The application of aviation as the main mobile firepower in achieving the objectives of military operations today is obvious and necessary. In solving a number of important tasks for aviation in practice, it is necessary to be able to assess the effectiveness of the aviation group. The effectiveness of aviation application depends on a number of factors, most of which are unmanageable. Therefore, the authors chose the level of flight crew readiness in the study as the main factor that can be influenced by taking into account the indicators of crew readiness and their subsequent appointment on missions with different levels of complexity. In the article, the authors analyzed the indicators that affect the readiness of the flight crew, which make up the specifics of a particular task. Qualitative indicators of the level of readiness and their compliance with the complexity of the relevant missions are determined. Quantitative values of qualitative indicators of the level of crew readiness were also obtained. The article proposes a method of determining the readiness level of the flight crew, taking into account: the total flight hours, the flight hours for 12 months, the flight hours of personal improvement, breaks in flights and the age of a pilot. The methodology considered in the article could increase the efficiency of fighter aviation application by reducing the time for the commander in the decision-making process and the exclusion of the subjective approach in the decision-making process. An algorithm for methodology for determining the level of crew readiness has been developed. The algorithm of this technique is implemented by the software package MATLAB: Simulink and Fuzzy Logic Designer.
Taking into account the influence of the flight
crew readiness degree on the success of the flight
task (combat missions) is carried out by entering
the appropriate coefficient . In the works [
To perform calculations by the fuzzy logic apparatus, it is necessary to create an algorithm of determining the readiness level, which will allow assigning flight crews on different types of missions based on the results of determining their readiness level. This algorithm is shown in Figure 1.
Start
Input Flight crew database 1. 2. 3. 4.
Determining the indicators that affect the readiness level and determining the value of the weight factor of these indicators Formalization of defined indicators (linguistic variables)
<Ɛj, T, K, G> Construction membership functions of linguistic variables
G={μƐ(X)|X} Creating a database of rules for linguistic variables that forms the linguists variables ‘level of readiness’ (if-then)
Construction of a fuzzy logic systems Calculation the readiness level of the flight crew
Assigning flight crews by missions
End Checking the results for reasonable
Adjusting database of rules
No Yes
Are results reasonable?
To determine the effectiveness of the fighter aviation application it is necessary to investigate all the factors that affect the readiness level of the flight crew and identify the main ones. But these factors have no clear boundaries. In conditions when there are no clear boundaries, the problem can be solved quite successfully using fuzzy logic. The fuzzy logic theory is one of the most suitable mathematical theories designed to formalize indefinite information for solving these kinds of issues. 2.1. steps
Step 1. Selection of indicators to determine the readiness and formation of input data. That is, to decide the flight crew appointment to perform the particular mission will be used to quantify the readiness of the crew. The group of experts determines by voting the five indicators that have the greatest impact on the level of readiness of the flight crew.
Step 2. Formalization of the assessment of
input readiness indicators as a tuple is carried out,
<Ɛj, T, K, G> where Ɛj – name, T – terms, K –
boundaries, G={μƐ(X)|X} – membership
functions [
Definition of terms for linguistic variables that characterize the level of readiness of the flight crew and the linguistic variable “level of readiness”.
Step 3. Construction of membership functions of linguistic variables. At this step, the limits of the terms selected to determine the level of readiness and for the linguistic variable "level of readiness" are also set. The construction of membership functions is carried out based on regulatory requirements and expert assessments.
Step 4. Determining the relationship between input and output data in the form of linguistic rules "if - then".
Step 5. Building a fuzzy logic system for
each subsystem using the graphical toolkit Fuzzy
Logic Designer, from the MATLAB software
package. In this application there is a choice of
either Sugeno or Mamdani system [
Step 6. The initial readiness level of the flight crew is calculated and its values are checked for reasonable. The operation of each fuzzy logic block is checked so that it gives the expected initial values and, therefore, confirms that the developed method of analysis is acceptable.
After that, need to run several launches with different input values, and compare the results with each other. The aim is to determine whether the results are reasonable for the model to give realistic and consistent results. After confirming this, the result should be checked for acceptable limits set for the type of operation. If necessary, appropriate adjustments are made.
The assessment of the initial level of readiness is being formalized. Also, values are determined, as well as the choice of the required fuzzy inference algorithm.
Step 7. The obtained values of the readiness level are compared with quantitative indicators that correspond to the values of the linguistic variable "level of readiness" and then appoint flight crew on a mission with an applicable level of complexity.
Next, consider an example of calculating the readiness level of flight crews for fighter aviation.
Since quantitative values of variables are
required to formalize t-he decision-making
algorithm, use fuzzy logic methods to assess the
qualitative indicator of the level of readiness [
2. low (corresponds to the value 2 – the pilot is able to perform disruption (violation) of enemy air freight cargo missions)
3. medium (corresponds to a value 3 – the pilot is able to perform missions of defeating enemy airborne troops in the air)
4. sufficient (corresponds to the value 4 – the pilot is able to perform the missions of destroying the air threat means of the enemy over own territory)
5. high (corresponds to value 5 – the pilot is able to perform the missions of destroying the air threat means of the enemy over hostile territory) the research process based on the results of solving the problem. The most common are triangular, trapezoidal and bell-shaped membership function, which will be used in the proposed model. Predetermined intervals of fuzzy sets are the basis for constructing the membership function of input linguistic variables.
The membership functions for each value of the linguistic variable “level of readiness” are shown in Figure 2.
From the obtained diagram it is possible to determine quantitative indicators that correspond to the values of the linguistic variable “level of readiness”. Display these values in Table 4.
That is, to decide on the appointment of the crew to perform the mission, the commander will use the quantitative assessment of the readiness of the crew from Table 4.
The directive documents regulating the
procedure for training and conducting flight
training in the state aviation of Ukraine stipulate
that the preparation for flight crew flights is the
process of bringing the flight crew into readiness
for flight tasks [
In research authors found that the readiness level of flight crew to perform combat action that constitute the specifics of each type of mission depends on a large number (more than 30) indicators. However, for the study, experts identified 5 main indicators that have a greater impact on the final result. These include the total flight hours, the flight hours for 12 months, the flight hours of personal improvement, breaks in flights and the age of a pilot.
Using the same method used to determine the readiness level of the flight crew, the authors calculated these indicators. 4.1.
The total flight hours is compared to the experience, the larger it is the easier it is for the pilot to perform any task that he has encountered in his flying activities before.
The total flight hours in the study is considered as a pilot's flight hours on all types of aircraft for all years of flight activity, including training in Air Force Academy and flight schools outside the service in the Armed Forces of Ukraine.
The flight hours on simulators and the operating time on the ground during engine' star up and taxiing are not taken into account.
To describe the membership function of the linguistic variable “Total flight hours” the terms were named T = {dangerously low; low; medium; sufficient; high} and their limits in flight hours K = [50, 700] were determined.
The maximum value of each term was taken as 1.
The membership function for the linguistic variable “The total flight hours” built in Microsoft Excel is shown in Figure 3.
The flight hours for 12 months in the study is considered to be flight hours on all types of aircraft on which the pilot has flown in the last year at the time of data input.
The authors and experts also took into account the organizational and methodological recommendations of the Command of the Air Force on the implementation of annual flight hours per year.
To describe the membership function of the
linguistic variable “The flight hours for 12
months” the terms were named T = {dangerously
low; low; medium; sufficient; high} and their
limits in flight hours K = [
The membership function for the linguistic variable “The flight hours for 12 months” built in Microsoft Excel is shown in Figure 4.
The flight hours of personal improvement is the percentage of flights performed by the pilot in the interest of advancing on the appropriate training course. The value of the flight hours of personal improvement is taken into account for the last year at the time of data input in percentage in relation to the flight hours for 12 months only on the main type of aircraft (combat aircraft).
To describe the membership function of the
linguistic variable “The flight hours of personal
improvement” the terms were named T =
{dangerously low; low; medium; sufficient; high}
and their limits in percentage K = [
The membership function for the linguistic variable “The flight hours of personal improvement” built in Microsoft Excel is shown in Figure 5.
In considering the linguistic variable “Breaks in flight”, the author and experts, in addition to the empirical approach, took into account the requirements of the Fighter Training Course and the Rules of State Aviation. These documents set requirements for breaks in flights by type of training and meteorological conditions.
To describe the membership function of the linguistic variable “Breaks in flight” the terms were named T = {dangerously high; high; medium; acceptable; slight} and their limits in days K = [3, 42] were determined. The maximum value of each term was taken as 1.
The membership function for the linguistic variable “Breaks in flight” built in Microsoft Excel is shown in Figure 6.
To perform tasks, the specifics of which are the speed of reaction and the body's ability to resist g-force during combat maneuvering, an important indicator that affects the progress of the task of the flight crew is its age. The value of the linguistic variable “Pilot's age” is understood by authors and experts as the length of the period from birth to the time of data input.
To describe the membership function of the linguistic variable “Pilot's age” the terms were named T = {unsuitable; admissible; suitable; optimal; regular} and their limits in years K = [20, 70] were determined. The maximum value of each term was taken as 1.
The membership function for the linguistic variable “Pilot's age” built in Microsoft Excel is shown in Figure 7.
Based on the analysis of indicators that affect the level of crew readiness, the obtained quantitative indicators of the value of the linguistic variable "level of readiness" and processing of expert data, a hierarchical structure of the flight readiness level tree and their distribution by tasks in Simulink and shown in Figure 8.
Fuzzy control simulation is performed using the Fuzzy Inference System (FIS). For each FIS unit Model of calculation of the level of readiness and distribution of crews by tasks (Figure 8.) it is necessary to define a system of fuzzy rules.
Fuzzy rules “if-then” are the core of a fuzzy
logic system because they combine all the other
components and determine the output of the
system. When assessing the level of readiness,
input data are often assigned as indicators and
results as readiness. Then fuzzy rules “if – then”
are established for the ratio of readiness and set of
indicators with a certain level of linguistic
tolerance [
IF indicator 1 is low, AND indicator 2 is high, THEN readiness is average
The rules are built systematically, looking at all possible combinations of fuzzy sets of each input from the smallest to the largest. The consequences are adjusted so that the smallest sum of fuzzy sets is equal to the minimum, and the largest sum is equal to the maximum value of readiness. Subtotals are interpolated between these two values. The number of rules is the product of the number of fuzzy sets of each input. For example, for FIS “flight crew level of readiness” the number of logic inputs - 5, the number of terms of the output function - 5, the number of rules is 55 = 125.
The calculation of clear readiness values is carried out in the Simulink environment. The calculation model is presented in Figure 8. The input data are data of the total flight hours, the flight hours for 12 months, the flight hours of personal improvement, breaks in flights and the age of a pilot. At the output we get a numerical value of the readiness level from 0 to 1. An example of the obtained values is shown in Figure 9.
After receiving the numerical values of the crew readiness level, the obtained values are compared with the quantitative indicators of the “flight crew level of readiness”. Depending on the complexity of the task, this figure varies. In case of discrepancy between the level of complexity of the task and the level of readiness of the crew, appropriate changes are made to the input data, the crew or task is replaced. If the level of readiness and complexity of the task corresponds, the crew is assigning to a mission.
Thus, the authors analyzed the indicators that affect the readiness of the flight crews, which constitute the specifics of each particular task. Qualitative indicators of the level of readiness and their compliance with the complexity of the relevant missions are determined. Quantitative values of qualitative indicators of the readiness level of flight crew were also obtained.
The algorithm of determining the readiness level of the flight crew has been developed. The algorithm is implemented by the software package MATLAB: Simulink and Fuzzy Logic Designer.
The proposed methodology will allow to quantify the readiness level of the flight crew, to take timely measures to organize effective training of crews for possible tasks. By reducing the time in decision-making process in assigning flight crews on missions, taking into account the level of readiness of crews, and exclusion of a subjective approach in solving this task, the methodology could increase the efficiency of fighter aviation.
[1] A.S. Bonin, Osnovnye polozheniya metodicheskih podhodov k ocenke boevyh potencialov i boevyh vozmozhnostej aviacionnyh formirovanij, 1nd. ed., Voennaya mysl', Voen. Izd, Moscow, 2008, pp. 43-47. [2] N.M. Skomorohov (Ed.), Bor'ba za gospodstvo v vozduhe, Voenizdat, Moscow, 1990. [3] B.I. Semon, Suchasnyi metod boiovykh potentsialiv v prykladnykh zadachakh planuvannia rozvytku ta zastosuvannia taktychnoi aviatsii, NAOU, Kyiv, 2009.