CN113051723A - Swarm unmanned aerial vehicle fault propagation analysis method based on time sequence network - Google Patents
Swarm unmanned aerial vehicle fault propagation analysis method based on time sequence network Download PDFInfo
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Abstract
The invention discloses a swarm unmanned aerial vehicle fault propagation analysis method based on a time sequence network, and relates to the technical field of unmanned aerial vehicle fault analysis. The method solves the problem of difficult fault propagation modeling of the swarm unmanned aerial vehicle, can effectively describe the fault condition of the unmanned aerial vehicle, understand the propagation process of the fault in the swarm unmanned aerial vehicle, and provide a theoretical basis for the prevention and solution of the fault of the swarm unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle fault analysis, in particular to a swarm unmanned aerial vehicle fault propagation analysis method based on a time sequence network.
Background
The unmanned aerial vehicle is an unmanned and reusable aircraft, has the advantages of low manufacturing cost, small volume, light weight, high maneuverability, strong adaptability and the like, is widely applied to military and civil fields such as reconnaissance, measurement, artificial rainfall and the like, and particularly has complex environments or dangerous tasks. Although the unmanned aerial vehicle shows great advantages in task execution, the unmanned aerial vehicle is small in size, light in weight and low in load, so that the information processing capability is weaker than that of the unmanned aerial vehicle, and the task execution capability of the unmanned aerial vehicle is limited.
Along with unmanned aerial vehicle operation environment is complicated day by day and the task is diversified, single unmanned aerial vehicle can't satisfy the demand, consequently needs a plurality of unmanned aerial vehicles to carry out the task in coordination with expansion capacity, improvement execution efficiency. The unmanned aerial vehicle cluster can integrate the advantages of a single unmanned aerial vehicle through a group combat method, and can realize autonomous decision and task planning without human intervention. The swarm unmanned aerial vehicle can effectively acquire task information through local information interaction between individuals, so that quick and efficient decision and execution are realized, and the applicable range of the unmanned aerial vehicle is greatly widened. As a large-scale interconnection system, if a certain unmanned aerial vehicle in a swarm suffers malicious attack or breaks down, the interactive performance in the swarm may cause the broken unmanned aerial vehicle to affect the consistency and the connectivity of the surrounding unmanned aerial vehicle and even the whole swarm, and the overall advantages of the swarm unmanned aerial vehicle are greatly destroyed. Therefore, the fault propagation condition of the swarm unmanned aerial vehicle system when suffering malicious attack is analyzed, the cluster safety and stability are favorably improved, and a theoretical guidance effect is provided for the prevention and the solution of the fault.
Aiming at the problem of fault propagation, at present, researches are mainly focused on aspects such as a power system, a traffic network, a control system and a communication network, and the research method is mainly based on fault propagation models such as a graph theory, a Bayesian network and a complex network. The fault propagation research method based on the graph theory can describe the fault propagation process through intuitive structural characteristics, such as models of a directed graph, a Petri network, a bonded graph and the like, but the problems of more fault redundant solutions, poorer precision and the like exist at the same time; the Bayesian network-based fault propagation model defines the relationship strength among nodes through conditional probability, overcomes uncertain factors and carries out qualitative analysis on the propagation problem, but the Bayesian network model needs the support of prior probability, and the calculation result is greatly influenced by subjective factors, so that the accuracy is not high enough; the fault propagation model based on the complex network abstracts an actual system into the complex network, can effectively analyze a system structure, identify a fault source and reduce the complexity of propagation problem research, but the complex topological network structure is generally in a fixed state, so that the application range is generally limited to the fields of power grids, chemical production and the like.
Disclosure of Invention
The invention provides a swarm unmanned aerial vehicle fault propagation analysis method based on a time sequence network, when an unmanned aerial vehicle is maliciously attacked, according to the dynamics and the local interaction characteristic of a swarm unmanned aerial vehicle system, the speed and the position information of the swarm unmanned aerial vehicle are combined, and the fault propagation rule of the swarm unmanned aerial vehicle system is obtained, so that the influence degree of the fault unmanned aerial vehicle on the whole swarm is analyzed, the fault prevention and the fault solution are facilitated, and the harm effect of the fault on the swarm unmanned aerial vehicle system is reduced.
The technical scheme of the invention is as follows:
a swarm unmanned aerial vehicle fault propagation analysis method based on a time sequence network comprises the following steps:
establishing a swarm unmanned aerial vehicle system model, and determining a swarm control potential energy function according to the swarm unmanned aerial vehicle system model;
analyzing the form of malicious attack unmanned aerial vehicles by combining a swarm unmanned aerial vehicle system model and a bee-hive control potential energy function;
establishing a time sequence network model according to the swarm unmanned aerial vehicle system model and the characteristics of the topological network thereof;
determining a fault propagation direction and a fault influence degree distribution rule of a time sequence network model;
judging individual fault conditions according to the fault influence degree of each unmanned aerial vehicle;
and carrying out simulation experiments on the analysis method based on matlab.
The further technical scheme is that a swarm unmanned aerial vehicle system model is established, and a swarm control potential energy function is determined according to the swarm unmanned aerial vehicle system model, and the method comprises the following steps:
carrying out feedback linearization processing on the swarm unmanned aerial vehicle system dynamic model, and adopting a second-order system model to represent the swarm unmanned aerial vehicle system model:
wherein q isiIs the position of drone i, piIs the velocity vector of drone i, and qi、pi∈R3，uiControl input quantity of the unmanned aerial vehicle i;
the control input is in the form of a bee-hive control, expressed as follows:
wherein,
and
respectively representing the position and velocity feedback obtained by the ith unmanned aerial vehicle, NiIs a neighborhood set of drone i, phiαControlling potential energy function, vector, for bee-hives
aij(q) is an element of an adjacency matrix a of the swarm drone system topology;
to construct a non-negative smoothly derivable potential energy function, the mathematical definition defining the norm σ is:
wherein z is a defined independent variable;
the elements of adjacency matrix a are defined as:
wherein r isαFor the maximum communication range of the unmanned aerial vehicle, ρ h () is a smooth function between 0 and 1, and is defined as:
wherein h ∈ (0, 1);
the bee-hive control potential energy function is defined as:
φα(z)＝ρh(z/rα)φ(z-dα)
wherein,
parameter 0<a≤b，
d is the desired relative distance between drones, and dα＝||d||σ。
The further technical scheme is that a time sequence network model is established according to the characteristics of the swarm unmanned aerial vehicle system model and the topological network thereof, and the time sequence network model comprises the following steps:
setting a static topological network model of the swarm unmanned aerial vehicle system as G ═ { v, e }, wherein v ═ v, e }1,v2,. is a node set, e ═ e1,e2,. } is an edge set; when the swarm unmanned aerial vehicle system fails, the static topological network of the swarm unmanned aerial vehicle system dynamically changes along with time, and the swarm is free of faultsUnmanned aerial vehicle individuals in the man-machine system are taken as nodes of a time sequence network, time constraint is added into an edge set to establish a time sequence network model, the time sequence network model is defined through a quadruple (i, j, t, delta t) to represent that the individuals i and the individuals j are in the [ t, t + delta t ]]If interaction exists in the time period, all quadruplets representing interaction events are arranged according to the time sequence to form a time sequence network model of the swarm unmanned aerial vehicle;
and obtaining a time-varying topological structure of the swarm unmanned aerial vehicle system through a time sequence network model, wherein snapshots of the time-varying topological structure in each time window are represented by a two-dimensional adjacency matrix sequence.
The further technical scheme is that the interaction between the individual i and the individual j in the [ t, t + delta t ] time period comprises the following steps:
observing period [ t ] of time sequence network model1,t1+T]Dividing the time window into M time windows, wherein the length delta T of each time window is T/M, and obtaining M continuous time windows which are equal in length and do not overlap { [ T ]1,t1+Δt),[t2,t2+Δt),...,[tM,tM+ Δ t) }, where t isi＝t1+(i-1)Δt；
For a certain time window tm,tm+ Δ t), if an interaction (i, j, τ, δ τ) satisfies one of the following three conditions:
tm≤τi<tm+Δt (1)
tm≤τi+δτi<tm+Δt (2)
τi<tm<tm+Δt≤τi+δτi (3)
wherein, the condition (1) represents that the start time of the interaction is in the window, the condition (2) represents that the end time of the interaction is in the window, and the condition (3) represents that the interaction is always in the window, then the time window [ t ] between the node i and the node j is considered to bem,tm+ Δ t) there is an interaction, i.e. there is one connected edge, all connected edges are grouped together to form an edge set em。
The further technical scheme is that the method for determining the fault propagation direction and the fault influence degree distribution rule of the time sequence network model comprises the following steps:
according to the influence and the sweep principle of the fault, the fault propagation direction principle is defined as follows:
the unmanned aerial vehicle individuals in the swarm unmanned aerial vehicle system are regarded as nodes of a time sequence network, the hop count between the nodes is taken as a distance, the minimum hop count between any two nodes is calculated through a Dijkstra algorithm and is taken as a standard for judging the fault propagation direction, and then the fault is propagated from the low-hop-count node to the high-hop-count node only;
the fault influence degree can only be propagated to the unmanned aerial vehicle which interacts with the fault unmanned aerial vehicle, and then the distribution rule of the fault influence degree is determined as follows:
the fault influence degree distribution weight is defined as:
wherein u isi' denotes the control input u of drone i to drone jiLength of vector projection in the general control input u direction of drone j, NjA neighborhood set representing drone j;
degree of influence f of unmanned aerial vehicle i on fault of unmanned aerial vehicle jijIs defined as:
fij＝fi*Lij
wherein f isiRepresenting the fault influence degree of the unmanned aerial vehicle i;
the influence degree of unmanned aerial vehicle with different faults on the same unmanned aerial vehicle can be superposed, and then unmanned aerial vehicle j is at moment t1Degree of influence f from faultj(t1) Expressed as:
wherein t is0Is the time of occurrence of the failure.
Its further technical scheme does, judges the individual fault condition according to each unmanned aerial vehicle trouble influence degree, includes:
the unmanned aerial vehicle state comprises a normal state and a fault state, wherein the normal state is represented by '0', and the fault state is represented by '1'; according to the time sequence network model, obtaining the fault influence degree of each unmanned aerial vehicle in the swarm unmanned aerial vehicle system along with the change of time, and judging whether the unmanned aerial vehicle individual has faults or not and the fault influence degree according to the fault influence degree;
state variable E for unmanned aerial vehicle i at time ti(t) represents:
wherein f isi(t) shows the fault influence degree of the unmanned aerial vehicle i at the moment t, delta is a threshold value set according to the relative distance and the speed adjusting range of the swarm unmanned aerial vehicles, fi>Delta denotes that drone i enters a fault state; otherwise, the normal state is kept.
The further technical scheme is that a simulation experiment is carried out on the analysis method based on matlab, and the simulation experiment comprises the following steps:
initializing the fault influence degree of the swarm unmanned aerial vehicle system;
calculating the fault influence degree of the fault unmanned aerial vehicle on the neighboring unmanned aerial vehicle to distribute weight according to the position and speed information of the swarm unmanned aerial vehicle system at the current moment and the two-dimensional adjacent matrix, and forming a fault propagation matrix of the swarm unmanned aerial vehicle system;
calculating the fault influence degree of the neighboring unmanned aerial vehicles after superposition according to the fault propagation matrix, updating the fault influence degrees of all the unmanned aerial vehicles, and entering the next moment;
judging whether the current moment reaches a set maximum simulation moment, if so, finishing simulation, and outputting the fault influence degrees of all unmanned aerial vehicles; and otherwise, calculating the fault influence degree of the fault unmanned aerial vehicle on the neighboring unmanned aerial vehicle to distribute weight according to the position and speed information of the swarm unmanned aerial vehicle system at the current moment and the two-dimensional adjacent matrix.
The further technical scheme is that the analysis of the form of the malicious attack unmanned aerial vehicle comprises the following steps:
when the swarm unmanned aerial vehicle system is in a normal state, each unmanned aerial vehicle can only perform position and speed information interaction with other unmanned aerial vehicles within the communication range of the unmanned aerial vehicle, and the state of the unmanned aerial vehicle is adjusted through the swarm control, so that the consistency of the swarm speed and the relative distance is realized;
when the individual controller of the unmanned aerial vehicle breaks down, the control input quantity of the failed unmanned aerial vehicle is changed to be irrelevant to neighborhood information or to follow a certain unmanned aerial vehicle in the neighborhood, so that the failed individual deviates from a group consistency target, and other normal unmanned aerial vehicles are influenced through local information interaction, and the fault is propagated in the swarm unmanned aerial vehicle system.
The beneficial technical effects of the invention are as follows:
when a certain unmanned aerial vehicle in the swarm unmanned aerial vehicle is attacked maliciously, a fault individual can propagate faults in the cluster in a position and speed information local interaction mode. Due to the characteristics of dynamics, local interaction and the like of the swarm unmanned aerial vehicle system, the topological network change and the fault propagation process of the swarm unmanned aerial vehicle system after the fault occurs can be reflected in real time by utilizing the characteristics of flexibility, instantaneity and the like of the time sequence network; according to the characteristics of the swarm unmanned aerial vehicle system adopting the swarm control, the rule of the fault propagation direction and the distribution rule of the fault influence degree are defined, the problem that the swarm unmanned aerial vehicle fault propagation problem is difficult to model is solved, the fault propagation condition after the fault of the swarm unmanned aerial vehicle occurs can be more accurately described, the fault can be prevented and solved, and the harm consequence of the fault to the swarm unmanned aerial vehicle system is reduced.
Drawings
Fig. 1 is a schematic diagram of the spatial distribution of swarm drones.
Fig. 2 is a fault propagation path diagram of the swarm drone.
Fig. 3 is a diagram of an allocation rule based on the degree of influence of a fault projected by a control input vector.
Fig. 4 is a simulation flowchart of a swarm unmanned aerial vehicle fault propagation method based on a time sequence network.
Fig. 5(a) - (c) are three-axis velocity profiles of swarm drones.
Fig. 6 is a relative distance map of swarm drones.
Fig. 7(a) and (b) are an initial time position diagram and a collision time position diagram of the swarm drone, respectively.
Fig. 8 is a graph showing the variation of the influence degree of the fault of the swarm unmanned aerial vehicle.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
The application discloses a swarm unmanned aerial vehicle fault propagation analysis method based on a time sequence network, which comprises the following steps:
step 1: establishing a swarm unmanned aerial vehicle system model, determining a swarm control potential energy function according to the swarm unmanned aerial vehicle system model, and analyzing the form of malicious attack on the unmanned aerial vehicle by combining the swarm unmanned aerial vehicle system model and the swarm control potential energy function.
Specifically, feedback linearization processing is carried out on a swarm unmanned aerial vehicle system dynamic model, and a second-order system model is adopted to represent the swarm unmanned aerial vehicle system model:
wherein q isiIs the position of drone i, piIs the velocity vector of drone i, and qi、pi∈R3，uiIs the control input of drone i.
The control input is in the form of a bee-hive control, expressed as follows:
wherein,
and
the position and the speed feedback that show ith frame unmanned aerial vehicle and obtain respectively can realize that distance and speed match between unmanned aerial vehicle. N is a radical ofiNeighborhood set for drone i, denoted Ni＝{j|||qi-qj||≤r,j∈N}
φαControlling potential energy function, vector, for bee-hives
aij(q) is an element of the adjacency matrix a of the swarm drone system topology.
To construct a non-negative smoothly derivable potential energy function, the mathematical definition defining the norm σ is:
wherein z is a defined argument.
Adjacency matrix a ═ aij]The elements of (a) are defined as:
wherein r isαFor the maximum communication range of the unmanned aerial vehicle, ρ h () is a smooth function between 0 and 1, and is defined as:
where h ∈ (0, 1). Element a of the adjacency matrix when the relative distance between drones varies continuouslyij(q) at [0,1]With continuously changing intervals, neighborhood set N of individual iiAlso changing dynamically.
The bee-hive control potential energy function is defined as:
φα(z)＝ρh(z/rα)φ(z-dα)
wherein,
parameter 0<a≤b，
d is the desired relative distance between drones, and dα＝||d||σ。
The malicious attack unmanned aerial vehicle formal analysis is as follows:
when bee colony unmanned aerial vehicle system is in normal condition, every unmanned aerial vehicle can only carry out position and speed information interaction with other unmanned aerial vehicles in self communication range to be loose, the form of coupling distributes in the space when normal condition, as shown in fig. 1. The state of the swarm is adjusted through the bee colony control, the consistency of the swarm speed and the relative distance is realized, and the swarm unmanned aerial vehicle system integrally presents certain cooperative behavior.
When the individual controller of the unmanned aerial vehicle breaks down, the control input quantity of the failed unmanned aerial vehicle is changed to be irrelevant to neighborhood information or to follow a certain unmanned aerial vehicle in the neighborhood, so that the failed individual deviates from a group consistency target, and other normal unmanned aerial vehicles are influenced through local information interaction, and the fault is propagated in the swarm unmanned aerial vehicle system.
Step 2: and establishing a time sequence network model according to the swarm unmanned aerial vehicle system model and the characteristics of the topological network thereof.
According to the analysis on the malicious attack form, when the swarm unmanned aerial vehicle is attacked maliciously, the swarm unmanned aerial vehicle which is originally distributed in a loose and coupled form in the space can be influenced by faults, the original static topological network can dynamically change along with time, and the time sequence network is introduced for analysis.
Setting a static topological network model of the swarm unmanned aerial vehicle system as G ═ { v, e }, wherein v ═ v, e }1,v2,. is a node set, e ═ e1,e2,.. When the swarm unmanned aerial vehicle system fails, the position and speed information of the failed unmanned aerial vehicleThe change can cause the change of the connection relation with the neighboring unmanned aerial vehicle, namely the edges among the nodes of the static topological network can be disconnected or new edges can be generated, the unmanned aerial vehicle individuals in the swarm unmanned aerial vehicle system are taken as the nodes of the time sequence network, time constraint is added into the edge set to establish a time sequence network model, the time sequence network model is defined through a quadruple (i, j, t, delta t) to represent that the individual i and the individual j are in the position of [ t, t + delta t ]]And if interaction exists in the time period, arranging all quadruples representing interaction events according to the time sequence to form a time sequence network model of the swarm unmanned aerial vehicle. And obtaining a time-varying topological structure of the swarm unmanned aerial vehicle system through the time sequence network model, wherein snapshots of the time-varying topological structure in each time window are represented by a two-dimensional adjacency matrix sequence.
Specific conditions for the interaction between the individual i and the individual j in the [ t, t + deltat ] time period include:
observing period [ t ] of time sequence network model1,t1+T]Dividing the time window into M time windows, wherein the length delta T of each time window is T/M, and obtaining M continuous time windows which are equal in length and do not overlap { [ T ]1,t1+Δt),[t2,t2+Δt),...,[tM,tM+ Δ t) }, where t isi＝t1+(i-1)Δt；
For a certain time window tm,tm+ Δ t), if an interaction (i, j, τ, δ τ) satisfies one of the following three conditions:
tm≤τi<tm+Δt (1)
tm≤τi+δτi<tm+Δt (2)
τi<tm<tm+Δt≤τi+δτi (3)
wherein, the condition (1) represents that the start time of the interaction is in the window, the condition (2) represents that the end time of the interaction is in the window, and the condition (3) represents that the interaction is always in the window, then the time window [ t ] between the node i and the node j is considered to bem,tm+ Δ t) there is an interaction, i.e. there is one connected edge, all connected edges are grouped together to form an edge setem。
Let Gm＝(v,em) In time window [ t ] for time sequence network modelm,tm+ Δ t) snapshot, a sequence of sequential network snapshots { G1,G2,...,GMEach element in the time-series network model is a snapshot of the time-series network model in a corresponding time window. Each snapshot Gm＝(v,em) Using an adjacency matrix A [ t ]m]Indicates if node i and node j are at tm,tmThere is interaction within a time period of + Δ t), eij(tm)∈emThen the adjacency matrix A [ t ]m]Element a in (1)ij(tm) 1, otherwise aij(tm) 0. Thus, the sequence of time-series network snapshots G1,G2,...,GMCan be represented by a two-dimensional adjacency matrix sequence { A [ t }1],A[t2],...,A[tM]Represents it.
And step 3: and determining the fault propagation direction and the fault influence degree distribution rule of the time sequence network model.
In the swarm unmanned aerial vehicle network, faults are propagated in a local information interaction mode among unmanned aerial vehicles, and the faults are spread to other individuals in a cluster layer by taking a fault source individual as a center. According to the influence and the sweep principle of the fault, the fault propagation direction principle is defined as follows:
as shown in fig. 2, the hop count between the nodes of the time-series network is used as a distance, the Dijkstra algorithm is used to calculate the minimum hop count between any two nodes, and the minimum hop count is used as a standard for judging the propagation direction of the fault, so that the fault is propagated to the high-hop-count node only from the low-hop-count node, and no propagation behavior exists between the nodes with the same hop count.
According to the form of bee-crowd control, the control law of the swarm unmanned aerial vehicles is directly related to the position and speed information of the neighboring unmanned aerial vehicles, and when the distance between any two unmanned aerial vehicles is smaller than or larger than an expected distance, the control input can generate corresponding force to realize the separation and aggregation of the corresponding unmanned aerial vehicles.
Fault influence degree only can propagate to have interactive unmanned aerial vehicle with trouble unmanned aerial vehicle on, carry out the analysis through the control input to unmanned aerial vehicle, confirm that fault influence degree distribution rule is:
the fault influence degree distribution weight is defined as:
wherein, as shown in FIG. 3, ui' denotes the control input u of drone i to drone jiLength of vector projection in the direction of the general control input u of drone j, uk' parameter means and ui' same principle, NjRepresenting a neighborhood set of drone j.
Degree of influence f of unmanned aerial vehicle i on fault of unmanned aerial vehicle jijIs defined as:
fij＝fi*Lij
wherein f isiIndicating the degree of influence of the failure of drone i.
The influence degree of unmanned aerial vehicle with different faults on the same unmanned aerial vehicle can be superposed, and then unmanned aerial vehicle j is at moment t1Degree of influence f from faultj(t1) Expressed as:
wherein t is0Is the time of occurrence of the failure.
And 4, step 4: and judging the individual fault condition according to the fault influence degree of each unmanned aerial vehicle.
The unmanned aerial vehicle state comprises a normal state and a fault state, wherein the normal state is represented by '0', and the fault state is represented by '1'; according to the time sequence network model, obtaining the fault influence degree of each unmanned aerial vehicle in the swarm unmanned aerial vehicle system along with the change of time, and judging whether the unmanned aerial vehicle individual has faults or not and the fault influence degree according to the fault influence degree;
state variable E for unmanned aerial vehicle i at time ti(t) represents:
wherein f isi(t) shows the fault influence degree of the unmanned aerial vehicle i at the moment t, delta is a threshold value set according to the relative distance and the speed adjusting range of the swarm unmanned aerial vehicles, fi>Delta denotes that drone i enters a fault state; otherwise, the normal state is kept.
And 5: a simulation experiment is carried out on the analysis method based on matlab, wherein a simulation flow chart is shown in FIG. 4, and the method specifically comprises the following steps:
step 51: and initializing the fault influence degree of the swarm unmanned aerial vehicle system.
When t equals 0, it is "1" to assume the initial fault influence degree of trouble unmanned aerial vehicle, and other unmanned aerial vehicle's initial fault influence degree is "0", and all individuals except the trouble individual are normal individual at the initial moment promptly.
Step 52: and calculating the fault influence degree of the fault unmanned aerial vehicle on the neighbor unmanned aerial vehicle to distribute weight according to the position and speed information of the swarm unmanned aerial vehicle system at the current moment and the two-dimensional adjacency matrix, and forming a fault propagation matrix of the swarm unmanned aerial vehicle system.
Step 53: and calculating the fault influence degree of the neighboring unmanned aerial vehicle after superposition according to the fault propagation matrix, updating the fault influence degrees of all the unmanned aerial vehicles, and entering the next moment, namely, making t equal to t + 1.
Step 54: and judging whether the current moment reaches a set maximum simulation moment t _ max, if so, finishing simulation, and outputting the fault influence degrees of all the unmanned aerial vehicles. Otherwise, the step of calculating the fault influence degree of the fault unmanned aerial vehicle on the neighboring unmanned aerial vehicle to distribute weight according to the position and speed information of the swarm unmanned aerial vehicle system at the current moment and the two-dimensional adjacent matrix is executed again, namely the step 52 is returned.
Carrying out simulation experiments according to the simulation method, setting the colony scale to be 20 frames and the maximum communication range to be 200 meters in a matlab-based simulation model, randomly generating the initial position and speed of the colony, controlling the colony unmanned aerial vehicle system by adopting the colony control, and setting the expected relative distance to be 120 meters, wherein the total simulation time t belongs to 0,200 s. When t is 100s, injecting a fault into the controller for unmanned aerial vehicle No. 8, wherein the fault is in the form of following a certain unmanned aerial vehicle in the neighborhood of the unmanned aerial vehicle and lasts for 50 s. The three-axis speed change condition of the swarm unmanned aerial vehicle is shown in fig. 5, and it can be seen from the figure that as the No. 8 unmanned aerial vehicle is attacked maliciously in 100s, the speed and the position of the unmanned aerial vehicle deviate from the expected values, and the consistency of the swarm unmanned aerial vehicle system is damaged. The relative distance change situation of the swarm unmanned aerial vehicle is shown in fig. 6, and it can be seen from the graph that the fault is propagated to other unmanned aerial vehicles after occurrence and causes the whole swarm to generate serious consequences such as splitting and collision.
The initial moment of trouble of bee colony unmanned aerial vehicle and collision moment flight position are shown in fig. 7(a), (b), can see from the picture that the former uniformity of bee colony unmanned aerial vehicle is destroyed to because the trouble is propagated and is made No. 1 unmanned aerial vehicle and No. 8 unmanned aerial vehicle to have produced the collision consequence, when producing the collision, follow-up trouble influences no longer analysis. The individual fault influence degree change curve in the swarm unmanned aerial vehicle system is shown in fig. 8, and according to the fault influence degree value of each individual, the following collision time unmanned aerial vehicle state tables can be obtained.
| Numbering | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| State (E)i) | 1 | 1 | 1 | 0 | 0 | 1 | 1 |
| |
8 | 9 | 10 | 11 | 12 | 13 | 14 |
| State (E)i) | 1 | 0 | 0 | 0 | 1 | 1 | 0 |
| |
15 | 16 | 17 | 18 | 19 | 20 | |
| State (E)i) | 0 | 0 | 0 | 0 | 0 | 0 |
According to the fault propagation analysis method provided by the application, the fault propagation phenomenon in the whole swarm after a certain unmanned aerial vehicle in the swarm unmanned aerial vehicles breaks down is described by using the time sequence network. After the trouble takes place, swarm unmanned aerial vehicle uniformity is destroyed, and topological network is in the dynamic change state, utilizes the time sequence network can reflect the topological mutual condition of swarm unmanned aerial vehicle in real time. According to the characteristics of local information interaction and bee-colony control of the swarm unmanned aerial vehicles, the direction of the unmanned aerial vehicle fault propagation path and the distribution rule of the fault influence degree are determined, the problem that the swarm unmanned aerial vehicle fault propagation problem is difficult to model is solved, the fault degree of each unmanned aerial vehicle in the swarm unmanned aerial vehicles can be reasonably described, the prevention and the solution of faults are facilitated, and the harm consequences of the faults to the swarm unmanned aerial vehicle system are reduced.
What has been described above is only a preferred embodiment of the present application, and the present invention is not limited to the above embodiment. It is to be understood that other modifications and variations directly derivable or suggested by those skilled in the art without departing from the spirit and concept of the present invention are to be considered as included within the scope of the present invention.
Claims (8)
1. A swarm unmanned aerial vehicle fault propagation analysis method based on a time sequence network is characterized by comprising the following steps:
establishing a swarm unmanned aerial vehicle system model, and determining a swarm control potential energy function according to the swarm unmanned aerial vehicle system model;
analyzing the form of malicious attack unmanned aerial vehicles by combining the swarm unmanned aerial vehicle system model and the bee-hive control potential energy function;
establishing a time sequence network model according to the swarm unmanned aerial vehicle system model and the characteristics of the topological network thereof;
determining a fault propagation direction and a fault influence degree distribution rule of the time sequence network model;
judging individual fault conditions according to the fault influence degree of each unmanned aerial vehicle;
and carrying out simulation experiments on the analysis method based on matlab.
2. The timing network-based swarm unmanned aerial vehicle fault propagation analysis method of claim 1, wherein the establishing a swarm unmanned aerial vehicle system model, and determining a swarm control potential energy function according to the swarm unmanned aerial vehicle system model, comprises:
carrying out feedback linearization processing on the swarm unmanned aerial vehicle system dynamic model, and adopting a second-order system model to represent the swarm unmanned aerial vehicle system model:
wherein q isiIs the position of drone i, piIs the velocity vector of drone i, and qi、pi∈R3，uiControl input quantity of the unmanned aerial vehicle i;
the control input quantity is in the form of bee-hive control and is expressed as follows:
wherein,
and
respectively representing the position and velocity feedback obtained by the ith unmanned aerial vehicle, NiIs a neighborhood set of drone i, phiαControlling potential energy function, vector, for bee-hives
aij(q) is an element of an adjacency matrix a of the swarm drone system topology;
to construct a non-negative smoothly derivable potential energy function, the mathematical definition defining the norm σ is:
wherein z is a defined independent variable;
the elements of the adjacency matrix a are defined as:
wherein r isαFor the maximum communication range of the unmanned aerial vehicle, ρ h () is a smooth function between 0 and 1, and is defined as:
wherein h ∈ (0, 1);
the bee-congestion control potential energy function is defined as:
φα(z)＝ρh(z/rα)φ(z-dα)
wherein,
parameter 0<a≤b，
d is the desired relative distance between drones, and dα＝||d||σ。
3. The sequential network-based swarm unmanned aerial vehicle fault propagation analysis method of claim 1, wherein the establishing of the sequential network model according to the swarm unmanned aerial vehicle system model and the characteristics of the topological network thereof comprises:
setting a static topological network model of the swarm unmanned aerial vehicle system as G ═ { v, e }, wherein v ═ v, e }1,v2,. is a node set, e ═ e1,e2,. } is an edge set; when the swarm unmanned aerial vehicle system breaks down, the static topological network of the swarm unmanned aerial vehicle system dynamically changes along with time, unmanned aerial vehicle individuals in the swarm unmanned aerial vehicle system are regarded as nodes of a time sequence network, time constraint is added into the edge set to establish a time sequence network model, and the time sequence network model is defined through a four-tuple (i, j, t, delta t) to represent that the individual i and the individual j are in the [ t, t + delta t ]]If interaction exists in the time period, arranging all quadruples representing interaction events according to the time sequence to form a time sequence network model of the swarm unmanned aerial vehicle;
and obtaining a time-varying topological structure of the swarm unmanned aerial vehicle system through the time sequence network model, wherein snapshots of the time-varying topological structure in each time window are represented by a two-dimensional adjacency matrix sequence.
4. The sequential network-based swarm drone fault propagation analysis method of claim 3, wherein an individual i and an individual j have interaction within a [ t, t + δ t ] time period, comprising:
an observation period [ t ] of the time-series network model1,t1+T]Dividing the time window into M time windows, wherein the length delta T of each time window is T/M, and obtaining M continuous time windows which are equal in length and do not overlap { [ T ]1,t1+Δt),[t2,t2+Δt),...,[tM,tM+ Δ t) }, where t isi＝t1+(i-1)Δt；
For a certain time window tm,tm+ Δ t), if an interaction (i, j, τ, δ τ) satisfies one of the following three conditions:
tm≤τi<tm+Δt (1)
tm≤τi+δτi<tm+Δt (2)
τi<tm<tm+Δt≤τi+δτi (3)
wherein, the condition (1) represents that the start time of the interaction is in the window, the condition (2) represents that the end time of the interaction is in the window, and the condition (3) represents that the interaction is always in the window, then the time window [ t ] between the node i and the node j is considered to bem,tm+ Δ t) there is an interaction, i.e. there is one connected edge, all connected edges are grouped together to form an edge set em。
5. The method according to claim 1, wherein the determining the fault propagation direction and fault influence degree distribution rule of the time series network model comprises:
according to the influence and the sweep principle of the fault, the fault propagation direction principle is defined as follows:
the unmanned aerial vehicle individuals in the swarm unmanned aerial vehicle system are regarded as nodes of a time sequence network, the hop count between the nodes is taken as a distance, the minimum hop count between any two nodes is calculated through a Dijkstra algorithm and is taken as a standard for judging the fault propagation direction, and then the fault is propagated from the low-hop-count node to the high-hop-count node only;
the fault influence degree can only be propagated to the unmanned aerial vehicle which interacts with the fault unmanned aerial vehicle, and then the distribution rule of the fault influence degree is determined as follows:
the fault influence degree distribution weight is defined as:
wherein u isi' denotes the control input u of drone i to drone jiLength of vector projection in the general control input u direction of drone j, NjA neighborhood set representing drone j;
degree of influence f of unmanned aerial vehicle i on fault of unmanned aerial vehicle jijIs defined as:
fij＝fi*Lij
wherein f isiRepresenting the fault influence degree of the unmanned aerial vehicle i;
the influence degree of unmanned aerial vehicle with different faults on the same unmanned aerial vehicle can be superposed, and then unmanned aerial vehicle j is at moment t1Degree of influence f from faultj(t1) Expressed as:
wherein t is0Is the time of occurrence of the failure.
6. The sequential network-based swarm unmanned aerial vehicle fault propagation analysis method of claim 1, wherein the determining of individual fault conditions according to the fault influence degree of each unmanned aerial vehicle comprises:
the unmanned aerial vehicle state comprises a normal state and a fault state, wherein the normal state is represented by '0', and the fault state is represented by '1'; according to the time sequence network model, obtaining the fault influence degree of each unmanned aerial vehicle in the swarm unmanned aerial vehicle system along with the change of time, and judging whether the unmanned aerial vehicle individual has faults or not and the fault influence degree according to the fault influence degree;
state variable E for unmanned aerial vehicle i at time ti(t) represents:
wherein f isi(t) shows the fault influence degree of the unmanned aerial vehicle i at the moment t, delta is a threshold value set according to the relative distance and the speed adjusting range of the swarm unmanned aerial vehicles, fi>Delta denotes that drone i enters a fault state; otherwise, the normal state is kept.
7. The sequential network-based swarm unmanned aerial vehicle fault propagation analysis method of claim 1, wherein the matlab-based simulation experiment of the analysis method comprises:
initializing the fault influence degree of the swarm unmanned aerial vehicle system;
calculating the fault influence degree of the fault unmanned aerial vehicle on the neighboring unmanned aerial vehicle to distribute weight according to the position and speed information of the swarm unmanned aerial vehicle system at the current moment and the two-dimensional adjacent matrix, and forming a fault propagation matrix of the swarm unmanned aerial vehicle system;
calculating the fault influence degree of the neighboring unmanned aerial vehicles after superposition according to the fault propagation matrix, updating the fault influence degrees of all the unmanned aerial vehicles, and entering the next moment;
judging whether the current moment reaches a set maximum simulation moment, if so, finishing simulation, and outputting the fault influence degrees of all the unmanned aerial vehicles; and otherwise, re-executing the step of calculating the fault influence degree of the fault unmanned aerial vehicle on the neighboring unmanned aerial vehicles to distribute weight according to the position and speed information of the swarm unmanned aerial vehicle system at the current moment and the two-dimensional adjacent matrix.
8. The sequential network-based swarm drone fault propagation analysis method according to claim 1 or 2, wherein the analysis of malicious attack drone forms comprises:
when the swarm unmanned aerial vehicle system is in a normal state, each unmanned aerial vehicle can only perform position and speed information interaction with other unmanned aerial vehicles within the communication range of the unmanned aerial vehicle, and the state of the unmanned aerial vehicle is adjusted through the swarm control, so that the consistency of the swarm speed and the relative distance is realized;
when the controller of the unmanned aerial vehicle individual breaks down, the control input quantity of the broken unmanned aerial vehicle is changed to be irrelevant to neighborhood information or to follow a certain unmanned aerial vehicle in the neighborhood, so that the broken unmanned aerial vehicle individual deviates from a group consistency target, and other normal unmanned aerial vehicles are influenced through local information interaction, and the fault is propagated in the swarm unmanned aerial vehicle system.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113743565A (en) * | 2021-08-05 | 2021-12-03 | 中国人民解放军火箭军工程大学 | Unmanned aerial vehicle swarm counter-control method based on swarm architecture |
| CN113741511A (en) * | 2021-08-02 | 2021-12-03 | 中国人民解放军军事科学院国防科技创新研究院 | Unmanned aerial vehicle cluster deduction and fault diagnosis method and system |
| CN115077594A (en) * | 2022-08-09 | 2022-09-20 | 南京航空航天大学 | Swarm unmanned aerial vehicle fault detection method based on LSTM and neighbor trust mechanism |
| CN115167147A (en) * | 2022-08-09 | 2022-10-11 | 南京航空航天大学 | Distributed fault diagnosis method for swarm unmanned aerial vehicle based on thrust residual error estimation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105867415A (en) * | 2016-04-20 | 2016-08-17 | 沈阳航空航天大学 | Cooperative control policy based on secure communication of multiple unmanned aerial vehicles |
| CN107092987A (en) * | 2017-04-18 | 2017-08-25 | 中国人民解放军空军工程大学 | A kind of small and medium size unmanned aerial vehicles autonomous landing on the ship wind speed forecasting method |
| CN108427401A (en) * | 2018-03-29 | 2018-08-21 | 南京航空航天大学 | A kind of flight control system collaborative fault diagnosis method with joint connection topology |
| CN110146108A (en) * | 2019-05-10 | 2019-08-20 | 南京航空航天大学 | A kind of On-line Fault appraisal procedure for unmanned plane bee colony collaborative navigation |
-
2021
- 2021-03-12 CN CN202110269419.7A patent/CN113051723B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105867415A (en) * | 2016-04-20 | 2016-08-17 | 沈阳航空航天大学 | Cooperative control policy based on secure communication of multiple unmanned aerial vehicles |
| CN107092987A (en) * | 2017-04-18 | 2017-08-25 | 中国人民解放军空军工程大学 | A kind of small and medium size unmanned aerial vehicles autonomous landing on the ship wind speed forecasting method |
| CN108427401A (en) * | 2018-03-29 | 2018-08-21 | 南京航空航天大学 | A kind of flight control system collaborative fault diagnosis method with joint connection topology |
| CN110146108A (en) * | 2019-05-10 | 2019-08-20 | 南京航空航天大学 | A kind of On-line Fault appraisal procedure for unmanned plane bee colony collaborative navigation |
Non-Patent Citations (6)
| Title |
|---|
| QIDA YU等: "An efficient and globally optimal method for camera pose estimation using line features", 《MACHINE VISION AND APPLICATIONS》 * |
| 刘广瑞等: "基于改进人工蜂群算法的多无人机协同任务规划", 《郑州大学学报(工学版)》 * |
| 吴国锋: "蜂拥算法及其在协同自动驾驶中的应用", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》 * |
| 张如佩等: "基于符号有向图的故障样本选取方法", 《控制工程》 * |
| 徐一凡: "基于复杂网络的产业共生水网络故障传播特性研究", 《中国优秀硕士学位论文全文数据库 (基础科学辑)》 * |
| 陈严波等: "基于有向图的蜂群无人机故障影响", 《航空学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113741511A (en) * | 2021-08-02 | 2021-12-03 | 中国人民解放军军事科学院国防科技创新研究院 | Unmanned aerial vehicle cluster deduction and fault diagnosis method and system |
| CN113743565A (en) * | 2021-08-05 | 2021-12-03 | 中国人民解放军火箭军工程大学 | Unmanned aerial vehicle swarm counter-control method based on swarm architecture |
| CN115077594A (en) * | 2022-08-09 | 2022-09-20 | 南京航空航天大学 | Swarm unmanned aerial vehicle fault detection method based on LSTM and neighbor trust mechanism |
| CN115167147A (en) * | 2022-08-09 | 2022-10-11 | 南京航空航天大学 | Distributed fault diagnosis method for swarm unmanned aerial vehicle based on thrust residual error estimation |
| CN115167147B (en) * | 2022-08-09 | 2023-04-18 | 南京航空航天大学 | Distributed fault diagnosis method for swarm unmanned aerial vehicle based on thrust residual error estimation |
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