Issue 1 (215), article 5

DOI:

Cybernetics and Computer Engineering, 2024,1(215)

Shepetukha D.O., PhD (Technics),
Senior Researcher of Intelligent Control Department
https://orcid.org/0000-0002-6256-5248
e-mail: yshep@meta.ua

Semenog R.V., PhD Student,
Researcher of Intelligent Control Department
https://orcid.org/0000-0002-6714-0644
e-mail: ruslansemenog20@icloud.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Sciences
of Ukraine and the Ministry of Education and Science of Ukraine,
40, Akad. Glushkov av., Kyiv, 03187, Ukraine

SEQUENTIAL STRUCTURING METHOD FOR BUILDING DYNAMIC OBJECTS MANAGEMENT SYSTEMS

Introduction. The growing role of information technologies makes them an important part of modern facilities management systems and contributes to increasing their efficiency, security and their ability to adapt to changes. A new type of information systems is emerging that uses advanced technologies to automate and optimize management processes, including artificial intelligence and others — Intelligent Management Systems(IMS), is emerging now.

The next step in this direction is complex systems that allow dynamic objects to be controlled independently of external intervention — autonomous control systems (ACS). SACs use a variety of sensors, data processing and decision-making algorithms and are widely used in the automotive industry (for example, self-driving cars), in unmanned aerial vehicles (drones), and many other industries where independent and efficient control of objects is required.

The purpose of the article is to investigate modern concepts of building autonomous control systems for dynamic objects and to describe methods of intellectualization of such systems.

The results. A modern approach to the construction of systems of autonomous control of dynamic objects, based on sequential structuring, was studied. Methods of creating systems aimed at optimizing automatic management of dynamic objects are highlighted.

Conclusions. A promising direction of research is the development of a new generation of intelligent information technologies that use information processing mechanisms that are based on the method of sequential structuring in the construction of automatic control systems.

Concepts for building automatic control systems should ensure the application of meaningful data processing methods and use components of synergistic interaction of human-machine control systems. The application of the methodology of sequential structuring of weakly formalized components of intellectual problems in visual management systems allows achieving some unification in solving a certain class of intellectual management problems.

The further direction of research consists in the development of a new generation of information technologies and the corresponding toolkit of automatic control, which will apply methods of meaningful data processing, in particular, the method of sequential structuring for the intellectualization of automatic control systems.

Keywords: intelligent information technology, artificial intelligence, intelligent control, dynamic object, imaginative thinking, autonomy.

Download full text!

REFERENCES

1. Imam I. F., Kondratoff  Y. Intelligent adaptive agents: a highlight of the AAAI-96 workshop.
AI Mag. 1997, Vol. 18, No. 3, pp. 75–80. DOI: 10.1609/aimag.v18i3.1307. 

2. Pigozzi G., Tsoukias A., Viappiani P. Preferences in artificial intelligence. Annals Math. and Artif. Intell. 2016, Vol. 77, No. 3/4, pp. 361–401. 

3. He H., Li P., Wang H. Advances in knowledge discovery and data analysis for artificial intelligence. Journal of Experimental & Theoretical Artificial Intelligence. 2011, Vol. 23,
№1, pp.1–3.

4. Revesz P.Z. Artificial intelligence basic research directions at the U.S. Air Force. International Journal on Artificial Intelligence Tools. 2014, Vol. 23, №6. Access mode:  http://cse.unl.edu/~revesz/papers/IJAIT14.pdf.  – name from the screen.

5. V.L. Deepak, S. Nayak, S. Patra. Development of obstacle-avoiding robots using
RF technology. Intern. J. Intell. Unmanned Syst. 2016, Vol. 4, No. 4, pp. 214–225.
DOI: 10.1108/IJIUS-08-2016-0007.

6. Hanratty T.P., Newcomb E.A., Hammell R.J. A fuzzy-based approach to support decision making in complex military environments. Intern. J. Intell. Inform. Technol. 2016, Vol. 12,
No. 1, pp. 1–30. DOI: 10.4018/IJIIT.2016010101.

7. Mukhlish F., Page J., Bain M. Evolutionary-learning framework: improving automatic swarm robotics design. Intern. J. Intell. Unmanned Syst. 2018, Vol. 6, No. 4, pp. 197–215.
DOI: 10.1108/IJIUS-06-2018-0016.

8. Walczak S. Society of agents: a framework for multi-agent collaborative problem solving. Intern. J. Intell. Inform. Technol. 2018, Vol. 14, No. 4, pp. 1–23. DOI: 10.4018/IJIIT.2018100101.

9. Groumpos P. P. Complex systems and intelligent control: Issues and challenges. Large Scale Systems: Theory and Applications 2001 : 9th IFAC symp. : 18–20 July, 2001, Bucharest, Romania, pp. 29–36. DOI: 10.1016/S1474-6670(17)40790-7.

10. Gonzales D., Harting S. Designing unmanned systems with greater autonomy. RAND Corporation, Santa Monica, CA, USA. 2014. Access mode: https://www.rand.org/content/ dam/rand/pubs/research_reports/RR600/RR626/RAND_RR626.pdf

11. Mostafa  S.A., Ahmad M.S., Mustapha A. Adjustable autonomy: a systematic literature review. Artificial Intelligence Review. 2019, Vol. 51, №3, pp. 149–186.

12. Ghallaba M., Nau D., Traverso P. The actor’s view of automated planning and acting: a position paper. Artificial Intelligence. 2014,  Vol. 208, pp. 1–17.

13. Mertoguno J.S. Human decision making model for autonomic cyber systems. International Journal on Artificial Intelligence Tools. 2014, Vol. 23, № 6. Access mode:  https://www.worldscientific.com/doi/abs/10.1142/ S0218213014600239.

14. Shepetukha Yu.M., Volkov O.Ye., Komar M.M.. Intellectualization of decision making processes in autonomous control systems. Cybernetics and computer engineering. 2021,
№ 2 (204),  pp. 49-63 http://jnas.nbuv.gov.ua/article/UJRN-0001262408

Received 04.01.2024

Issue 1 (215), article 4

DOI:

Cybernetics and Computer Engineering, 2024,1(215)

Volosheniuk D.O., PhD (Technics), Senior Researcher,
Head of the Unmanned Aircraft Systems Laboratory
https://orcid.org/0000-0003-3793-7801,
e-mail: p-h-o-e-n-i-x@ukr.net

Tymchyshyn R.M., PhD Student,
Researcher of Intelligent Control Department
https://orcid.org/0000-0002-4243-4240,
e-mail: romantymchyshyn.rt@gmail.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Sciences
of Ukraine and the Ministry of Education and Science of Ukraine,
40, Akad. Glushkov av., Kyiv, 03187, Ukraine

INTELLIGENT INFORMATION TECHNOLOGY
FOR TRANSPORT INFRASTRUCTURE MONITORING

Introduction. The rapid growth in the number of UAV developments in recent years is associated with serious technological advances in various fields. In particular, successes in the field of geographic information systems (GIS) raise the problem of dynamic modeling of reality and maintenance of cartographic information in an up-to-date state. To eliminate it, it is necessary to solve the problem of prompt introduction of changes. If the information contained in the GIS is inaccurate or outdated, its analysis will not correspond to the real situation, which can lead to wrong decisions.

That is why data about objects in GIS need systematic updating. Without updating the GIS data, they cannot perform the tasks for which they are intended. Therefore, the joint use of UAVs and GIS becomes an important component of a unified information and communication environment.

The purpose of the article is to investigate the information technology of ensuring the process of monitoring the transport infrastructure using the optical systems of an unmanned aerial vehicle (UAV).

The results. The issue of the need to create a new perspective system for controlling unmanned aerial vehicles and the development of methods for its intellectualization was considered. The ideas of applying the theory of invariance and autonomy for the synthesis of promising control systems are proposed, as well as a number of methods for ensuring a high level of their intellectualization. An approach to solving the problem is proposed, based on the theory of high-precision remote control of dynamic objects, as well as on the complex interaction of the methods of the theory of autonomy, adaptive control, and intellectualization of the processes of control of dynamic objects.

Conclusions. The obtained research results indicate the possibility of successful application of the developed transport infrastructure monitoring technology, in particular for the management of engineering networks. The solutions used in the developed technology are universal and capable of solving complex tasks for electric, water, heat and gas networks, as well as for roads and railways.

Prospects for further research are to improve the quality of thematic processing methods to improve the classification result. The developed methods can be used instead of expert decryption of monitoring data with similar and even higher accuracy.

Keywords: unmanned aerial vehicle, control system, invariance, intellectualization, autonomy.

Download full text!

REFERENCES

1. Modern information technologies in the tasks of navigation and guidance of unmanned maneuverable aircraft / Edited by: M.N. Krasylshchikova, H.G. Serebryakova M.: FIZMATLYT, 2009. 556 p.

2. Berezyna S.I. Blynychkin K.V. Automation of the process of rejecting data received from unmanned aerial vehicles. Science and technology of the Air Force of the Armed Forces of Ukraine. 2014. No. 1(14). P. 82–89.

3. Conte G., Doherty P. Vision-based unmanned aerial vehicle navigation using georeferenced information. EURASIP Journal on Advances in Signal Processing. 2009. Vol. 2009. P. 10.

4. Bhanu B.  Automatic target recognition State of the art survey  IEEE Trans. Aerosp. Electron. Syst.  Vol. AES-22. July 1986.  Р. 364–379.

5. Technical vision in mobile object management systems 2010. Proceedings of the scientific and technical conference-seminar. issue 4. Moscow: KDU, 2011. P. 328.

6. Polovynkin P.N. Course: Development of multimedia applications using OpenCV and IPP libraries. Lecture: Detectors and descriptors of key points. Image classification algorithms. The problem of object detection in images and methods of its solution. 2013.

7. Dolya K. V., Dolya O. E. Geoinformation systems on transport. Kharkiv: XNUMX named after O. M. Beketova, 2018. 230 p. [In Ukrainian]

8. Inozemtsev D.P. Automated aerial photography using the “GeoScan-PhotoScan” hardware and software complex. CAD and GIS of highways. 2014. No. 1(2). P. 46–51

9. Filippov D.V., Velikzhanina K.Yu., Gryadunov D.A. UAV studies the condition of highways. The roads Innovations in construction. No. 20. July. 2012. pp. 74–78. [In Ukrainian]

Received 03.01.2024

Issue 1 (215), article 3

DOI:

Cybernetics and Computer Engineering, 2024,1(215)

Komar M.M., PhD (Engineering), Senior Researcher 
Deputy Director for Scientific and Organizational Work,
https://orcid.org/0000-0001-9194-2850,
e-mail:nickkomar08@gmail.com

Chepizhenko V.I., DSc (Engineering), Senior Researcher,
Leading Researcher of the Intelligent Control Department,
https://orcid.org/0000-0001-8797-4868,
e-mail: chepizhenko.valeriy@gmail.com

Bogachuk Yu.P., PhD (Engineering), Senior Researcher,
Senior Researcher of the Intelligent Control Department
https://orcid.org/0000-0002-3663-350X,
e-mail: dep185@irtc.org.ua

Soloviev M.V., PhD Student,
Leading Engineer of the Intelligent Control Department
https://orcid.org/0009-0003-5131-7497,
e-mail: 19Leviathan90@gmail.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Sciences
of Ukraine and the Ministry of Education and Science of Ukraine,
40, Akad. Glushkov av., Kyiv, 03187, Ukraine

DEVELOPMENT OF THE MULTI PURPOSE SIMULATION COMPLEX
FOR TRAINING OF UNMANNED SYSTEMS OPERATORS

Introduction. This paper discusses the development of a multi-domain simulation complex for training unmanned systems operators. Active use of unmanned systems, their improvement, and complication of designs require the creation and development of simulators and modeling equipment, the use of which ensures effective training of competent operators.

The purpose of the paper is development of the multipurpose simulation complex for training of unmanned systems operators and for performing experimental and research works.

The methods. The following methods were used during the work: methods of automatic control, theory of navigation, theory of group decision making, theory of construction of distributed control systems for aircraft in a network-centric environment, methods of semi-natural modeling, methods of software engineering, methods for evaluating the piloting characteristics and stability and controllability characteristics of simulators, methods for evaluating the visualization systems of simulators.

The results. As a result of the work, a prototype of the complex was created, which can be used for the development and research of aircraft control systems, training of unmanned systems  operators, and conducting experimental research.

Conclusions. The developed prototype of the multi-domain simulation complex is a tool for solving the problem of quality training of operators. The complex allows operators to be trained in a safe environment, which reduces the risk of equipment damage and injuries to people. In addition, the complex allows for the study of aircraft control systems and the development of new control algorithms.

Keywords: Unmanned system, Training complex, Virtual environment, Operator, Simulation, Simulation complex, Control systems.
Download full text!

REFERENCES

1. https://www.polskieradio.pl/398/7856/Artykul/3077550

2. Kevin W. Williams. A Summary of Unmanned Aircraft Accident/Incident Data: Human Factors Implications, Final Report. DOT/FAA/AM-04/24. Office of Aerospace Medicine Washington, DC 20591, 2004.

3. Liang, X., Wang, Y. Design and development of ground station for UAV/UGV heterogeneous collaborative system. Ain Shams Engineering Journal. 2021.

4. Qi, S., et al. Unmanned Aircraft System Pilot/Operator Qualification Requirements and Training Study. Proceedings of the MATEC Web of Conferences.
Wuhan, China. 2008, Vol. 179, 03006

5. H. Ji, P. et al. Simulation of Unmanned Aircraft System Performing Surveillance Mission Based on Advanced Distributed Architecture, 2018. IEEE CSAA Guid., Nav. and Cont. Conf. 2018, pp. 1–4.

6. Bergmann K. Way forward unclear for lead-in fighter training system. Asia-Pacific Defence Reporter. 2021, 47(1), pp. 21–22.

7. Mairaj A., Baba A.I., Javaid A.Y. Application specific drone simulators: Recent advances and challenges. Simulation Modelling Practice and Theory. 2019, 94, pp. 100–117.

8. Karatanov O. et al. Implementation of augmented reality technologies in the training process with the design of aircraft equipment. Radioelectronic and Computer Systems. 2021, (1), pp. 110–118.

9. Pinchas G., Tishler A. The Israeli defense industry. The Economics of the Global Defence Industry. Routledge. 2019, pp. 354–377

10. Selecký M., et al. Analysis of using mixed reality simulations for incremental development of multi-uav systems. Journal of Intelligent & Robotic Systems. 2019, 95(1), pp. 211–227.

11. Drone Racing League. FLY-DRL Sim. URL:https://thedroneracingleague.com/ (Last access: 29.11.2023)

12. RDS. REAL DRONE SIMULATOR. URL: https://www.realdronesimulator.com/ (Last access: 29.11.2023)

13. H-SIM. SIMDRONE. URL: http://www.h-sim.com/ (Last access: 29.11.2023)

14. Quantum3D. Quantum3D UAV Simulator. URL: https://quantum3d.com/uav-simulator/ (Last access: 29.11.2023)

15. Garzón M., Valente J., Roldán J.J., Garzón-Ramos D., de León J., Barrientos A., del Cerro J. Using ROS in Multi-robot Systems: Experiences and Lessons Learned from Real-World Field Tests. Robot Operating System (ROS): The Complete Reference; Koubaa, A., Ed.; Springer International Publishing: Cham, Switzerland. 2017, Volume 2, pp. 449–483. 

16. Mueller M., Smith N., Ghanem B. A Benchmark and Simulator for UAV Tracking. Proceedings of the Computer Vision—ECCV 2016, Amsterdam, The Netherlands, 8–16 Oct 2016; Leibe B., Matas J., Sebe N., Welling M., Eds.; Springer International Publishing: Cham, Switzerland. 2016, pp. 445–461. 

17. Olivares-Mendez M.A., Kannan S., Voos H. Setting up a testbed for UAV vision based control using V-REP & ROS: A case study on aerial visual inspection. Proceedings of the 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL, USA, 27–30 May 2014. 2014, pp. 447–458. 

18. Meyer J., Sendobry A., Kohlbrecher S., Klingauf U., von Stryk O. Comprehensive Simulation of Quadrotor UAVs using ROS and Gazebo. Proceedings of the 3rd International Conference on Simulation, Modeling and Programming for Autonomous Robots (SIMPAR), Tsukuba, Japan, 5–8 Nov 2012. 

19. Huang H.; Sturm J., Tum Simulator. 2018. URL: http://wiki.ros.org/tum_simulator (Last  access: 29.11.2023)

20. Nayak D., Patra S. Development of obstacle-avoiding robots using RF technology. International Journal of Intelligent Unmanned Systems. 2016, Vol. 4, №4, pp. 214–225.

21. Tang S., Kubo N., Kawanishi N., Furukawa R., Hasegawa A., Takeuchi Y. Cooperative relative positioning for intelligent transportation system. International Journal of Intelligent Transportation Systems Research. 2015, Vol. 13, №3, pp. 131–142.

22. Becerra V.M. Autonomous control of unmanned aerial vehicles. Electronics.  2019, Vol. 8, №452. URL: https://www.researchgate.net/publication/332588499_Autonomous_Control_of_ Unmanned_Aerial_Vehicles. (Last  access: 29.11.2023)

23. Warren R. D. Application of artificial intelligence techniques in uninhabited
aerial vehicle flight. Proceedings of the Digital Avionics Systems Conference.
Indianapolis, IN, USA. 2003. URL: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/ 20040082071.pdf. (Last access: 29.11.2023)

24. Pelosi M., Kopp C., Brown M. Range-limited UAV trajectory using terrain masking under radar detection risk. Applied Artificial Intelligence.  2012, Vol. 26, №8, pp. 743–759.

25. Roldán J., del Cerro J., Barrientos A. Using process mining to model multi-UAV missions through the experience. IEEE Intelligent Systems. 2017, Vol. 32, №4, pp. 40–47.

26. Pham H., La H.M., Feil-Seifer D., Nguyen L.V. Autonomous UAV navigation using reinforcement learning. Proceedings of the 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Philadelphia, PA, USA. 2018. URL: https://ieeexplore.ieee.org/document/8468611. (Last access: 29.11.2023)

27. Volkov O., Komar M., Synytsya K., & Volosheniuk D. The UAV simulation complex for operator training. Multi Conference on Computer Science and Information Systems, MCCSIS 2019-Proceedings of the International Conference on e-Learning 2019. 2019, pp. 313–316.

Received 29.12.2023

Issue 1 (215), article 2

DOI:

Cybernetics and Computer Engineering, 2024,1(215)

Dzhebrailov R.Yu., PhD Student,
Junior Researcher of the Research Laboratory of Unmanned Complexes and Systems,
https://orcid.org/0000-0002-4473-9670,
e-mail: rombik1197@gmail.com

Gospodarchuk O.Yu.,
Senior Researcher of the Intelligent Control Department
https://orcid.org/0000-0001-6619-2277,
e-mail: olexago@gmail.com

International Research and Training Center
for Information Technologies and Systems
of the National Academy of Science of Ukraine
and the Ministry of Education and Science of Ukraine.
40, Acad. Glushkov av., 03187, Kyiv, Ukraine

DETECTION OF SPECIAL ZONES AS A BASIS FOR THE METHOD OF TOPOGRAPHIC AFFINITY OF IMAGES

Introduction. The satellite and inertial navigation systems of an unmanned aerial vehicle (UAV) or unmanned aircraft system (UAS) have their drawbacks. Attempts to eliminate these shortcomings are to develop an autonomous navigation system. The officially patented model of an autonomous navigation system, as it turned out, also has its drawbacks. Accordingly, there is a need to improve such an autonomous navigation system.

The purpose of the paper is to develop and study a method for determining the topographic affinity of images based on the detected special zones in images of the natural landscape for autonomous UAV navigation.

Results. A method of topographic affinity of visual images based on the detection of special zones by searching for local maxima of the Laplace operator in the image has been developed. The method of topographic affinity of images allows  involving a smaller number of special points for comparison, which reduces the amount of required memory resources and increases performance.

Conclusions. The proposed method of topographic affinity of images based on the detection of special zones (blob detection methods) based on the principle of searching for local maxima of the Laplace operator can be used to build an autonomous navigation system for UAVs. The algorithmic implementation of the method has shown that it can work with a large number of complex and diverse images of the earth’s surface obtained during UAV flights, is effective by increasing the processing speed of the studied images, and can be implemented to create full-fledged UAV autonomous navigation systems.

Keywords: unmanned aerial vehicle, unmanned aerial vehicle complex, autonomous navigation, special points, special zones, method of special image zones analysis.

Download full text!

REFERENCES

  1. Irani G., Christ J. Image processing for Tomahawk scene matching. Johns Hopkins APL Technical Digest. 1994, No, 3, pp. 250–264.
  2. Durrant-Whyte H., Bailey T. Simultaneous localization and mapping: part I. IEEE Robotics and Automation Magazine. 2006, No. 2, pp. 99–110.
  3. Durrant-Whyte H., Bailey T. Simultaneous localization and mapping (SLAM): part II. IEEE Robotics and Automation Magazine. 2006, No. 3, pp. 108–117.
  4. SVO: Semi-Direct Visual Odometry for Monocular and Multi-Camera Systems / C. Forster et al. IEEE Transactions on Robotics. 2017, Vol. 33, no. 2, pp. 249–265.
  5. Scaramuzza D., Fraundorfer F. Visual odometry [tutorial]. Part I: The first 30 years and fundamentals. IEEE Robotics and Automation Magazine. 2011, No. 4, pp. 80–92.
  6. Scaramuzza D., Fraundorfer F. Visual odometry: part II: matching, robustness, optimization, and applications. IEEE Robotics and Automation Magazine. 2012, No. 2, pp. 78–90.
  7. Efficient on-board Stereo SLAM through constrained-covisibility strategies / G. Castro et al. Robotics and Autonomous Systems. 2019, No. 116, pp. 192–205.
  8. Qin T., Li P., Shen S. VINS-Mono: a robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics. 2017, No. 4, pp. 1004–1020.
  9. VIMO: simultaneous visual inertial model-based odometry and force estimation / B. Nisar et al. IEEE Robotics and Automation Letters. 2019, No. 3, pp. 2785–2792.
  10. Autonomous navigation system for unmanned aerial vehicle based on topographic clustering of visual images : pat. UA 121833 C2 Ukraine: 2020.01. № a 2019 05904 ; application for application filed 29.05.2019 ; published 27.07.2020, Bulletin No. 14. 18 p.
  11. Intelligent information technology of autonomous navigation of an unmanned aerial vehicle / O. Volkov et al. Actual problems of automation and instrumentation: materials of the 2nd International scientific and technical conference, Kharkiv, December 6. 2018. 2018, pp. 18-19.
  12. Methodological recommendations “Combating unmanned aerial vehicles” (based on the experience of the Joint Forces Operation (formerly ATO). Kyiv: Center for Operational Standards and Methods of Preparation. Arms. Forces of Ukraine in cooperation with the Main Directorate of Training. Armed Forces of Ukraine. Armed Forces of Ukraine, 2019. 44 p.
  13. Gerasimov S., Kolomiytsev O., Pustovarov V. Features of determining the measurement accuracy of inertial coordinate determination devices. Control, navigation and communication systems. 2018, № 6(52), pp. 3-8.
  14. Zarubin A., Astin V. Analysis of total instrumental errors of inertial navigation systems. Systems of weapons and military equipment. 2006, № 1, pp. 68-71.
  15. Han K. T. M., Uyyanonvara B. A Survey of Blob Detection Algorithms for Biomedical Image. 2016 7th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), pp. 57–60.
  16. Zavadil J., Tuma J., Santos V. Traffic signs detection using blob analysis and pattern recognition. Proceedings of the 13th International Carpathian Control Conference (ICCC), High Tatras, 28–31 May 2012. 2012, pp. 776–779.

Received 19.12.2023

Issue 1 (215), article 1

DOI:

Cybernetics and Computer Engineering, 2024,1(215)

Volkov O.Ye., PhD (Engineering), Senior Researcher
Director
https://orcid.org/0000-0002-5418-6723,
e-mail: alexvolk@ukr.net

Simakhin V.M., PhD Student,
Senior Researcher of the Laboratory of Unmanned Complexes and Systems
https://orcid.org/0000-0003-4497-0925,
e-mail: thevladsima@gmail.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Sciences
of Ukraine and the Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

ALGORITHM FOR CONTROLLING THE FULL ENERGY OF AN UNMANNED AERIAL VEHICLE

Introduction. The development of unmanned aviation requires constant active implementation of new technologies and systems. Autonomous control and navigation are among the most relevant areas of development of unmanned aerial vehicles (UAVs). Various approaches and tools are used to increase the level of intellectualization of UAV control, including are full-energy control systems.

The purpose of the paper is to develop a full-energy control algorithm for UAVs to enhance control intellectualization through dynamic regulation of the altitude and flight speed of the aircraft. 

Methods. Theory of intelligent control, automatic control, theory of UAV flight dynamics.

Results. To develop the algorithm for controlling the full energy of UAVs, the theoretical basis of full-energy control systems was considered, and the development of the concept of such modern systems was analyzed. On the basis of the general laws of aircraft control, a full-energy control algorithm for UAVs was synthesized, which operates in three modes: full energy control, altitude control, and flight speed control. 

Conclusions. The developed full-energy control algorithm covers the main necessary UAV control modes for performing flight tasks in a volatile environment. The use of such an algorithm in modern navigation and flight systems will increase the efficiency and intellectualize the UAV control process.

Keywords: Unmanned Aerial Vehicle, Control Algorithm, Full Energy Control, Navigation and Piloting Complex, TECS.

Download full text!

REFERENCES

  1. Kevin R. Bruce. (NASA-CR-778285) NASA B737 Flight Test Results of the Total Energy Control System (Boeing Commercial Airplane Cc.). Seattle, Washington, 1987. 103 p.
  2. Lambregts, Antonius A. TECS Generalized Airplane Control System Design – An Update. In Advances in Aerospace Guidance, Navigation and Control, 2013, pp. 503–534.
  3. Lambregts, Antonius A., Flight envelope protection strategies for automatic and augmented manual control, in Proc. of the CEAS Conf. on Guidance, Navigation and Control. Delft, The Netherlands, 2013, pp. 1364–1383.
  4. Jimenez, P., Lichota, P., Agudelo, D. and Rogowski, K. Experimental Validation of Total Energy Control System for UAVs. In Energies, 2019, Vol. 13, Number 1, 14. p.
  5. Lai, Y.-C. and Ting, W. Design and Implementation of an Optimal Energy Control System for Fixed-Wing Unmanned Aerial Vehicles. In Applied Sciences, 2016, Vol. 6, Number 11, 369. p.
  6. Volkov, O. Ye., Shepetukha, Y. M., Bogachuk, Y. P., Komar, M. M., & Volosheniuk, D. O. Experience in Development and Implementation of Intelligent Systems for Control of Dynamic Objects. In Control Systems and Computers, 2022, Issue 1 (297), pp. 64–81.
  7. Volkov, O., Komar, M., Rachkovskij, D., & Volosheniuk, D. Technology of Autonomous Take-Off and Landing for the Modern Flight and Navigation Complex of an Unmanned Aerial Vehicle. In Cybernetics and Systems Analysis. 2022, Vol. 58, Issue 6, pp. 882–888.

Received 13.12.2023