Unmanned aerial vehicle autonomous response control method and system and medium

Abstract

The invention provides an unmanned aerial vehicle autonomous response control method and system and a medium, and belongs to the technical field of unmanned aerial vehicles, and the method comprises the steps: taking an unmanned aerial vehicle cluster in an unmanned aerial vehicle system as a plurality of agents according to the communication topological relation of the unmanned aerial vehicle system, and constructing a multi-agent system model under network attack according to the attack type; when the attack type is a physical domain attack type, constructing an elastic control protocol based on constraint indexes meeting energy consumption on the premise of ensuring safety; the influence of physical domain attack on flight control of the unmanned aerial vehicle system is counteracted through an elastic control protocol; when the attack type is an information domain attack type, constructing an adaptive elastic formation control protocol; and regulating and controlling the current state of each multi-agent through a self-adaptive elastic formation control protocol, and controlling the unmanned aerial vehicle system formation. According to the method, the influence of the attack is eliminated or weakened by changing the control protocol, so that the security control under the attack condition is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an autonomous response control method, system and medium for unmanned aerial vehicles. Background Art

[0002] As a high-dimensional complex system that integrates information, physics, and mission domains, the drone multi-domain system is inevitably subject to security threats from complex environments such as the ground, air, and jungle, as well as malicious network attacks when performing tasks such as fixed-point delivery, jungle crossing, cargo delivery, ultra-low-altitude flight, and penetration operations. This will affect the safe flight of the drone.

[0003] There are many ways to attack drone multi-domain systems, including attacks on the physical domain and information domain. In the physical domain, deception attacks and false data injection attacks are common means. For example, by spoofing GPS signals, attackers can mislead the navigation system of drones and cause them to deviate from the intended route. False data injection attacks inject false data into the drone's sensor system, causing it to make wrong judgments and reactions. These attacks can not only disrupt the normal operation of drones, but may also cause serious safety accidents.

[0004] Existing technologies have many deficiencies in dealing with these multi-domain attacks. The lack of a rapid response mechanism within multiple domains is a major problem. When a drone system is attacked, if the attack cannot be quickly identified and responded to, the impact of the attack will often expand rapidly. Existing defense mechanisms are often static and lack dynamic adaptability. In a complex multi-domain attack environment, the attacker's means and strategies may change constantly, and static defense mechanisms are difficult to respond effectively. Existing defense technologies also have deficiencies in autonomous response. Drone systems need to be able to automatically respond appropriately when attacked, rather than relying entirely on human intervention. Summary of the invention

[0005] To solve the above problems, the present invention provides a method, system and medium for autonomous response control of drones. The method realizes a defense mechanism that can respond quickly, dynamically adapt and have autonomous response capabilities when facing complex and changeable attacks. By improving the existing technology and enhancing the comprehensive defense capability of the system, the security and reliability of the drone multi-domain system can be effectively improved to ensure its normal operation.

[0006] To achieve the above object, the present invention provides the following technical solutions.

[0007] A method for autonomous response control of a UAV comprises the following steps: According to the communication topology of the UAV system, the UAV cluster in the UAV system is regarded as multiple agents, and a multi-agent system model under network attack is constructed according to the attack type; in this model, one agent is designated as the leader and the other agents are followers; When the UAV system is attacked by the physical domain, a distributed attack estimation observer is designed according to the multi-agent system model, and the network attack is reconstructed based on the distributed attack estimation observer to obtain the estimated result of the attack; after obtaining the attack estimation result, a flexible control protocol is constructed based on the constraint index of energy consumption under the premise of ensuring safety to offset the impact of the physical domain attack on the flight control of the UAV system and ensure that the UAV can still maintain a stable flight state when attacked; wherein the types of physical domain attacks include deception attacks and false data injection attacks; When the UAV system is attacked by information domain, a formation function is defined to determine the set of channels attacked by information domain; the union of the attack time of the attacked channel and the attack time of the non-attacked channel is constructed; based on the union, an adaptive elastic formation control protocol is constructed to regulate the current status of each multi-agent and control the UAV system formation; wherein the types of information domain attacks include denial of service attacks and malware attacks.

[0008] Preferably, constructing a multi-agent system model under network attack according to the attack type comprises the following steps: When the UAV system is attacked in the physical domain, the multi-agent system model is: Leaders: ; Followers: ; in, and is the known system matrix, is the attack distribution matrix, It is The physical domain network attack signal suffered by the intelligent agent, For the leader dynamics model, is the follower dynamics model, Represents the number of followers, and Represent the leader’s state, output vector and Lipschitz nonlinear term respectively, and Respectively represent The system state of the followers, the control protocol to be designed, the system output and the Lipschitz nonlinear term.

[0009] Preferably, when the UAV system is attacked in the physical domain, a distributed attack estimation observer is designed according to the multi-agent system model, and the network attack is reconstructed based on the distributed attack estimation observer to obtain an estimated result of the attack, including the following steps: It is The physical domain network attack signal suffered by an agent is described as the following bounded and unknown nonlinear function: ; in, is the unknown constant matrix, It is with time t ,state x and output y The unknown time-varying function involved; For the multi-agent system model, the following state observer is designed: ; in, and is the known system matrix, is the attack distribution matrix, It is The follower agent outputs, and They are follower agent state estimates and output estimates, is the estimated value of the system state, For the control protocol, and are the output and output estimate of the initial follower agent, and are the outputs and output estimates of the agent’s neighbors, is the Lipschitz nonlinear term estimate, It is The set of neighbors of an agent; It is The interaction weight between each agent and the leader, Representative and i The weight of the interactions between agents; is the observer gain matrix, It is The relative output estimation error of the followers is, It is The attack estimation on an agent follows the following rules: ; in, is a given adaptive learning rate, is a constant matrix, for The first derivative at time t is for The first derivative at time t.

[0010] Preferably, after obtaining the attack estimation result, under the premise of ensuring safety, a flexible control protocol is constructed based on the constraint index of energy consumption, as shown below: ; Among them, when hour, ,otherwise ; is the relative output error, is the controller gain matrix, is the adaptive parameter, is called the control input signal, i.e. the formation protocol, Represent the system output of the leader, Followers and The system output of the follower, Represents a satisfaction The constant matrix of is the attack distribution matrix, It is Attack estimation on agents; It is The interaction weights between the agents and the leader; It is The agent and The weight of the interaction between agents, is a positive definite gain matrix, Represents the input signal The transpose of and Represent the actual energy consumption and the given upper bound of the maximum energy constraint. The control goal is to meet the energy consumption constraint index under the premise of ensuring safety. accomplish , and They are The state of the agent and the Agent state.

[0011] Preferably, constructing a multi-agent system model under network attack according to the attack type comprises the following steps: When the UAV system is attacked by the information domain, the multi-agent system model is: Consider 1 leader, followers, given the following multi-agent system model: ; in, and represents the position and velocity vector of the leader, is the agent label, and Respectively represent The position vector and velocity vector of each agent; Representative The control protocol of the intelligent agent, is a known constant; For the leader dynamics model, is the follower dynamics model; is the output of the leader agent, is the output of the follower agent.

[0012] Preferably, the formation function is defined to determine the set of channels that are attacked by the information domain; and the union of the attack time of the attacked channel and the attack time of the non-attacked channel is constructed, including the following steps: definition is a continuously differentiable formation function, is the position-dependent formation function, is the speed-dependent formation function, and the states of the leader and follower are defined as symbol represents the transpose of a vector or matrix, are the error between the leader and the formation function and the error between the follower and the formation function, respectively. are the errors between the position and velocity vectors and the formation function, considering is 0, then: ; in, and for and The first derivative at time t; , represents the Kronecker product, It is a flexible formation control protocol to be designed; is the first-order derivative of the formation function at time t; Consider multiple channels being attacked by denial of service at the same time, define As a set of sequences from no attack to attack, then: ; in, Indicates time interval, It is in The duration of the time interval attack; consider ,but: ; in, and Respectively represent the time interval The collection of time intervals between communication rejection and communication connection; The expression, Indicates that at time arrive The multiple attacked time periods within the time interval of , which is also the time period during which communications are refused; Indicates from arrive time interval, excluding all time intervals under attack , i.e., the time interval of the communication connection; definition As in During the time period, the channel The set of denial-of-service attacks on , assuming: ; in, , Represents the attack strength. , Stands for Denial of Service Attack; Definition The set of channels that are attacked at any moment F( t )for: ; in, is the set of all edges; definition The union of the attack time of the attacked channel and the attack time of the non-attacked channel within the time period: ; in, for The time when the attack occurred on the attacked channel, for The time when the channel was not attacked.

[0013] Preferably, the adaptive elastic formation control protocol is:

[0014] ; in, To control the amount, is the error between the leader and the formation function, and For intelligent agents and Agent as the error between the follower and the formation function; and is the gain matrix, is a known constant; Represents the performance function of the entire multi-agent system; and are the controller gain matrix and adaptive weight gain matrix to be designed; Leaders and Agents The weight of the effect, Leaders and Agents The adaptive change coefficient of the action weight; It is an intelligent agent To Agent The weight of the action; if the agent Receive information from agent j, ,otherwise, ; It is an intelligent agent To Agent The adaptive change coefficient of the action weight, its initial value , yes The first derivative with respect to time t; The core idea of ​​the performance formation control protocol is to determine the upper bound of the performance index. At the same time, realize the formation of multiple agents; is the first derivative of the velocity-related formation function with respect to time t; By deducing the , and select appropriate adjustment parameters , the allowable attack intensity is obtained according to the following linear matrix inequality: ; in, is the attenuation rate, and attack strength are scalars, Refers to the set of all edges Removal The set of channels that are attacked at any given moment After the collection.

[0015] An autonomous response control system for an unmanned aerial vehicle, the system comprising: processor; a memory having stored thereon a computer program executable on the processor; Among them, when the computer program is executed by the processor, the steps of the UAV autonomous response control method are implemented.

[0016] A computer-readable storage medium stores a data processing program, and when the data processing program is executed by a processor, the steps of the autonomous response control method of the unmanned aerial vehicle are implemented.

[0017] Beneficial effects of the present invention: The present invention proposes a method, system and medium for autonomous response control of unmanned aerial vehicles. The method adopts a distributed attack estimation observer to monitor the status of each intelligent agent of the unmanned aerial vehicle system in real time, identify abnormal behaviors and reconstruct attack patterns. Through the reconstructed attack estimation results, the system can quickly adjust the control strategy and adopt a flexible control protocol to respond to attacks to ensure the flight safety and stability of the unmanned aerial vehicle. For information domain attack types, such as denial of service (DoS) attacks and malware attacks, we define a formation function to determine the time distribution of attacked channels and unattacked channels. The adaptive flexible formation control protocol can dynamically adjust the communication and collaboration methods between the intelligent agents according to the attack situation of the channel, ensuring that the unmanned aerial vehicle cluster can still maintain effective formation and coordination under information domain attacks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of a method for autonomous response control of a UAV according to an embodiment of the present invention; Figure 2 Reconstruct the simulation results for the attack of Agent 1 and Agent 2, where Figure 2 (a) is the attack reconstruction simulation result of agent 1; Figure 2 (b) is the attack reconstruction simulation result of agent 2; Figure 3 Reconstruct the simulation results for the agent attack of Agent 3 and Agent 4, where Figure 3 (a) is the attack reconstruction simulation result of agent 3; Figure 3 (b) is the attack reconstruction simulation result of agent 4; Figure 4 is the state error between the leader and follower of Agent 1 and Agent 2, where Figure 4 (a) is the state error between the leader and follower of agent 1; Figure 4(b) is the state error between the leader and follower of agent 2; Figure 5 is the state error between the leader and follower of Agent 3 and Agent 4, where Figure 5 (a) is the state error between the leader and follower of agent 3; Figure 5 (b) is the state error between the leader and follower of agent 4; Figure 6 Topology for multi-agents; Figure 7 It is a multi-channel DoS attack signal; Figure 8 for and The trajectory of Figure 8 (a) is X Direction The trajectory of Figure 8 (b) is Y Direction trajectories; Fig. 9 for and The trajectory of Fig. 9 (a) is Z Direction The trajectory of Fig. 9 (b) is X Direction trajectories; Fig.10 for and The trajectory of Fig.10 (a) is Y Direction The trajectory of Fig.10 (b) is Z Direction trajectories; Fig.11 is the position of the agent at different times; Fig.12 is the performance function and the upper bound of the guaranteed performance. DETAILED DESCRIPTION

[0019] The research on autonomous, safe, rapid response and iterative control of UAV systems is mainly based on a hierarchical and graded full-state indicator system, forming an intra-domain rapid response mechanism based on the combinatorial optimization of the plan library, and constructing a real-time full-state feedback loop. In the multi-domain threat state space, with multi-domain security as the overall goal, the full-state safe collaborative control of the UAV system is achieved through layer-by-layer and step-by-step iterative optimization. The specific research idea is to consider the multi-domain system of UAVs as a high-dimensional complex system that integrates information, physics, and mission domains. When performing tasks such as fixed-point delivery, jungle crossing, cargo delivery, ultra-low-altitude flight, and penetration operations, it is inevitably subject to security threats from complex environments such as the ground, air, and jungle, as well as malicious network attacks, which will affect the safe flight of the UAV. To this end, this embodiment proposes an autonomous response control method for UAVs under attacks in the information domain and physical domain, such as Figure 1 As shown. Specifically including the following steps: According to the communication topology of the UAV system, the UAV cluster in the UAV system is regarded as multiple agents, and a multi-agent system model under network attack is constructed according to the attack type; in this model, one agent is designated as the leader and the other agents are followers; When the UAV system is attacked by the physical domain, a distributed attack estimation observer is designed according to the multi-agent system model, and the network attack is reconstructed based on the distributed attack estimation observer to obtain the estimated result of the attack; after obtaining the attack estimation result, a flexible control protocol is constructed based on the constraint index of energy consumption under the premise of ensuring safety to offset the impact of the physical domain attack on the flight control of the UAV system and ensure that the UAV can still maintain a stable flight state when attacked; wherein the types of physical domain attacks include deception attacks and false data injection attacks; When the UAV system is attacked by information domain, a formation function is defined to determine the set of channels attacked by information domain; the union of the attack time of the attacked channel and the attack time of the non-attacked channel is constructed; based on the union, an adaptive elastic formation control protocol is constructed to regulate the current status of each multi-agent and control the UAV system formation; wherein the types of information domain attacks include denial of service attacks and malware attacks.

[0020] Specifically, the autonomous response control method of a UAV under a physical domain network attack includes the following steps: S1: According to the communication topology of the UAV system, the UAV cluster in the UAV system is regarded as multiple agents, and a multi-agent system model under network attack is constructed according to the attack type; in this model, one agent is designated as the leader and the other agents are followers: Leaders:

[0021] Followers:

[0022] in, is the known system matrix, is the attack distribution matrix, It is The physical domain network attack signal suffered by the intelligent agent, For the leader dynamics model, is the follower dynamics model, Represents the number of followers, and Represent the leader’s state, output vector and Lipschitz nonlinear term respectively, and Respectively represent The system state of the followers, the control protocol to be designed, the system output and the Lipschitz nonlinear term.

[0023] S2: Design a distributed attack estimation observer based on the multi-agent system model, obtain the relative output estimation error through the attack estimation observer, reconstruct the network attack suffered, and obtain the attack estimation result: It is The physical domain network attack signal suffered by an agent is described as the following bounded and unknown nonlinear function: .

[0024] in, is the unknown constant matrix, It is with time ,state and output The relevant unknown time-varying function; For the multi-agent system model, the following state observer is designed: .

[0025] in, and is the known system matrix, is the attack distribution matrix, It is The follower agent outputs, and They are follower agent state estimates and output estimates, is the estimated value of the system state, For the control protocol, and are the output and output estimate of the initial follower agent, and are the outputs and output estimates of the agent’s neighbors, is the Lipschitz nonlinear term estimate, It is The set of neighbors of an agent; It is The interaction weight between each agent and the leader, Representative and i The weight of the interactions between agents; is the observer gain matrix, It is The relative output estimation error of the followers is, It is The attack estimation on an agent follows the following rules: ; in, is a given adaptive learning rate, is a constant matrix, for The first derivative at time t is for The first derivative at time t.

[0026] S3: After obtaining the attack estimation results, under the premise of ensuring safety, based on the constraint indicators of energy consumption, a flexible control protocol is constructed to offset the impact of physical domain attacks on the flight control of the UAV system, ensuring that the UAV can still maintain a stable flight state when attacked:

[0027] Among them, when hour, ,otherwise ; is the relative output error, is the controller gain matrix, is the adaptive parameter, is called the control input signal, i.e. the formation protocol, Represent the system output of the leader, Followers and The system output of the follower, Represents a satisfaction The constant matrix of is the attack distribution matrix, It is Attack estimation on agents; It is The interaction weights between the agents and the leader; It is The agent and The weight of the interaction between agents, is a positive definite gain matrix, Represents the input signal The transpose of and Represent the actual energy consumption and the given upper bound of the maximum energy constraint. The control goal is to meet the energy consumption constraint index under the premise of ensuring safety. accomplish , and They are The state of the agent and the Agent state.

[0028] In this embodiment, the scheme is simulated and verified by taking a multi-agent with 1 leader and 4 followers as an example, which are numbered as and , each agent has a 4-dimensional state vector. Assume that the attack occurs at follower 1 and follower 4. The attack signal and its estimation are as follows: Figure 2 and Figure 3 shown. Figure 2 (a) Figure 2 (b) Figure 3 (a) and Figure 3 (b) shows the attack reconstruction simulation results of Agent 1-Agent 4, where the attacks occur on Agent 1 and Agent 4.

[0029] After the attack reconstruction, the designed control protocol is used to offset the impact of the attack on the system, thereby achieving elastic security consistency control. The simulation results are as follows: Figure 4 and Figure 5 As shown, Figure 4 (a) Figure 4 (b) Figure 5 (a) and Figure 5 (b) is the state error between the leader and followers of Agent 1 to Agent 4. Under the attack condition, the state vectors of the four followers are consistent with the leader state, that is, the error between the follower state vector and the leader state vector approaches 0.

[0030] Specifically, the autonomous response control method of the UAV under the information domain network attack specifically includes the following steps: S1: According to the communication topology of the UAV system, the UAV cluster in the UAV system is regarded as multiple agents, and a multi-agent system model under network attack is constructed according to the attack type; one of the agents is the leader and the other agents are followers: Consider 1 leader, followers, given the following multi-agent system model:

[0031] in, and represents the position and velocity vector of the leader, is the agent label, Respectively represent The position vector and velocity vector of each agent; Representative The control protocol of the intelligent agent, is a known constant; For the leader dynamics model, is the follower dynamics model; is the output of the leader agent, is the output of the follower agent.

[0032] S2: When the UAV system is attacked by the information domain, a formation function can be defined to determine the set of channels that are attacked by the information domain. For the formation function: definition is a continuously differentiable formation function, is the position-dependent formation function, is the speed-dependent formation function, and the states of the leader and follower are defined as symbol represents the transpose of a vector or matrix, are the error between the leader and the formation function and the error between the follower and the formation function, respectively. are the errors between the position and velocity vectors and the formation function, considering is 0, then:

[0033] in, and for and The first derivative at time t; , represents the Kronecker product, It is a flexible formation control protocol to be designed; is the first-order derivative of the formation function at time t; Consider multiple channels being attacked by denial of service at the same time, define As a set of sequences from no attack to attack, then: .

[0034] in, Indicates time interval, It is in The duration of the time interval attack; consider ,but: .

[0035] in, and Respectively represent the time interval The collection of time intervals between communication rejection and communication connection; The expression, Indicates that at time arrive The multiple attacked time periods within the time interval of , which is also the time period during which communications are refused; Indicates from arrive time interval, excluding all time intervals under attack , which is the time interval of the communication connection.

[0036] definition As in During the time period, the channel The set of denial-of-service attacks on , assuming: .

[0037] in, , Represents the attack strength. , Stands for Denial of Service Attack; Definition The set of channels that are attacked at any moment F( t )for: .

[0038] in, is the set of all edges; definition The union of the attack time of the attacked channel and the attack time of the non-attacked channel within the time period: .

[0039] in, for The time when the attack occurred on the attacked channel, for The time when the channel was not attacked.

[0040] S3: The adaptive elastic formation control protocol is:

[0041] in, To control the amount, is the error between the leader and the formation function, and For intelligent agents and Agent as the error between the follower and the formation function; and is the gain matrix, is a known constant; Represents the performance function of the entire multi-agent system; and are the controller gain matrix and adaptive weight gain matrix to be designed; Leaders and Agents The weight of the effect, Leaders and Agents The adaptive change coefficient of the action weight; It is an intelligent agent To Agent The weight of the action; if the agent Receive information from agent j, ,otherwise, ; It is an intelligent agent To Agent The adaptive change coefficient of the action weight, its initial value , yes The first derivative with respect to time t; The core idea of ​​the performance formation control protocol is to determine the upper bound of the performance index. At the same time, realize the formation of multiple agents; is the first derivative of the velocity-related formation function with respect to time t.

[0042] By deducing the And select the appropriate , according to the following linear matrix inequality: ; ; .

[0043] Gets the allowed attack strength.

[0044] in, is the attenuation rate, and attack strength are scalars, Refers to the set of all edges Removal The set of channels that are attacked at any given moment After the collection.

[0045] S4: The current status of each multi-agent is regulated through an adaptive elastic formation control protocol, and the UAV system formation is controlled.

[0046] In this embodiment, the feasibility of the scheme is verified by simulation. Taking 1 leader and 3 followers as an example, the connection form between agents and the form of attack on each channel are as follows: Figure 6 and Figure 7 As shown in Figure 2. Under the designed flexible formation control protocol, the simulation results are shown in Figure 2. Figure 8 , Fig. 9 and Fig.10 As shown, Direction and The trajectory of Figure 8 (a) is X Direction The trajectory of Figure 8 (b) is Y Direction The trajectory of Fig. 9 (a) is Z Direction The trajectory of Fig. 9 (b) is X Direction The trajectory of Fig.10 (a) is Y Direction The trajectory of Fig.10 (b) is Z Direction From Figure 8 , Fig. 9 and Fig.10 It can be seen that the position is related to the speed All of them eventually converge to the leader’s state, achieving a safe formation.

[0047] Fig.11represents the position of the agents in three-dimensional space when they form formations at different times, and Fig.12 It represents the performance and its upper limit. The left axis corresponds to the performance function, and the right axis corresponds to the upper limit of performance. It can be seen from the figure that the performance function indicator is much smaller than its upper limit, that is, the performance of the formation is guaranteed while ensuring safety.

[0048] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for autonomous response control of a UAV, characterized in that: The following steps are involved: According to the communication topology of the UAV system, the UAV cluster in the UAV system is regarded as multiple agents, and a multi-agent system model under network attack is constructed according to the attack type; in this model, one agent is designated as the leader and the other agents are followers; When the UAV system is attacked by the physical domain, a distributed attack estimation observer is designed according to the multi-agent system model, and the network attack is reconstructed based on the distributed attack estimation observer to obtain the estimated result of the attack; after obtaining the attack estimation result, a flexible control protocol is constructed based on the constraint index of energy consumption under the premise of ensuring safety to offset the impact of the physical domain attack on the flight control of the UAV system and ensure that the UAV can still maintain a stable flight state when attacked; wherein the types of physical domain attacks include deception attacks and false data injection attacks; When the UAV system is attacked by information domain, a formation function is defined to determine the set of channels attacked by information domain; the union of the attack time of the attacked channel and the attack time of the non-attacked channel is constructed; based on the union, an adaptive elastic formation control protocol is constructed to regulate the current status of each multi-agent and control the UAV system formation; wherein the types of information domain attacks include denial of service attacks and malware attacks.

2. The autonomous response control method of a UAV according to claim 1, characterized in that: The method of constructing a multi-agent system model under network attack according to the attack type includes the following steps: When the UAV system is attacked in the physical domain, the multi-agent system model is: Leaders: ； Followers: ； in, is the known system matrix, is the attack distribution matrix, It is The physical domain network attack signal suffered by the intelligent agent, For the leader dynamics model, is the follower dynamics model, Represents the number of followers, and Represent the leader’s state, output vector and Lipschitz nonlinear term respectively, and Respectively represent The system state of the followers, the control protocol to be designed, the system output and the Lipschitz nonlinear term.

3. The autonomous response control method of a UAV according to claim 2, characterized in that: When the UAV system is attacked in the physical domain, a distributed attack estimation observer is designed according to the multi-agent system model, and the network attack is reconstructed based on the distributed attack estimation observer to obtain an estimated result of the attack, including the following steps: It is The physical domain network attack signal suffered by an agent is described as the following bounded and unknown nonlinear function: ； in, is the unknown constant matrix, It is with time t ,state x and output y The unknown time-varying function involved; For the multi-agent system model, the following state observer is designed: ； in, and is the known system matrix, is the attack distribution matrix, It is The follower agent outputs, and They are follower agent state estimates and output estimates, is the estimated value of the system state, For the control protocol, and are the output and output estimate of the initial follower agent, and are the outputs and output estimates of the agent’s neighbors, is the Lipschitz nonlinear term estimate, It is The set of neighbors of an agent; It is The interaction weight between each agent and the leader, Representative and i The weight of the interactions between agents; is the observer gain matrix, It is The relative output estimation error of the followers is, It is The attack estimation on an agent follows the following rules: ； in, is a given adaptive learning rate, is a constant matrix, for The first derivative at time t is for The first derivative at time t.

4. The autonomous response control method of a UAV according to claim 3, characterized in that: After obtaining the attack estimation result, under the premise of ensuring safety, a flexible control protocol is constructed based on the constraint index of energy consumption, as shown below: ； Among them, when hour, ,otherwise ; is the relative output error, is the controller gain matrix, is the adaptive parameter, is called the control input signal, i.e. the formation protocol, Represent the system output of the leader, Followers and The system output of the follower, Represents a satisfaction The constant matrix of is the attack distribution matrix, It is Attack estimation on agents; It is The interaction weights between the agents and the leader; It is The agent and The weight of the interaction between agents, is a positive definite gain matrix, Represents the input signal The transpose of and Represent the actual energy consumption and the given upper bound of the maximum energy constraint. The control goal is to meet the energy consumption constraint index under the premise of ensuring safety. accomplish , and They are The state of the agent and the Agent state.

5. The autonomous response control method of a UAV according to claim 1, characterized in that: The method of constructing a multi-agent system model under network attack according to the attack type includes the following steps: When the UAV system is attacked by the information domain, the multi-agent system model is: Consider 1 leader, followers, given the following multi-agent system model: ； in, and represents the position and velocity vector of the leader, is the agent label, and Respectively represent The position vector and velocity vector of each agent; Representative The control protocol of the intelligent agent, is a known constant; For the leader dynamics model, is the follower dynamics model; is the output of the leader agent, is the output of the follower agent.

6. The autonomous response control method of a UAV according to claim 5, characterized in that: The formation function is defined to determine the set of channels that are attacked by the information domain; and the union of the attack time of the attacked channel and the attack time of the non-attacked channel is constructed, including the following steps: definition is a continuously differentiable formation function, is the position-dependent formation function, is the speed-dependent formation function, and the states of the leader and follower are defined as symbol represents the transpose of a vector or matrix, are the error between the leader and the formation function and the error between the follower and the formation function, respectively. are the errors between the position and velocity vectors and the formation function, considering is 0, then: ； in, and for and The first derivative at time t; , represents the Kronecker product, It is a flexible formation control protocol to be designed; is the first-order derivative of the formation function at time t; Consider multiple channels being attacked by denial of service at the same time, define As a set of sequences from no attack to attack, then: ； in, Indicates time interval, It is in The duration of the time interval attack; consider ,but: ； in, and Respectively represent the time interval The collection of time intervals between communication rejection and communication connection; The expression, Indicates that at time arrive The multiple attacked time periods within the time interval of , which is also the time period during which communications are refused; Indicates from arrive time interval, excluding all time intervals under attack , i.e., the time interval of the communication connection; definition As in During the time period, the channel The set of denial-of-service attacks on , assuming: ； in, , Represents the attack strength. , Stands for Denial of Service Attack; Definition The set of channels that are attacked at any moment F( t )for: ； in, is the set of all edges; definition The union of the attack time of the attacked channel and the attack time of the non-attacked channel within the time period: ； in, for The time when the attack occurred on the attacked channel, for The time when the channel was not attacked.

7. The autonomous response control method of a UAV according to claim 6, characterized in that: The adaptive elastic formation control protocol is: ； in, To control the amount, is the error between the leader and the formation function, and For intelligent agents and Agent as the error between the follower and the formation function; and is the gain matrix, is a known constant; Represents the performance function of the entire multi-agent system; and are the controller gain matrix and adaptive weight gain matrix to be designed; Leaders and Agents The weight of the effect, Leaders and Agents The adaptive change coefficient of the action weight; It is an intelligent agent To Agent The weight of the action; if the agent Received Agent j Information, ,otherwise, ; It is an intelligent agent To Agent The adaptive change coefficient of the action weight, its initial value , yes The first derivative with respect to time t; The core idea of ​​the performance formation control protocol is to determine the upper bound of the performance index. At the same time, realize the formation of multiple agents; is the first derivative of the velocity-related formation function with respect to time t; By deducing the , and select appropriate adjustment parameters , the allowable attack intensity is obtained according to the following linear matrix inequality: ； in, is the attenuation rate, and attack strength are scalars, Refers to the set of all edges Removal The set of channels that are attacked at any given moment After the collection.

8. An autonomous response control system for a drone, characterized in that: The system comprises: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the autonomous response control method of the unmanned aerial vehicle as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a data processing program, and when the data processing program is executed by the processor, the steps of the autonomous response control method of the unmanned aerial vehicle according to any one of claims 1 to 7 are implemented.

Images