A quantitative consistency control method for unmanned boat event triggering under DoS attack

Abstract

The present invention discloses an event-triggered quantization consistency control method for an unmanned boat under a DoS attack. A state space model of the unmanned boat considering external environmental interference and input delay caused by a communication network transmission delay is established, and the model is transformed. The output feedback state observer of the unmanned boat is established by using the transformed model without input delay. For situations where the state cannot be directly strategized, the state observer is used to reconstruct the state of the unmanned boat. At the same time, a uniform quantizer is established to effectively reduce the communication frequency and bandwidth occupation. Based on the communication network topology diagram of the unmanned boat state space model, the attack error of the unmanned boat within the duration of the DoS network attack is obtained, the event triggering condition is constructed, and a consistency protocol based on the consistency sufficient condition of the event-triggered quantization of the unmanned boat is established, which can achieve consistency of multiple unmanned boats in a variety of complex communication situations. The control performance of the unmanned boat is greatly improved, and it can resist the situation where multiple channels are attacked by independent DoS attacks, providing important technical support for the widespread application of unmanned boats.

Description

Technical Field

[0001] The present invention relates to the field of cooperative control of unmanned boats, and in particular to an event-triggered quantitative consistency control method for unmanned boats under DoS attacks. Background Art

[0002] With the rapid development of science and technology and the growing demand for ocean exploration, unmanned surface vessels have gradually become an indispensable tool in the fields of marine scientific research, military strategic deployment and commercial shipping. In the process of performing diverse and complex tasks, unmanned vessels usually act in formation, cluster and collaboration, and the consistency of multi-agent systems is one of the key technologies to achieve these tasks.

[0003] Due to the complex and changeable marine environment, the communication network between unmanned surface vehicles (USVs) often has communication delays, limited communication resources, and even network attacks. In the control process of the unmanned boat, the transmission speed of information is often slower than the movement speed of the unmanned boat, which leads to input delay. If the input delay cannot be effectively handled, the control accuracy and stability of the unmanned boat will be affected. Limited bandwidth is also a situation where communication resources are limited. In practical applications, communication between unmanned boats often needs to be achieved through wireless communication, and the bandwidth of wireless communication is limited. If the limited bandwidth cannot be effectively utilized, the communication quality and efficiency between unmanned boats will be affected. When facing network attacks, communication resources will be severely restricted, which may lead to communication interruption between unmanned boats or information tampering, thereby affecting the control effect. Therefore, it is of great significance to solve the problem of consistency control of unmanned boats under complex communication conditions.

[0004] At present, a lot of research has been done on the consistency of unmanned boats under communication conditions such as input delay, limited bandwidth, and network attacks. However, the following problems still exist in the existing solutions:

[0005] Based on the existing consistency control schemes for multiple unmanned boats, most of them only consider the situation where some communications are restricted, without considering the situation where communication resources are limited, such as network attacks, input delays, and limited bandwidth. Therefore, in practical applications, limited communication resources cannot be effectively used for control. In addition, in the existing consistency control schemes for multiple unmanned boats, the consistency control protocols mostly directly use the system state without considering the situation where the state is unmeasurable. Therefore, it is necessary to adopt a state observer to improve the robustness of the consistency control to deal with the situation where the state is unmeasurable, so as to better cope with complex practical application scenarios. In addition, in the existing consistency control schemes for multiple unmanned boats, the event trigger mechanism reduces the communication frequency to a certain extent. Summary of the invention

[0006] The present invention provides an event-triggered unmanned boat quantitative consistency control method under DoS attack to overcome the above technical problems.

[0007] In order to achieve the above object, the technical solution of the present invention is:

[0008] A method for controlling the quantified consistency of an unmanned boat event trigger under a DoS attack comprises the following steps:

[0009] S1: construct a state space model of the unmanned boat that takes into account external environmental interference and input delay caused by communication network transmission delay, and transform the state space model of the unmanned boat into a model without input delay to obtain the real-time motion state of the unmanned boat;

[0010] The motion state includes the lateral speed, heading speed, heading angle, rolling speed and rolling angle of the unmanned boat;

[0011] S2: constructing an output feedback state observer of the unmanned boat for estimating the motion state of the unmanned boat according to the model without input delay, so as to obtain an estimated value of the motion state of the unmanned boat;

[0012] S3: constructing a uniform quantizer for quantizing the motion state of the unmanned boat according to the estimated value of the motion state of the unmanned boat, and obtaining the quantized motion state of the unmanned boat;

[0013] S4: Obtaining a topological diagram of the unmanned boat communication network based on the state space model of the unmanned boat to obtain a Laplace matrix;

[0014] S5: According to the unmanned boat communication network topology map, obtain the DoS network attack duration of the channel in the unmanned boat communication network topology map; and obtain the attack error of the unmanned boat within the DoS network attack duration;

[0015] S6: constructing an event triggering condition according to the attack error, the quantized motion state of the unmanned boat, and the Laplace matrix;

[0016] S7: According to the event triggering conditions, based on the consistency sufficient conditions of the event triggering quantification of the unmanned boat, a consistency protocol is constructed to achieve consistent control of the unmanned boat.

[0017] Furthermore, in S7, the consistency protocol is constructed as follows:

[0018]

[0019] Where: u i (t) represents the control input; K represents the control protocol gain matrix, K = μR -1 D TP, μ>0 indicates the control gain coefficient, R -1 is a self-selected matrix, P represents the solution of the Riccati equation; represents the sum of the event-triggered quantized estimated state errors between node i and other nodes; represents the kth triggering moment of the i-th unmanned boat.

[0020] Furthermore, in S7, the method for obtaining the consistent sufficient conditions for the event triggering quantification of the unmanned boat is as follows:

[0021] First, obtain the Lyapunov function as follows:

[0022]

[0023]

[0024]

[0025] Where: V represents the Lyapunov function; δ(t) represents the consistency state error; E I represents the I-order unit matrix; P represents the solution of the Riccati equation; z(t) represents the state of the unmanned boat; represents the state average value,

[0026] Secondly, solve the Lyapunov function:

[0027]

[0028] Where: |Θ(0,t)| represents the attack duration when the communication topology is connected; |ι(0,t)| represents the attack duration when the communication topology is disconnected; -β1 represents the attenuation rate of the Lyapunov function differential equation when the communication topology is connected; β2 represents the attenuation rate of the Lyapunov function when the communication topology is disconnected; V(0) represents the initial value of the Lyapunov function; γ represents the constant coefficient, where, N0 represents the set of natural numbers; ξ k Indicates the moment when the attack mode is switched and the communication topology is disconnected after that; Θ(ξ k +v k ,t) represents the duration of the existence of the minimum spanning tree; ξ k +υ k Indicates the attack mode switching moment and the subsequent moment when the communication topology is connected; |ι(ξ k ,t)| represents the duration when there is no minimum spanning tree; χ is a bounded function; β Γ represents the attenuation rate of the Lyapunov function differential equation under different attack modes; |Ξ Γ(0,t)| indicates the total duration of whether there is a minimum spanning tree in the time interval (0,t); |Ξ Γ (ξ k ,t)| represents the time interval (ξ k ,t) whether there is a minimum spanning tree; v k Indicates a time period;

[0029] Finally, the sufficient conditions for obtaining consistency are as follows,

[0030]

[0031]

[0032]

[0033] in, is the connectivity constant; is the non-connectivity constant; β Γ represents the decay rate of the Lyapunov function differential equation corresponding to the attack mode; Indicates the attack strength.

[0034] Furthermore, in S1, the state space model of the unmanned boat is established as follows:

[0035]

[0036] Among them, x i (t)=[υ(t) r(t) ψ(t) p(t) φ(t)] T ,x i (t)∈R n represents the motion state matrix of the unmanned boat, where υ(t), r(t), ψ(t), p(t), and φ(t) represent the lateral velocity, heading velocity, heading angle, roll velocity, and roll angle of the unmanned boat, respectively. n represents n-dimensional vector space; A represents the state matrix; B represents the input matrix; E represents the perturbation matrix; u i (th) represents a control protocol with delay, where u i (th)∈R m , R m represents an m-dimensional vector space; represents the external disturbance, where R p represents a p-dimensional vector space, ω ψ (t) represents the roll disturbance, ω φ (t) represents heading disturbance; h≥0 represents delay time; t represents time; y i(t) represents the output of the state space model system of the unmanned boat, y i (t)∈R l , R l represents the l-dimensional vector space; C represents the coefficient matrix; Represents x i The derivative of (t);

[0037] in:

[0038]

[0039] Among them, T r ,T v Both represent time constants; K vr is the lateral and bow coupling characteristic coefficient, K υp is the lateral and roll coupling characteristic coefficient, K dv is the lateral characteristic coefficient, K dr is the heading characteristic coefficient, K dp is the roll characteristic coefficient; is the damping ratio; ω n is the natural frequency.

[0040] Furthermore, in S1, the no-input-delay model is obtained as follows:

[0041]

[0042] Where: z i (t) is the motion state of the unmanned boat, where τ represents the integration variable; represents the output information of the model without input delay; D represents the input matrix, where D = e -Ah B.

[0043] Furthermore, in S2, the output feedback state observer of the unmanned boat is constructed as follows:

[0044]

[0045] Where: An estimated value representing the motion state of the unmanned boat; represents the differential of the estimated value of the motion state of the unmanned boat; u i (t) represents the control input; G represents the gain matrix; express The estimated value of .

[0046] Furthermore, in S3, the uniform quantizer is constructed as follows:

[0047]

[0048] Where: represents the quantified motion state of the unmanned boat; q u represents a uniform quantizer for quantizing the motion state of the unmanned boat; △ represents a quantization parameter; represents the set of estimated values ​​of the motion state of I unmanned boats, and its quantization error is

[0049] Furthermore, in S5, the duration of the DoS network attack is obtained as follows:

[0050]

[0051] Where: It is the sum of the attack time in the network channel between the i-th unmanned boat and the j-th unmanned boat, that is, the attack duration; Represents a real number greater than or equal to 0, R ≥0 represents the set of real numbers not less than 0; Indicates attack strength, R >1 Represents a set of real numbers greater than 1; t1 and t2 are the start time and end time of the time period, respectively, and t2≥t1.

[0052] Furthermore, in S6, the event triggering condition is constructed as follows:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] In the formula, f i Indicates event triggering conditions; Indicates the estimated state error at the time of event triggering; represents the sum of the event-triggered quantized estimated state errors between node i and other nodes; represents the quantization error; ω i (t) represents an exponential function, where a i >0 indicates exponential, b i >0 indicates the function gain coefficient; ψ i(t) represents the attack error function, represents the attack error; ||·|| represents the norm; represents the set of neighbor nodes of the i-th unmanned boat; a ij is the edge (v i ,v j )’s weight; An estimated value representing the motion state of the unmanned boat; represents the estimated state at the moment of attack; represents the sum of the event-triggered quantized estimated state errors between node i and other nodes; Indicates the node j event triggers the quantitative estimation state; Indicates the event triggering quantization estimation state of node i; represents the kth triggering moment of the i-th unmanned boat; represents the kth triggering moment of the jth unmanned boat; Represents the quantization of the estimated state of node j at the time when the event is triggered; Represents the quantization of the estimated state of node i at the time of event triggering; α i represents the node error coefficient; l i represents the quantization error coefficient; Represents the edge (v j ,v i ) The moment of the mth attack, Indicates that the i-th unmanned boat is The estimated state difference between time t and time t; Represent the estimated states of node i and node j at the time of event triggering respectively;

[0061]

[0062] Where: Indicates the next moment; represents the current moment; L0 represents the Laplace matrix of the initial topology; L Γ Represents the topological Laplacian matrix in attack mode; represents the Kronecker product; K represents the control protocol gain matrix; Table 1 shows the sum of the estimated state errors between node i and other nodes triggered by events. represents the sum of the estimated state errors between node i and other nodes, express and The difference between

[0063] Beneficial effects: The present invention provides an event-triggered quantization consistency control method for an unmanned boat under a DoS attack. The state space model of the unmanned boat considering the external environmental interference and the input delay caused by the transmission delay of the communication network is established, and the state space model is transformed. The output feedback state observer of the unmanned boat is established by using the transformed model without input delay. For the situation where the state cannot be directly strategized, the state observer is used to reconstruct the state of the unmanned boat. At the same time, a uniform quantizer is established to effectively reduce the communication frequency and bandwidth occupancy, and the input delay problem is overcome by using a reduction transformation. Based on the communication network topology diagram of the unmanned boat state space model, the attack error of the unmanned boat within the duration of the DoS network attack is obtained, the event triggering condition is constructed, and a consistency protocol based on the consistency sufficient condition of the event-triggered quantization of the unmanned boat is established, which can achieve consistency among multiple unmanned boats in a variety of complex communication situations. The control performance of the unmanned boat is greatly improved, and it can resist the situation where multiple channels are attacked by independent DoS attacks, providing important technical support for the widespread application of unmanned boats. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0065] Figure 1 is a flow chart of the consistency control method of the present invention;

[0066] Figure 2 is a communication topology structure block diagram in an embodiment of the present invention;

[0067] Figure 3 is a block diagram of the consistency control structure of the unmanned boat in an embodiment of the present invention;

[0068] Figure 4 is the consistency effect of the three unmanned boats υ(t) in the embodiment of the present invention;

[0069] Figure 5 is the consistency effect of the three unmanned boats r(t) in the embodiment of the present invention;

[0070] Figure 6 is the consistency effect of the three unmanned boats ψ(t) in the embodiment of the present invention;

[0071] Figure 7 is the consistency effect of the three unmanned boats p(t) in the embodiment of the present invention;

[0072] Figure 8is the consistency effect of the three unmanned boats φ(t) in the embodiment of the present invention;

[0073] Fig. 9 is a schematic diagram of event triggering time in an embodiment of the present invention;

[0074] Fig.10 Schematic diagram of attack sequence and attack mode in an embodiment of the present invention;

[0075] Fig.11 is a schematic diagram of a consistency protocol in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] This embodiment discloses a method for controlling the consistency of an unmanned boat event triggered by a DoS attack, including the following steps: Figure 1 As shown:

[0078] Specifically, in this embodiment, DoS means Denial of Service, which means denial of service. This embodiment discusses the unmanned boat event-triggered quantitative consistency control method under a denial of service attack.

[0079] S1: constructing a state space model of the unmanned boat considering external environmental interference and input delay caused by communication network transmission delay, and converting the state space model of the unmanned boat with input delay into a model without input delay to obtain the real-time motion state of the unmanned boat;

[0080] The motion state includes the lateral speed, heading speed, heading angle, rolling speed and rolling angle of the unmanned boat;

[0081] Specifically, in this embodiment, a state space model of the unmanned boat is constructed and used as the controlled object, wherein the state space model of the unmanned boat includes external environmental disturbances caused by sea waves and input delays caused by transmission delays of the communication network;

[0082] Preferably, the state space model of the unmanned boat is established as follows:

[0083]

[0084] Among them, x i(t)=[υ(t) r(t) ψ(t) p(t) φ(t)] T ,x i (t)∈R n represents the motion state matrix of the unmanned boat, where υ(t), r(t), ψ(t), p(t), and φ(t) represent the lateral velocity, heading velocity, heading angle, roll velocity, and roll angle of the unmanned boat, respectively. n represents n-dimensional vector space; A represents the state matrix; B represents the input matrix; E represents the perturbation matrix; u i (th) represents a control protocol with delay, where u i (th)∈R m , R m represents an m-dimensional vector space; represents the external disturbance, where R p represents a p-dimensional vector space, ω ψ (t) represents the roll disturbance, ω φ (t) represents heading disturbance; h≥0 represents delay time; t represents time; y i (t) represents the output of the state space model system of the unmanned boat, y i (t)∈R l , R l represents the l-dimensional vector space; C represents the coefficient matrix; Represents x i The derivative of (t);

[0085] in:

[0086]

[0087] Among them, T r ,T v Both represent time constants; K vr is the lateral and bow coupling characteristic coefficient, K υp is the lateral and roll coupling characteristic coefficient, K dv is the lateral characteristic coefficient, K dr is the heading characteristic coefficient, K dp is the roll characteristic coefficient; is the damping ratio; ω n is the natural frequency.

[0088] Preferably, in order to overcome the influence of input delay, the model without input delay is obtained according to Artstein-Kwon-Pearson reduction transformation as follows:

[0089]

[0090] Where: z i(t) is the motion state of the unmanned boat, where τ represents the integration variable; represents the output information of the model without input delay; D represents the input matrix, where D = e -Ah B.

[0091] S2: constructing an output feedback state observer of the unmanned boat for estimating the motion state of the unmanned boat according to the model without input delay, so as to obtain an estimated value of the motion state of the unmanned boat;

[0092] Specifically, in order to reconstruct the motion state of the unmanned boat when the motion state is unmeasurable, the output feedback and control input of the unmanned boat are used to construct a state observer, thereby estimating the real-time motion state of the unmanned boat;

[0093] Preferably, the output feedback state observer of the unmanned boat is constructed as follows:

[0094]

[0095] Where: An estimated value representing the motion state of the unmanned boat; represents the differential of the estimated value of the motion state of the unmanned boat; u i (t) represents the control input; G represents the gain matrix; express An estimated value of

[0096] S3: constructing a uniform quantizer for quantizing the motion state of the unmanned boat according to the estimated value of the motion state of the unmanned boat, and obtaining the quantized motion state of the unmanned boat; the quantized estimated state and quantization error will be used for constructing subsequent event trigger conditions,

[0097] Specifically, according to the estimated value of the motion state of the unmanned boat, a suitable uniform quantizer and quantization parameters are selected to quantize the estimated state, thereby reducing the bandwidth occupation. The subsequent event trigger conditions and consistency protocols will be designed using the estimated state quantization value and quantization error.

[0098] Preferably, the uniform quantizer is constructed as follows:

[0099]

[0100] Where: represents the quantified motion state of the unmanned boat; q u represents a uniform quantizer for quantizing the motion state of the unmanned boat; △ represents a quantization parameter; represents the set of estimated values ​​of the motion state of I unmanned boats, and its quantization error is

[0101] S4: Obtaining a topological diagram of the unmanned boat communication network based on the state space model of the unmanned boat to obtain a Laplace matrix;

[0102] Specifically, according to the information interaction of the state space model of the unmanned boat, an unmanned boat communication network topology map is constructed, and the Laplace matrix is ​​obtained, and then the eigenvalue of the Laplace matrix (i.e., the second smallest eigenvalue) can be obtained to judge the connectivity of the communication network topology map, wherein the communication network topology map is an undirected connected graph.

[0103] Specifically, in this embodiment, based on the state space model of the unmanned boat, according to the communication state information of each individual in the multi-unmanned boat system, the information connection relationship between the unmanned boats is expressed, and the communication network topology of the unmanned boat system is established, thereby obtaining the adjacency matrix and the Laplace matrix, so as to provide a basis for the establishment of the network attack mode and the consistency analysis;

[0104] Specifically, the communication network topology diagram constructed is: in, represents the set of nodes, that is, the set of unmanned boats, v i represents the i-th unmanned boat; ε represents the set of edges; represents the adjacency matrix, a ij is the edge (v i ,v j ) weight. If (ν i ,ν j )∈ε,a ij =1; otherwise, a ij =0, where i and j are the index numbers of the unmanned boats; in the undirected communication network topology graph, a ij =a ji . Define the set of neighbor nodes

[0105] The obtained Laplace matrix is in, represents the adjacency matrix; represents the degree matrix, d i represents the in-degree of the communication node, R I×I represents an I×I dimensional vector space;

[0106] S5: According to the unmanned boat communication network topology map, obtain the DoS network attack duration of the channel in the unmanned boat communication network topology map; and obtain the attack error of the unmanned boat within the DoS network attack duration;

[0107] Specifically, in order to study the situation where each channel in the communication network topology is attacked by an independent denial of service network attack, namely DoS network attack, the duration of DoS network attack on each channel is obtained respectively; DoS network attack in each channel can form different attack modes, and the state error generated by the attack is subsequently used to construct event triggering conditions, and the conditions for system stability under different attack modes are analyzed; considering non-periodic attacks, the attacker needs to enter the sleep mode after launching the attack to accumulate energy for subsequent attacks. Therefore, the entire attack cycle can be divided into two different stages: active attack stage and sleep stage.

[0108] Preferably, the DoS network attack duration is obtained as follows:

[0109]

[0110] Where: It is the sum of the attack time in the network channel between the i-th unmanned boat and the j-th unmanned boat, that is, the attack duration; Represents a real number greater than or equal to 0, R ≥0 represents the set of real numbers not less than 0; Indicates attack strength, R >1 Represents a set of real numbers greater than 1; t1 and t2 are the start time and end time of the time period, respectively, and t2≥t1.

[0111] Furthermore, according to the duration of the DoS network attack, the attack mode can be obtained as follows:

[0112]

[0113] Where: Γ(t) represents the attack mode at time t; (v i ,v j ) represents the edge in the communication network topology graph; ε represents the set of edges; where the attack mode is the combination of the attacked channels at the attack time, and the sum of the attack modes is

[0114] Among them, the set of DoS network attack duration and non-DoS network attack duration is

[0115]

[0116] in, Indicates the total duration of non-DoS network attacks; \ indicates exclusion;

[0117] S6: constructing an event triggering condition according to the attack error, the quantized motion state of the unmanned boat, and the Laplace matrix;

[0118] According to the quantization state, quantization error and attack state error generated by the quantizer, the event trigger condition is constructed. When the trigger condition is met, the unmanned boat updates its state.

[0119] Specifically, in order to save communication resources and reduce the frequency of information interaction between unmanned boats, this embodiment constructs the event triggering conditions as follows:

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] In the formula, f i Indicates event triggering conditions; Indicates the estimated state error at the time of event triggering; represents the sum of the event-triggered quantized estimated state errors between node i and other nodes; represents the quantization error; ω i (t) represents an exponential function, where a i >0 indicates exponential, b i >0 indicates the function gain coefficient; ψ i (t) represents the attack error function, represents the attack error; ||·|| represents the norm; represents the set of neighbor nodes of the i-th unmanned boat; a ij is the edge (v i ,v j )’s weight; An estimated value representing the motion state of the unmanned boat; represents the estimated state at the moment of attack; represents the sum of the event-triggered quantized estimated state errors between node i and other nodes; Indicates the node j event triggers the quantitative estimation state; Indicates the event triggering quantization estimation state of node i; represents the kth triggering moment of the i-th unmanned boat; represents the kth triggering moment of the jth unmanned boat; Represents the quantization of the estimated state of node j at the time when the event is triggered; Represents the quantization of the estimated state of node i at the time of event triggering; α i represents the node error coefficient; l i represents the quantization error coefficient; Represents the edge (v j ,v i ) The moment of the mth attack, Indicates that the i-th unmanned boat is The estimated state difference between time t and time t; Represent the estimated states of node i and node j at the time of event triggering respectively;

[0128] In order to exclude the occurrence of Zeno behavior, that is, frequent information interaction, we have:

[0129]

[0130] Where: Indicates the next moment; represents the current moment; L0 represents the Laplace matrix of the initial topology; L Γ Represents the topological Laplacian matrix in attack mode; represents the Kronecker product; K represents the control protocol gain matrix; Table 1 shows the sum of the estimated state errors between node i and other nodes triggered by events. represents the sum of the estimated state errors between node i and other nodes, express and The difference between

[0131] Specifically, due to the existence of the positive lower bound, it can be guaranteed that Zeno behavior will not occur.

[0132] S7: According to the event triggering conditions, based on the consistency sufficient conditions of the event triggering quantification of the unmanned boat, a consistency protocol is constructed to achieve consistent control of the unmanned boat.

[0133] Specifically, S7 includes providing a consistency protocol based on event triggering and information quantification to make the real-time motion state of the unmanned boat tend to be consistent.

[0134]

[0135] Where: u i (t) represents the control input; K represents the control protocol gain matrix, K = μR -1 D T P, μ>0 indicates the control gain coefficient, R -1is a self-selected matrix and P represents the solution of the Riccati equation.

[0136] Preferably, the sufficient consistency condition for the event triggering quantification of the unmanned boat is obtained as follows:

[0137] According to the real-time motion state of the unmanned boat, the motion state error δ(t) of the unmanned boat is obtained to obtain the Lyapunov function based on the motion state error of the unmanned boat, and according to the Laplace matrix, the differential equation of the Lyapunov function is calculated by the second smallest eigenvalue of the Laplace matrix to obtain the consistent sufficient conditions for realizing the event triggering quantization of the unmanned boat under different DoS attack modes.

[0138] Specifically, the motion state error between multiple unmanned boats is constructed to design the Lyapunov function. The consistency is analyzed in two cases. One case is that under a DoS network attack, the communication topology is still connected, and the second smallest eigenvalue of the Laplace matrix of the communication topology is not 0; the other case is that under a DoS network attack, the communication topology is not connected, that is, there is no minimum spanning tree, and the second smallest eigenvalue of the Laplace matrix of the communication topology is 0. Combining the two cases, the input-state stability of the system is finally achieved, thereby ensuring the consistency of multiple unmanned boats;

[0139] Specifically, the Lyapunov function is obtained as follows:

[0140]

[0141]

[0142]

[0143] Where: V represents the Lyapunov function; δ(t) represents the consistency state error; E I represents the I-order unit matrix; P represents the solution of the Riccati equation; z(t) represents the state of the unmanned boat; represents the state average value,

[0144] According to the Laplace matrix, the Lyapunov function differential equation is solved when the second smallest eigenvalue of the Laplace matrix is ​​not 0 and the second smallest eigenvalue of the Laplace matrix is ​​0, and finally the following is obtained:

[0145]

[0146] Where: |Θ(0,t)| represents the attack duration when the communication topology is connected; |ι(0,t)| represents the attack duration when the communication topology is disconnected; -β1 represents the attenuation rate of the Lyapunov function differential equation when the communication topology is connected; β2 represents the attenuation rate of the Lyapunov function when the communication topology is disconnected; V(0) represents the initial value of the Lyapunov function; Υ represents the constant coefficient, where, N0 represents the set of natural numbers. k Indicates the moment when the attack mode is switched and the communication topology is disconnected after that; Θ(ξ k +v k ,t) represents the duration of the existence of the minimum spanning tree; ξ k +υ k Indicates the attack mode switching moment and the subsequent moment when the communication topology is connected; |ι(ξ k ,t)| represents the duration when there is no minimum spanning tree; χ is a bounded function. β Γ represents the attenuation rate of the Lyapunov function differential equation under different attack modes; |Ξ Γ (0,t)| indicates the total duration of whether there is a minimum spanning tree in the time interval (0,t); |Ξ Γ (ξ k ,t)| represents the time interval (ξ k ,t) whether there is a minimum spanning tree; v k Indicates a time period.

[0147] According to (10), when When , the consistency state error system is stable, and the sufficient condition for consistency can be obtained as follows:

[0148]

[0149]

[0150]

[0151] in, is the connectivity constant; is the non-connectivity constant; β Γ Represents the decay rate of the Lyapunov function differential equation under different attack modes; Indicates the attack strength.

[0152] Specifically, a specific simulation experiment embodiment of the present invention is as follows:

[0153] Communication topology of multiple unmanned boat systems Figure 2 As shown in the figure, three unmanned boats are selected to form a communication system. The model parameters of the unmanned boat are

[0154]

[0155] B=[0.149 -0.052 0 -0.2167 0] T

[0156]

[0157] C=[1 0.8 1 -1 0.6]

[0158] The disturbance in the rolling and heading motions is ω φ =0.1cos(t), Where ω1(t) is a Gaussian random signal with a mean square value of 0.1. The initial states of the three unmanned boats are: x1 = [3 3 3 33], x2 = [2 2 2 2 2], x3 = [1 1 1 1 1]. φ represents the heading disturbance; ω ψ represents the roll disturbance; s represents the complex frequency variable;

[0159] According to the communication topology, there are 8 attack modes, which are represented as follows:

[0160]

[0161] The simulation time is 20s. The attack duration is less than 5s. The relevant parameters of consistency are R=1, Q=100·I5, K=[0.1371 -1.6506 -5.0000 -1.2721 -2.4098]. The relevant parameters of the event trigger condition are b i =1,a i =0.1. The quantizer parameter is △=0.06, and the input delay time is h=0.1.

[0162] Figure 3 This is a block diagram of the consistency control structure of a channel unmanned boat. The unmanned boats exchange state information through a wireless communication network, and the network channel is subject to non-periodic DoS attacks. The control system of each unmanned boat consists of a control protocol, a state observer, an event trigger mechanism, and a quantizer. As the state of the unmanned boat changes, the observer estimates its state in real time. When the event trigger condition is met, the estimated state is quantized and sent to the neighboring node. After the unmanned boat receives the quantized estimated state information from the neighboring node, it forms a consistency control protocol based on its own quantized estimated state and acts on the unmanned boat.

[0163] Figure 4-11 is a simulation effect diagram, where Figure 4-8The consistency effect of the unmanned boats is demonstrated, and it can be seen that the corresponding states of each unmanned boat tend to be consistent. Fig. 9 In order to take into account the quantization error of the unmanned boat event triggering moment, the update frequency of the unmanned boat status is greatly reduced. Fig.10 Figure 2 is the DoS attack sequence and attack mode of each channel. It can be seen that the attack sequence of each channel is different, and the attack mode is switched between mode 1-mode 8. Fig.11 This is a consistency protocol based on event triggering and information quantization. The present invention proposes a quantitative consistency control method for multiple unmanned boats based on an event triggering mechanism. Considering the situations where multiple channels are attacked by independent DoS, input delays, communication resources are limited, and states are unmeasurable, a consistency protocol is designed by combining event triggering mechanism, quantization mechanism, and state observer, and the consistency of multiple unmanned boats in a complex communication environment is achieved.

[0164] Compared with the existing unmanned boat consistency method, this embodiment takes into account the consistency control of unmanned boats when multiple channels are attacked by independent denial of service networks, input delays, and limited communication resources. For situations where the state is not directly measurable, the state observer is used to reconstruct the state of the unmanned boat. On this basis, an event trigger mechanism and a quantizer are introduced to effectively reduce the communication frequency and bandwidth occupancy, and a reduction transformation is used to overcome the input delay problem. At the same time, the designed consistency protocol can resist the situation where multiple channels are attacked by independent DoS. In summary, the present invention can achieve consistency of multiple unmanned boats in a variety of complex communication situations. This greatly improves the control performance of unmanned boats and provides important technical support for the widespread application of unmanned boats.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the quantization consistency of unmanned boat event triggering under DoS attack, characterized in that: The steps include: S1: construct a state space model of the unmanned boat that takes into account external environmental interference and input delay caused by communication network transmission delay, and transform the state space model of the unmanned boat into a model without input delay to obtain the real-time motion state of the unmanned boat; The motion state includes the lateral speed, heading speed, heading angle, rolling speed and rolling angle of the unmanned boat; In S1, the state space model of the unmanned boat is established as follows: Among them, x i (t)=[υ(t)r(t)ψ(t)p(t)φ(t)] T ,x i (t)∈R n represents the motion state matrix of the unmanned boat, where υ(t), r(t), ψ(t), p(t), and φ(t) represent the lateral velocity, heading velocity, heading angle, roll velocity, and roll angle of the unmanned boat, respectively. n represents n-dimensional vector space; A represents the state matrix; B represents the input matrix; E represents the perturbation matrix; u i (th) represents a control protocol with delay, where u i (th)∈R m , R m represents an m-dimensional vector space; represents the external disturbance, where R p represents a p-dimensional vector space, ω ψ (t) represents the roll disturbance, ω φ (t) represents heading disturbance; h≥0 represents delay time; t represents time; y i (t) represents the output of the state space model system of the unmanned boat, y i (t)∈R l , R l represents the l-dimensional vector space; C represents the coefficient matrix; Represents x i The derivative of (t); in: Among them, T r ,T v Both represent time constants; K vr is the lateral and bow coupling characteristic coefficient, K υp is the lateral and roll coupling characteristic coefficient, K dv is the lateral characteristic coefficient, K dr is the heading characteristic coefficient, K dp is the roll characteristic coefficient; ζ is the damping ratio; ω n is the natural frequency; The no input delay model is obtained as follows: Where: z i (t) is the motion state of the unmanned boat, where τ represents the integration variable; represents the output information of the model without input delay; D represents the input matrix, where D = e -Ah B; S2: constructing an output feedback state observer of the unmanned boat for estimating the motion state of the unmanned boat according to the model without input delay, so as to obtain an estimated value of the motion state of the unmanned boat; S3: constructing a uniform quantizer for quantizing the motion state of the unmanned boat according to the estimated value of the motion state of the unmanned boat, and obtaining the quantized motion state of the unmanned boat; S4: Obtaining a topological diagram of the unmanned boat communication network based on the state space model of the unmanned boat to obtain a Laplace matrix; S5: According to the unmanned boat communication network topology map, obtain the DoS network attack duration of the channel in the unmanned boat communication network topology map; and obtain the attack error of the unmanned boat within the DoS network attack duration; S6: constructing an event triggering condition according to the attack error, the quantized motion state of the unmanned boat, and the Laplace matrix; S7: According to the event triggering condition, based on the consistency sufficient condition of the event triggering quantification of the unmanned boat, a consistency protocol is constructed to achieve consistent control of the unmanned boat; In S7, the consistency protocol is constructed as follows: Where: u i (t) represents the control input; K represents the control protocol gain matrix, K = μR -1 D T P, μ>0 indicates the control gain coefficient, R -1 is a self-selected matrix, P represents the solution of the Riccati equation; represents the sum of the event-triggered quantized estimated state errors between node i and other nodes; represents the kth triggering moment of the i-th unmanned boat; In S7, the method for obtaining the consistent sufficient conditions for the event triggering quantification of the unmanned boat is as follows: First, obtain the Lyapunov function as follows: Where: V represents the Lyapunov function; δ(t) represents the consistency state error; E I represents the I-order unit matrix; P represents the solution of the Riccati equation; z(t) represents the state of the unmanned boat; represents the state average value, Secondly, solve the Lyapunov function: Where: |Θ(0,t)| represents the attack duration when the communication topology is connected; |ι(0,t)| represents the attack duration when the communication topology is disconnected; -β1 represents the attenuation rate of the Lyapunov function differential equation when the communication topology is connected; β2 represents the attenuation rate of the Lyapunov function when the communication topology is disconnected; V(0) represents the initial value of the Lyapunov function; γ represents the constant coefficient, where, N0 represents the set of natural numbers; ξ k Indicates the moment when the attack mode is switched and the communication topology is disconnected after that; Θ(ξ k +v k ,t) represents the duration of the existence of the minimum spanning tree; ξ k +υ k Indicates the attack mode switching moment and the subsequent moment when the communication topology is connected; ι(ξ k ,t)| represents the duration when there is no minimum spanning tree; χ is a bounded function; β Γ represents the attenuation rate of the Lyapunov function differential equation under different attack modes; |Ξ Γ (0,t)| indicates the total duration of whether there is a minimum spanning tree in the time interval (0,t); Ξ Γ (ξ k ,t)| represents the time interval (ξ k ,t) whether there is a minimum spanning tree; v k Indicates a time period; Finally, the sufficient conditions for obtaining consistency are as follows, in, is the connectivity constant; is the non-connectivity constant; β Γ represents the decay rate of the Lyapunov function differential equation corresponding to the attack mode; Indicates the attack strength.

2. The method for controlling the quantization consistency of an unmanned boat event triggered by a DoS attack according to claim 1 is characterized in that: In S2, the output feedback state observer of the unmanned boat is constructed as follows: Where: An estimated value representing the motion state of the unmanned boat; represents the differential of the estimated value of the motion state of the unmanned boat; u i (t) represents the control input; G represents the gain matrix; express The estimated value of .

3. The method for controlling the quantization consistency of an unmanned boat event triggered by a DoS attack according to claim 1 is characterized in that: In S3, the uniform quantizer is constructed as follows: Where: Represents the quantified motion state of the unmanned boat; q u represents a uniform quantizer for quantizing the motion state of the unmanned boat; △ represents a quantization parameter; represents the set of estimated values ​​of the motion state of I unmanned boats, and its quantization error is 4. The method for controlling the quantization consistency of an unmanned boat event triggered by a DoS attack according to claim 1 is characterized in that: In S5, the duration of the DoS network attack is obtained as follows: Where: It is the sum of the attack time in the network channel between the i-th unmanned boat and the j-th unmanned boat, that is, the attack duration; Represents a real number greater than or equal to 0, R ≥0 represents the set of real numbers not less than 0; Indicates attack strength, R >1 Represents a set of real numbers greater than 1; t1 and t2 are the start time and end time of the time period, respectively, and t2≥t1.

5. The method for controlling the quantization consistency of an unmanned boat event triggered by a DoS attack according to claim 1 is characterized in that: In S6, the event triggering condition is constructed as follows: In the formula, f i Indicates event triggering conditions; Indicates the estimated state error at the time of event triggering; represents the sum of the event-triggered quantized estimated state errors between node i and other nodes; represents the quantization error; ω i (t) represents an exponential function, where a i >0 indicates exponential, b i >0 indicates the function gain coefficient; ψ i (t) represents the attack error function, represents the attack error; ||·|| represents the norm; represents the set of neighbor nodes of the i-th unmanned boat; a ij is the edge (v i ,v j )’s weight; An estimated value representing the motion state of the unmanned boat; represents the estimated state at the moment of attack; represents the sum of the event-triggered quantized estimated state errors between node i and other nodes; Indicates the node j event triggers the quantitative estimation state; Indicates the event triggering quantization estimation state of node i; represents the kth triggering moment of the i-th unmanned boat; represents the kth triggering moment of the jth unmanned boat; Represents the quantization of the estimated state of node j at the time when the event is triggered; Represents the quantization of the estimated state of node i at the time of event triggering; α i represents the node error coefficient; l i represents the quantization error coefficient; Represents the edge (v j ,v i ) The moment of the mth attack, Indicates that the i-th unmanned boat is The estimated state difference between time t and time t; Represent the estimated states of node i and node j at the time of event triggering respectively; Where: Indicates the next moment; represents the current moment; L0 represents the Laplace matrix of the initial topology; L Γ Represents the topological Laplacian matrix in attack mode; represents the Kronecker product; K represents the control protocol gain matrix; Table 1 shows the sum of the estimated state errors between node i and other nodes triggered by events. represents the sum of the estimated state errors between node i and other nodes, express and The difference between

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