CN112929205A - Swarm unmanned aerial vehicle fault propagation method based on cellular automaton - Google Patents

Swarm unmanned aerial vehicle fault propagation method based on cellular automaton Download PDF

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CN112929205A
CN112929205A CN202110088613.5A CN202110088613A CN112929205A CN 112929205 A CN112929205 A CN 112929205A CN 202110088613 A CN202110088613 A CN 202110088613A CN 112929205 A CN112929205 A CN 112929205A
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程月华
汪志军
姜斌
杨浩
陈严波
薛琪
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a swarm unmanned aerial vehicle fault propagation method based on a cellular automaton, and belongs to the technical field of calculation, calculation or counting. The method utilizes the cellular automata to describe fault propagation caused by malicious attack on the swarm unmanned aerial vehicle. According to the characteristics of the structure and dynamics of the swarm unmanned aerial vehicle, the cellular automata model is expanded, and then the cellular automata model is built. And circularly updating the states of all the cells until the conditions are met and exiting the circulation. By the method, the problem of difficulty in fault propagation modeling of the swarm unmanned aerial vehicle is solved, faults in the swarm unmanned aerial vehicle can be effectively described, and a theoretical basis is provided for effectively avoiding damage of the faults to the swarm unmanned aerial vehicle system.

Description

Swarm unmanned aerial vehicle fault propagation method based on cellular automaton
Technical Field
The invention relates to a fault propagation technology, in particular to a swarm unmanned aerial vehicle fault propagation method based on a cellular automaton, and belongs to the technical field of calculation, calculation or counting.
Background
With the increasing complexity of unmanned aerial vehicle combat environments and the increasing diversification of executed tasks, because the platform load of a single unmanned aerial vehicle is relatively small and the information processing capacity is relatively weak, multiple unmanned aerial vehicles are required to cooperatively execute tasks in a swarm manner to expand the task capacity and improve the execution efficiency. The possibility that the faults occur can be increased when the swarm unmanned aerial vehicle collectively executes the task, so that the fault propagation mode of the swarm unmanned aerial vehicle needs to be researched, and a theoretical basis is provided for effectively avoiding the influence of the faults on the whole swarm.
The existing swarm unmanned aerial vehicle research mainly focuses on control, the research on faults is limited to the faults of actuators or sensors of single individuals in the unmanned aerial vehicle formation, and the swarm unmanned aerial vehicle and the unmanned aerial vehicle formation are different in nature. Firstly, the topology of the swarm unmanned aerial vehicle is characterized by looseness, coupling and time variation, and the topology of the unmanned aerial vehicle formation is preset and does not change along with time, so that the swarm unmanned aerial vehicle has greater flexibility. Secondly, the quantity of swarm unmanned aerial vehicle is more than the unmanned aerial vehicle formation, through distributing on a large scale, possesses stronger situation perception, discernment and survivability.
Because bee colony unmanned aerial vehicle possesses stronger survivability than the unmanned aerial vehicle formation, so the influence of the form that traditional single individual takes place executor or sensor trouble is very little to bee colony unmanned aerial vehicle, can not cause very big harm. Referring to the malicious attack form in the field of multi-agent, the failure form considered by the invention is external malicious attack, which can cause the change of a part of individual controllers in a swarm, thereby affecting the states of the swarm and neighbors thereof and causing great harm to the swarm.
At present, the research on fault propagation mainly focuses on network systems, chemical engineering, power systems, electronic circuits and the like, and mainly comprises fault propagation models based on graph theory, Petri network and the like. The fault propagation model based on the graph theory is used for representing the structure of the system by a graph and analyzing a fault propagation mechanism by combining various algorithms, such as a fault propagation directed graph and a fault symbol directed graph, but the actual system has high complexity and density, and the conversion into the directed graph is very complicated and has large workload, so the graph theory is only suitable for fault propagation analysis of a simple system; the fault propagation model based on the Petri network can be divided into two categories, namely a fault analysis method based on knowledge and a fault analysis method based on an object model, if the system is too complex, nodes in the Petri network are too many, and the model analysis is difficult.
The cellular automata has excellent performance in simulating a complex system, a synchronous parallel process and nonlinear science, does not need to establish and solve a complex partial differential equation, has a simple structure, is easy to calculate, and is often applied to some dynamic systems generated by space coupling action and time causal relationship, such as infectious disease transmission, traffic flow evolution and Internet fault transmission. The method considers the complexity of the structural network of the swarm unmanned aerial vehicle system and the coupling between individuals, and aims to analyze the fault propagation model of the swarm unmanned aerial vehicle by adopting a cellular automaton method.
Disclosure of Invention
The invention aims to provide a fault propagation method of a swarm unmanned aerial vehicle based on a cellular automaton, which aims to overcome the defects of the background technology, and comprises the steps of establishing a cellular model according to the communication topology of the swarm unmanned aerial vehicle, injecting a fault into the cellular model to simulate the swarm unmanned aerial vehicle to be maliciously attacked, calculating the real-time state of each cell by evaluating the damage degree of the injection fault to the speed and relative distance consistency of the swarm unmanned aerial vehicle, further obtaining the state feedback of adjacent cells to the injection fault through the communication topology, and obtaining the fault propagation rule of the swarm unmanned aerial vehicle system by carrying out statistical analysis on the state feedback of each cell, so that the influence of the fault on the swarm unmanned aerial vehicle system is conveniently analyzed, thereby timely taking effective measures to avoid the damage of the fault to the swarm unmanned aerial vehicle system, and solving the technical problem of fault propagation simulation of the.
The invention adopts the following technical scheme for realizing the aim of the invention:
swarm unmanned aerial vehicle model and malicious attack form analysis
The swarm drone system dynamics employs a second order model as shown below:
Figure BDA0002911892470000021
wherein q isi、pi∈R3Position and velocity vectors of the individual i, respectively, and control law u thereofiAdopting the form of bee-brood control:
Figure BDA0002911892470000022
the 1 st item and the 2 nd item in the control law are respectively position and speed feedback of the ith individual and the jth individual in the neighborhood of the ith individual, wherein N isiSet of neighbors for individual i, nijThe weight of the individual j on the individual i position feedback influence,
Figure BDA0002911892470000023
let z be qj-qi. Defining a non-negative mapping, σ being a norm, which is mathematically defined as:
Figure BDA0002911892470000024
ε is a constant greater than 0. a isij(q) is an element in the swarm drone system communication topology adjacency matrix, which is defined as:
Figure BDA0002911892470000031
in the formula, rαρ h is a smooth function between 0 and 1 for the communication distance between individuals:
Figure BDA0002911892470000032
wherein the threshold h ∈ (0, 1). The elements a of the adjacency matrix when the relative distance between individuals varies continuouslyijIn [0,1 ]]Interval continuously changing, individuali neighbor set NiAlso changing dynamically.
The bee-hive control potential energy function:
Φ(z)＝ρh(||z||σ/rα)φ(||z||σ-dα)，
Figure BDA0002911892470000033
wherein the content of the first and second substances,
Figure BDA0002911892470000034
a is more than 0 and less than or equal to b,
Figure BDA0002911892470000035
so as to satisfy phi (0) being 0, dαThe relative distance between individuals is desired.
Different from unmanned aerial vehicle formation, the swarm unmanned aerial vehicle system considered by the invention has no fixed formation constraint and neighbor number, individuals are in a loose and coupled distribution state in space, and each individual interacts with other individuals of which the relative distance is smaller than the perception distance. The overall consistency is realized through local information interaction between the swarm unmanned aerial vehicles, the speed is consistent, and the relative distance is kept unchanged. The fault form of the invention is considered from the level of a swarm unmanned aerial vehicle system, and is different from the faults of the existing single individual actuator and sensor, for example, when an unmanned aerial vehicle cluster executes a battle task, the unmanned aerial vehicle cluster may be attacked maliciously by electromagnetic interference of an enemy and the like aiming at the unmanned aerial vehicle sensor. When the swarm unmanned aerial vehicle is attacked maliciously, the attacked individual sensor breaks down, the state measurement of the sensor per se is wrong, and the controller is led to follow the swarm controller uiSwitching to attacked unmanned aerial vehicle self controller uf。
By uiThe control of the individual i is not only influenced by the state of the individual i, but also related to the information of the neighbors. Controller u after a fault compared to a normal controllerfRegardless of the information of the neighbors, the control form can lead the fault individual to deviate from the goal of the consistency of the bee colony and influence the fault individualAnd the neighbor individuals enable the fault to be propagated in the bee colony. Because the swarm unmanned aerial vehicle system does not have a fixed formation, the individuals only carry out information interaction with the individuals in the communication range, an exact mathematical model of the swarm unmanned aerial vehicle system cannot be established, and the faults are difficult to analyze in a model mode. The cellular automaton does not need to establish and solve a complex partial differential equation, so the method of the cellular automaton is adopted to analyze the fault propagation.
(II) establishing cellular automata according to the characteristics of swarm unmanned aerial vehicles
According to the analysis of the swarm unmanned aerial vehicle model in the step (a), the individual distribution in the swarm unmanned aerial vehicle system is different from the cellular space in the traditional cellular unmanned aerial vehicle model, the cellular space has no regular grid shape, the cellular space is in a loose and coupled state, and the number of neighbors is not fixed, so that the definition of the individual neighbors in the swarm cannot be described in the form of common Von Neumann, Moore and the like.
Defining an individual in the swarm drone as a cell, and defining a CA model as (L, S, N, f) by a quadruple;
l represents a cellular space, and the swarm unmanned aerial vehicle is scattered on a three-dimensional space under an inertial coordinate system and is in a loose and coupled state, so that the cellular space is expanded into the three-dimensional space;
s represents a cellular state, and when the swarm unmanned aerial vehicle is attacked maliciously, whether the unmanned aerial vehicle breaks down or not needs to be determined by judging the cellular state;
n represents a neighbor set of cells, all cells which are around one cell in the cell space and can influence the cell are called neighbors of the cell, and according to the form of the swarm unmanned aerial vehicle controller, individuals in the unmanned aerial vehicle communication range influence the state of the unmanned aerial vehicle. Therefore, the neighbors of the cells are not limited to spatially adjacent cells, but are expanded to be connected by edges in the communication topological graph to be neighbors;
f is a state transition rule (local mapping set), and as known from the controller form of the swarm drone, the state of a cell at the next moment is not only related to the current state but also affected by the neighbor state, so a state transition function shown as the following formula is adopted:
Si(t+1)＝f(Si(t),Sj1(t),...,SjN(t))，
wherein S isi(t +1) is the state of the ith cell at time t +1, j1, …, jN is the neighbor of the cell, { Sj1(t),...,SjN(t) } is Si(t) a set of states for all neighboring cells; neighbors are defined as all individuals that can communicate with an individual i, i.e. the relative distance to i is in the communication range rαThe mathematical expression is as follows:
Ni＝{j|||qi-qj||≤rα,j＝1,2，…，N}。
expression form of (III) cellular state and state conversion rule
When the swarm unmanned aerial vehicle is attacked maliciously, the controller of the attacked individual is changed, so that the consistency of the original speed and the relative distance between the unmanned aerial vehicles is destroyed, and the individual is considered to have a fault. Defining the state S of a celli(t) is the individual fault degree value:
Figure BDA0002911892470000051
wherein p isiWhich is indicative of the velocity of the individual i,
Figure BDA0002911892470000052
speed when indicating consistency of swarm drones, dij＝||qi-qjI represents the relative distance between an individual i and a neighboring individual j, dαRepresenting the expected relative distance between individuals when the swarm drones reach consistency, Ni(t) represents the set of neighbors of the individual i at time t.
Individual in the swarm unmanned aerial vehicle realizes consistency through information interaction with neighbor individuals, and after a fault occurs, the information of the fault individual also influences the neighbor individuals through information interaction, so that the fault is propagated in the swarm unmanned aerial vehicle system. The state of the cell at the next moment is not only related to the current state, but also affected by the neighbor states.
Therefore, the state transition rule f is defined as:
Figure BDA0002911892470000053
(IV) judging whether the individual has a fault according to the state of the cell
The individual is divided into two states, a normal state and a fault state, the normal state being denoted by 0 and the fault state being denoted by 1. Each individual in the swarm unmanned aerial vehicle corresponds to one cell of the cellular automaton, the output state set A of the cell is made to be {0,1}, and whether the individual has a fault or not is judged according to the output state of the cell. A is used as the output state variable of the node i at the time ti(t) represents:
Figure BDA0002911892470000054
Si(t) represents the fault degree value of the individual i at the time t, delta is a threshold value set according to experience, and when S isi> δ indicates that individual i has failed; otherwise, the state is normal.
By adopting the technical scheme, the invention has the following beneficial effects:
(1) the invention discloses a fault propagation method of a swarm unmanned aerial vehicle based on a cellular automaton, which is characterized in that a cellular model is established according to the communication characteristics of the swarm unmanned aerial vehicle, when the swarm unmanned aerial vehicle is attacked maliciously, the controller of the attacked individual can be changed, and the normal individual of the neighbor can be influenced, so that the fault can be propagated in a cluster.
(2) Due to the complexity of the structure network of the swarm unmanned aerial vehicle and the coupling between individuals, the modeling difficulty is high based on methods such as a graph theory, the traditional cellular automata model is expanded by utilizing the characteristics of flexibility, simplicity and the like of the cellular automata according to the characteristics of the structure and dynamics of the swarm unmanned aerial vehicle and the neighborhood definition mode of individual information interaction, so that the distribution of cells can be flexibly and randomly distributed without being limited to the grid shape in the regular CA when the model is established, the cell distribution is closer to the real swarm unmanned aerial vehicle model, and the fault propagation process of the swarm unmanned aerial vehicle can be truly reflected.
Drawings
Fig. 1 is a schematic diagram of the spatial distribution of swarm drones.
Fig. 2 is an initial topology of swarm drones.
Fig. 3 is a flowchart of a swarm unmanned aerial vehicle fault propagation method based on a cellular automaton.
Fig. 4(a), 4(b), and 4(c) are velocity profiles of the drone individuals in the swarm in the x direction, the y direction, and the z direction, respectively.
Fig. 5 is a change curve diagram of the fault degree value of the swarm unmanned aerial vehicle.
Detailed Description
In order to explain the technical scheme disclosed by the invention in detail, the invention is explained in detail below with reference to the attached drawings.
The invention discloses a fault propagation method of a swarm unmanned aerial vehicle based on a cellular automaton, which describes fault propagation caused by malicious attack on individuals in a swarm unmanned aerial vehicle system by using the cellular automaton. According to the characteristics of the structure and dynamics of the swarm unmanned aerial vehicle, the cellular automata model is expanded, the cellular space is expanded into a three-dimensional space, and the cells are not limited to a certain specific position in the space and are scattered on the three-dimensional space; the neighbor set of the cells is expanded to have edges connected to form neighbors, and the cells are not limited to spatially adjacent cells. The problem of swarm unmanned aerial vehicle fault propagation modeling difficulty is solved, the fault in the swarm unmanned aerial vehicle can be effectively described, and a theoretical basis is provided for effectively avoiding the fault from harming the swarm unmanned aerial vehicle system.
Aiming at the spatial distribution characteristics of the swarm unmanned aerial vehicle shown in FIG. 1, the invention adopts the swarm unmanned aerial vehicle fault propagation method based on cellular automata shown in FIG. 3 to realize the simulation of the swarm unmanned aerial vehicle fault propagation process, and the method comprises the following steps:
(1) at the start time of the simulation, all individual speeds and positions are initialized, and t is set to 0,
Figure BDA0002911892470000061
dαδ, fault start time tsAnd the initial state S of all individualsiAll the individuals are set to be 0, namely all the individuals are normal individuals at the initial moment, and the fault degree values are all 0;
(2) judging whether t is larger than tsIf the voltage is larger than the preset value, injecting a fault; otherwise go to (3);
(3) calculating the fault degree value S of all the individuals at the next moment according to the evolution rulei(t+1)；
(4) Sequentially judging the fault degree values S of all individualsi(t +1) if it is greater than delta, if so, making the individual output AiIs 1, otherwise AiIs 0;
(5) judging whether the simulation is finished or not, if so, finishing; otherwise, t is t +1, and (2) is returned.
Example 1
Randomly initializing the speed and the position of 20 individuals, selecting No. 8 individuals to be subjected to malicious attacks according to the communication topology shown in FIG. 2, and setting the starting time of the fault as ts150s, fault form ufThe threshold δ is 0.3, and the individual speed changes as shown in fig. 4(a) to 4 (c). As can be seen in fig. 4, all individuals in the swarm drone system had reached consistency by 150 s. Since the number 8 individual suffers from malicious attack in 150s, the speed of the individual deviates from the speed of the consistency of the swarm unmanned aerial vehicle system, and meanwhile, the speeds of other individuals in the swarm unmanned aerial vehicle system deviate from the expected value along with the number 8 individual.
The individual fault degree value change curve in the swarm unmanned aerial vehicle system is shown in fig. 5, and a fault time table shown in the following table can be obtained according to the difference of the time for each individual to reach the threshold value.
Numbering 8 3 9 7 13 2 4
Time to failure t/s 157.9 160.2 160.75 160.85 160.85 161.3 162.15
14 12 10 6 18 15 1
162.55 163.3 164.05 164.15 164.35 164.45 164.75
11 5 17 19 16 20
164.85 164.85 166.65 169.05 173.55 174.95
As can be seen from the table, since the failure source is the number 8 individual, the failure has the largest influence on the state after the failure occurs, and is determined as the failed individual first. 3. No. 7, No. 9, No. 13 individuals are neighbors of No. 8 individuals, the state of the No. 8 individuals changes after the faults occur, and the No. 3, No. 7, No. 9, No. 13 individuals and the No. 8 fault source individuals carry out information interaction, so that the four individuals are affected by the faults only second to the fault source.
When the individuals No. 3, 7, 9 and 13 are in fault, the individual No. 2 simultaneously exchanges information with the individuals No. 3 and No. 7 affected by the fault, and the individual No. 6 only exchanges information with the individual No. 7 affected by the fault, so that the influence of the fault on the individual No. 2 is greater than that of the individual No. 6. According to the difference of individual fault time in the table, the propagation direction of the fault in the swarm unmanned aerial vehicle system can be obtained, and a theoretical basis is provided for analyzing the influence of the fault on the swarm unmanned aerial vehicle system, so that effective measures are taken in time to avoid the damage of the fault on the swarm unmanned aerial vehicle system.

Claims (7)

1. A swarm unmanned aerial vehicle fault propagation method based on a cellular automaton is characterized in that individuals in a swarm unmanned aerial vehicle are used as cells, a cell model is defined as a quadruple for recording real-time three-dimensional position information, real-time state information, a real-time neighbor set and a state conversion rule of each cell, the real-time neighbor set of each cell comprises all neighbors communicated with the cell at the current moment, the real-time state information of each cell is a real-time fault degree value corresponding to the individual, a fault is injected after the real-time state information of each cell is initialized, the fault degree value of each cell after the fault is injected is deduced according to the state conversion rule, and whether each individual in the swarm unmanned aerial vehicle has a fault or not is judged according to the fault degree value of each cell after the fault is injected.
2. The cellular automata-based swarm unmanned aerial vehicle fault propagation method of claim 1, wherein the real-time neighbor set of each cell is according to an expression: n is a radical ofi＝{j|||qi-qj||≤rαJ-1, 2, …, N } determination, NiSet of neighbors for the ith individual, qi、qjIs the location of the ith individual, the jth individual, rαThe communication distance between individuals in the swarm unmanned aerial vehicle is N, and the N is the total number of neighbor nodes.
3. The cellular automata-based swarm drone fault propagation method of claim 1, wherein the real-time state information of each cell is obtained by evaluating the degree of deviation of individual speed from the speed of swarm drone to reach consistency and the degree of deviation of adjacent individual relative distance from communication distance.
4. The cellular automata-based swarm unmanned aerial vehicle fault propagation method according to claim 3, wherein the real-time state information of each cell is as follows:
Figure FDA0002911892460000011
Si(t) is the real-time status information of the ith individual at the time t, namely the fault degree value of the ith individual at the time t, piIs the speed of the ith individual and is,
Figure FDA0002911892460000012
speed when reach consistency for swarm unmanned aerial vehicle, Ni(t) is the set of neighbors of the ith individual at time t, dijIs the relative distance of the ith individual from the jth individual, dαFor communication distance, α1Weight of degree of speed, alpha, at which individual speed deviates from swarm drone to reach consistency2A weight of the degree to which the relative distance of adjacent individuals deviates from the communication distance.
5. The cellular automata-based swarm unmanned aerial vehicle fault propagation method according to claim 4, wherein the state transition rule is expressed by an expression
Figure FDA0002911892460000013
Determination of Si(t +1) is the real-time status information of the ith individual at time t +1, Sj(t) is the real-time status information of the jth individual at time t, | Ni(t) | is the total number of neighbor individuals included in the neighbor set of the ith individual at time t.
6. The method as claimed in claim 5, wherein the formula for determining whether each cell in the swarm unmanned aerial vehicle has a fault according to the fault degree value of each cell after the fault is injected is as follows:
Figure FDA0002911892460000021
Ai(t) is a state value of whether or not the ith individual is faulty at time t, and δ is a threshold value.
7. The cellular automata-based swarm unmanned aerial vehicle fault propagation method according to any one of claims 1 to 6, wherein the fault propagation direction of the swarm unmanned aerial vehicle is determined according to the time when each cell fails after fault injection and the information interaction state between the cells.
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CN114598608A (en) * 2022-03-04 2022-06-07 重庆擎羚科技有限公司 Information propagation method under rigid topology of unmanned aerial vehicle network
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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