CN114647188A - Cooperative competition multi-agent system security cooperative control method under denial of service attack - Google Patents
Cooperative competition multi-agent system security cooperative control method under denial of service attack Download PDFInfo
- Publication number
- CN114647188A CN114647188A CN202210194476.8A CN202210194476A CN114647188A CN 114647188 A CN114647188 A CN 114647188A CN 202210194476 A CN202210194476 A CN 202210194476A CN 114647188 A CN114647188 A CN 114647188A
- Authority
- CN
- China
- Prior art keywords
- agent
- denial
- service attack
- cooperative
- agent system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000004891 communication Methods 0.000 claims abstract description 71
- 230000002457 bidirectional effect Effects 0.000 claims abstract description 15
- 230000003993 interaction Effects 0.000 claims abstract description 10
- 238000013178 mathematical model Methods 0.000 claims abstract description 7
- 239000003795 chemical substances by application Substances 0.000 claims description 261
- 239000011159 matrix material Substances 0.000 claims description 57
- 230000001960 triggered effect Effects 0.000 claims description 16
- 239000013598 vector Substances 0.000 claims description 14
- 230000009466 transformation Effects 0.000 claims description 9
- 230000002860 competitive effect Effects 0.000 claims description 8
- 239000000126 substance Substances 0.000 claims description 8
- 238000009795 derivation Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 238000010187 selection method Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 241000207961 Sesamum Species 0.000 description 1
- 235000003434 Sesamum indicum Nutrition 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000002195 synergetic effect Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Multi Processors (AREA)
- Computer And Data Communications (AREA)
Abstract
The invention discloses a cooperative competition multi-agent system security cooperative control method under denial of service attack, which comprises the following steps: establishing a mathematical model for describing individual motion characteristics of the intelligent agent and a topological graph for describing interaction between the intelligent agents; constructing a state estimator for acquiring the state of a neighbor agent at a non-trigger moment; aiming at the influence of the denial-of-service attack with limited energy on a multi-agent system communication network, determining an effective denial-of-service attack subinterval and establishing a denial-of-service attack model; designing an event trigger communication controller, and judging the next communication time of the intelligent agent; respectively designing a corresponding controller 1 and a controller 2 aiming at two conditions of no denial of service attack and existence of denial of service attack; based on the Lyapunov stability theory, a sufficiency condition for realizing the bidirectional consistency of the multi-agent system is obtained. The method aims at the multi-agent system with cooperation and competition relations, and effectively ensures that the cooperation and competition multi-agent system realizes bidirectional consistency.
Description
Technical Field
The invention belongs to the field of multi-agent cooperative control, and particularly relates to a safe cooperative control method of a multi-agent system under denial of service attack, wherein the multi-agent system has cooperation and competition relations at the same time.
Background
At present, the problem of multi-agent cooperative control is widely concerned in practical application. With the rapid development of science and technology, intelligent devices such as intelligent robots, automatic driving automobiles, unmanned aerial vehicles and unmanned naval vessels are widely applied to the military and civil fields, and the requirement for the cooperative control of a plurality of intelligent systems such as multiple robots, multiple vehicles and the like is increasingly improved, so that the control system tends to be complicated and diversified. Compared with a single agent, the multi-agent system can complete more complex tasks and improve the working efficiency and robustness of multi-agent formation through the synergistic effect among the agents.
The prior art mainly focuses on the situation that all the agents are in cooperative relationship, but in practical application, the agents inevitably have competitive relationship. For example, the cooperative confrontation of multiple unmanned aerial vehicles, the bidirectional danger avoidance area formed by the multiple unmanned aerial vehicles has higher agility compared with the same-direction avoidance, and the like. Therefore, the research on the cooperative control problem of the multi-agent system with the cooperation and competition relationship has obvious practical application value. Since the cooperative control of the multi-agent system highly depends on the reliable communication between the agents, but in the real environment, with the rapid development of the wireless communication technology, attacks against the communication network are more frequent and diversified, wherein a Denial of Service attack (Denial of Service), which is one of the main attack forms, causes communication interruption by blocking information transmission between the agents, thereby destroying the consistency target of the multi-agent system and even causing system instability. Effects similar to denial of service attacks may also be caused by environmental factors, such as: regional strong electromagnetic interference, terrain shielding, inclement weather, and the like. Research is carried out by taking denial of service attack as a typical factor, and a series of similar external interferences can be overcome at the same time. Therefore, for the cooperative competition multi-agent system under the attack of denial of service, how to design a set of safe and effective cooperative control method to ensure that the multi-agent system can still realize the established control target under the attack, and the method has important significance for improving the reliability and the safety of the cooperative competition multi-agent system.
Disclosure of Invention
In view of the above, the present invention provides a secure cooperative control method based on event-triggered communication, so as to solve the cooperative control problem of a multi-agent system having a cooperative and competitive relationship when the multi-agent system is under a denial of service attack, and reduce the communication burden and energy resource consumption of the agents. Aiming at denial of service attack, the invention mainly considers a situation of energy limitation, namely the energy of an attacker is limited, and the attacker needs to be switched into a sleep period for charging after a period of attack, so as to prepare for the next attack. This energy-limited situation is considered for two main reasons: (1) the energy-limited denial-of-service attack is more consistent with the situation of most attacks in practice; (2) the denial of service attack with limited energy can describe not only the denial of service attack itself, but also various other actually existing communication interferences, such as terrain shielding, local strong electromagnetic environments and the like. Therefore, research on the denial of service attack with limited energy has wider application value.
In order to achieve the above object, the present invention provides a cooperative competition multi-agent system security cooperative control method under denial of service attack, comprising the following steps:
step S1: obtaining a mathematical model for describing the individual motion characteristics of the intelligent agents and a topological graph for describing the interaction between the intelligent agents according to the dynamic characteristics of the individuals of the multi-intelligent-agent system and the cooperative competition relationship between the individuals of the intelligent agents;
step S2: constructing a state estimator for acquiring the state of a neighbor agent at a non-trigger moment;
step S3: aiming at the influence of the denial-of-service attack with limited energy on a multi-agent system communication network, determining an effective denial-of-service attack subinterval and establishing a denial-of-service attack model;
step S4: aiming at the denial of service attack model in the step S3, designing an event trigger communication controller, and judging the next communication time of the intelligent agent;
step S5: respectively designing corresponding controllers 1 and 2 aiming at two conditions of no denial of service attack and existence of denial of service attack based on the time trigger communication controller in the step S4 and the denial of service attack model in the step S3;
step S6: based on the Lyapunov stability theory, a sufficiency condition for realizing the bidirectional consistency of the multi-agent system is obtained.
Further, the multi-agent system in step S1 is composed of N agents, and the mathematical model of the individual motion characteristics of the agents is represented as:
wherein x isi(t)∈RnFor the ith agent state, A belongs to Rn×nFor the system matrix, B ∈ Rn×lFor the system input matrix, ui(t)∈RlFor control input, (A, B) may be calm.
Further, the topology describing the interaction between the agents in step S1 specifically includes:
describing cooperative competition relationship between the intelligent agents by using topological graph with signs, wherein positive weight is used for describing cooperative relationship between the intelligent agents, negative weight is used for describing competition relationship between the intelligent agents, and therefore the adjacent matrix of the intelligent agents is determined
aijSetting the information exchange between the multi-agent systems to be bidirectional for the weighted adjacent matrix element of the ith row and the jth column of the multi-agent system to represent the interactive relation between the ith agent and the jth agent, so that the adjacent matrix element
Is a symmetric matrix, and the Laplace matrix of the corresponding topological graph is expressed as L epsilon RN×N(ii) a The set of neighbor agents of agent i represents the set of all other agents that have information flowing to that agent, and the set of neighbor agents of agent i is represented as
All intelligence in a cooperative competition multi-agent systemThe set of volumes is V, dividing the set V into V1And V2Two groups, satisfy: v1∪V2＝V，
And for vi∈V1,vj∈V1Or vi∈V2,vj∈V2Weighted adjacency matrix element aijNot less than 0; for vi∈V1,vj∈V2Or vi∈V2,vj∈V1Weighted adjacency matrix element aijLess than or equal to 0; wherein v isiFor individuals i, v in a multi-agent systemjIs an individual j in the multi-agent system; and the topological diagram of the multi-agent system is set to be strongly connected, namely, each agent can directly or indirectly receive the information of all other agents.
Further, the state estimator of step S2 is:
wherein the content of the first and second substances,
for the state estimate for agent i at time t,
and
respectively representing the k-th of agent iiAnd k isi+1 event trigger times, the event trigger time sequence for agent i is represented as:
t0indicating the initial time.
Further, the establishing of the denial of service attack model in step S3 specifically includes:
the mth valid denial of service attack time interval is represented as: [ t ] ofm,tm+τm) (ii) a Will time interval t0,t]The set of all valid denial of service attack time intervals above is denoted as: phi (t)0,t)＝∪[tm,tm+τm)∩[t0,t]M is an element of N; wherein, tmDenotes the m-th attack start time, τmRepresents the mth attack duration;
setting 1: in the time interval [ T1,T2]In the above, the attack frequency of the effective denial of service attack is set to M, and the frequency F of occurrence of the denial of service attack is seta(T1,T2) The following conditions are satisfied:
setting 2: in the time interval [ T1,T2]In the above, the total time length of the effective denial of service attack is set to satisfy the following conditions:
wherein T is0，τaIs a positive constant.
Further, the event-triggered communication controller in step S4 specifically includes:
based on the state estimator in step S2, the following event trigger function is adopted:
wherein
Represents the error of the state estimation of agent i, represents the error between the state estimation value and the true value of the state of the agent at the current moment, betaiThe parameters are triggered for the event of agent i,
sign () is a sign function for the deviation of the state estimate of agent i from the state estimate of the neighbor agent;
based on the event trigger function, agent i next trigger time is determined by the function:
wherein the content of the first and second substances,
bia positive constant can ensure that the event trigger interval is strictly greater than zero; theta is a time constant for the system to carry out fixed-period attack detection after the communication between the intelligent agents is attacked by denial of service, namely, the system detects the communication network once every theta after suffering the attack of denial of service;
indicating event trigger time
Is not subject to a denial-of-service attack,
indicating event trigger time
Just within the denial of service attack time interval.
Further, in step S5, the controller 1 and the controller 2 specifically include:
ui(t)＝0 (8)
wherein, the first and the second end of the pipe are connected with each other,
sign () is a sign function, which is the deviation of the state estimate of agent i from the state estimate of the neighbor agent; k is BTP is a gain matrix, and the P matrix is obtained by solving the following algebraic Riccati equation:
PA+ATP-εPBBTP+ωI＝0 (9)
wherein ε and ω are predetermined constants greater than zero, ε < 2 λ2(L)，λ2(L) represents the minimum non-zero eigenvalue of the laplace matrix L, I represents the identity matrix;
the controller 1 and the controller 2 work under the condition that the system is not attacked by denial of service or is attacked by denial of service respectively, and the state of the multi-agent system is regulated and controlled.
Further, the step S6 includes the following sub-steps:
step 1: triggering the communication controller according to the event designed in step S4
Time, i.e. the trigger time is just in the m-th denial of service attack interval tm,tm+τm) At the moment, the communication triggering mode is converted from event triggering into fixed period triggering, and the fixed period is theta; the longest time interval for influencing the system by a single denial of service attack is set as follows: [ t ] ofm,tm+τm+ theta) that the attack interval occurs, dividing the whole time interval t based on the characteristics of the energy-limited denial of service attack0,t]The method is divided into two parts according to whether the attack is influenced or not:
wherein the content of the first and second substances,
a set of time intervals representing the system's exposure to a denial of service attack,
a set of time intervals representing a system not affected by a denial of service attack;
step 2: for multi-agent system in time interval
Respectively constructing Lyapunov functions, and obtaining the segmented continuous Lyapunov functions of the whole system as shown below;
wherein P, S is two positive definite matrices, INRepresenting a unit vector of order N, the matrix S being formed by solving the algebraic Riccati inequality SA + ATS-α2I < 0, where I is the identity matrix, alpha2Is a preset constant and satisfies the following conditions: 0 < a1＜α2；
For all the agent state difference vectors, 1NRepresenting column vectors, symbols with all 1 elements of order N
Representing the kronecker product, x (t) representing a vector formed by arranging all state variables of the agent in an individual order
Step 3: constructing a standard transformation matrix D: normalized transformation matrix D ═ diag { D ═ D1,d2,...,dN}，diE {1, -1}, wherein1, 2.., N, corresponding to the i-th agent; diagonal element diThe selection method comprises the following steps: for the
Selection of d i1 for
Selection of d i1, is ═ 1; or for
Selection of diIs equal to-1, for
Selection of di＝1；
Step 4: the following specification transformations are performed on the cooperative competition multi-agent system:
converting the cooperative competition multi-agent system into a multi-agent system corresponding thereto and having only a cooperative relationship, wherein z (t) represents a state variable of the cooperative multi-agent system,
representing all of the agent status difference vectors of the cooperative multi-agent system;
constructing a corresponding Lyapunov function for the transformed multi-agent system:
by pairs
With respect to t derivation, can be obtained
Constant number
α2＞α1(ii) a Further obtain
A sufficient condition is established wherein
Is that
Psi is a real number greater than zero; based on the Lyapunov stability theory, sufficient conditions for ensuring the bidirectional consistency of the cooperative competition multi-agent system are obtained as follows:
boundary conditions for event-triggered parameters:
(1)
(2)
wherein the content of the first and second substances,
γ1and gamma2Two constants greater than zero and satisfying gamma1+γ1＝γ＜1，λN(. cndot.) represents the Nth largest eigenvalue of the matrix.
Upper bound on the frequency and duration of denial of service attacks:
(1)
(2)
wherein eta is*Is a constant and satisfies 0 < eta*＜α1，
λmax(. and λ)min(. cndot.) represents the maximum eigenvalue and minimum eigenvalue of the matrix, respectively.
The invention has the characteristics and beneficial effects that:
1. the safety cooperative control method based on event-triggered communication provided by the invention can be suitable for a multi-agent system with cooperation and competition relations, and ensures that the cooperation and competition multi-agent system realizes bidirectional consistency.
2. The safety cooperative control method based on the event-triggered communication can effectively resist various external interferences including energy-limited (intermittent) denial of service attacks and various environmental factors with similar characteristics, and simultaneously adopts fixed-period communication and asynchronous event-triggered communication strategies aiming at the moments suffering from the denial of service attacks and not suffering from the denial of service attacks, thereby effectively reducing the communication, calculation and energy consumption of the system. A Lyapunov stability theory and a linear matrix inequality tool are utilized to provide sufficient conditions for realizing bidirectional consistency of the system.
Drawings
FIG. 1 is a schematic flow chart of a cooperative and competitive multi-agent system security cooperative control method under denial of service attack;
FIG. 2 is a schematic view of a undirected topology of an embodiment of the present invention;
FIG. 3 is a diagram of a state trajectory over time for an agent in accordance with an embodiment of the present invention; wherein FIG. 3(a) is a plot of a first state component of five agents as a function of time, FIG. 3(b) is a plot of a second state component of five agents as a function of time, and FIG. 3(c) is a plot of a third state component of five agents as a function of time;
fig. 4 is a dot diagram of the triggering time of the agent according to the embodiment of the present invention.
Detailed Description
For better understanding of the technical solutions of the present invention, the technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application, and it is obvious that the following examples are intended to facilitate understanding of the present invention and are not intended to limit the present application.
The invention aims to provide a safe cooperative control method aiming at a multi-agent system with cooperation and competition relations, under the non-ideal communication environment suffering from external interference such as denial of service attack and the like, and overcomes the defect that the cooperation and competition multi-agent system can deal with the communication interference such as the external denial of service attack and the like.
As shown in fig. 1, the security cooperative control method for a cooperative competition multi-agent system under the attack of denial of service provided by this embodiment includes the following steps:
step S1: and obtaining an individual mathematical model in a state space form for describing the motion characteristics and a topological graph of the interaction relation between the intelligent agents according to the individual dynamic characteristics and the relations between the individuals in the multi-intelligent-agent system.
For the dynamic behavior of individuals in a multi-agent system, a generally linear system is considered. A cooperation competition multi-agent system is composed of N agents, and a dynamic characteristic state space description model of each agent is as follows:
wherein x isi(t)∈RnFor the ith agent state, A belongs to Rn×nFor the system matrix, B ∈ Rn×lFor the system input matrix, ui(t)∈RlIs a control input and (a, B) may be calm.
The interaction relation between agents is described by adopting a topological graph, the agent individuals correspond to nodes in the topological graph, edges in the nodes represent the interaction relation between the agents, edges of two nodes are connected to represent that the corresponding agents have the interaction relation, meanwhile, the connection weight represents the tightness of the connection between the agents, the topological graph connection weight is different from the topological graph connection weight of a multi-agent system only having the cooperation relation, and the topological graph connection weight is positiveUsing a topological graph with symbols to describe cooperative competition relations among the agents; preferably, the adjacent matrix of the agents is determined by describing cooperative relationship among the agents by positive weight and describing competitive relationship among the agents by negative weight
aijThe weighted adjacent matrix element of ith row and jth column of the multi-agent system represents the interactive relation between ith agent and jth agent, and the information exchange between the multi-agents is bidirectional, so that the adjacent matrix element
Is a symmetric matrix. The corresponding Laplace matrix of the topological graph is expressed as L epsilon RN×N. The set of neighbor agents of agent i represents all other agents having information flowing to that agent, and represents the set of neighbor agents of agent i as
The set of all agents in the cooperative competition multi-agent system is V, and the set V is divided into V1And V2Two groups, satisfy agent set V1And V2There is a competitive relationship between the two groups, and V1And V2The internal agents of each group are in cooperative relationship, and the corresponding mathematical description is as follows: v1∪V2＝V，
And for vi∈V1,vj∈V1Or vi∈V2,vj∈V2Has aijNot less than 0; for vi∈V1,vj∈V2Or vi∈V2,vj∈V1Has aijLess than or equal to 0. Wherein v isiFor individuals i, v in a multi-agent systemjIs an individual j in a multi-agent system. And the topology of the multi-agent system is strongly connectedI.e. each agent can receive information of all other agents, either directly or indirectly.
Therefore, mathematical model description of motion dynamic characteristics is carried out on individuals in a cooperative and competitive multi-agent system in a reasonable and effective mode, and the interaction relation among the agents is visually depicted by adopting a topological graph so as to be used for analyzing a safety cooperative control method of a subsequent system.
Step S2: adding a state estimator:
wherein the content of the first and second substances,
for the estimate of agent i at time t,
and
is the event trigger time of agent i. The sequence of event trigger times for agent i is represented as:
t0indicating the initial time. The state estimator is used for estimating the state value of the neighbor intelligent agent at the non-triggering moment, so that the situation that the event triggers the communication controller to continuously acquire the state information of the neighbor intelligent agent can be effectively avoided, and the communication between the intelligent agents is realized only at the triggering moment.
Step S3: and considering the influence of the denial of service attack on the multi-agent system communication network, and establishing a denial of service attack model.
Due to the adoption of event-triggered communication control based on state estimatorTherefore, the multi-agent system is only influenced by the denial of service attack at the event triggering moment, and the denial of service attack occurring in the time interval of the event triggering interval is invalid for the multi-agent system. In order to more conveniently depict denial of service attack and model the denial of service attack, the attack interval only represents the attack interval containing the event triggering moment of the intelligent agent, namely the time interval containing the effective attack of the event triggering moment of the intelligent agent, which can successfully block communication between the intelligent agents. The mth valid denial of service attack time interval is represented as: [ t ] ofm,tm+τm) T since the attack cannot be continuedm+1＞tm+τm(ii) a Will time interval t0,t]The set of all valid denial of service attack time intervals above is denoted as: phi (t)0,t)＝∪[tm,tm+τm)∩[t0,t]And m is equal to N. Wherein, tmDenotes the m-th attack start time, τmRepresenting the duration of the mth attack.
Setting 1: in the time interval [ T1,T2]In the above, the attack frequency of the effective denial of service attack is set to be M, and the frequency F of the occurrence of the denial of service attack is seta(T1,T2) The upper bound is:
setting 2: in a time interval [ T1,T2]In the above, the upper bound of the total duration of the effective denial-of-service attack is set as follows:
wherein T is0，τaIs a positive constant.
Step S4: considering the influence of the denial of service attack on the multi-agent system communication network, an event trigger communication controller is designed.
Step 1: events within a time interval that is not subject to a denial of service attack are designed to trigger the communications controller.
Based on the state estimator in step S2, the following event trigger function is employed:
wherein
Represents the error of the state estimation of the agent i, represents the error between the state estimation value and the true value of the state of the agent at the current moment, and betaiThe parameters are triggered for the event of agent i,
sign () is a sign function that is the deviation of the state estimate for agent i from the state estimate for the neighbor agent.
When the event trigger function is not less than 0, the measured state values of the agent are transmitted to the neighboring agents and the state estimator is updated. To avoid the event-triggered communication controller from performing the carnot activity, i.e. the controller performs an infinite number of triggers in a finite time, we set a minimum event-trigger interval b to the controlleriThus, the sesame behavior is excluded. Thus, under the action of the event-triggered communication controller, the next trigger time of agent i is determined by the function:
wherein max {. is } represents the largest element in {. and biA positive constant can ensure that the event trigger interval is strictly greater than zero.
At this time, when the multi-agent system communication network is normal, the agent i can determine the next trigger time according to the function (19), and when the trigger time is reached, the agent i updates the state estimator and sends the state value of the agent i to the neighbor agents through the communication network.
During the time that a denial of service attack is occurring, the communication network between agents is blocked, at which point no communication will be possible between agents causing the controller to not update, and therefore the event triggered pass policy in Step1 no longer applies. Aiming at the period that a multi-agent system suffers from denial of service attack, a fixed period monitor with a time period of a constant theta is adopted to monitor a multi-agent system communication network, namely the fixed period monitor detects the communication network of the system once every theta time, whether the denial of service attack is finished or not and whether the communication network is recovered to be normal or not are judged, if the communication network is recovered to be normal, the system immediately triggers a communication update state estimator, otherwise, the communication network state is continuously detected once every theta time. The duration theta of the fixed period can be set according to actual requirements, and if the value theta is increased, the anti-attack capability of the system is weakened, but the resources of the system are relatively saved; if the value of theta is reduced, the attack resistance of the system is enhanced, but the cost is that the energy consumption of the system is increased. Therefore, in the time interval in which the system is under denial of service attack, the next trigger time of agent i is determined by the following function:
step 3: comprehensively considering the time interval of the system suffering from the denial of service attack and not suffering from the denial of service attack, setting a communication trigger controller of the system as follows:
wherein the content of the first and second substances,
indicating that the event trigger time has not suffered a denial of service attack,
indicating that the event trigger time is just within the denial of service attack time interval.
The next trigger moment judgment process: firstly, judging whether an event trigger function is satisfied, and if so, further judging the current moment t and the last trigger moment
Time interval of
Whether or not it is greater than biIf the current time is greater than the trigger time, the current time is the trigger time, and if the current time is not greater than the trigger time, the next trigger time is determined as the trigger time
And then further judging whether the communication network is normal at the triggering moment, if so, carrying out communication updating on the state of the estimator, if the network is attacked to cause communication interruption, then switching the communication triggering controller to a fixed period mode to monitor the state of the network at a fixed period theta, and when detecting that the communication network is recovered, immediately triggering the communication by the system.
Step S5: the controller 1 and the controller 2 are respectively adopted aiming at two situations that the multi-agent system is not attacked by denial of service and is attacked by denial of service.
The specific form of the controller 1 is as follows:
wherein
The state estimation value of the agent i is the deviation from the state estimation value of the neighbor agent, sign (·) is a sign function, and K equals to BTP is a gain matrix, which can be obtained by solving the following algebraic Riccati equation:
PA+ATP-εPBBTP+ωI＝0 (23)
where ε and ω are predetermined constants greater than zero, preferably, the upper bounds of the coefficients are determined: epsilon < 2 lambda2(L) wherein λ2(L) represents the minimum non-zero eigenvalue of the Laplace matrix L, and I represents an identity matrix of appropriate order.
The specific form of the controller 2 is as follows:
ui(t)＝0 (24)
specifically, when the system is not attacked by denial of service, the intelligent agents can normally transmit data through the communication network, at this time, the system adopts the controller 1 to regulate and control the states of the intelligent agents, when the communication network of the system is attacked by denial of service, the communication between the intelligent agents is interrupted, and at this time, the state of the intelligent agents in the system is regulated and controlled by switching to the controller 2. When the communication network of the multi-agent system replies, the controller 1 is switched back to regulate and control the state of the agents in the system.
Step S6: based on the Lyapunov stability theory, a sufficiency condition for realizing the bidirectional consistency of the multi-agent system is obtained.
Step 1: the time interval is divided into two parts according to whether the multi-agent system is affected by a denial of service attack or not.
Specifically, the method comprises the following steps: triggering the communication controller according to the event designed in step S4
Time, i.e. the trigger time is just in the m-th denial of service attack interval tm,tm+τm) In this case, the communication trigger mode is converted from event trigger to fixed period trigger, where the fixed period is θ, so that the denial of service attack may be terminated, but the system may not be detected until the next detection time. Preferably, the longest time interval during which a single denial of service attack affects the system is: [ t ] ofm,tm+τm+ θ). Based on the characteristics of the energy-limited denial-of-service attack, the attack interval occurs, so that it is possible toThe whole time interval t0,t]The method is divided into two parts according to whether the attack is influenced or not:
wherein the content of the first and second substances,
a set of time intervals representing the system's exposure to a denial of service attack,
representing a set of time intervals during which the system is not affected by a denial of service attack.
Step 2: for multi-agent system in time interval
Respectively constructing Lyapunov functions, and obtaining the segmented continuous Lyapunov functions of the whole system as shown below;
wherein P, S is two positive definite matrices, INRepresenting a unit vector of order N, the matrix S being formed by solving the algebraic Riccati inequality SA + ATS-α2I < 0, where I is an identity matrix of appropriate order, alpha2Is a preset constant and satisfies the following conditions: 0 < a1＜α2；
Is a state difference vector, 1NRepresenting column vectors, symbols with all 1 elements of order N
Representing the Kronecker product, x (t) representing a vector of all agent state variables arranged in an individual order
Step 3: constructing a standard transformation matrix D: normalized transformation matrix D ═ diag { D ═ D1,d2,...,dN}，diE {1, -1}, where i ═ 1, 2.., N, corresponds to the ith agent; diagonal element diThe selection mode is as follows: for the
Selection of d i1 for
Selection of di-1; or to
Selection of diAs to-1, for
Selection of di＝1；
Step 4: carrying out the following standard transformation on the cooperative competition multi-agent system;
it can be converted into a cooperative-only multi-agent system corresponding to the same phase of the cooperative multi-agent, where z (t) can represent the state variables of the cooperative multi-agent system,
all of the agent status difference vectors of the cooperative multi-agent system may be represented. Then, the corresponding lyapunov function is constructed for the transformed system:
by pairs
With respect to t derivation, one can obtain
Constant number
α2＞α1(ii) a And then can obtain
A sufficient condition is established wherein
Is that
Is a real number greater than zero. Based on the Lyapunov stability theory, sufficient conditions for ensuring the bidirectional consistency of the cooperative competition multi-agent system are obtained as follows:
boundary conditions for event-triggered parameters:
(1)
(2)
wherein, the first and the second end of the pipe are connected with each other,
γ1and gamma2Two constants greater than zero and satisfying gamma1+γ1＝γ＜1，λN(. cndot.) represents the Nth largest eigenvalue of the matrix.
Upper bound on the frequency and duration of denial of service attacks:
(1)
(2)
wherein eta is*Is a constant and satisfies 0 < eta*＜α1，
λmax(. and λ)min(. cndot.) represents the maximum eigenvalue and minimum eigenvalue of the matrix, respectively.
Therefore, the cooperative competition multi-agent system security cooperative control method under the condition of denial of service attack is provided.
Example (b):
and then carrying out numerical simulation on the cooperative competition multi-agent system security cooperative control method under the denial of service attack designed by the invention to verify the effectiveness of the cooperative competition multi-agent system security cooperative control method. The specific simulation steps are as follows:
step 1: multi-agent system setup
Consider a multi-agent system consisting of 5 third-order agents whose inter-agent topology is shown in fig. 2. The dynamic characteristic parameters of the single agent are set as follows:
the adjacency matrix L of the topological graph is:
the initial state of each agent is shown in table 1 below.
TABLE 1
Step 2: the parameter setting and selecting parameters of the controller are as follows: with 2 for epsilon and 1 for omega, the gain matrix for controller 1 is calculated using the LMI toolbox of matlab as:
and then setting an event triggering parameter betai＝0.0724，biThe fixed period monitoring interval θ is 0.2, which is 0.01.
Step 3: setting up denial of service attack interval
The setting of the denial of service attack section is shown as a bar-shaped shaded area in fig. 3.
Step 4: analysis of simulation results
Fig. 3 shows the variation traces of the three state components of the agent in the case of the denial of service attack in the cooperative competition multi-agent system, and it can be seen from fig. 3 that the system trace can be converged to the bidirectional consistency state faster although the system is subjected to the denial of service attack. Fig. 4 shows a point diagram of the triggering time when an event triggers the communication controller, and it can be seen from fig. 4 that the designed controller can effectively reduce the communication frequency of the system, save system resources, and increase the capability of the system to resist denial of service attack. Therefore, the effectiveness of the cooperative competition multi-agent system security cooperative control method under the condition of denial of service attack is verified through the simulation experiment.
The above is only a preferred embodiment of the present invention, it should be noted that the above embodiment does not limit the present invention, and various changes and modifications made by workers within the scope of the technical idea of the present invention fall within the protection scope of the present invention.
Claims (8)
1. A cooperative competition multi-agent system security cooperative control method under the attack of denial of service is characterized by comprising the following steps:
step S1: obtaining a mathematical model for describing the individual motion characteristics of the intelligent agents and a topological graph for describing the interaction between the intelligent agents according to the dynamic characteristics of the individuals of the multi-intelligent-agent system and the cooperative competition relationship between the individuals of the intelligent agents;
step S2: constructing a state estimator for acquiring the state of a neighbor agent at a non-trigger moment;
step S3: aiming at the influence of the denial-of-service attack with limited energy on a multi-agent system communication network, determining an effective denial-of-service attack subinterval, and establishing a denial-of-service attack model;
step S4: aiming at the denial of service attack model in the step S3, designing an event trigger communication controller, and judging the next communication time of the intelligent agent;
step S5: respectively designing corresponding controllers 1 and 2 aiming at two conditions of no denial of service attack and existence of denial of service attack based on the time trigger communication controller in the step S4 and the denial of service attack model in the step S3;
step S6: based on the Lyapunov stability theory, a sufficiency condition for realizing the bidirectional consistency of the multi-agent system is obtained.
2. The cooperative competition multi-agent system security cooperative control method under denial of service attack as claimed in claim 1, wherein the multi-agent system in the step S1 is composed of N agents, and the mathematical model of the individual motion characteristics of the agents is represented as:
wherein x isi(t)∈RnFor the ith agent state, A belongs to Rn×nFor the system matrix, B ∈ Rn×lFor the system input matrix, ui(t)∈RlFor control input, (A, B) may be calm.
3. The cooperative and competitive security cooperative control method for multi-agent system under denial of service attack as claimed in claim 2, wherein the topology describing interaction between agents in the step S1 is specifically:
describing cooperative competition relationship between the intelligent agents by using topological graph with signs, wherein positive weight is used for describing cooperative relationship between the intelligent agents, negative weight is used for describing competition relationship between the intelligent agents, and therefore the adjacent matrix of the intelligent agents is determined
aijSetting the information exchange between the multi-agent systems to be bidirectional for the weighted adjacent matrix element of the ith row and the jth column of the multi-agent system to represent the interactive relation between the ith agent and the jth agent, so that the adjacent matrix element
Is a symmetric matrix, and the Laplace matrix of the corresponding topological graph is expressed as L epsilon RN×N(ii) a The set of neighbor agents of agent i represents all other agents having information flowing to that agent, and represents the set of neighbor agents of agent i as
The set of all agents in the cooperative competition multi-agent system is V, and the set V is divided into V1And V2Two groups, satisfy: v1∪V2＝V，
And for vi∈V1,vj∈V1Or vi∈V2,vj∈V2Weighted adjacency matrix element aijNot less than 0; for vi∈V1,vj∈V2Or vi∈V2,vj∈V1Weighted adjacency matrix element aijLess than or equal to 0; wherein v isiFor individuals i, v in a multi-agent systemjIs an individual j in the multi-agent system; and the topology of the multi-agent system is set to be strongly connected, i.e. each agent is connectedThe information of all other agents can be received directly or indirectly.
4. The cooperative competition multi-agent system security coordination control method under the denial of service attack as claimed in claim 3, wherein the state estimator of the step S2 is:
wherein the content of the first and second substances,
for the state estimate for agent i at time t,
and
respectively representing the k-th of agent iiAnd k isi+1 event trigger times, the sequence of event trigger times for agent i is represented as:
t0indicating the initial time.
5. The cooperative competition multi-agent system security cooperative control method under the denial of service attack as claimed in claim 4, wherein the establishing of the denial of service attack model in step S3 is specifically:
the mth valid denial of service attack time interval is represented as: [ t ] ofm,tm+τm) (ii) a Will time interval t0,t]In case of all valid denial of service attacksThe set of intervals is represented as: phi (t)0,t)＝∪[tm,tm+τm)∩[t0,t]M is an element of N; wherein, tmDenotes the m-th attack start time, τmRepresents the mth attack duration;
setting 1: in the time interval [ T1,T2]In the above, the attack frequency of the effective denial of service attack is set to be M, and the frequency F of the occurrence of the denial of service attack is seta(T1,T2) The following conditions are satisfied:
setting 2: in the time interval [ T1,T2]In the above, the total time length of the effective denial of service attack is set to satisfy the following conditions:
wherein T is0，τaIs a positive constant.
6. The cooperative and competitive security cooperative control method for multi-agent system under denial of service attack as claimed in claim 5, wherein the event-triggered communication controller in step S4 is specifically:
based on the state estimator in step S2, the following event trigger function is adopted:
wherein
Represents the error of the state estimation of agent i, represents the error between the state estimation value and the true value of the state of the agent at the current moment, betaiAs agent iThe event-triggered parameter(s) of (c),
sign () is a sign function for the deviation of the state estimate of agent i from the state estimate of the neighbor agent;
based on the event trigger function, agent i next trigger time is determined by the function:
wherein, the first and the second end of the pipe are connected with each other,
bia positive constant can ensure that the event trigger interval is strictly greater than zero; theta is a time constant for the system to carry out fixed-period attack detection after the communication between the intelligent agents is attacked by denial of service, namely, the system detects the communication network once every theta after suffering the attack of denial of service;
indicating event trigger time
Is not subject to a denial-of-service attack,
indicating event trigger time
Just within the denial of service attack time interval.
7. The cooperative competition multi-agent system security cooperative control method under the denial of service attack as claimed in claim 6, wherein the controller 1 and the controller 2 in the step S5 are specifically:
ui(t)＝0 (8)
wherein, the first and the second end of the pipe are connected with each other,
sign () is a sign function for the deviation of the state estimate of agent i from the state estimate of the neighbor agent; k is BTP is a gain matrix, and the P matrix is obtained by solving the following algebraic Riccati equation:
PA+ATP-εPBBTP+ωI＝0 (9)
wherein ε and ω are predetermined constants greater than zero, ε < 2 λ2(L)，λ2(L) represents the minimum non-zero eigenvalue of the laplace matrix L, I represents the identity matrix;
the controller 1 and the controller 2 work under the condition that the system is not attacked by denial of service or is attacked by denial of service respectively, and the state of the multi-agent system is regulated and controlled.
8. The cooperative competition multi-agent system security coordination control method under denial of service attack as claimed in claim 1, wherein the step S6 comprises the following sub-steps:
step 1: triggering the communication controller according to the event designed in step S4
Time, i.e. the trigger time is just in the m-th denial of service attack interval tm,tm+τm) At the moment, the communication triggering mode is converted from event triggering into fixed period triggering, and the fixed period is theta; the longest time interval for which a single denial of service attack affects the system is set as follows: [ t ] ofm,tm+τm+ theta), based on the characteristics of the energy-limited denial-of-service attack, attackIntervals occur, dividing the entire time interval t0,t]The method is divided into two parts according to whether the attack is influenced or not:
wherein, the first and the second end of the pipe are connected with each other,
a set of time intervals representing the system's exposure to a denial of service attack,
a set of time intervals representing a system not affected by a denial of service attack;
step 2: for multi-agent system in time interval
Respectively constructing Lyapunov functions to obtain segmented continuous Lyapunov functions of the whole system as shown in the specification;
wherein P, S is two positive definite matrices, INRepresenting a unit vector of order N, the matrix S being formed by solving the algebraic Riccati inequality SA + ATS-α2I < 0, where I is the identity matrix, alpha2Is a preset constant and satisfies the following conditions: 0 < a1＜α2；
For all the agent state difference vectors, 1NRepresenting column vectors, symbols, with all 1 elements of order N
Representing the kronecker product, x (t) representing all smart shapesVector formed by arranging state variables in individual sequence
Step 3: constructing a standard transformation matrix D: normalized transformation matrix D ═ diag { D ═ D1,d2,...,dN}，diE {1, -1}, wherein i ═ 1, 2., N, corresponds to the ith agent; diagonal element diThe selection method comprises the following steps: for the
Selection of di1 for
Selection of di-1; or for
Selection of diAs to-1, for
Selection of di＝1；
Step 4: performing the following specification transformation on the cooperative competition multi-agent system:
converting the cooperative competition multi-agent system into a multi-agent system corresponding thereto and having only a cooperative relationship, wherein z (t) represents a state variable of the cooperative multi-agent system,
representing all of the agent status difference vectors of the cooperative multi-agent system;
constructing a corresponding Lyapunov function for the transformed multi-agent system:
by making a pair
With respect to t derivation, can be obtained
Constant number
α2＞α1(ii) a Further obtain
A sufficient condition is established wherein
Is that
Psi is a real number greater than zero; based on the Lyapunov stability theory, sufficient conditions for ensuring the bidirectional consistency of the cooperative competition multi-agent system are obtained as follows:
boundary conditions for event-triggered parameters:
(1)
(2)
wherein, the first and the second end of the pipe are connected with each other,
γ1and gamma2Is two constants greater than zero andsatisfy gamma1+γ1＝γ＜1，λN(. cndot.) represents the Nth largest eigenvalue of the matrix.
Upper bound on the frequency and duration of denial of service attacks:
(1)
(2)
wherein eta is*Is a constant and satisfies 0 < eta*＜α1，
λmax(. and λ)min(. cndot.) represents the maximum eigenvalue and minimum eigenvalue of the matrix, respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210194476.8A CN114647188B (en) | 2022-03-01 | 2022-03-01 | Cooperative competition multi-intelligent system safety cooperative control method under denial of service attack |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210194476.8A CN114647188B (en) | 2022-03-01 | 2022-03-01 | Cooperative competition multi-intelligent system safety cooperative control method under denial of service attack |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114647188A true CN114647188A (en) | 2022-06-21 |
| CN114647188B CN114647188B (en) | 2024-06-21 |
Family
ID=81993776
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210194476.8A Active CN114647188B (en) | 2022-03-01 | 2022-03-01 | Cooperative competition multi-intelligent system safety cooperative control method under denial of service attack |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114647188B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115378648A (en) * | 2022-07-18 | 2022-11-22 | 深圳市人工智能与机器人研究院 | Network attack prevention containment control method and device for multi-agent system and terminal |
| CN115826417A (en) * | 2023-02-20 | 2023-03-21 | 北京航空航天大学 | Robot formation control method for resisting rejection service attack based on topology reconstruction |
| CN118070294A (en) * | 2024-04-25 | 2024-05-24 | 济南源根科技有限公司 | Safety operation and maintenance big data processing system based on multidimensional data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070270983A1 (en) * | 2006-05-18 | 2007-11-22 | Honda Motor Co., Ltd. | Control apparatus, control method, and engine control unit |
| CN109491249A (en) * | 2018-11-30 | 2019-03-19 | 沈阳航空航天大学 | It is a kind of that there are the design methods of multi-agent system event trigger controller when DoS attack |
| CN110297497A (en) * | 2019-06-11 | 2019-10-01 | 沈阳航空航天大学 | The control method of multi-agent system consistency based on mixing trigger mechanism under network attack |
| CN111679572A (en) * | 2020-05-11 | 2020-09-18 | 南京财经大学 | Network control system security control method based on hybrid triggering under multi-network attack |
| CN113467332A (en) * | 2021-07-28 | 2021-10-01 | 南京市初仁智能科技有限公司 | Design method of event trigger controller of information physical system under denial of service attack |
-
2022
- 2022-03-01 CN CN202210194476.8A patent/CN114647188B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070270983A1 (en) * | 2006-05-18 | 2007-11-22 | Honda Motor Co., Ltd. | Control apparatus, control method, and engine control unit |
| CN109491249A (en) * | 2018-11-30 | 2019-03-19 | 沈阳航空航天大学 | It is a kind of that there are the design methods of multi-agent system event trigger controller when DoS attack |
| CN110297497A (en) * | 2019-06-11 | 2019-10-01 | 沈阳航空航天大学 | The control method of multi-agent system consistency based on mixing trigger mechanism under network attack |
| CN111679572A (en) * | 2020-05-11 | 2020-09-18 | 南京财经大学 | Network control system security control method based on hybrid triggering under multi-network attack |
| CN113467332A (en) * | 2021-07-28 | 2021-10-01 | 南京市初仁智能科技有限公司 | Design method of event trigger controller of information physical system under denial of service attack |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115378648A (en) * | 2022-07-18 | 2022-11-22 | 深圳市人工智能与机器人研究院 | Network attack prevention containment control method and device for multi-agent system and terminal |
| CN115826417A (en) * | 2023-02-20 | 2023-03-21 | 北京航空航天大学 | Robot formation control method for resisting rejection service attack based on topology reconstruction |
| CN115826417B (en) * | 2023-02-20 | 2023-05-12 | 北京航空航天大学 | Robot formation control method for resisting service attack based on topology reconstruction |
| CN118070294A (en) * | 2024-04-25 | 2024-05-24 | 济南源根科技有限公司 | Safety operation and maintenance big data processing system based on multidimensional data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114647188B (en) | 2024-06-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114647188A (en) | Cooperative competition multi-agent system security cooperative control method under denial of service attack | |
| Shao et al. | Fuzzy adaptive event-triggered secure control for stochastic nonlinear high-order MASs subject to DoS attacks and actuator faults | |
| Li et al. | Event-triggered consensus control for multi-agent systems against false data-injection attacks | |
| Zhang et al. | Fault-tolerant containment control for IT2 fuzzy networked multiagent systems against denial-of-service attacks and actuator faults | |
| Wang et al. | Event-triggered consensus of general linear multiagent systems with data sampling and random packet losses | |
| CN113467332B (en) | Design method of event trigger controller of information physical system under denial of service attack | |
| Xu | Resilient secure control of networked systems over unreliable communication networks | |
| Cheng et al. | Resilient distributed secure consensus control for uncertain networked agent systems under hybrid DoS attacks | |
| Ding et al. | Secure synchronization of complex networks under deception attacks against vulnerable nodes | |
| CN108681247B (en) | Fully distributed performance-guaranteed communication fault-tolerant formation control method | |
| CN112865752A (en) | Filter design method based on adaptive event trigger mechanism under hybrid network attack | |
| Wang et al. | Anomaly detection in connected and automated vehicles using an augmented state formulation | |
| CN111781822A (en) | Privacy protection grouping consistency control method of multi-agent system | |
| Xu et al. | Data-based collaborative learning for multiagent systems under distributed denial-of-service attacks | |
| Wang et al. | Event-triggered cooperative adaptive neural control for cyber–physical systems with unknown state time delays and deception attacks | |
| Wang et al. | Sampled-data consensus control of nonlinear multi-agent systems with Markovian switching topologies based on extended looped functional | |
| Li et al. | Event-triggered optimal Kalman consensus filter with upper bound of error covariance | |
| Yang et al. | Dynamic event-triggered scaled consensus of multi-agent systems in reliable and unreliable networks | |
| Zhou et al. | Consensus of NMASs with MSTs subjected to DoS attacks under event-triggered control | |
| Li et al. | Design of non-smooth consensus protocol for multi-agent systems under DoS attacks | |
| CN114912108A (en) | Method for realizing leaderless consistency of multi-Euler-Lagrange system under deception attack | |
| Jiang et al. | Intelligent Consensus Asymptotic Tracking Control for Nonlinear Multiagent Systems Under Denial-of-Service Attacks | |
| Caiazzo et al. | Distributed resilient control for autonomous connected vehicles platoon over uncertain V2X communication links | |
| Guo et al. | Observer‐based event‐triggered consensus control of nonlinear cyber‐physical systems under backlash‐like hysteresis and denial‐of‐service attacks | |
| Zhang et al. | Event-triggered security consensus of continuous-time multi-agent systems against complex cooperative attacks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |