking control of targets relies on a secure network communication environment, but the network channel is inevitably attacked by malicious attackers. DoS attacks are the most common type of network attacks. They can interrupt the transmission of communication channels, making it impossible to update information normally. Therefore, it is of great significance to study the state estimation and tracking problem of collaborative targets of single UAVs under DoS attacks. However, DoS attacks do not always remain in an attacking state. Therefore, non-periodic communication systems can tolerate DoS signals characterized by frequency and duration. Non-periodic communication methods based on event triggering mechanisms are effective technical means to save communication resources. Common event triggering mechanisms include point-based event triggering mechanisms and edge-based event triggering mechanisms.

[0003] However, the event triggering mechanism based on point triggering used in existing content still has the following shortcomings:

[0004] First, when the difference in state between adjacent nodes falls below a preset threshold, the trigger mechanism still forces communication, resulting in repeated transmission of similar data. For example, if the positions of adjacent unmanned boats are highly synchronized, the trigger rule will still execute position updates, resulting in wasted bandwidth resources. This means that the convergence of neighboring states leads to redundant communication.

[0005] Second, if the system uses a global threshold instead of a local threshold, the triggering of one node may cause multiple adjacent nodes to passively update their states, forming a chain communication and causing multi-node triggering timing conflicts.

[0006] Third, the traditional point trigger mechanism only relies on the current state to determine whether communication has occurred, lacks dynamic evaluation of historical state changes, and has low utilization of historical data. Summary of the Invention

[0007] The purpose of this application is to provide a method and device for layered collaborative control of unmanned boats triggered by edge events under DoS attacks, which can reduce the communication frequency and communication burden through a distributed event edge trigger observer, and effectively reduce the impact of DoS attacks on system status updates through a state estimator.

[0008] To achieve the above objectives, this application provides the following solutions:

[0009] First, this application provides a layered collaborative control method for edge event triggering of unmanned boats under DoS attacks, including:

[0010] Obtain a periodic DoS attack module, a kinematic model, and a dynamic model of a multi-UAV system consisting of several follower UAVs and a target UAV;

[0011] According to the periodic DoS attack module, the kinematic model and dynamic model of the multi-UAV system, the state of each UAV in the multi-UAV system is monitored based on the state predictor;

[0012] When the state change of any unmanned boat in the multi-unmanned boat system exceeds a set threshold, the current state information of the abnormal unmanned boat is sent to the non-abnormal unmanned boats in the multi-unmanned boat system; the abnormal unmanned boat is the unmanned boat whose state change exceeds the set threshold;

[0013] According to the current status information of the abnormal unmanned boat, a distributed edge event for DoS attacks is constructed based on the edge event triggering method;

[0014] Triggering ESO according to the distributed edge event to obtain status data of the target unmanned boat;

[0015] According to the state data of the target unmanned boat and based on the line-of-sight guidance principle, the relative dynamic equation of the target unmanned boat under DoS attack is established;

[0016] According to the relative dynamic equation, the uncertain disturbance function of the target unmanned vehicle is predicted by reducing the dimension of ESO;

[0017] According to the uncertain disturbance function and based on the anti-interference position tracking law of the dimension-reduced ESO, the follower unmanned boat is controlled to track the position of the target unmanned boat.

[0018] Optionally, the kinematic model of the target unmanned boat in the kinematic model of the multi-unmanned boat system is:

[0019]

[0020] Where x0, y0, and ψ0 represent the X-axis coordinate, Y-axis coordinate, and yaw angle of the target unmanned boat in the earth coordinate system, respectively; u0, v0, and r0 represent the longitudinal velocity, transverse drift velocity, and yaw angular velocity of the target unmanned boat in the unmanned boat body coordinate system, respectively.

[0021] Optionally, the kinematic model and dynamic model of the multi-unmanned boat system are:

[0022]

[0023] Where m ui ,mvi ,m ri represents the mass and inertia of the i-th following unmanned boat; f ui (·),f vi (·),f ri (·) represents an unknown nonlinear function; u i ,v i ,r i They represent the longitudinal velocity, lateral drift velocity and bow angular velocity of the i-th following unmanned boat in the body coordinate system, respectively, and x i ,y i ,ψ i They represent the X-axis coordinate, Y-axis coordinate and bow angle of the i-th following unmanned boat in the earth coordinate system; τ ui ,τ ri They represent the moments of the i-th following unmanned boat in the longitudinal thrust and bow rolling directions respectively; τ uwi ,τ vwi ,τ rwi Represents the external ocean environment disturbance of the i-th unmanned boat.

[0024] Optionally, the periodic DoS attack module is:

[0025] A d (T1,T2)=∪A k ∩[T1,T2],k∈R + ;

[0026] Where, k represents the kth DoS attack, A d represents the attack time, and (T1, T2) represents the given time range.

[0027] Optionally, the expression of the state predictor is:

[0028]

[0029] Where, and Respectively The estimated values ​​for edge (0,i) and edge (i,j).

[0030] Optionally, the edge event trigger method is:

[0031]

[0032] Where A s Indicates the non-DoS attack time.

[0033] Optionally, the formula for triggering ESO by a distributed edge event is:

[0034]

[0035] Where k1∈R 2×2 ,k2∈R 2×2 , c∈R + .

[0036] Optionally, the relative dynamic equation is expressed as:

[0037]

[0038] Where, ρ i represents the sight distance, β i Indicates angle, l i It represents the expected distance between the i-th following unmanned boat and the i-th virtual point after the deviation setting, β si represents the sideslip angle, e ui represents the speed error, e ri represents the tracking error, α ui and α ri represents the virtual control law.

[0039] Optionally, the formula expression of dimensionality reduction ESO is:

[0040]

[0041] Where s ρi ,s βi ,s ui ,s ri is the auxiliary state of dimensionality reduction ESO, is the gain of dimensionality-reduced ESO, ζ ui ,ζ ri ,g ui ,g ri estimated value.

[0042] Second, the present application provides a layered collaborative control device for triggering edge events of unmanned boats under DoS attacks, including:

[0043] Acquisition module, used to acquire the periodic DoS attack module of the multi-unmanned boat system, the kinematic model and dynamic model of the multi-unmanned boat system; the multi-unmanned boat system consists of several follower unmanned boats and a target unmanned boat

[0044] The state monitoring module is used to monitor the state of each unmanned vehicle in the multi-unmanned vehicle system based on the periodic DoS attack module, the kinematic model and dynamic model of the multi-unmanned vehicle system, and the state estimator;

[0045] The state synchronization module is used to send the current state information of the abnormal unmanned boat to the non-abnormal unmanned boats in the multi-unmanned boat system when the state change of any unmanned boat in the multi-unmanned boat system exceeds the set threshold; the abnormal unmanned boat is the unmanned boat whose state change exceeds the set threshold;

[0046] A distributed edge event construction module is used to construct distributed edge events for DoS attacks based on the current status information of abnormal unmanned boats and the edge event triggering method;

[0047] A state calculation module is used to trigger the ESO according to the distributed edge event and obtain the state data of the target unmanned boat;

[0048] The relative dynamic equation building module is used to establish the relative dynamic equation of the target unmanned boat under DoS attack based on the state data of the target unmanned boat and the line-of-sight guidance principle;

[0049] An uncertain disturbance function determination module is used to predict the uncertain disturbance function of the target unmanned vehicle through dimension reduction ESO based on the relative dynamic equation;

[0050] The tracking module is used to control the follower unmanned boat to track the position of the target unmanned boat according to the uncertain disturbance function and the anti-interference position tracking law of the reduced-dimensional ESO.

[0051] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0052] This application provides a method and device for edge-event-triggered hierarchical collaborative control of unmanned boats (UAVs) under DoS attacks. First, the kinematic and dynamic models of the periodic DoS attack module and the multi-UAV system are acquired. These models provide the basis for subsequent state monitoring and event triggering. Next, a state predictor is used to monitor the state of each UAV in the multi-UAV system in real time. The state predictor can use historical data and current input to predict the future state of the system, effectively addressing communication interruptions and data loss caused by DoS attacks. When the state change of any UAV in the system exceeds a set threshold, an edge event is triggered. At this point, the current state information of the abnormal UAV is sent to all non-anomalous UAVs in the system. This step implements distributed event triggering, reduces communication frequency, and reduces the communication burden. Subsequently, a distributed edge-event trigger observer (ESO) is constructed based on the current state information of the abnormal UAV. This distributed edge-event triggering observer (ESO) can be used to obtain the state data of the target UAV. This data provides key information for subsequent position tracking. Next, based on the principle of line-of-sight guidance, a relative dynamic equation for the target UAV under DoS attacks is established. This step takes into account the impact of the DoS attack on the motion state of the target unmanned boat. Then, the uncertain disturbance function of the target unmanned boat is predicted by the dimension-reduced ESO. The dimension-reduced ESO can maintain an effective estimate of the uncertain disturbance of the system while reducing the computational complexity. Finally, according to the uncertain disturbance function and the anti-interference position tracking law of the dimension-reduced ESO, the follower unmanned boat is controlled to track the position of the target unmanned boat. This application reduces the communication frequency and burden by combining a distributed event edge-triggered observer and a state predictor, and effectively copes with the impact of DoS attacks on system state updates, thereby realizing collaborative control of unmanned boats under DoS attacks. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 A flowchart of a hierarchical collaborative control method for edge event triggering of an unmanned boat under DoS attack provided in one embodiment of the present application;

[0055] Figure 2 A schematic diagram of multiple unmanned boats coordinating single target tracking according to an embodiment of the present application;

[0056] Figure 3 A communication topology network is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0059] Example 1

[0060] like Figure 1 As shown, this embodiment provides a layered collaborative control method for edge event triggering of an unmanned boat under DoS attack, including:

[0061] Step 101: Obtain a periodic DoS attack module, a kinematic model, and a dynamic model of a multi-UAV system; the multi-UAV system consists of several follower UAVs and a target UAV;

[0062] Step 102: Based on the periodic DoS attack module, the kinematic model and the dynamic model of the multi-UAV system, and the state estimator, the state of each UAV in the multi-UAV system is monitored;

[0063] Step 103: When the state change of any unmanned boat in the multi-unmanned boat system exceeds a set threshold, the current state information of the abnormal unmanned boat is sent to the non-abnormal unmanned boats in the multi-unmanned boat system; the abnormal unmanned boat is the unmanned boat whose state change exceeds the set threshold;

[0064] Step 104: Based on the current status information of the abnormal unmanned boat and the edge event triggering method, a distributed edge event for DoS attacks is constructed;

[0065] Step 105: triggering the ESO according to the distributed edge event to obtain the status data of the target unmanned boat;

[0066] Step 106: Based on the state data of the target unmanned boat and the line-of-sight guidance principle, a relative dynamic equation of the target unmanned boat under DoS attack is established;

[0067] Step 107: Predicting the uncertain disturbance function of the target unmanned vehicle based on the relative dynamic equation by using the dimension-reduced ESO;

[0068] Step 108: According to the uncertain disturbance function and based on the anti-interference position tracking law of the dimension-reduced ESO, the following unmanned boat is controlled to track the position of the target unmanned boat.

[0069] When executing steps 101-108, the specific steps may be as follows:

[0070] When building a periodic DoS attack module, define t k represents the kth transmission attempt of the information. Assuming that the transmission attempt of the information is performed periodically, the transmission interval can be obtained as:

[0071] Δ=t k+1 -t k (k>0)(1).

[0072] Among them, the time interval of the kth DoS attack is Therefore, for a given time (T1, T2) and t2>t1, the DoS attack time A d for:

[0073] A d (T1,T2)=∪A k ∩[T1,T2],k∈R + (2).

[0074] Due to the limited sampling rate, t k The first successful attempt will take some time to complete, so the upper bound of the k-th attack duration can be defined as and in:

[0075]

[0076] Among them, when constructing the kinematic model and dynamic model of the multi-unmanned boat system, the specific methods can be as follows:

[0077] Resilient controller structure and method for unmanned boats under DoS attacks, Figure 2 The idea of ​​cooperative single-target tracking by underactuated multiple unmanned boats is shown. The kinematic model of the target unmanned boat in a multi-unmanned boat system consisting of N underactuated follower unmanned boats and a single target unmanned boat is as follows:

[0078]

[0079] Where x0, y0, and ψ0 represent the X-axis coordinate, Y-axis coordinate, and yaw angle of the target unmanned boat in the earth coordinate system, respectively; u0, v0, and r0 represent the longitudinal velocity, transverse drift velocity, and yaw angular velocity of the target unmanned boat in the unmanned boat body coordinate system, respectively.

[0080] The kinematic and dynamic models of the following unmanned boat can be summarized as follows:

[0081]

[0082] Where x i ,y i ,ψ i They represent the X-axis coordinate, Y-axis coordinate and bow angle u of the i-th following unmanned boat in the earth coordinate system respectively. i ,v i ,r i They represent the longitudinal velocity, lateral drift velocity and bow angular velocity of the i-th following unmanned vehicle in the unmanned vehicle body coordinate system. ui ,m vi ,m ri represents the mass and inertia of the i-th following unmanned boat f ui (·),f vi (·),f ri (·) represents an unknown nonlinear function. τ ui ,τ ri Respectively represent the torque of the i-th following unmanned boat in the longitudinal thrust and bow rolling directions. uwi ,τ vwi ,τ rwi Represents the external ocean environment disturbance of the i-th unmanned boat.

[0083] When executing steps 103-105, the specific steps may be as follows:

[0084] According to the kinematic model, a state estimator is introduced to monitor the status of multiple unmanned boats. When the state change exceeds a certain threshold, a communication event is triggered and the latest state information is sent to other unmanned boats.

[0085] Define q0 = [u0cos(ψ0)-v0sin(ψ0),u0sin(ψ0)+v0cos(ψ0)] T , p0=[x0,y0] T , and are the estimated values ​​of the position p0 and speed q0 of the non-cooperative target unmanned boat for the i-th virtual point. In order to trigger the event function, the following state estimator is introduced:

[0086]

[0087] Where, and They are For the estimated values ​​of edge (0,i) and edge (i,j), the estimation error of the i-th virtual point corresponding to different edges is defined as:

[0088]

[0089] The communication trigger function can be defined as:

[0090]

[0091] Mode In order to save communication resources, the following edge event triggering mechanism is designed:

[0092]

[0093] Where A s This is the non-DoS attack time.

[0094] Then, combined with the state estimator, the following distributed edge-triggered ESO is designed:

[0095]

[0096] Where k1∈R 2×2 ,k2∈R 2×2 , c∈R + . and Satisfies the following formula:

[0097]

[0098] Where A d is the DoS attack time. The specific physical meaning of formula (12) is: using the above event trigger mechanism (10), when there is a DoS attack in the communication channel at this time, the distributed edge trigger ESO uses the estimated value of the edge (0, i) at the previous moment p0 On the contrary, if there is no DoS attack in the channel, the current state is updated normally using the event trigger mechanism. Similarly, the physical meaning of formula (13) is: using the event trigger mechanism (10), when there is a DoS attack in the communication channel, the distributed edge trigger ESO uses the estimated value of the edge (j, i) at the previous moment Design, otherwise, use the state triggered by the current event Make a design.

[0099] When executing step 106, the specific steps may be as follows:

[0100] Based on the distributed edge events in the communication layer, ESO is triggered to obtain the state data of the target unmanned boat under DoS attack. The relative dynamic equation designed in the control layer can better represent the position tracking error e between the virtual point and the unmanned boat. ρi and angular tracking error e βi, so as to facilitate the dimension reduction of ESO position tracking error e ρi and angular tracking error e βi Estimates.

[0101] The position data of the target unmanned boat obtained after observation is obtained through the line-of-sight guidance principle p0=[x0,y0] T The visual distance ρ between the i-th virtual point i and angle β i as follows:

[0102]

[0103] Where Δ yi , Δ xi It represents the expected deviation between the designed i-th virtual point and the target unmanned boat.

[0104] The position tracking error e can be obtained ρi and angular tracking error e βi for:

[0105]

[0106] Where, l i It represents the expected distance between the i-th following unmanned boat and the i-th virtual point after the deviation setting, β si is the sideslip angle. Then, define the velocity tracking error e ui and e ri for:

[0107]

[0108] Where α ui and α ri is a virtual control law and will be designed below. According to the dynamic model, the dynamic equations of position error, angle error and speed error can be obtained as follows:

[0109]

[0110] For the convenience of analysis, the above dynamic equation is simplified into the following form:

[0111]

[0112] Where:

[0113]

[0114] Due to factors such as unknown sideslip angle, unknown model parameters and ocean environment disturbance, the function ζ ui (·),ζ ri (·), g ui(·),g ri (·) is unmeasurable. In order to estimate the above unknown function, we design a reduced dimension ESO:

[0115]

[0116] Where s ρi ,s βi ,s ui ,s ri is the auxiliary state of dimensionality reduction ESO, is the gain of dimensionality-reduced ESO, ζ ui ,ζ ri ,g ui ,g ri estimated value.

[0117] When executing step 108, the specific steps may be as follows:

[0118] In order to control the follower UAV to keep up with the target UAV, that is, the speed and angle of the UAV are consistent with those of the target UAV, and the position and distance between the UAV and the target UAV are kept at a set level, an anti-interference position tracking law based on dimension-reduced ESO is designed in the control layer, as shown below:

[0119]

[0120] Where, in

[0121] This embodiment considers a multi-UAV network system consisting of four underactuated follower UAVs and a single target UAV. In this simulation, it is assumed that the communication channel between UAV No. 1 and UAV No. 3 will be interrupted by a DoS attack. That is, when the DoS attack occurs, UAV No. 3 cannot sense the position of UAV No. 1. The communication topology relationship is as follows: Figure 3 shown.

[0122] The initial state of the unmanned boat is shown in Table 1. The amplitude of the external disturbance is set to h ui ,h vi ,h ri The initial state of the target unmanned boat is (x0, y0, ψ0) = (5, 0, 0), (u0, v0, r0) = (0.5, 0, 0.03sin(0.04t)). The expected distance of the virtual point after the deviation conversion of the unmanned boat is set to l1 = l2 = l3 = l4 = 1m, and the expected tracking distance deviation between the following unmanned boat and the virtual point is set to: Δ p1 =[-10,-10] T , Δ p2 =[10,-10]T , Δ p3 =[-10,10] T , Δ p4 =[10,10] T .

[0123] The specific control parameters in this example are as follows:

[0124] Table 1 Following the status of unmanned boat

[0125] i＝1,…,4 Unmanned Boat 1 Unmanned Boat 2 Unmanned Boat 3 Unmanned Boat 4 <![CDATA[(x i (0),and i (0))]]> (5,5) (-6,8) (6,-6) (-8,-6) <![CDATA[ψ i ]]> π / 4 π / 6 0 π / 6 <![CDATA[(u i (0),v i (0),r i (0))]]> (0,0,0) (0,0,0) (0,0,0) (0,0,0)

[0126] Table 2 Control structure parameters

[0127]

[0128] Example 2

[0129] This embodiment provides a hierarchical collaborative control device for triggering edge events of an unmanned boat under DoS attacks, including:

[0130] An acquisition module is used to acquire a periodic DoS attack module, a kinematic model, and a dynamic model of a multi-unmanned boat system; the multi-unmanned boat system is composed of several follower unmanned boats and a target unmanned boat;

[0131] The state monitoring module is used to monitor the state of each unmanned vehicle in the multi-unmanned vehicle system based on the periodic DoS attack module, the kinematic model and dynamic model of the multi-unmanned vehicle system, and the state estimator;

[0132] The state synchronization module is used to send the current state information of the abnormal unmanned boat to the non-abnormal unmanned boats in the multi-unmanned boat system when the state change of any unmanned boat in the multi-unmanned boat system exceeds the set threshold; the abnormal unmanned boat is the unmanned boat whose state change exceeds the set threshold;

[0133] A distributed edge event construction module is used to construct distributed edge events for DoS attacks based on the current status information of abnormal unmanned boats and the edge event triggering method;

[0134] A state calculation module is used to trigger the ESO according to the distributed edge event and obtain the state data of the target unmanned boat;

[0135] The relative dynamic equation building module is used to establish the relative dynamic equation of the target unmanned boat under DoS attack based on the state data of the target unmanned boat and the line-of-sight guidance principle;

[0136] An uncertain disturbance function determination module is used to predict the uncertain disturbance function of the target unmanned vehicle through dimension reduction ESO based on the relative dynamic equation;

[0137] The tracking module is used to control the follower unmanned boat to track the position of the target unmanned boat according to the uncertain disturbance function and the anti-interference position tracking law of the reduced-dimensional ESO.

[0138] In summary, this application has the following technical effects:

[0139] This application studies the state estimation and cooperative tracking of distributed targets of multiple unmanned boats under DoS attacks, and proposes a hierarchical cooperative single target estimation and control structure based on a state predictor. Using the proposed control structure, four following unmanned boats can safely track non-cooperative targets in a specific formation under DoS attacks. The introduction of a state predictor and an edge-triggered event triggering mechanism in the distributed ESO design not only achieves distributed estimation of the speed and position of non-cooperative target unmanned boats under communication link obstruction, but also further reduces the frequency of network communication triggering by checking the trigger conditions in different edge links.

[0140] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A hierarchical collaborative control method for edge event triggering of unmanned boats under DoS attacks, characterized by: include: Obtain a periodic DoS attack module, a kinematic model, and a dynamic model of a multi-UAV system consisting of several follower UAVs and a target UAV; According to the periodic DoS attack module, the kinematic model and dynamic model of the multi-UAV system, the state of each UAV in the multi-UAV system is monitored based on the state predictor; When the state change of any unmanned boat in the multi-unmanned boat system exceeds a set threshold, the current state information of the abnormal unmanned boat is sent to the non-abnormal unmanned boats in the multi-unmanned boat system; the abnormal unmanned boat is the unmanned boat whose state change exceeds the set threshold; According to the current status information of the abnormal unmanned boat, a distributed edge event for DoS attacks is constructed based on the edge event triggering method; Triggering ESO according to the distributed edge event to obtain status data of the target unmanned boat; According to the state data of the target unmanned boat and based on the line-of-sight guidance principle, the relative dynamic equation of the target unmanned boat under DoS attack is established; According to the relative dynamic equation, the uncertain disturbance function of the target unmanned vehicle is predicted by reducing the dimension of ESO; According to the uncertain disturbance function and based on the anti-interference position tracking law of the dimension-reduced ESO, the follower unmanned boat is controlled to track the position of the target unmanned boat.

2. The layered collaborative control method for edge event triggering of unmanned boats under DoS attacks according to claim 1 is characterized in that: The kinematic model of the target unmanned boat in the kinematic model of the multi-unmanned boat system is: Where x0, y0, and ψ0 represent the X-axis coordinate, Y-axis coordinate, and yaw angle of the target unmanned boat in the earth coordinate system, respectively; u0, v0, and r0 represent the longitudinal velocity, transverse drift velocity, and yaw angular velocity of the target unmanned boat in the unmanned boat body coordinate system, respectively.

3. The layered collaborative control method for edge event triggering of unmanned boats under DoS attacks according to claim 2 is characterized in that: The kinematic model and dynamic model of the multi-unmanned boat system are: Where m ui ,m vi ,m ri represents the mass and inertia of the i-th following unmanned boat; f ui (·),f vi (·),f ri (·) represents an unknown nonlinear function; u i ,v i ,r i They represent the longitudinal velocity, lateral drift velocity and bow angular velocity of the i-th following unmanned boat in the body coordinate system, respectively, and x i ,y i ,ψ i They represent the X-axis coordinate, Y-axis coordinate and bow angle of the i-th following unmanned boat in the earth coordinate system; τ ui ,τ ri They represent the moments of the i-th following unmanned boat in the longitudinal thrust and bow rolling directions respectively; τ uwi ,τ vwi ,τ rwi Represents the external ocean environment disturbance of the i-th unmanned boat.

4. The layered collaborative control method for edge event triggering of unmanned boats under DoS attacks according to claim 3 is characterized in that: The periodic DoS attack module is: Ad(T1,T2)=∪Ak∩[T1,T2],k∈R + ; Where, k represents the kth DoS attack, A d represents the attack time, and (T1, T2) represents the given time range.

5. The layered collaborative control method for edge event triggering of unmanned boats under DoS attacks according to claim 4 is characterized in that: The expression of the state predictor is: Where, and Respectively The estimated values ​​for edge (0,i) and edge (i,j).

6. The layered collaborative control method for edge event triggering of unmanned boats under DoS attacks according to claim 5 is characterized in that: The edge event trigger method is: Where A s Indicates the non-DoS attack time.

7. The layered collaborative control method for edge event triggering of unmanned boats under DoS attacks according to claim 6 is characterized in that: The formula for distributed edge events triggering ESO is: Where k1∈R 2×2 ,k2∈R 2×2 , c∈R + .

8. The layered collaborative control method for edge event triggering of unmanned boats under DoS attacks according to claim 7 is characterized in that: The formula of the relative dynamic equation is: Where, ρ i represents the sight distance, β i Indicates angle, l i It represents the expected distance between the i-th following unmanned boat and the i-th virtual point after the deviation setting, β si represents the sideslip angle, e ui represents the speed error, e ri represents the tracking error, α ui and α ri represents the virtual control law.

9. The method for layered collaborative control of unmanned boats triggered by edge events under DoS attacks according to claim 8, characterized in that: The formula expression of dimensionality reduction ESO is: Where s ρi ,s βi ,s ui ,s ri is the auxiliary state of dimensionality reduction ESO, is the gain of dimensionality-reduced ESO, ζ ui ,ζ ri ,g ui ,g ri estimated value.

10. A hierarchical collaborative control device for edge event triggering of unmanned boats under DoS attacks, characterized by: include: An acquisition module is used to acquire a periodic DoS attack module, a kinematic model, and a dynamic model of a multi-unmanned boat system; the multi-unmanned boat system is composed of several follower unmanned boats and a target unmanned boat; The state monitoring module is used to monitor the state of each unmanned vehicle in the multi-unmanned vehicle system based on the periodic DoS attack module, the kinematic model and dynamic model of the multi-unmanned vehicle system, and the state estimator; The state synchronization module is used to send the current state information of the abnormal unmanned boat to the non-abnormal unmanned boats in the multi-unmanned boat system when the state change of any unmanned boat in the multi-unmanned boat system exceeds the set threshold; the abnormal unmanned boat is the unmanned boat whose state change exceeds the set threshold; A distributed edge event construction module is used to construct distributed edge events for DoS attacks based on the current status information of abnormal unmanned boats and the edge event triggering method; A state calculation module is used to trigger the ESO according to the distributed edge event and obtain the state data of the target unmanned boat; The relative dynamic equation building module is used to establish the relative dynamic equation of the target unmanned boat under DoS attack based on the state data of the target unmanned boat and the line-of-sight guidance principle; An uncertain disturbance function determination module is used to predict the uncertain disturbance function of the target unmanned vehicle through dimension reduction ESO based on the relative dynamic equation; The tracking module is used to control the follower unmanned boat to track the position of the target unmanned boat according to the uncertain disturbance function and the anti-interference position tracking law of the reduced-dimensional ESO.

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