Multi-agent system mean square consistency control method under hybrid attack

Abstract

The invention provides a multi-agent system mean square consistency control method under hybrid attacks, which relates to the technical field of intelligent control, and comprises the following steps: establishing a multi-agent system model, designing a controller, and on this basis, constructing an error system between a follower agent and a leader, and establishing a mean square consistency condition of the multi-agent system under the spoofing attack and the DOS attack, thereby controlling the multi-agent system to realize leader following mean square consistency. According to the method, the control strategy is improved by comprehensively considering the spoofing attack and the DOS attack possibly existing in the working period of the multi-agent system, so that the mean square bounded consistency of the leader-follower is realized, and the control efficiency and the control precision are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a mean square consistency control method for a multi-agent system under hybrid attack. Background Art

[0002] In the past two decades, the cooperative control of multi-agent systems has attracted widespread attention. A multi-agent system consists of a large number of agents with communication connections, which achieve common goals by exchanging information with each other. In practical applications, such as sensor networks, social networks, and distributed control, the consistency between agents is the basis for achieving collaborative work. However, due to factors such as device differences, communication interference, and noise, the information between agents may deviate during the transmission process. Mean square consistency refers to the fact that in a multi-agent system, the states or information of all agents tend to be consistent in a certain statistical sense. Commonly, it can be measured by calculating the mean square error (MSE) between them. The mean square error is the average of the squares of the difference between the state or information of each agent and the average state or information, reflecting the degree of consistency between agents.

[0003] However, deception attacks and DOS attacks may occur during the communication between agents. Deception attacks occur between followers and neighbor followers. They refer to the attacker using false information or disguised behavior to mislead, manipulate or destroy the collaboration and decision-making between multiple agents, thereby achieving the purpose of malicious attacks. In such a system, each agent completes complex tasks through communication and collaboration, so deception attacks will seriously affect the performance and security of the system. DOS attacks occur between leaders and followers. Attackers usually send a large number of invalid requests to the target system or consume a large amount of resources to overload it, causing the target system to be unable to provide normal services. How to reduce the adverse effects of attacks on the communication process between agents has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the communication process between agents in the current multi-agent system is adversely affected when an attack occurs. The present invention provides a mean square consistency control method for a multi-agent system under a hybrid attack.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] A first aspect of an embodiment of the present application provides a method for controlling mean square consistency of a multi-agent system under a hybrid attack, comprising:

[0007] S110, establish a multi-agent system model;

[0008] Consider a multi-agent system consisting of N followers and 1 leader, whose dynamic equation is as follows:

[0009]

[0010] Among them, s i (t)∈R n ,u i (t) respectively represent the state and control input of the ith follower. s0(t)∈R n is the leader state, g(·): R n →R is a continuous nonlinear function, A and B are preset parameter matrices;

[0011] S120, design controller;

[0012] s0 represents the status value of the leader, s i Represents the state value of other agents in the multi-agent system. The leader is not affected by the control protocol, and each follower complies with the control protocol; the control protocol of the controller is as follows:

[0013]

[0014] Among them, r ij is a random function that satisfies the Bernoulli distribution to decide whether to send fraudulent data, p i Represents the deceptive data information from neighbor followers, Satisfies ||P(t)||≤p, p is a normal number, f i is the pulse intensity, c i represents the communication strength with the leader, m i (t) represents the random function of whether a DOS attack occurs between the ith agent and the leader, δ(·) is the Dirac function, and a ij Closely related to the topology of multi-agent systems. <t0<t1<…<t k <t k+1 <…,θ1≤t k -t k-1 ≤θ2, where θ1 and θ2 are positive constants and the system is left continuous;

[0015] S130, constructing an error system between the follower agent and the leader;

[0016] The multi-agent system model can be converted into the following form:

[0017]

[0018] The error η between the follower agent and the leader i (t) = si (t)-s0(t), the error system is:

[0019]

[0020] The vector form of the error system is:

[0021]

[0022] Where F = diag[f i ],C=diag[c i ],M(t k )=diag[m i (t k )],R(t k )=[r ij (t k )] N×N , G(t, η(t))=[[g(s1(t))-g(s0(t))] T , [g(s2(t))-g(s0(t))] T , ..., [g(s N (t))]-g(s0(t))] T ] T , Ψ is a square matrix with all diagonal elements being 0, and Φ is a diagonal matrix;

[0023] S140, establish the mean square consistency condition of the multi-agent system under deception attack and DOS attack;

[0024] The given error converges to When the following inequality is satisfied:

[0025]

[0026] The multi-agent system is controlled to achieve leader-follower mean square consistency.

[0027] Optionally, the deception attack is described as follows, defining the injected fake data as p i (t), random function r ij (t) satisfies the Bernoulli distribution, which is defined as follows:

[0028]

[0029] When 0<α ij <1, random function r ij The probabilities of (t) are Prob{r ij (t) = 1} = α ij 、Prob{r ij(t) = 0} = 1-α ij .

[0030] Optionally, the DOS attack is described as follows, defining the random function m i (t) satisfies the Bernoulli distribution:

[0031]

[0032] When 0<β i <1, random function m i The probabilities of (t) are Prob{m i (t) = 1} = β i 、Prob{m i (t) = 0} = 1-β i .

[0033] Optionally, after establishing the mean square consistency condition of the multi-agent system under deception attack and DOS attack, the method further includes:

[0034] The range of mean square safety consistency is determined based on impulse control theory, convex hull theory and Lyapunov stability theory.

[0035] The second aspect of the embodiment of the present application provides a mean square consistency control device for a multi-agent system under a hybrid attack, comprising: a model building module, a control design module, an error system building module and a consistency control module, wherein:

[0036] The model building module is configured to build a multi-agent system model;

[0037] Consider a multi-agent system consisting of N followers and 1 leader, whose dynamic equation is as follows:

[0038]

[0039] Among them, s i (t)∈R n ,u i (t) respectively represent the state and control input of the ith follower. s0(t)∈R n is the leader state, g(·): R n →R is a continuous nonlinear function, A and B are preset parameter matrices;

[0040] The control design module is configured to design a controller;

[0041] s0 represents the status value of the leader, s i Represents the state value of other agents in the multi-agent system. The leader is not affected by the control protocol, and each follower complies with the control protocol; the control protocol of the controller is as follows:

[0042]

[0043] Among them, r ij is a random function that satisfies the Bernoulli distribution to decide whether to send fraudulent data, p i Represents the deceptive data information from neighbor followers, Satisfies ||P(t)||≤p, p is a normal number, f i is the pulse intensity, c i represents the communication strength with the leader, m i (t) represents the random function of whether a DOS attack occurs between the ith agent and the leader, δ(·) is the Dirac function, and a ij Closely related to the topology of multi-agent systems. <t0<t1<…<t k <t k+1 <…,θ1≤t k -t k-1 ≤θ2, where θ1 and θ2 are positive constants and the system is left continuous;

[0044] The error system building module is configured to build an error system between the follower agent and the leader;

[0045] The multi-agent system model can be converted into the following form:

[0046]

[0047] The error η between the follower agent and the leader i (t) = s i (t)-s0(t), the error system is:

[0048]

[0049] The vector form of the error system is:

[0050]

[0051] Where F = diag[f i ],C=diag[c i ],M(t k )=diag[m i (t k )],R(t k )=[r ij (t k )] N×N , G(t, η(t))=[[g(s1(t))-g(s0(t))] T, [g(s2(t))-g(s0(t))] T , ..., [g(s N (t))]-g(s0(t))] T ] T , Ψ is a square matrix with all diagonal elements being 0, and Φ is a diagonal matrix;

[0052] The consistency control module is configured to establish a mean square consistency condition for the multi-agent system under deception attack and DOS attack;

[0053] The given error converges to When the following inequality is satisfied:

[0054]

[0055] The multi-agent system is controlled to achieve leader-follower mean square consistency.

[0056] A third aspect of an embodiment of the present application provides an electronic device, comprising a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the mean square consistency control method of a multi-agent system under a hybrid attack as described in the first aspect.

[0057] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0058] Compared with the prior art, the technical solution provided by this application has the following beneficial effects:

[0059] The present invention provides a method and device for controlling mean square consistency of a multi-agent system under a hybrid attack. By establishing a multi-agent system model and designing a corresponding controller, an error system between a follower agent and a leader is constructed on this basis, and a mean square consistency condition of the multi-agent system under a deception attack and a DOS attack is established, thereby controlling the multi-agent system to achieve leader-follower mean square consistency. The present invention improves the control strategy by comprehensively considering the deception attack and DOS attack that may exist during the operation of the multi-agent system, thereby achieving mean square bounded consistency of the leader-follower, reducing the adverse effects of the communication process between agents when encountering an attack, and improving the control efficiency and control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flow chart of a method for controlling mean square consistency of a multi-agent system under a hybrid attack provided in an embodiment of the present application;

[0061] Figure 2A schematic diagram of the structure of a mean square consistency control device for a multi-agent system under a hybrid attack provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] Below, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present application.

[0064] The terms used herein are only for describing specific embodiments and are not intended to limit the present application. The terms "include", "comprising", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0065] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0066] Some block diagrams and / or flow charts are shown in the accompanying drawings. It should be understood that some blocks or combinations thereof in the block diagrams and / or flow charts may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that these instructions, when executed by the processor, may create a device for implementing the functions / operations described in these block diagrams and / or flow charts.

[0067] In some embodiments, see Figure 1 , Figure 1 A flow chart of a method for controlling the mean square consistency of a multi-agent system under a hybrid attack provided in an embodiment of the present application; the method for controlling the mean square consistency of a multi-agent system under a hybrid attack provided in an embodiment of the present application comprises:

[0068] S110, establish a multi-agent system model;

[0069] Consider a multi-agent system consisting of N followers and 1 leader, whose dynamic equation is as follows:

[0070]

[0071] Among them, s i (t)∈R n ,u i (t) respectively represent the state and control input of the ith follower. s0(t)∈R n is the leader state, g(·):R n →R is a continuous nonlinear function, A and B are preset parameter matrices;

[0072] Step S120, designing a controller;

[0073] s0 represents the status value of the leader, s i Represents the state value of other agents in the multi-agent system. The leader is not affected by the control protocol, and each follower complies with the control protocol; the control protocol of the controller is as follows:

[0074]

[0075] Among them, r ij is a random function that satisfies the Bernoulli distribution to decide whether to send fraudulent data, p i Represents the deceptive data information from neighbor followers, Satisfies ||P(t)||≤p, p is a normal number, f i is the pulse intensity, c i represents the communication strength with the leader, m i (t) represents the random function of whether a DOS attack occurs between the ith agent and the leader, δ(·) is the Dirac function, and a ij Closely related to the topology of multi-agent systems. <t0<t1<…<t k <t k+1 <…,θ1≤t k -t k-1 ≤θ2, where θ1 and θ2 are positive constants and the system is left continuous;

[0076] S130, constructing an error system between the follower agent and the leader;

[0077] The multi-agent system model can be converted into the following form:

[0078]

[0079] The error η between the follower agent and the leader i (t) = s i (t)-s0(t), the error system is:

[0080]

[0081] The vector form of the error system is:

[0082]

[0083] Where F = diag[f i ],C=diag[c i ],M(t k )=diag[m i (t k )],R(t k )=[r ij (t k )] N×N , G(t, η(t))=[[g(s1(t))-g(s0(t))] T , [g(s2(t))-g(s0(t))] T , ..., [g(s N (t))]-g(s0(t))] T ] T , Ψ is a square matrix with all diagonal elements being 0, and Φ is a diagonal matrix;

[0084] S140, establish the mean square consistency condition of the multi-agent system under deception attack and DOS attack;

[0085] The given error converges to When the following inequality is satisfied:

[0086]

[0087] Controlling multi-agent systems to achieve leader-follower mean square consensus.

[0088] In some embodiments, the spoofing attack is described as follows, defining the injected false data as p i (t), random function r ij (t) satisfies the Bernoulli distribution, which is defined as follows:

[0089]

[0090] When 0<α ij <1, random function r ij The probabilities of (t) are Prob(r ij (t) = 1} = α ij 、Prob(r ij (t) = 0} = 1-α ij .

[0091] In some embodiments, the DOS attack is described as follows, defining a random function m i (t) satisfies the Bernoulli distribution:

[0092]

[0093] When 0<β i <1, random function m i The probabilities of (t) are Prob(m i (t) = 1} = β i 、Prob(m i (t) = 0} = 1-β i .

[0094] In some embodiments, after establishing the mean square consistency condition of the multi-agent system under deception attack and DOS attack, the method further includes:

[0095] The range of mean square safety consistency is determined based on impulse control theory, convex hull theory and Lyapunov stability theory.

[0096] In an alternative embodiment, the Lyapunov function is constructed When t∈(t k-1 , t k ], its derivative is:

[0097]

[0098] From this, we can get Then we can get:

[0099]

[0100] When t = t k hour:

[0101]

[0102] in, V1(t k ) = η T (t k )Q(t k )Q(t k )η(t k ), V2(t k ) = η T (t k )Q(t k )Y(t k )+Y T (t k )Q(t k )η(t k ), V3(tk )=Y T (t k )Y(t k ),right and Have the following discussion:

[0103]

[0104] Where μ1=μ min (ZL), μ2=μ min (ZCΦ), μ3=μ max (ZCΦ), μ4=μ max (ZL). Similarly, we can get:

[0105]

[0106] Where μ5=μ max (ZΨ), similarly we can get:

[0107]

[0108] So we can get:

[0109]

[0110] And then:

[0111]

[0112] When t∈[t0,t1], and,

[0113]

[0114] When t∈(t1, t2],

[0115]

[0116] and,

[0117]

[0118] By derivation, we can get that when t∈(t k , t k+1 ], there is

[0119]

[0120] If 0<ε<1, and when t∈(t k , t k+1 ], we can get:

[0121]

[0122] based on It can be seen that when t→+∞, In addition, if ε ≥ 1 and t∈(t k , t k+1 ], similarly, we can get:

[0123]

[0124] according to When t→+∞, we can get Therefore, we can get that when t→+∞, In summary, the above multi-agent system can achieve mean square bounded consistency, and the error system can converge to the set

[0125] In one example, a multi-agent system includes 1 leader and 4 followers, where each agent can communicate with the leader, and the Laplace matrix is:

[0126]

[0127] Select the initial value g(t, s i (t)) = cot(s i (t))+cos(s i (t))sin(s i (t)), Select Parameters f i =0.25, c1=0.84, c2=0.69, c3=0.87, c4=0.62, p=0.8, θ1=0.18<t k -t k-1 =0.2<0.22=θ2, assuming ε=0.32, The error threshold can be obtained

[0128] The embodiment of the present application establishes a multi-agent system model, designs a corresponding controller, and on this basis, constructs an error system between the follower agent and the leader, and establishes the mean square consistency condition of the multi-agent system under deception attack and DOS attack, thereby controlling the multi-agent system to achieve leader-follower mean square consistency. The present invention improves the control strategy by comprehensively considering the deception attack and DOS attack that may exist during the operation of the multi-agent system, thereby achieving the mean square bounded consistency of the leader-follower, reducing the adverse effects of the communication process between agents when encountering attacks, and improving the control efficiency and control accuracy.

[0129] In some embodiments, see Figure 2 , Figure 2 The schematic diagram of the structure of a mean square consistency control device for a multi-agent system under a hybrid attack provided in an embodiment of the present application. The mean square consistency control device 200 for a multi-agent system under a hybrid attack provided in an embodiment of the present application comprises: a model building module 210, a control design module 220, an error system building module 230 and a consistency control module 240, wherein:

[0130] A model building module 210, configured to build a multi-agent system model;

[0131] Consider a multi-agent system consisting of N followers and 1 leader, whose dynamic equation is as follows:

[0132]

[0133] Among them, s i (t)∈R n ,u i (t) respectively represent the state and control input of the ith follower. s0(t)∈R n is the leader state, g(·): R n →R is a continuous nonlinear function, A and B are preset parameter matrices;

[0134] A control design module 220 configured to design a controller;

[0135] s0 represents the status value of the leader, s i Represents the state value of other agents in the multi-agent system. The leader is not affected by the control protocol, and each follower complies with the control protocol; the control protocol of the controller is as follows:

[0136]

[0137] Among them, r ij is a random function that satisfies the Bernoulli distribution to decide whether to send fraudulent data, p i Represents the deceptive data information from neighbor followers, Satisfies ||P(t)||≤p, p is a normal number, f i is the pulse intensity, c i represents the communication strength with the leader, m i (t) represents the random function of whether a DOS attack occurs between the ith agent and the leader, δ(·) is the Dirac function, and a ij Closely related to the topology of multi-agent systems. <t0<t1<…<t k <t k+1 <…,θ1≤t k -tk-1 ≤θ2, where θ1 and θ2 are positive constants and the system is left continuous;

[0138] an error system building module 230, configured to build an error system between the follower agent and the leader;

[0139] The multi-agent system model can be converted into the following form:

[0140]

[0141] The error η between the follower agent and the leader i (t) = s i (t)-s0(t), the error system is:

[0142]

[0143] The vector form of the error system is:

[0144]

[0145] Where F = diag[f i ],C=diag[c i ],M(t k )=diag[m i (t k )],R(t k )=[r ij (t k )] N×N ,

[0146] G(t, η(t))=[[g(s1(t))-g(s0(t))] T , [g(s2(t))-g(s0(t))] T , ..., [g(s N (t))]-g(s0(t))] T ] T , Ψ is a square matrix with all diagonal elements being 0, and Φ is a diagonal matrix;

[0147] A consistency control module 240, configured to establish a mean square consistency condition of the multi-agent system under deception attack and DOS attack;

[0148] The given error converges to When the following inequality is satisfied:

[0149]

[0150] The multi-agent system is controlled to achieve leader-follower mean square consistency.

[0151] In some embodiments, the spoofing attack is described as follows, defining the injected false data as p i (t), random function r ij (t) satisfies the Bernoulli distribution, which is defined as follows:

[0152]

[0153] When 0<α ij <1, random function r ii The probabilities of (t) are Prob{r ij (t) = 1} = α ij 、Prob{r ij (t) = 0} = 1-α ij .

[0154] In some embodiments, the DOS attack is described as follows, defining a random function m i (t) satisfies the Bernoulli distribution:

[0155]

[0156] When 0<β i <1, random function m i The probabilities of (t) are Prob{m i (t) = 1} = β i 、Prob{m i (t) = 0} = β i .

[0157] In some embodiments, the multi-agent system mean square consistency control device 200 under hybrid attack further includes a range determination module, and the range determination module is specifically configured as follows:

[0158] The range of mean square safety consistency is determined based on impulse control theory, convex hull theory and Lyapunov stability theory.

[0159] The mean square consistency control device for a multi-agent system under a hybrid attack provided in the embodiment of the present application can implement each process in the embodiment corresponding to the mean square consistency control method for a multi-agent system under a hybrid attack mentioned above, and will not be described again here to avoid repetition.

[0160] It should be noted that the mean square consistency control device for a multi-agent system under a hybrid attack provided in an embodiment of the present application and the mean square consistency control method for a multi-agent system under a hybrid attack provided in an embodiment of the present application are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the mean square consistency control method for a multi-agent system under a hybrid attack, and the repeated parts will not be repeated.

[0161] In some embodiments, see Figure 3 , Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. An electronic device 300 provided in an embodiment of the present application includes a processor 310 and a memory 320; the memory 320 stores a computer program, wherein the computer program implements the above-mentioned mean square consistency control method of a multi-agent system under a hybrid attack when executed by the processor.

[0162] Specifically, the processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 310 may also include an onboard memory for cache purposes. The processor 310 may be a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiment of the present application.

[0163] The memory 320 may be any medium capable of containing, storing, conveying, propagating or transmitting instructions. For example, the memory 320 may include, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device, component or propagation medium. Specific examples of the memory 320 include: a magnetic storage device, such as a magnetic tape or a hard disk (HDD); an optical storage device, such as a compact disk (CD-ROM); a random access memory (RAM) or flash memory; and / or a wired / wireless communication link.

[0164] The present application also provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the method for controlling the mean square consistency of a multi-agent system under the above-mentioned hybrid attack is implemented. The computer-readable medium may be included in the device / apparatus / system described in the above-mentioned embodiment; or it may exist independently without being assembled into the device / apparatus / system. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed, the method according to the embodiment of the present application is implemented.

[0165] According to an embodiment of the present application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical cable, radio frequency signal, etc., or any suitable combination of the above.

[0166] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present application. Therefore, the scope of the present application should not be limited to the above-described embodiments, but should be determined not only by the appended claims, but also by the equivalents of the appended claims.

Claims

1. A mean square consistency control method for a multi-agent system under a hybrid attack, characterized in that: include: S110, establish a multi-agent system model; Consider a multi-agent system consisting of N followers and 1 leader, whose dynamic equation is as follows: Among them, s i (t)∈R n ,u i (t) respectively represent the state and control input of the ith follower. s0(t)∈R n is the leader state, g(·): R n →R is a continuous nonlinear function, A and B are preset parameter matrices; S120, design controller; s0 represents the status value of the leader, s i Represents the state value of other agents in the multi-agent system. The leader is not affected by the control protocol, and each follower complies with the control protocol; the control protocol of the controller is as follows: Among them, r ij is a random function that satisfies the Bernoulli distribution to decide whether to send fraudulent data, p i Represents the deceptive data information from neighbor followers, Satisfies ||P(t)||≤p, p is a normal number, f i is the pulse intensity, c i represents the communication strength with the leader, m i (t) represents the random function of whether a DOS attack occurs between the i-th agent and the leader. δ(·) is the Dirac function, a ij Closely related to the topology of the multi-agent system, <t0<t1<…<t k <t k+1 <…,θ1≤t k -t k-1 ≤θ2, where θ1 and θ2 are positive constants and the system is left continuous; S130, constructing an error system between the follower agent and the leader; The multi-agent system model can be converted into the following form: The error η between the follower agent and the leader i (t) = s i (t)-s0(t), the error system is: The vector form of the error system is: Where F = diag[f i ],C=diag[c i ],M(t k )=diag[m i (t k )],R(t k )=[r ij (t k )] N×N , G(t, η(t))=[[g(s1(t))-g(s0(t))] T , [g(s2(t))-g(s0(t))] T , ..., [g(s N (t))]-g(s0(t))] T ] T , Ψ is a square matrix with all diagonal elements being 0, and Φ is a diagonal matrix; S140, establish the mean square consistency condition of the multi-agent system under deception attack and DOS attack; The given error converges to When the following inequality is satisfied: The multi-agent system is controlled to achieve leader-follower mean square consistency.

2. The mean square consistency control method of a multi-agent system under a hybrid attack according to claim 1 is characterized in that: The deception attack is described as follows, defining the injected false data as p i (t), random function r ij (t) satisfies the Bernoulli distribution, which is defined as follows: When 0<α ij <1, the probability of the random function rij(t) is Prob{r ij (t) = 1} = α ij 、Prob{r ij (t) = 0} = 1-α ij .

3. The mean square consistency control method of a multi-agent system under a hybrid attack according to claim 1 is characterized in that: The DOS attack is described as follows: define the random function m i (t) satisfies the Bernoulli distribution: When 0<βi<1, the random function m i The probabilities of (t) are Prob{m i (t) = 1} = β i 、Prob{m i (t) = 0} = 1-β i .

4. The mean square consistency control method of a multi-agent system under a hybrid attack according to claim 1 is characterized in that: After establishing the mean square consistency condition of the multi-agent system under deception attack and DOS attack, the method further includes: The range of mean square safety consistency is determined based on impulse control theory, convex hull theory and Lyapunov stability theory.

5. A mean square consistency control device for a multi-agent system under a hybrid attack, characterized in that: include: Model building module, control design module, error system building module and consistency control module, among which, The model building module is configured to build a multi-agent system model; Consider a multi-agent system consisting of N followers and 1 leader, whose dynamic equation is as follows: Among them, s i (t)∈R n ,u i (t) respectively represent the state and control input of the ith follower. s0(t)∈R n is the leader state, g(·): R n →R is a continuous nonlinear function, A and B are preset parameter matrices; The control design module is configured to design a controller; s0 represents the status value of the leader, s i Represents the state value of other agents in the multi-agent system. The leader is not affected by the control protocol, and each follower complies with the control protocol; the control protocol of the controller is as follows: Among them, r ij is a random function that satisfies the Bernoulli distribution to decide whether to send fraudulent data, p i Represents the deceptive data information from neighbor followers, Satisfies ||P(t)||≤p, p is a normal number, f i is the pulse intensity, c i represents the communication strength with the leader, m i (t) represents the random function of whether a DOS attack occurs between the i-th agent and the leader. δ(·) is the Dirac function, a ij Closely related to the topology of the multi-agent system, <t0<t1<…<t k <t k+1 <…,θ1≤t k -t k-1 ≤θ2, where θ1 and θ2 are positive constants and the system is left continuous; The error system building module is configured to build an error system between the follower agent and the leader; The multi-agent system model can be converted into the following form: The error η between the follower agent and the leader i (t) = s i (t)-s0(t), the error system is: The vector form of the error system is: Where F = diag[f i ],C=diag[c i ],M(t k )=diag[m i (t k )],R(t k )=[r ij (t k )] N×N , G(t, η(t))=[[g(s1(t))-g(s0(t))] T , [g(s2(t))-g(s0(t))] T , ..., [g(s N (t))]-g(s0(t))] T ] T , Ψ is a square matrix with all diagonal elements being 0, and Φ is a diagonal matrix; The consistency control module is configured to establish a mean square consistency condition for the multi-agent system under deception attack and DOS attack; The given error converges to When the following inequality is satisfied: The multi-agent system is controlled to achieve leader-follower mean square consistency.

6. An electronic device comprising a processor and a memory; the memory stores a computer program, wherein: When the computer program is executed by the processor, the computer program implements the mean square consistency control method of a multi-agent system under a hybrid attack according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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