CN109582040B - Unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system - Google Patents
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Abstract
The invention provides an unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system. Comprises the following steps of 1: acquiring information of an unmanned aerial vehicle cluster; step 2: constructing an improved control model of the unmanned aerial vehicle cluster; and step 3: combining the improved control model of the unmanned aerial vehicle cluster in the step 2, and constructing a formation evaluation function of the cluster according to the formation driving force; and 4, step 4: calculating the vulnerability of the cluster formation to interference; according to the method, the reaction degree of the cluster to different interferences is reflected by quantifying the vulnerability degree of the formation and the vulnerability degree of the cluster performance, so that the cluster nodes are improved in a targeted manner during cluster design, and the task completion efficiency of the unmanned aerial vehicle cluster is improved.
Description
Technical Field
The invention belongs to the field of overall performance evaluation of unmanned aerial vehicle clusters, and particularly relates to an unmanned aerial vehicle cluster formation and performance vulnerability evaluation method and system.
Background
In recent years, small-sized and low-cost unmanned aerial vehicle clusters receive wide attention, compared with a single unmanned aerial vehicle, the unmanned aerial vehicle clusters have obvious advantages in the aspects of flexibility, fault tolerance, collaboration, task diversity and the like, so that the unmanned aerial vehicle clusters are more and more widely applied, such as unmanned aerial vehicle lamplight show, express delivery, forest fire fighting, personnel search and rescue and the like in the civil field, and the unmanned aerial vehicle clusters are used for detection, interference, hunting, bombing, sudden defense and the like in the military field. At present, for an unmanned aerial vehicle cluster, how to analyze the viability, the communication technology, the cluster control, the overall performance and the like of the unmanned aerial vehicle cluster is the focus of leading edge attention of the unmanned aerial vehicle technology.
In the overall performance analysis technology of the unmanned aerial vehicle cluster, attention focuses on differences in stability, reliability, survivability, toughness and the like are different. For example, the stability research system has better anti-interference capability on the fluctuation of the system; the reliability research shows that the system can complete the capacity of a preset function under a specified condition, and the self fault influence is considered more; the survivability of the system was studied for its resistance to blows; and the toughness is used for researching the recovery capability of the system after the system is partially disturbed, attacked or damaged. At present, most of the performances are explored from the perspective of a Complex network, and a Complex network survivability model under the condition of incomplete Information is two special cases of the model, namely, the classical random failure and the intentional attack, as described in the documents [1] "Wu J, Deng H-Z, J Tan Y, et al.Vulnerability of Complex network under interactive attack with the incomplete Information [ M ].40.2007:2665 ] and [2 ]" Wu J, Deng H-Z, Tan Y-J, et al.Attack Vulnerability of Complex network Based on Local Information [ M ].21.2007: 1014- ] ". The literature [1] and the literature [2] use a probability mother function method to analyze and deduce survivability measurement parameters of an arbitrary degree distribution generalized random network under the condition of incomplete information, propose and establish an optimal attack model of a complex network under the condition of incomplete information, and use the probability mother function method to analyze and deduce the optimal attack strategy of a scale-free network under the condition of incomplete information. The document [3] "Tran H T.A complex networks to design responsive system-of-systems [ D ]. Georgia Institute of Technology, 2015." studies how to reasonably measure the toughness of complex network systems using the indexes such as system recovery time and system performance. Although the drone cluster and the communication can be regarded as a complex network, the features of the drone cluster and the communication are different, the drone cluster has the characteristics of a formation and a specific dynamic mode besides the network, so that the most core features of the drone cluster cannot be completely described by directly evaluating the drone cluster by using the related performance of the complex network. At present, comprehensive vulnerability analysis is directly carried out on the whole unmanned aerial vehicle cluster, and the comprehensive vulnerability analysis method and the comprehensive vulnerability analysis system are relatively blank fields, so the method and the system for evaluating the vulnerability of the unmanned aerial vehicle cluster are provided by comprehensively considering the characteristics of complex networks, formation and dynamics of the unmanned aerial vehicle cluster.
The basic dynamics model of the dynamics performance of the self-organizing unmanned aerial vehicle cluster which is widely applied at present is a Cuker-Small (CS) model, such as the models described in the documents [4] "Cucker F, Small S.Emergent Behavior in locks [ J ]. IEEE Transactions on Automatic Control,2007,52(5): 852-. CS is one of the most practical mathematical theoretical models because of its good explanatory properties. Supposing that there are clusters of N unmanned aerial vehicles, the CS basic theoretical control model is:
wherein xi(t) and viAnd (t) the position and the speed of the ith unmanned aerial vehicle at the time t respectively, and a constant alpha is used for measuring the interaction size among particles in the group. a isij(x) The information exchange weight between the unmanned aerial vehicle individuals, also called a correlation function, depends on the relative positions of the individual i and the individual j and is used for describing the interaction strength between the two unmanned aerial vehicles. Since formation, repulsive force, and destination driving force are not considered in the classical CS model, the unmanned aerial vehicle cluster dynamics model cannot be completely described yet with the classical CS model. To the unmanned aerial vehicle cluster of design, adopt which kind of communication mode, hardware technology level reaches which kind of level, just can guarantee that the unmanned aerial vehicle cluster has good interference killing feature, whether can also accomplish the task under the condition of partly unmanned aerial vehicle functional failure in the cluster, become the problem that unmanned aerial vehicle cluster user cared most. Therefore, from the viewpoint of self-organization dynamics of the unmanned aerial vehicle cluster, the unmanned aerial vehicle cluster forming the formation to execute the task needs to be subjected to overall performance analysis such as vulnerability analysis, so that the efficiency and success rate of task completion can be ensured.
Disclosure of Invention
The invention aims to solve the technical problem of how to analyze the vulnerability of the formation and the performance of the unmanned aerial vehicle cluster forming the formation, ensure the task completion efficiency and the success rate, and provide an unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system.
In order to solve the problem, the technical scheme adopted by the invention is as follows:
an assessment method for vulnerability of formation of unmanned aerial vehicle cluster comprises the following steps:
step 1: acquiring information of unmanned aerial vehicle clusters, including the initial number, formation mode, spatial position and communication topological structure of the unmanned aerial vehicle clusters;
step 2: constructing an improved control model of the unmanned aerial vehicle cluster according to the acquired information of the unmanned aerial vehicle cluster;
wherein:
wherein x isi(t) and vi(t) the position and speed of the ith unmanned aerial vehicle at the moment t respectively, a constant alpha is used for measuring the interaction size among particles in the groupij(x) The information exchange weight between the unmanned aerial vehicle individuals is dependent on the relative position between the unmanned aerial vehicle individual i and the unmanned aerial vehicle individual j, and is used for describing the interaction strength between the two unmanned aerial vehicles. bijDenotes the repulsive force coefficient between drones during cluster evolution, C denotes a distance constant, and when the distance between drones is less than this value, there is a repulsive force between them. N represents the number of unmanned planes, | | · | | represents the 2 norm of the vector, and ε is aNormal number, usually less than 0.5, F (x)i) Is formation driving force, beta and gamma are respectively repulsive force and destination driving force, theta is the direction of an included angle bisector between the individual speed direction of the unmanned aerial vehicle and the destination direction, and the value is [0, pi ]]Beta and gamma are constants representing the degree of dependence of the system on these two terms, and are usually taken separately
And 1.
And step 3: combining the improved control model of the unmanned aerial vehicle cluster of the step 2, and driving force F (x) according to the formationi) The specific form of (1) constructing a queue evaluation function of the cluster:
where Exp denotes a power function with the natural number e at the base,
representing the ideal position of the ith unmanned aerial vehicle at the moment t, wherein the ideal position is formed by models (2) to (4) according to the formation mode of the cluster
Determining; the formula is only suitable for the situation that the unmanned aerial vehicle cluster only has one communication connected subset, and the communication connected subset is the whole cluster, and if the cluster has a plurality of communication connected subsets, the formation evaluation f of the whole cluster is calculated by the following formula (6):
where n denotes the number of connected subsets, kiRepresenting the proportion of the number of i-th connected subset unmanned aerial vehicles to the total number of unmanned aerial vehicles, fiAnd (3) representing the queue shape evaluation of the ith connected subset of the cluster, and calculating by formula (5), wherein N in formula (5) is equal to the number of the unmanned aerial vehicles in the connected subset. The communication connectivity subset is composed of none capable of mutual communicationA collection of human-machine components, including,
and 4, step 4: calculating the vulnerability of the cluster formation to interference according to the acquired information through the following formula;
Rf＝(fh-fmin)(fh-fl) (7)
wherein f ishEvaluation value of formation of cluster before interference, fminMinimum value representing the evaluation of the formation of the cluster after the cluster has been disturbed, flEvaluation value of formation after cluster is stabilized by disturbance, fh、fmin、flThe three parameters are calculated by formula (5) or (6) according to whether the cluster has the connected subset.
Considering the case that the formation mode of the cluster of drones is circular, the formation driving force function in step 2 is:
wherein R is the convergence radius of the circular formation,
the communication connectivity subset is a set of unmanned aerial vehicles capable of communicating with each other.
Similarly, when the drone cluster formation pattern is circular, the evaluation function is:
the invention also provides a system for evaluating the formation vulnerability according to the unmanned aerial vehicle cluster formation vulnerability evaluation method, which comprises the following modules:
a cluster information acquisition module: acquiring information of unmanned aerial vehicle clusters through a radar, wherein the information comprises the initial number, the formation mode, the spatial position and the communication topological structure of the unmanned aerial vehicle clusters;
a cluster control module: constructing a control model of the unmanned aerial vehicle cluster through the unmanned aerial vehicle cluster information acquired by the cluster information acquisition module;
a cluster formation evaluation module: evaluating the cluster formation according to the unmanned aerial vehicle cluster control model output by the cluster control module, and outputting an evaluation value;
the formation vulnerability degree calculation module: and aiming at different formation interference, obtaining space position information and a communication topological structure of the cluster through a cluster information acquisition module, calculating formation evaluation values under different interference, and then calculating and outputting the vulnerability of the cluster.
The invention also provides a method for evaluating the performance vulnerability by adopting the unmanned aerial vehicle cluster formation vulnerability evaluation method, which comprises the following steps:
s1: constructing a vulnerability analysis factor of a communication connection subset according to a formation evaluation function (5):
wi＝ckifi (10)
k is the proportion of the number of the unmanned aerial vehicles in the communication network connected subset to the number of the unmanned aerial vehicles in the unmanned aerial vehicle cluster, and c is a constant and represents the cluster influence strength of the analysis factor;
s2: constructing a performance function of a connected subset of the cluster according to the vulnerability analysis factor;
wherein eta is the performance intensity of the cluster, and the overall change of the cluster performance is controlled; the performance function of the cluster as a whole can then be expressed as:
wherein n is*Indicating the number of connected subsets.
S3: calculating the performance vulnerability of the unmanned plane cluster to interference by the following formula (13):
Rp＝(ph-pmin)(ph-pl) (13)
wherein: p is a radical ofhRepresenting the cluster performance value, p, before interferenceminRepresenting the minimum value of the cluster performance value, p, after the cluster has been disturbedlIndicating the performance value after the cluster has stabilized from interference. p is a radical ofh、pmin、plThe three parameters are calculated by equation (12).
The invention also provides a system for evaluating the performance vulnerability by using the unmanned aerial vehicle cluster performance vulnerability evaluation method, which comprises the following modules:
a cluster information acquisition module: acquiring information of unmanned aerial vehicle clusters through a radar, wherein the information comprises the initial number, the formation mode, the spatial position and the communication topological structure of the unmanned aerial vehicle clusters;
a cluster control module: constructing a control model of the unmanned aerial vehicle cluster through the unmanned aerial vehicle cluster information acquired by the cluster information acquisition module;
a cluster formation evaluation module: evaluating the cluster formation according to the unmanned aerial vehicle cluster control model output by the cluster control module, and outputting an evaluation value;
a cluster performance calculation module: calculating a cluster performance vulnerability analysis factor according to the queue evaluation value output by the cluster queue evaluation module, and then calculating a cluster performance value according to a cluster performance function;
a cluster performance vulnerability calculation module: inputting different interferences, acquiring the spatial position and the communication topological structure of a cluster through a cluster information acquisition module, calculating the performance vulnerability degree under different interferences according to a vulnerability degree calculation function through the cluster performance values calculated by the modules, and outputting the performance vulnerability degree.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system, wherein an improved unmanned aerial vehicle cluster system dynamics model is established based on a classical CS model by obtaining formation information and a communication topological structure of an unmanned aerial vehicle cluster, a formation assessment function and a performance assessment function are established according to a specific form of formation driving force, then the unmanned aerial vehicle cluster formation and performance vulnerability are calculated by giving different interferences, and further the vulnerability assessment of the cluster is obtained.
Drawings
FIG. 1 is a diagram of a cluster formation vulnerability assessment system;
FIG. 2 is a diagram of a cluster performance vulnerability assessment system;
FIG. 3 is a communication topology network of an example cluster of drones;
fig. 4 shows the formation evaluation of the unmanned aerial vehicle cluster under the condition that different numbers of backbone nodes fail;
FIG. 5 shows unmanned aerial vehicle cluster performance evaluation under the condition that different numbers of backbone nodes fail;
fig. 6 shows formation evaluation of a cluster of unmanned aerial vehicles in the case of failure of backbone nodes at different positions;
fig. 7 shows the performance evaluation of the unmanned aerial vehicle cluster under the condition that backbone nodes at different positions fail.
Detailed Description
Fig. 1 to 7 show a specific embodiment of the method for evaluating formation and performance vulnerability of a drone cluster according to the present invention. This embodiment explains with unmanned aerial vehicle cluster formation mode as circular, specifically includes the following steps:
an assessment method for vulnerability of formation of unmanned aerial vehicle cluster comprises the following steps:
step 1: acquiring information of unmanned aerial vehicle clusters, including the initial number, formation mode, spatial position and communication topological structure of the unmanned aerial vehicle clusters;
step 2: constructing a control model of the unmanned aerial vehicle cluster according to the acquired information of the unmanned aerial vehicle cluster;
wherein:
wherein x isi(t) and vi(t) the position and speed of the ith unmanned aerial vehicle at the moment t respectively, a constant alpha is used for measuring the interaction size among particles in the groupij(x) The information exchange weight between the unmanned aerial vehicle individuals is dependent on the relative position between the unmanned aerial vehicle individual i and the unmanned aerial vehicle individual j, and is used for describing the interaction strength between the two unmanned aerial vehicles. bijDenotes the repulsive force coefficient between drones during cluster evolution, C denotes a distance constant, and when the distance between drones is less than this value, there is a repulsive force between them. N represents the number of unmanned planes, | | · | | represents a 2 norm of a vector, epsilon is a normal number, and the value is usually less than 0.5, F (x)i) Is formation driving force, beta and gamma are respectively repulsive force and destination driving force, theta is the direction of an included angle bisector between the individual speed direction of the unmanned aerial vehicle and the destination direction, and the value is [0, pi ]]Beta and gamma are constants representing the degree of dependence of the system on these two terms, and are usually taken separately
And (1) a reaction product of (A) and (B),
the geometric center of the unmanned aerial vehicle individuals in the same communication connected subset, wherein the communication connected subset is a set formed by unmanned aerial vehicles capable of communicating with each other, and when only one communication connected subset exists in the clusterThe whole cluster is formed into a team; when there are a plurality of disconnected subsets of communication connections after a cluster is disturbed, each subset forms a formation, so that here there is a formation
The geometric centers of the individuals of the unmanned aerial vehicles described as the same communication connected subset are not only coordinated when information is communicated, but the driving force is determined according to the geometric center of the connected subset.
And step 3: combining the improved control model of the unmanned plane cluster of step 2, according to F (x)i) The specific form of (1) constructing a cluster formation evaluation function:
where Exp denotes a power function with a natural number e as the base,
representing the ideal position of the ith unmanned aerial vehicle at the moment t, wherein the ideal position is controlled by cluster control models (2) - (4) according to the formation mode of the cluster
Time is determined;
in this embodiment, as shown in fig. 3, when the formation mode of the cluster of drones is circular, the evaluation function of the circular formation is used:
wherein Abs represents an absolute value; of course, the evaluation can also be carried out by using the formula (5), and only the formula (9) is used with strong pertinence and higher accuracy.
When the cluster has a plurality of connected subsets, the overall formation evaluation f of the cluster is calculated by the following formula (6):
where n denotes the number of connected subsets, kiRepresenting the proportion of connected subsets, fiAnd (3) representing the formation evaluation of a connected subset, and calculating by the formula (9), wherein N in (9) represents the number of the unmanned aerial vehicles in the connected subset.
And 4, step 4: calculating the vulnerability of the cluster formation to interference through the following formula;
Rf＝(fh-fmin)(fh-fl) (7)
wherein f ishEvaluation value of formation of cluster before interference, fminMinimum value representing the evaluation of the formation of the cluster after the cluster has been disturbed, flRepresenting the formation evaluation value after the cluster is stabilized by interference, fmin、flThe value of (c) is an evaluation value calculated using the formula (5) or the formula (6) according to the spatial position and communication topology information of the cluster after the cluster is interfered, since the present embodiment uses a circular formation, f in the present embodiment ismin、flThe calculation was performed using the equations (9) and (6).
This example also addresses the assessment of performance vulnerability of an unmanned aerial vehicle cluster, including the steps of:
s1: constructing a vulnerability analysis factor of a communication connected subset of the cluster according to a formation evaluation function:
wi＝ckifi (10)
wherein k isiThe number of the unmanned aerial vehicles in the communication network connected subset accounts for the proportion of the number of the unmanned aerial vehicles in the unmanned aerial vehicle cluster, and k is the number of the unmanned aerial vehicles in only one connected subsetiC is a constant to represent the cluster influence strength, and c is 1 in this embodiment, but may be adjusted according to data statistics or the rationality of the final vulnerability assessment result;
s2: constructing a performance function of a connected subset of the cluster according to the vulnerability analysis factor;
wherein eta is the performance intensity of the cluster, and the overall change of the cluster performance is controlled; the performance of the cluster as a whole is calculated by:
s3: constructing a vulnerability function of interference on unmanned aerial vehicle cluster performance according to the formation evaluation function;
Rp＝(ph-pmin)(ph-pl) (13)
wherein: p is a radical ofhRepresenting the cluster performance value, p, before interferenceminRepresenting the minimum value of the cluster performance value, p, after the cluster has been disturbedlIndicating the performance value after the cluster has stabilized from interference. p is a radical ofh、pmin、plThe three parameters are calculated from the cluster information after the cluster is interfered by using the formula (12).
The invention also provides a system for evaluating the formation vulnerability according to the unmanned aerial vehicle cluster formation vulnerability evaluation method, which comprises the following modules as shown in figure 1:
a cluster information acquisition module: acquiring information of unmanned aerial vehicle clusters through a radar, wherein the information comprises the initial number, the formation mode, the spatial position and the communication topological structure of the unmanned aerial vehicle clusters;
a cluster control module: constructing a control model of the unmanned aerial vehicle cluster through the unmanned aerial vehicle cluster information acquired by the cluster information acquisition module;
a cluster formation evaluation module: evaluating the cluster formation according to the unmanned aerial vehicle cluster control model output by the cluster control module, and outputting an evaluation value;
the formation vulnerability degree calculation module: and aiming at different formation interference, obtaining space position information and a communication topological structure of the cluster through a cluster information acquisition module, calculating formation evaluation values under different interference, and then calculating and outputting the vulnerability of the cluster.
The invention also provides a system for evaluating the performance vulnerability by using the unmanned aerial vehicle cluster performance vulnerability evaluation method, as shown in fig. 2, the system comprises the following modules:
a cluster information acquisition module: acquiring information of unmanned aerial vehicle clusters through a radar, wherein the information comprises the initial number, the formation mode, the spatial position and the communication topological structure of the unmanned aerial vehicle clusters;
a cluster control module: constructing a control model of the unmanned aerial vehicle cluster through the unmanned aerial vehicle cluster information acquired by the cluster information acquisition module;
a cluster formation evaluation module: evaluating the cluster formation according to the unmanned aerial vehicle cluster control model output by the cluster control module, and outputting an evaluation value;
a cluster performance calculation module: calculating a cluster performance vulnerability analysis factor according to the queue evaluation value output by the cluster queue evaluation module, and then calculating a cluster performance value according to a cluster performance function;
a cluster performance vulnerability calculation module: inputting different interferences, acquiring the spatial position and the communication topological structure of a cluster through a cluster information acquisition module, calculating the performance vulnerability degree under different interferences according to a vulnerability degree calculation function through the cluster performance values calculated by the modules, and outputting the performance vulnerability degree.
The interference of 2 backbones, 4 backbones and 6 backbone node failures is implemented on the unmanned aerial vehicle cluster, and the information of the space position and the communication network communication subset number of the cluster after the cluster is interfered is obtained through equipment such as a radar. Then, the formation vulnerability and the performance vulnerability under different interference conditions are calculated by the method and the system, and compared with the non-interference, i.e. 0 backbone failures, and the results are shown in table 1. Fig. 4 and 5 show the queue evaluation and performance evaluation curves of the unmanned aerial vehicle cluster under the condition that different numbers of backbone nodes fail.
TABLE 1 Cluster formation and Performance vulnerability values after failure of different numbers of backbone nodes
RfAnd RpA larger value indicates that the cluster is more vulnerable to this interference. The more the failure number of the backbone nodes is, the worse the formation and performance of the unmanned aerial vehicle cluster is. The less likely the task will be completed.
The unmanned aerial vehicle cluster formation and performance evaluation conditions are calculated by implementing the interference of the failure of backbone nodes at different positions of 2-phase connection, 2-phase dispersion, 4-phase connection, 4-phase dispersion and the like on the unmanned aerial vehicle cluster, and the information of the space position and the communication network connected subset number of the cluster after the cluster is interfered is obtained through equipment such as a radar and the like. Then, the formation vulnerability and the performance vulnerability under different interference conditions are calculated by the above method, and compared with 0 backbone failures without interference, and the results are shown in table 2. Fig. 6 and 7 show graphs of formation evaluation and performance evaluation of the unmanned aerial vehicle cluster under the condition that backbone nodes at different positions fail.
TABLE 2 vulnerability values of backbone node failures at different locations to interfering cluster formation and performance
As can be seen from tables 1 and 2, as the number of failed backbone nodes increases and the distribution is relatively large, the larger the vulnerability value of the cluster formation and performance, the stronger the response of the cluster to such interference, and the larger the influence of the interference. Meanwhile, it is also stated that the backbone nodes are the vulnerabilities of the cluster, so that the increase of the anti-interference capability of the backbone nodes should be considered during the design of the cluster, and the cluster is more favorable for completing the established tasks.
As can be seen from fig. 4 and 5, as the number of failed backbone drones increases, the formation evaluation and performance of the cluster decreases dramatically, indicating that the communication backbone has a significant impact on the performance of the drone cluster. As can be seen from fig. 6 and 7, when the failed communication backbones of the drones are more dispersed, the formation evaluation and performance of the clusters are more degraded, which indicates that the drone cluster in this mode has a weaker capability to resist multi-party interference. In general, this communication mode is vulnerable, and the success of the task has a strong dependence on the interference rejection capability of the communication backbone.
According to the method and the system for evaluating the formation and the performance vulnerability of the unmanned aerial vehicle cluster, the formation information and the communication topological structure or the communication sub-set structure of the unmanned aerial vehicle cluster are obtained, the formation evaluation function and the performance evaluation function are used according to an improved unmanned aerial vehicle cluster control model, the formation and the performance vulnerability of the unmanned aerial vehicle cluster under different interference conditions are calculated, and the reaction conditions of the cluster to different interferences are reflected through quantification, so that the cluster nodes are improved in a targeted manner during cluster design, and the task completion efficiency of the unmanned aerial vehicle cluster is improved.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (6)
1. An assessment method for vulnerability of formation of unmanned aerial vehicle cluster is characterized in that: the method comprises the following steps:
step 1: acquiring information of unmanned aerial vehicle clusters, including the initial number, formation mode, spatial position and communication topological structure of the unmanned aerial vehicle clusters;
step 2: constructing an improved control model of the unmanned aerial vehicle cluster according to the acquired information of the unmanned aerial vehicle cluster;
wherein
Wherein x isi(t) and vi(t) the position and speed of the ith unmanned aerial vehicle at the moment t respectively, a constant alpha is used for measuring the interaction size among particles in the groupij(x) The information exchange weight between the unmanned aerial vehicles is dependent on the relative position between the unmanned aerial vehicle individual i and the unmanned aerial vehicle individual j, the information exchange weight is used for describing the interaction strength between the two unmanned aerial vehicles, N represents the number of the unmanned aerial vehicles, b represents the number of the unmanned aerial vehicles, andijthe coefficient of repulsion force between unmanned aerial vehicles in the cluster evolution process is represented, C represents a distance constant, when the distance between the unmanned aerial vehicles is smaller than the value, the repulsion force exists between the unmanned aerial vehicles, | | | - | represents the 2 norm of the vector, epsilon is a positive constant, the value is usually smaller than 0.5, and F (x) is obtainedi) Is formation driving force, beta and gamma are respectively repulsive force and destination driving force, theta is the direction of an included angle bisector between the individual speed direction of the unmanned aerial vehicle and the destination direction, and the value is [0, pi ]]Beta and gamma are constants representing the degree of dependence of the system on these two terms, and are usually taken separately
And 1;
and step 3: control model and formation driving force F (x) of unmanned aerial vehicle cluster improved in step 2i) And constructing a formation evaluation function of the cluster:
when the cluster has only one communication connected subset, the queue evaluation function of the cluster is as follows:
where Exp denotes a power function with a natural number e as the base,
representing the ideal position of the ith unmanned aerial vehicle at the moment t, wherein the ideal position is formed by models (2) to (4) according to the formation mode of the cluster
Time is determined;
when the cluster has a plurality of communication connected subsets, and the communication connected subsets are sets formed by unmanned planes capable of communicating with each other, a formation evaluation function f of the whole cluster is calculated by the following formula (6):
where n denotes the number of connected subsets, kiRepresenting the proportion of the number of i-th connected subset unmanned aerial vehicles to the total number of unmanned aerial vehicles, fiRepresenting the queue form evaluation of the ith connected subset of the cluster, and calculating by (5), wherein N in (5) is equal to the number of the unmanned aerial vehicles in the connected subset;
and 4, step 4: calculating the vulnerability of the unmanned aerial vehicle cluster formation to interference according to the formation evaluation function;
Rf＝(fh-fmin)(fh-fl) (7)
fhevaluation value of formation of cluster before interference, fminMinimum value representing the evaluation of the formation of the cluster after the cluster has been disturbed, flRepresenting the formation evaluation value after the cluster is stabilized by interference, fh、fmin、flThe three parameters are calculated by formula (5) or formula (6) in step 3 according to whether the cluster has the connected subset.
2. The method of claim 1, wherein the method comprises: when the unmanned aerial vehicle cluster formation mode is circular, the formation driving force function in step 2 is as follows:
wherein R is the convergence radius of the circular formation,
the geometric centers of the individuals of the unmanned planes in the same communication connected subset.
3. The method of claim 2, wherein the method comprises: when the drone cluster formation mode is circular, the function is evaluated:
where Abs () represents an absolute value function.
4. An unmanned aerial vehicle cluster formation vulnerability assessment system for implementing the unmanned aerial vehicle cluster formation vulnerability assessment method of any one of claims 1-3, characterized in that: the system comprises the following modules:
a cluster information acquisition module: acquiring information of unmanned aerial vehicle clusters through a radar, wherein the information comprises the initial number, the formation mode, the spatial position and the communication topological structure of the unmanned aerial vehicle clusters;
a cluster control module: constructing a control model of the unmanned aerial vehicle cluster through the unmanned aerial vehicle cluster information acquired by the cluster information acquisition module;
a cluster formation evaluation module: evaluating the cluster formation according to the unmanned aerial vehicle cluster control model output by the cluster control module, and outputting an evaluation value;
the formation vulnerability degree calculation module: and aiming at different formation interference, obtaining space position information and a communication topological structure of the cluster through a cluster information acquisition module, calculating formation evaluation values under different interference, and then calculating and outputting the vulnerability of the cluster.
5. An unmanned aerial vehicle cluster formation performance vulnerability assessment method, which adopts the unmanned aerial vehicle cluster formation vulnerability assessment method of any one of claims 1-3, and is characterized in that: the method comprises the following steps:
s1: and (3) constructing a performance vulnerability analysis factor of a communication connected subset of the cluster according to a formation evaluation function:
wi＝ckifi (10)
wherein k isiC is a constant and represents the cluster influence strength of the analysis factor, wherein the proportion of the number of the unmanned aerial vehicles in the communication connected subset to the number of the unmanned aerial vehicles in the unmanned aerial vehicle cluster is shown;
s2: constructing a performance function of a connected subset according to the performance vulnerability analysis factor;
wherein eta is the performance intensity of the cluster, and the overall change of the cluster performance is controlled;
and the performance of the cluster as a whole is calculated by the following formula:
s3: calculating the performance vulnerability of the unmanned aerial vehicle cluster to interference through a formula (13);
Rp＝(ph-pmin)(ph-pl) (13)
wherein: p is a radical ofhRepresenting the cluster performance value, p, before interferenceminRepresenting the minimum value of the cluster performance value, p, after the cluster has been disturbedlRepresenting the performance value, p, of the cluster after it has stabilized against interferenceh、pmin、plThe three parameters are calculated by the formula (12).
6. An unmanned aerial vehicle cluster formation performance vulnerability assessment system for implementing the unmanned aerial vehicle cluster formation performance vulnerability assessment method of claim 5, comprising the following modules:
a cluster information acquisition module: acquiring information of unmanned aerial vehicle clusters through a radar, wherein the information comprises the initial number, the formation mode, the spatial position and the communication topological structure of the unmanned aerial vehicle clusters;
a cluster control module: constructing a control model of the unmanned aerial vehicle cluster through the unmanned aerial vehicle cluster information acquired by the cluster information acquisition module;
a cluster formation evaluation module: evaluating the cluster formation according to the unmanned aerial vehicle cluster control model output by the cluster control module, and outputting an evaluation value;
a cluster performance calculation module: calculating a cluster performance vulnerability analysis factor according to the queue evaluation value output by the cluster queue evaluation module, and then calculating a cluster performance value according to a cluster performance function;
a cluster performance vulnerability calculation module: inputting different interferences, acquiring the spatial position and the communication topological structure of a cluster through a cluster information acquisition module, calculating the performance vulnerability degree under different interferences according to a vulnerability degree calculation function through the cluster performance values calculated by the modules, and outputting the performance vulnerability degree.
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Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110162093B (en) * | 2019-06-13 | 2022-02-11 | 中国人民解放军军事科学院国防科技创新研究院 | High-adaptability formation control method based on geometric similarity design |
| CN110661667B (en) * | 2019-09-29 | 2020-07-03 | 北京航空航天大学 | Task reliability analysis method and system for unmanned aerial vehicle swarm |
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| CN112801160B (en) * | 2021-01-22 | 2022-09-06 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle formation vulnerability analysis method and system based on honeycomb graph model |
| CN113645143B (en) * | 2021-08-06 | 2023-11-21 | 浙江大学 | Optimization method and device for air trunking communication network |
| CN113625697B (en) * | 2021-09-15 | 2022-03-15 | 北京航空航天大学 | Unmanned aerial vehicle cluster reliability assessment method and system considering task capability change |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2161297A1 (en) * | 2008-09-08 | 2010-03-10 | Shin-Etsu Chemical Co., Ltd. | Method for preparing a primer composition and coated product |
| CN103995539A (en) * | 2014-05-15 | 2014-08-20 | 北京航空航天大学 | Unmanned aerial vehicle autonomous formation evaluation index and MPC formation control method |
| RU2583234C1 (en) * | 2014-12-31 | 2016-05-10 | Открытое акционерное общество "Государственный научно-исследовательский навигационно-гидрографический институт" (ОАО "ГНИНГИ") | System for illuminating ice conditions and preventing effect of ice formations on sea objects of business operations |
| CN106656300A (en) * | 2016-12-21 | 2017-05-10 | 中国航天时代电子公司 | UAV cluster combat system utilizing ad-hoc network data chain |
| CN106843269A (en) * | 2017-01-22 | 2017-06-13 | 北京航空航天大学 | A kind of unmanned plane formation method based on small birds cluster fly mechanics |
| CN107728643A (en) * | 2017-11-10 | 2018-02-23 | 西安电子科技大学 | A kind of unmanned aerial vehicle group distributed task dispatching method under dynamic environment |
| GB2559291A (en) * | 2016-01-25 | 2018-08-01 | Above Surveying Ltd | UVAs for detecting defects in solar panel arrays |
| CN108680063A (en) * | 2018-05-23 | 2018-10-19 | 南京航空航天大学 | A kind of decision-making technique for the dynamic confrontation of extensive unmanned plane cluster |
| CN108959072A (en) * | 2018-06-14 | 2018-12-07 | 北京航空航天大学 | A kind of group system elasticity assessment method based on complex network |
| CN109104737A (en) * | 2018-07-31 | 2018-12-28 | 北京航空航天大学 | A kind of cluster antagonism appraisal procedure based on time-varying network |
| CN109254592A (en) * | 2018-09-17 | 2019-01-22 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle formation change control method and system |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8186589B2 (en) * | 2006-01-11 | 2012-05-29 | Carmel-Haifa University Economic Corporation Ltd. | UAV decision and control system |
| US9679490B2 (en) * | 2015-08-04 | 2017-06-13 | Qualcomm Incorporated | Opportunistic unmanned autonomous vehicle energy harvesting |
| US9990854B1 (en) * | 2016-03-15 | 2018-06-05 | Rockwell Collins, Inc. | Unmanned aerial system mission flight representation conversion techniques and traffic management scheme |
| CN105871636B (en) * | 2016-05-27 | 2017-05-03 | 合肥工业大学 | Reconstruction method and system for unmanned-aerial-vehicle formation communication topology based on minimum arborescence |
| CN108170162B (en) * | 2017-12-30 | 2022-03-25 | 天津职业技术师范大学 | Performance evaluation method for multi-scale wind disturbance analysis unmanned aerial vehicle cluster coordination control system |
| CN108768745B (en) * | 2018-06-14 | 2021-08-20 | 北京航空航天大学 | Complex network-based cluster system brittleness evaluation method |
-
2019
- 2019-01-25 CN CN201910077285.1A patent/CN109582040B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2161297A1 (en) * | 2008-09-08 | 2010-03-10 | Shin-Etsu Chemical Co., Ltd. | Method for preparing a primer composition and coated product |
| CN103995539A (en) * | 2014-05-15 | 2014-08-20 | 北京航空航天大学 | Unmanned aerial vehicle autonomous formation evaluation index and MPC formation control method |
| RU2583234C1 (en) * | 2014-12-31 | 2016-05-10 | Открытое акционерное общество "Государственный научно-исследовательский навигационно-гидрографический институт" (ОАО "ГНИНГИ") | System for illuminating ice conditions and preventing effect of ice formations on sea objects of business operations |
| GB2559291A (en) * | 2016-01-25 | 2018-08-01 | Above Surveying Ltd | UVAs for detecting defects in solar panel arrays |
| CN106656300A (en) * | 2016-12-21 | 2017-05-10 | 中国航天时代电子公司 | UAV cluster combat system utilizing ad-hoc network data chain |
| CN106843269A (en) * | 2017-01-22 | 2017-06-13 | 北京航空航天大学 | A kind of unmanned plane formation method based on small birds cluster fly mechanics |
| CN107728643A (en) * | 2017-11-10 | 2018-02-23 | 西安电子科技大学 | A kind of unmanned aerial vehicle group distributed task dispatching method under dynamic environment |
| CN108680063A (en) * | 2018-05-23 | 2018-10-19 | 南京航空航天大学 | A kind of decision-making technique for the dynamic confrontation of extensive unmanned plane cluster |
| CN108959072A (en) * | 2018-06-14 | 2018-12-07 | 北京航空航天大学 | A kind of group system elasticity assessment method based on complex network |
| CN109104737A (en) * | 2018-07-31 | 2018-12-28 | 北京航空航天大学 | A kind of cluster antagonism appraisal procedure based on time-varying network |
| CN109254592A (en) * | 2018-09-17 | 2019-01-22 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle formation change control method and system |
Non-Patent Citations (4)
| Title |
|---|
| Receding horizon control for multiple UAV formation flight based on modified brain storm optimization;Huaxin Qiu 等;《Nonlinear Dynamics》;20140722;1-16 * |
| Wilson O. Cauesada 等.Leader-Follower Formation for UAV Robot Swarm Based on Fuzzy Logic Theory.《Springer International Publishing;AG, part of Springer Nature》.2018, * |
| 基于QABC-IFMADM算法的有人/无人机编队作战威胁评估;韩博文 等;《电子学报》;20180731;第46卷(第7期);1584-1592 * |
| 多无人机协同航迹规划及效能评估方法研究;吴静;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20130115(第1期);C031-33 * |
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