METHOD OF THE INTELLIGENT SYSTEM CONSTRUCTION OF AUTOMATIC CONTROL OF UNMANNED AIRCRAFT APPARATUS

Context. Military conflicts of the late XX – early XXI centuries are characterized by the using of a large number of new weapons, which allowed the warring parties to distance themselves as far as possible from the direct collision with each other. Unmanned aircraft apparatus (UAA) have become one of the latest weapons on the battlefield, which during military conflicts were proven to be more effective than manned planes, in conducting air reconnaissance and other combat tasks, as well as strike at the enemy. One of the ways to increase the efficiency of UAA is to increase the level of technical excellence of their control systems. Creating new approaches for designing navigation systems for unmanned aerial vehicles particular, based on a free-form inertial navigation system, is an urgent task, as it will allow automatic control of the UAA flight route in the absence of corrective signals from the global satellite navigation system. Objective. The purpose of this work is to develop a methodology for managing an unmanned aerial apparatus using an intelligent automatic control system. This technique will minimize the error of a free inertial navigation system due to the using of a fuzzy neural network system. The algorithm of the proposed method of constructing the intellectual system of automatic control of UAA navigation system using the fuzzy neural network apparatus in the MatLab 7 software environment was developed. A neural network training was conducted in the Python 3.6 software environment (Jupyter-notebook), as well as testing the UAA model in the robot operational system (ROS) simulator environment for comparison with existing methods. Method. To achieve this goal, the following methods were used: intelligent systems, the theory of automatic control, pseudospectral method; methods based on the genetic algorithm and apparatus of the fuzzy neural network. Results. The method of constructing the intelligent system of automatic control of an unmanned aerial apparatus for minimizing the error of a free-form inertial navigation system due to the application of the neural network has been developed. The work of the intellectual system of automatic control of the UAA navigational system using the neural network in the MatLab software environment based on the proposed implementation algorithm were tested. The possibility of practical application of the obtained results and comparison with traditional methods were investigated.


ABBREVIATIONS
NN is a neural network; NS is a navigation system; RM is a reference model; CS is a closed system; GSNS is a global satellite navigation system; UAA is an unmanned aircraft apparatus; INS is an inertial navigation system; ROS is a robot operational system; CM is a center mass of the controlling object.

INTRODUCTION
One of the most important subject areas is the management system of moving objects.
Special feature of which is that each new generation satisfies new requirements to the conditions, modes and quality of functioning, as well as extends the range of solvable tasks [1].
The variety of moving objects in the environment of their functioning and the importance of effective implementation lead to a large range of scientific problems arising in the development of traffic control systems.
Nowadays, the base of navigation systems for unmanned aerial apparatus is made up of receivers of global satellite navigation systems, integrated with a block of inertial sensors of spatial orientation. Such a system provides a very accurate determination of the UAA location and its parameters (up to 15 m) in the presence of receiving signals GSNS. If it is integrated with satellite navigation, cheap low-precision inertial systems equipped with micromechanical motion sensors (accelerometers and gyroscopes) are possible.
The cost of such a system is from 5 to 15 thousand dollars, depending on the accuracy of the sensors. It should be noted, that the inertial system of such a price range is not able to carry out an autonomous calculation of the traversed path due to the high speed of drift gyroscopic sensors. Some samples are capable of maintaining the accuracy within a few minutes (not more than 10) of the absence of a GSNS signal at a level of 100-150 m. In this case, generally, the mode of rectilinear motion is used without acceleration.
Thus, the presence of GSNS signals is currently a prerequisite for the implementation of unmanned aerial vehicles set tasks. The absence or deliberate suppression of navigation signals leads to the inability to determine their own coordinates accurately and, as a result, to perform flight on a given route. In the same case using of inertial systems of ultralow precision (especially on short-range UAA) in the UAA, the lack of corrective signals from the GSNS can lead to the complete cessation of the inertial system functioning and the UAA accident. Therefore, the suppression of the GSNS is considered as the main method of fighting the UAA with the newest type of inertial systems.
Using of high-precision inertial navigation systems also completely does not solve the problem for the following reasons: 1) such systems are expensive (30-50 thousand dollars); 2) the mass of the inertial system of "medium accuracy" on laser or fiber-optic gyroscopes is from 8 kg, which makes it problematic for their using in small and medium-range UAA; 3) the limitation of the INS is increasing in the error of coordinates determination with the time of autonomous operation. The accuracy of an autonomous number of coordinates for modern INS is about 2 kilometers per hour of flight (for systems of high accuracy), which does not allow to provide high-precision determination of the coordinates of goals.
The growing interest of scientists in intelligent control systems, based on artificial neural networks gives grounds to assert about the qualitative influence of the latter on the performance indicators of the UAA complex. At the same time, their using can significantly reduce the cost of such systems. Therefore, the intellectualization of control sys-tems in the present conditions is one of the main scientific and practical directions of their improvement.
Thus, the object of study is the system of automatic control of UAA movement.
The subject of the study is the UAA motion control process based on intelligent control systems based on artificial neural networks, without the use of GSNS signals.
The purpose of the study is to develop a method for an unmanned aerial apparatus control using an intelligent control system based on artificial neural networks to reduce the control time without using the GSNS signals.

PROBLEM STATEMENT
Focusing on the results of well-known scientific developments in accordance with the research topic, the development of a method for constructing an intellectual system for automatic control of the UAA navigation system in the MatLab software environment remains open and requires detailed elaboration [1][2][3][4][5][6][7][8][9][10].
The task of the control system synthesis is to select the structure and the control channels parameters, which ensure the receipt of a given quality of flight control, based on dynamic properties of the UAA.
To achieve the research aim, it is necessary to solve a number of the following interrelated scientific tasks: 1. To develop method of an unmanned aerial vehicle management with using an intelligent automatic control system to minimize the error of a platform-free inertial navigation system due to the using of a fuzzy neural network system; Suppose given the original sample as a set of functions and parameters: vector Y; spatial motion of the UAA is described by () . For a given sample of control parameters the problem of intelligent automatic control system synthesis can be presented as the problem of finding quadratic functional of intelligent automatic control system where the model structure W RM () usually specified by the user in practice, and the set of controlled parameters ( ) Learning in direct-acting systems is the result of the current numerical optimization of the main circuit. Target functionality of this optimization is selected simultaneously and as a criterion for object control and as a target condition for learning: ( ) ( ) ; To apply developed realization algorithm of the advanced method of the automatic control intellectual system of the UAA navigation system construction with the help of a neural network in the MatLab software environment; 3. Perform training on the neural network in the Python 3.6 (Jupyter-notebook) software environment, as well as testing the UAA model in the ROS simulator for comparison with traditional methods.

REVIEW OF THE LITERATURE
Nowadays, the prospects for the development, refinement and upgrading of modern aircraft are the main focus of researches by modern scientists. From the point of view of the intelligent automatic control systems introduction for UAA complexes, it is worth noting the achievements of scientists, which are published in the works [3][4][5][6][7][8][9][10], and others.
In [11], the work of the quadcopter is investigated, and the algorithms of stabilization are proposed for the automatic control of the trajectory motion. The results of the research show a satisfactory stability of the quadcopter flight and tracking of the given trajectories, which confirms the correctness and validity of the proposed control algorithm.
The works [12,13] investigate approaches to the implementation of the neural network regulator in the automatic control system of an unmanned aircraft apparatus. Within the framework of the work, researchers offer the structure of the neural network and simulate the result of training the neural network regulator.
In the works [14,15] the authors offer solutions to approach the issue of automatic control of the UAA navigation system under conditions of interference. The author reveals the formation mechanisms of the flight path. The efficiency of the proposed algorithms is confirmed by calculations.
However, despite the results of scientific developments in accordance with the research topic, the question of an effective algorithm formation for the implementation method of constructing an intelligent system of automatic control of UAA navigation system using a neural network, in the MatLab software environment requires detailed elaboration.

MATERIALS AND METHODS
Works [1][2][3][4][5][6] offer classic methods for building UAA control systems, with the main hardware for the trajectory planning are: -encoders; -inertia sensors; -devices of interception; -barometers, compasses, etc. The UAA traffic is controlled from the ground point by transmitting data through the radio communication channel. If for the generalization of the UAA control analysis to consider this apparatus as a "black box", it can be shown, that in the general case, the input of the apparatus enters the control signal and the influence of perturbations coming from the external environment. The source data can be considered backward radio communication with the item management and interaction with the external environment.
Tasks that are solving by the control point include the following: -reception and processing of received information on radiocommunication; -identifying and recognizing objects and building a map of the area; -determining the coordinates of the objects and the scale of the map; -formation of priorities and adjusting the flight plan; -providing control of UAA movement and aerial photography.
While operating a control station under the condition of conducting a military operation, it is often necessary to timely elaborate substantiated recommendations and makes decisions on UAA control in conditions of uncertainty and fuzzy assessment criteria. Solving this problem is possible with the using of intelligent systems at all stages of the support processes of the decision making (Fig. 1).
The preparation for the work of the intellectual decision support system can be divided into the following stages: -construction of a digital model of the area, which in the form of a three-dimensional geometric model is formed on the basis of aerial photography; -definition of main characteristics of UAA sensors, their ranges, sensitivity and resolution; -getting coordinates of the view point of the current image.
Working of the system begins with the determination of the orientation angle of the angular velocities of the sight line in the vertical and horizontal planes and the distance to the target using the navigation system. The object image is transferred to the point; which coordinates were obtained as a result of the calculation. This information is transmitted over the radio station in real time. The system compares the output images with the parameters included in the description, which leads to the problem of detection, recognition of the object.
It makes possible to form instructions and recommendations to the operator.
At the same time sensor control is conducted to target the target. The operator makes an appropriate correction to the flight plan of the UAA, which is taken into account while forming a control signal. The UAA goes into auto attendance mode. In case of error control signals are formed again according to information from the navigation system, but it takes into account the estimates received in the filtering block.
The intellectual system construction methodology of automatic control of the UAA navigation system is graphically represented as a flowchart in fig. 2. The intelligence of the method is based on the using of fully automated systems. Management is based on the principle of "guidance-stabilization". The navigation method is responsible for forming the flight direction and current coordination, which is based on sensor signals, orientation and navigation systems. The result is broadcast by "Autopilot", which solves the problem of stabilization. It is mandatory to constantly check the availability of communication over the radio channel.
The first block is a unit for forming the concept of a model and analyzing the initial data. This block provides for the collection of information and its analysis. The initial information at this stage includes mathematical models of UAA, external and internal excitations that affect the UAA during operation, models and data from sensors and sensors.
In the presence of all initial data the concept is formed of a management model, the main directions of movement, obstacles in the way, the projection of displacement, etc.
The second block assumes of the model implementation in the software environment. This block is the main part within the framework of the algorithm. There is a synthesis of the general concept, the laws of management and the navigation algorithms of the system are formed and generalized.
It takes into account the operating modes and conditions of the algorithm implementation that is being developed. A general model is created using mathematical models of dynamics and excitement. The resulting models are fundamental in the overall development algorithm, based on further analysis and the formation of the final model. At each individual stage mandatory verification and evaluation of the simulation results, as well as correction, if necessary parameters of the laws of management and navigation algorithms are carried out.
The third block envisages the formation of a knowledge base, system training and testing of the developed model with the analysis of the results obtained and the final result output, on the intellectual system effectiveness of automatic control of the UAA navigation system. Spatial motion of the UAA is described by the equations of forces, moments and kinematics of the flight. The force equation describes the movement of the CM in the vertical space: with this vertical acceleration at δ=0 equals The UAA rotation around the CM along the OZ axis, in accordance with the basic law of the dynamics of rotational motion: (2) The magnitude of the moment depends on the mass distribution with respect to the OZ rotation axis. For a circle with a radius r moment of inertia -2 0 mr I = .
Relying on the work [12], the principle of receiving the transfer function of the control channel of the UAA is based on dependence ( ) δ = α f taking into account the dynamics of UAA rotation around the axis . Angular velocity vector of UAA speed: where Then: The basis of the equation of the UAA rotation dynamics (2) will substitute the obtained values: Having done the transformation, we have: We group members in relative terms α, receive: Let's make Laplace's transformation: The obtained equation repeats the equation of oscillatory motion: where The transmission coefficient depends on the layout of the UAA, its speed and direction of movement, the boundary of the indicator from 0,4 to 0,6 dB, and the transmission coefficient shows at which angle the object δ = α α K will return; taking into account the angles Then: The mathematical model of the constructed neural controller with a transmitted function ( ) for the implementation of the proposed algorithm, the structural scheme of which is depicted in Fig. 3, has a look ( )

EXPERIMENTS
In the framework of this scientific research concerning the method implementation of the automatic control intellectual system of the UAA navigation system with the help of a neural network. The direct application of the solution to the problem is used in the MathLab software environment. Unlike the adaptive systems of the identification type, the systems with direct control algorithms do not contain models, which are configured according to the behavior of the main system, identifiers and evaluation devices. The information in the form of the current values estimations of the parameters and the state of the volume is not used to configure the main circuit [13]. Learning in directacting systems is the result of the current numerical optimization of the main circuit. Target functionality of this opti-mization is selected simultaneously and as a criterion for object management and as a target condition for learning.
Obligatory condition [20]: To study the neural network, we use the algorithm of the inverse distribution of the error with the Levenberg-Marquardt training method and the activation function is a hyperbolic tangent. After training we test and validate the network in the environment of Simulink (MatLab) visual modeling. The general procedure for forming a training algorithm is formed on the basis of a parameters selection that come from the block calculation output of the parameters sum of the reference model. These parameters are grouped by significance, thus forming the space of states and external control influence on the object. Signals on the output of the network that do not meet the conditions, again fall on the input of the system, thereby teaching the system.
The principle of the learning unit is based on customizing the generalized object by selecting the parameters within the range of training. The error is the difference from , and in general, the error serves as a fundamental indicator of the quality of learning. The error in its composition has information on the deviation of the key indicators ( ) ( ) Fig. 4 shows the automatic control system structural scheme of the UAA navigation system, which includes a regulator with transfer functions ( ) Minimization of a quadratic functional: (17) provided by the calculation of the derivative The functional scheme of the unit training system is shown at the Fig. 5.

RESULTS
The simulation results of the proposed algorithm for the method implementation of the intellectual system of the UAA navigation system automatic control using a neural network, in the MathLab software environment, are shown in Fig. 6.
The resulting error of management using the system's learning unit is ( ) , at the same time, the parameters of the controller of the intelligent automatic control system of the UAA navigation system tend to the parameters of the reference model.
In the Fig. 6: green is the object of control, blue is the reference model, violet is the error of control.
Comparing two systems of automatic control of the UAA navigation system with and without a unit training system it is worth noting that the first system has more stable performance. a b Figure 6 -The results of simulation of the proposed algorithm in the software environment MathLab: a) -with using of the training unit control system; b) -without using of the training unit control system

DISCUSSION
Within the framework of the work an algorithm for the method implementation of the intellectual system of the UAA navigation system automatic control with the help of the neural network in the MatLab software environment is disclosed. Structural and functional schemes of control system training are presented which are the basis of the implementation algorithm methodology of the automatic control intellectual system of direct-acting UAA navigation system.
The offered inertial navigator of automatic control flight of UAA works as with the use of MEMS sensors, that allows substantially to reduce in price the similar systems as compared to the systems [11][12][13][14][15].
In-process [23] an author examines a navigational, the methods of Kalman filtration are fixed in basis of that. These systems are able to provide high exactness of positioning of object substantially complicating a calculable process. In addition, such systems are bulky and outvalue considerably.
In respect of the offered system, an algorithm (Fig. 2) is fixed in basis of work of that, her exactness is arrived at by means of methods of artificial intelligence or training unit control system -technically.
From a fig. 6 we will notice that time of positioning with her use diminished substantially.
In the conditions of the use of UAA in fully autonomous behavior (in default of additional GSNS), block of UAA hours will be megascopic for lack of power expenses on the search of navigation signals and reduction of the calculable loading of navigational on the whole.
The control system described can be used as an intelligent automatic control system for UAA navigation system.
The proposed algorithm for the implementation of the methodology based on the direct control method with the help of the training unit, gives positive results in the range of normative change of parameters, greatly reduces the time and cost of design.

CONCLUSIONS
In the course of the conducted scientific research, the authors improved the methodology for building an automatic control intelligent system of an unmanned aerial vehicle, which allows to increase the efficiency of control systems for unmanned aerial vehicles up to 15% (time of decision making and positioning accuracy).
The scientific novelty. The proposed automatic control system using artificial intelligence makes it possible to provide control tasks in the mode of full autonomous flight of a UAA without regard to auxiliary positioning systems.
The testing of the work of the automatic control intellectual system of the UAA navigational system using the neural network in the MatLab software environment based on the proposed implementation algorithm.
The practical significance. The experimental results allow us to recommend the proposed algorithm for practical use in order to increase the stability of the automatic flight control system of the UAA in the autonomous operation mode.
The proposed algorithm for the implementation of the methodology based on the direct control method with the help of the training unit, gives positive results in the range of normative change of parameters, greatly reduces the time and cost of design.
The possibility of practical application of the obtained results and comparison with known methods was investigated.
The obtained scientific result is expedient to use in control systems of unmanned aerial vehicles in a complex signal-interfering environment.
Prospects for further research are to study the degree of parasitic effects of sensors on the control channel.

ACKNOWLEDGEMENTS
The staff of the authors expresses gratitude for the scientific and organizational assistance to the Honored worker of education of Ukraine, doctor of technical sciences, professor Romaniuk Valery Antonovich -deputy of head of the Military institute of telecommunications and informatization named after Heroes of Krut of scientific work and honored scientist of Ukraine, doctor of technical sciences, professor Slyusar Vadim Ivanovichmain scientist of the Central research institute of armament and military equipment of the Armed Forces of Ukraine.