Issue 1 (195), article 6


Kibern. vyčisl. teh., 2018, Issue 1 (195), pp.

Chernyshova T.A., physician,

Aviation Medical Center of the National Aviation University,
Komarova av., 1, Kyiv, 03058, Ukraine


Introduction. Modern advances in science and technology have substantially expanded the possibilities for detecting malignant neoplasms. A great number of methods for the detection and allocation of circulating tumor cells clearly indicates the interest of researchers to this problem.
The purpose of the work is to form a complex of criteria for tumor malignancy evaluation and to improve the method of detecting circulating tumor cells in human blood.
Results. The proposed method for determining circulating tumor cells, which is an improvement of ISET technology, combines two stages. At the first stage the improvement is in adding two additional polycarbonate filters with 5 and 3 micron diameter pores, and providing a mode of 100% sealing of the chamber with hemolysis, and constant pressure throughout the filtration process. At the second stage, we carried out the determination of malignancy degree of the isolated cells using the developed set of criteria. The use of the developed method in the automated system for the analysis of digital microscopic images of circulating tumor cells provides the detection and calculation of characteristic features for assigning an object to a certain class of malignancy and the creation of scanned images database with recorded existing cells or their entities, as well as the final verification of the results of tumor malignancy evaluation for template masks of circulating tumour cells and benign tumor cells.
Conclusions. The application of the proposed method for the detection of circulating tumor cells allows detecting smaller cells than in case of using traditional methods, ensures their integrity and intactness.

Keywords: circulating tumor cells, criteria for evaluation of tumormalignancy, method of determining circulating tumor cells in human blood.

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1. Krivacic R.. Ladanyi A., Curry D., Hsieh H., Kuhn P., Bergsrud D., Kepros J. A rare-cell detector for cancer. Proceedings of the National Academy of Sciences of the United States of America. 2004. V. 101, N 29. P. 10501-10504.

2. Harouaka R., Kang Z., Zheng S.Y., Cao L. Circulating tumor cells: advances in isolation and analysis, and challenges for clinical applications. Pharmacol Ther. 2014. Vol. 141. P. 209-221.

3. Tewes M., Aktas B., Welt A., Mueller S., Hauch S., Kimmig R., Kasimir-Bauer S. Molecular profiling and predictive value of circulating tumor cells in patients with metastatic breast cancer: an option for monitoring response to breast cancer related therapies. Breast cancer research and treatment. 2009. Vol. 115, N 3. P. 581-590.

4. Danila D.C., Heller G., Gignac G.A. et al. Circulating tumor cell number and prognosis in progressive castration-resistant prostate cancer. Clin Cancer Res. 2007. Vol. 13(23). P. 7053-7058.

5. Krivacic R.. Ladanyi A., Curry D., Hsieh H., Kuhn P., Bergsrud D., Kepros J. A rare-cell detector for cancer. Proceedings of the National Academy of Sciences of the United States of America. 2004. Vol. 101, N 29. P. 10501-10504.

6. Ntouroupi T., Ashraf S., McGregor S., Turney B., Seppo A., Kim Y., Wang X. Detection of circulating tumour cells in peripheral blood with an automated scanning fluorescence microscope. British journal of cancer. 2008. Vol. 99, N 5. P. 789-795.

7. Bauer K., de la Torre-Bueno J., Diel I., Hawes D., Decker W., Priddy C., Bossy B. Reliable and sensitive analysis of occult bone marrow metastases using automated cellular imaging. Clinical cancer research: an official journal of the American Association for Cancer Research. 2000. Vol. 6, N 9. P. 3552-3559.

8. Kagan M., Howard D., Bendele T., Mayes J., Silvia J., Repollet M., Doyle J. A Sample Preparation and Analysis System for Identification of Circulating Tumor Cells. Journal of Clinical Ligand Assay. 2002. Vol. 25, N 1. P. 104-110.

9. Andreopoulou E., Yang L.-Y., Rangel K., Reuben J., Hsu L., Krishnamurthy S., Valero V. Comparison of assay methods for detection of circulating tumor cells in metastatic breast cancer: AdnaGen AdnaTest BreastCancer Select. Detect versus Yeridex CellSearch system. Int. journal of cancer. 2012. Vol. 130, N 7. P. 1590-1597.

10. 10 Nezos A., Pissimisis N., Lembessis P., Sourla A., Dimopoulos P., Dimopoulos T., Tzelepis K. Detection of circulating tumor cells in bladder cancer patients. Cancer treatment reviews. 2009. V. 35, N 3. P. 272-279.

11. Tewes M., Aktas B., Welt A., Mueller S., Hauch S., Kimmig R., Kasimir-Bauer S. Molecular profiling and predictive value of circulating tumor cells in patients with metastatic breast cancer: an option for monitoring response to breast cancer related therapies. Breast cancer research and treatment. 2009. V. 115, N 3. P. 581-590.

12. Ignatiadis M, Lee M, Jeffrey SS. Circulating Tumor Cells and Circulating Tumor DNA: Challenges and Opportunities on the Path to Clinical Utility. Clin Cancer Res. 2015. No21(21), P. 4786-800.

13. Alix-Panabieres C, Pantel K. Technologies for detection of circulating tumor cells: facts and vision. Lab Chip. 2014. No 14(1):P. 57-62.

14. Ferreira M.M, Ramani V.C, Jeffrey S.S. Circulating tumor cell technologies. Mol Oncol. 2016. No10(3), P. 374-94

15. Gertler R., Rosenberg R., Fuehrer K., Dahm M., Nekarda H., Siewert J. Detection of circulating tumor cells in blood using an optimized density gradient centrifugation. Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer. 2003. Vol. 162. P. 149-155.

16. Tan S., Yobas L., Lee G., Ong C, Lira C. Microdevice for the isolation and enumeration of cancer cells from blood. Biomedical microdevices. 2009. V. 11, N 4. P. 883-892.

17. Tan S., Yobas L., Lee G., Ong C, Lira C. Microdevice for the isolation and enumeration of cancer cells from blood. Biomedical microdevices. 2009. V. 11, N 4. P. 883-892.

18. Kovalev A.A, Grudinskaya T.V., Kuznetsov T.P., Kovalev K.A. Heterogeneity of circulating tumor cells. Oncology. 2012. V.4. No. 2 12.12-12.(in Russian)

19. Pavlov S.V, Kozhemyako V.P, Burdenyuk I.I. Rami Rebhi Hamdi. Optical and electronic technologies for the analysis of biomedical images. Vinnitsa: VNTU, 2011. 166 p. (in Ukranian)

20. Nepomnyaschaya E.I., Kit O.I., Nistratova O.V., Novikova I.A. and others. Circulating tumor cells and some morpho-immuno-histochemical indices at colorectal cancer. Modern problems of science and education. 2016. No. 2. (in Russian)

21. G. Vona, C. Beroud, A. Benachi, A. Quenette, J.P. Bonnefont, Y. Dumez, B. Lacour, P. Paterlini-Brechot. Enrichment and genetic analyses of fetal cells circulating in the maternal blood by the ISET technique and single cell microdissection: a non-invasive tool for early prenatal diagnosis. Am. J. Pathol. 2002. No 160. P. 51-58.

22. Ismayilova G., Laget S., Paterlini-Brechot P. Diagnosis of circulating tumor cells using ISET technology and their molecular characteristics for fluid biopsy. URL: (Last accessed: 08.12.2018)

23. Ledov V.K., Skrinnikova MA, Popova O.P. Isolation of Circulating Tumor Cells by Isolated Size (ISET) (overview). Voprosy Oncologii, 2014. No60 (5). P.548-552. (in Russian)

24. Baikhenko A.K, Shaimardinova G. M., Popova N.V., Zhusinova B.T., Ismayilova G.N. Circulating Cancer Cells: Molecular Characteristics and Monitoring of Cancer Treatment. Clinical Medicine of Kazakhstan. 2013. Vol.4 No30. (in Russian)

25. Volchenko N.N. Cytological diagnostics of tumors of the mammary gland. Mammology, 2006, No. 1, P. 35-39. (in Russian)

26. Paterlini-Brechot P, Benali-Furet NL. Circulating tumor cells (CTC) detection : Clinical impact and future directions. Cancer Letter. 2007. No 253. P. 180-204.

27. Zubtsov D.A., Zubtsova J.I., Lavrov A.V., Legchenko E.V. et al. Circulating tumor cells (CAC) in breast cancer: prognostic significance and methods of excretion. Trudy MFTI. 2012. Volume 4. No. 3. P.18-26. (in Russian)

28. Chimitov A.A.,.Ryantsantsev N.V, Dambayev G.T., Khitricheev V.E. and others. Filtration of venous blood of boluses with the aim of cytological diagnostics of malignant neoplasms. Bulleten VSNI SO RAMN, 2010. No. 3 (73). (in Russian)

29. Laget S, Broncy L, Hormigos K, Dhingra DM, BenMohamed F, Capiod T, et al. Technical Insights into Highly Sensitive Isolation and Molecular Characterization of Fixed and Live Circulating Tumor Cells for Early Detection of Tumor Invasion. PLOS ONE, 2017, 12 (1): e0169427.

30. Ushakova G.O. Modern methods of clinical diagnostics. Guidelines. Dnipropetrovsk: DNU. 2003. 52 p.

31. Zlepko S.M., Chernyshova T.A., Timchik S.V., Krivonosov V.E., Zlepko O.S. Information system (IS) for receiving and processing microscopic images of circulating tumor cells (CTC). Achievements of clinical and experimental medicine, 2017, No 4, P. 39-46. (in Ukranian)

32. Azarhov O.Yu., Zlepko S.M., Timchik S.V., Chernyshova T.A.,. Danilkov S.O Methods and tools for computer analysis of microscopic images of circulating tumor cells. Bulletin of scientific researches. 2017. No. 4. P. 162-166. (in Ukranian)

33. Golovnya V.M., Zlepko S.M. The system of express diagnostics of formed blood elements. Measuring and computing engineering in technological processes. 2010, 2, P. 163-168. (in Ukranian)

34. Lyadov V.K., Ledin E.V., Skrypnikova M.A. Cytological diagnostics of pancreatic adenocarcinoma by the method of isolating the size of circulating tumor cells from peripheral blood: a message from practice. Clinical Laboratory Diagnostics, 2017. No. 11. P. 31-39. (in Russian)

35. Kit O.I., Novikova I.A, Selutina O.N., Dontsov V.A., Chernikova E.N., Samaneyeva N.Yu., Nistratova O.V. Investigation of the level of the central ankylosing spondylitis in epithelial tumors of various localizations. International Journal of Applied and Fundamental Researches. 2018. No. 12-5. P.817-820. (in Russian)

Received 26.12.2018

Issue 1 (195), article 5


Kibern. vyčisl. teh., 2018, Issue 1 (195), pp.

Kaplin I.V.1, ophthalmologist of the Kyiv Center for Eye Therapy and Microsurgery, PhD student of the Ophthalmology Department

Kochina M.L.2, DSc. (Biology), Professor,
Head of the Medical and Biological Basics of Sports and Physical Rehabilitation Department,

Demin Yu.A.1, DSc. (Medicine), Professor,
Head of Ophthalmology Department,

Firsov A.G.3, PhD (Technics), Chief Designer

1 Kharkiv Medical Academy of Postgraduate Education,
58, Amosova str., Kharkiv, Ukraine, 61000

2 Petro Mohyla Black Sea National University
10, 68-Desantniv str., Mykolaiv, Ukraine, 54000

1, Aviation str., ap.7, Kharkiv, Ukraine, 61166


Introduction. According to the World Health Organization (WHO), glaucoma accounts for 4–5% of the total ocular pathology, making it one of the most common eye diseases in the world. The first sign of the disease is a constant or periodic increase in intraocular pressure, which leads to the development of visual field defects, optic nerve atrophy, and dystrophic changes in eye tissues. Detection of glaucoma and ocular hypertension is done by measuring the intraocular pressure, which is the standard procedure for diagnosis of the condition of eyes in all patients over 40 years of age. Patients with a diagnosis of “glaucoma” should constantly measure the intraocular pressure, which is necessary to control the effectiveness of treatment, its correction and evaluation of the effectiveness of drugs.
The purpose of the article is to develop the system for assessing the intraocular pressure level using the interference pictures parameters observed on the eye cornea in the polarized light.
Results. The proposed system of two-level classification of the intraocular pressure level, which contains a pair of complementary fuzzy models, formalized in the form of logical rules and sets of numerical parameters of functions (membership and conclusion), and additional decisive rules that consist of a regression equation and a classification criterion.
Such a hybrid system adequately reflects the general communication of adjusted interference picture parameters with a measured value of intraocular pressure by classical Goldman tonometry, which allowed offering it to practical use as a basis for intraocular pressure express assessment.
Conclusion. Using the developed software module evaluation of intraocular pressure, based on the proposed concept of express assessment of intraocular pressure, integrates fuzzy models and decisive rules allowing to improve the results of glaucoma treatment at early detection of high level of intraocular pressure.

Keywords: intraocular pressure, central eye cornea thickness, interference pictures, express assessment.

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1. Tham Y.C., Li X., Wong T.Y., Quigley H.A., Aung T., Cheng C.Y. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014; 121:2081-90.

2. Leske M. C. Ocular perfusion pressure and glaucoma: clinical trial and epidemiologic findings. Curr. Opin. Ophthalmol. 2009. Vol. 20, N 2. P. 73-78.

3. Howard D. L., Kim M. M., Hartnett M. E. Predicting glaucoma diagnosis in an elderly sample: revisiting the established populations for epidemiologic studies of the elderly. J Natl Med Assoc .2011. Vol. 103. P. 332-341.

4. Day A.C., Baio G., Gazzard G.The prevalence of primary angle close glaucoma in European derived population: a systematic review. Br J Ophthalmol. 2012. Vol. 96. P. 1162-1167.

5. Ravi Thomas. Glaucoma a in developing countries. Indian J Ophthalmol. 2012. Vol. 60. 5. P. 446-450.

6. Rykov S.A., Vitovskaya O.P., Stepanyuk G.I. Incidence, prevalence of ophthalmopathology and eye disability in Ukraine. News of Glaucoma. 2009. No. 1. 34-35. (in Russian)

7. Povch Z.V. Contemporary regional features and dynamics of glaucoma morbidity of adult population of Ukraine, gender aspects. Health of Society. 2014.1 2. P. 36-40. (in Ukrainian)

8. Povch Z.V. Approaches to improving the glaucoma prevention taking into account the regional peculiarities of its prevalence among different age groups of the population of Ukraine. Health of Society. 2014. 1-2. P. 79. (in Ukrainian)

9. Rykov S.O. Medvedovskaya N.V., Troyanov D.P. Current state and dynamics of glaucoma incidence among adult population of Ukraine. Ukraine. The health of the Nation. 2012. 2-3. P. 119-121. (in Ukrainian)

10. Rykov S.A., Shargorodskaya I.V., I.I. bakdardin, Simchuk I.V. Diagnosis and treatment of glaucoma. Supplement to lectures; ed. S.O. Rykova. [2nd ed.]. K.: LLC “Firm ASAVA”, 2014. 72p. (in Russian)

11. Kaplin I.V., Kochina M.L., Demin Y.A., Firsov A.G. The conception of telemedicine system for express estimation of intraocular pressure’s level. Cybernetics and Computer Engineering. 2018. No 1 (191). P. 76-94. (in Ukrainian)

12. Brandt J. D., Gordon M.O., Beiser J. A. The Ocular Hypertension Treatment Study (OHTS) Group. Adjusting Intraocular Pressure for Central Corneal Thickness Does Not Improve Prediction Models for Primary Open-Angle Glaucoma .Ophthalmol. 2012. Vol. 119(3). P. 437-442.

13. Egorov E.A., Vasina M.V. The influence of the cornea thickness on the level of intraocular pressure among various groups of patients. Clinical Ophthalmology. 2006. No. 1. P. 16-19. (in Russian)

14. Eremina M.V., Erichev V.P., Yakubova L.V. The influence of the central thickness of the cornea on the level within the eye pressure is normal and with glaucoma (overview). Glaucoma. 2006. No. 4. P. 78-83. (in Russian)

15. Alekseev V. N., Litvin I. B. The influence of corneal thickness on the level of intraocular pressure and prognosis in primary open-angle glaucoma. Clinical ophthalmol. 2008. No. 4. P. 130-132. (in Russian)

16. Avetisov S.E., Petrov S.Yu., Bubnov I.A. Influence of the central cornea thickness on the results of tonometry (review of literature). Vestn. Ophthalmol. 2008. No. 5. P. 3-7. (in Russian)

17. Kochina M.L., Demin Y.A., Kaplin I.V., Kovtun N.M. Model of stress-deformation status of the eye corneaEast European Scientific Journal. 2017. 2(18). P. 61-66.

18. Shtovba S.D. Designing fuzzy systems by means of MATLAB. Horiachaia liniia – Telecom. M., 2007. 288p with pictures. (in Russian)

19. Yager R., Filev D. Essential of Fuzzy Modeling and Control. John Willey & Sons, 1994. 388 p.

20. Open source software for numerical computation. Access mode: (Last accessed: 21.06.2018.)

21. Fuzzy Logic Toolbox. Access mode: (Last accessed: 21.06.2018)

22. Fuzzy Logic Library for Microsoft .Net Access mode: (Last accessed: 21.06.2018)

Received 03.12.2018

Issue 1 (195), article 4


Kibern. vyčisl. teh., 2018, Issue 1 (195), pp.

Milyavsky Y.L., Senior Lecturer,
Department of the Mathematical Methods of System Analysis

National Technical University of Ukraine “I. Sikorsky Kyiv Polytechnic Institute”
37 Peremohy av., Kyiv, 03056, Ukraine


Introduction. Cognitive map is a popular way of modeling complex multivariate systems. Usually weights coefficients of edges connecting the cognitive map nodes are suggested by experts. But such a method is always inaccurate. In case when nodes coordinates are measured, there is the possibility for mathematical identification of these coefficients. However, the issue is that often not all nodes coordinates of a cognitive map are measured, but only a few of them. In this case the problem of identification is much more complicated.
The purpose of the article is to research and develop a method for identifying weights of cognitive map nodes in case when number of nodes is known, but not all of them are measured.
Results. Identification method based on 4SID method is suggested. It allows finding some realization of the system equivalent to the original cognitive map in its outputs, with the control observation matrices remaining unchanged.Invariants of the original and identified systems are analyzed. Practical example of identifying a cognitive map of an IT company is considered. It is shown what the accuracy of the suggested method depends on and under which conditions it is applicable.
Conclusions. As demonstrated in the research, the proposed method of identifying cognitive maps achieves almost full coincidence of measured coordinates between the original and the identified systems, although the incidence matrices themselves may not be equal. Invariants of the system, specifically eigenvalues, are identified with sufficient precision if the problem is well-conditioned, i.e. with sufficient number of measurable coordinates, sufficient number of measurement periods and low level of measurement noise. If these conditions are not fulfilled, the identification results become incorrect.

Keywords: cognitive map, identification, 4SID method, unmeasurable coordinates.

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1 Roberts F. Discrete Mathematical Models with Applications to Social, Biological, and Environmental Problems. Englewood Cliffs: Prentice-Hall, 1976. 559p.

2 V. Gubarev, V. Romanenko, Y. Milyavsky. Identification in cognitive maps in the impulse process mode with full information.Problems of control and informatics. 2018. N 4. P. 30-43 (in Russian).

3 Verhagen M., Dewilde P., Subspace model identification. Part I: the output-error state space model identification class of algorithms. Int. J. Control. 1992,. N56. P. 1187-1210.

4 V. Romanenko, Y. Milyavsky, M. Polyakov, Y. Letser, G. Shevchenko. Research of scenarios of IT company development based on decision-making in cognitive maps impulse process control mode. Proceedings of 1st international scientific and practical forum “Science and business”. (29-30 of June, 2015, Dnipropetrovsk), Dnipropetrovsk, 2015. P. 233-237.2015. P. 233-237.

Recieved 27.11.2018

Issue 1 (195), article 3


Kibern. vyčisl. teh., 2018, Issue 1 (195), pp.

Yefymenko M.V., PhD.,
associate professor of Zaporizhzhya National Technical University,
Chief Designer

Scientific Production Enterprise “HARTRON-YUKOM”
Soborny аv., 166, Zaporozhye, 69035, Ukraine


Introduction. There are a number of control objects, the movement of which in space can be interpreted as the movement of a point along a sphere of a given radius. As an example of such a motion, the angular motion of a spacecraft can be considered. Using the orientation quaternion and its derivative to describe the angular motion of a spacecraft, the angular motion can be represented as the motion of a point along a unit sphere in R4.

While controlling such objects, the methods for solving the basic problems of controlling the motion of a point along the unit sphere in the Rn space are of interest.

The purpose of the article is to build the following algorithms for controlling the motion of a point along the sphere:
–                algorithm of a point motion stabilization with respect to program trajectory;
–                algorithm of a point relocation from the current position to a specified position in minimum time;
–                algorithm of a point relocation from the current position to a specified position in fixed time.
Results. The methods for solving the various problems of controlling the motion of a point along the sphere have been proposed.

Conclusion. On the basis of main properties of point along the sphere movement, the methods for solving the problems of controlling the motion of a point along the unit sphere in n-dimensional space have been proposed. Using the proposed methods, the solutions for the following control tasks have been found:
–     problems of stabilizing the motion of a point along the sphere with respect to program trajectory;
–     speed problems taking place when a point moves on along the sphere;
–     problems of a point on the sphere relocation from the current position to a specified position in fixed time.
The efficiency of the proposed algorithms has been demonstrated on the example of spacecraft angular motion control. The results obtained can be applicable in the development of various control systems, the spacecraft angular motion control systems in particular.

Keywords: sphere, control, point projection, quaternion.

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1 Kirichenko N.V., Matvienko V.T. Algorithms of asymptotic terminal and adaptive stabilization of the rotational motions of a rigid body. Problems of Control and Informatics. 2003. No 1. P. 5-15. (in Russian).

2 Kirichenko N.F., Lepekha N.P. Perturbation of pseudoinverse and projection matrices and their application to the identification of linear and nonlinear dependencies. Problems of Control and Computer Science. 2001. N 1. P. 6-22.

3 Kirichenko N.F., Lepekha N.P. Pseudo-inversion in control and observation problems. Automation. 1993. N 5. P. 69-81.

4 Kirichenko N.F., Matvienko V.T. Optimal synthesis of structures for linear systems. Problems of Control and Informatics. 1996. N 1-2. P. 162-171.

5 Yefimenko N.V. Mathematical model of the angular motion of the spacecraft in the parameters of Rodrigues-Hamilton and its properties. Electronic modeling. 2018. Vol. 40. No 6. P. 21-36 (in Russian).

6 Quakernaak X., Sivan R. Linear optimal control systems. Moscow: Mir, 1977. 650 p. (in Russian).

7 Yefimenko N.V. Synthesis of the space-optimal time-reversal of a spacecraft using the dynamic equation of the rotational motion of a rigid body in the Rodrig Hamilton parameters. Problems of Control and Computer Science. 2017. No 3. P. 109-128. (in Russian).

8 Yefimenko N.V. Synthesis of spacecraft reorientation control algorithms using the dynamic equations of the rotational motion of a rigid body in the Rodrig Hamilton parameters. Problems of Control and Computer Science. 2015. No 3. P. 145-155. (in Russian).

Received 27.11.2018

Issue 1 (195), article 2


Kibern. vyčisl. teh., 2018, Issue 1 (195), pp.

Sukhoruchkina O.N., Senior Researcher,
Department of System Information Technologies

Progonnyi N.V., Researcher,
Department of System Information Technologies

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


Introduction. The problem of timely updating of laboratory means for research and training in robotics and intelligent technologies is considered. The information technology is proposed for organization of the laboratory complex with two types of components — remotely controlled robotics equipment and virtual means for corresponding practical research. Today, such approaches are the most optimal for providing research and training processes with modern resources for acquiring practical experience in rapidly developing scientific fields.

The purpose of the article is to consider the information technology capabilities in the organization of remote access to physical equipment and virtual means for practical research and training on robotics.

Methods. Methods of distributed information and computing processes, communication protocols, and web application programming are used.

Results. Two types of specialized means of our laboratory complex — physical equipment with remote access and virtual environments are considered. The general structures of autonomous mobile robot and sensor module that can be used remotely for certain research and practical training are presented. Some examples of web applications that are intended to familiarize students with certain types of robotics systems by their 3D models and to perform corresponding practical tasks with the automatic results checking are shown.

Conclusion. The use of the laboratory complex components according to the created technology leads to timely expansion of the resources for the state-of-the-art research and practical training on robotics or intelligent technologies by the students of many Ukraine technical universities.

Keywords: robotics, remote control technology, virtual laboratory, web applications.

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1 Guimaraes E., Maffeis A., Pereira J., Russo B., Cardozo E., Bergerman M. REAL: A virtual laboratory for mobile robot experiments. IEEE Transactions on Education. 2003. Vol. 46. No. 1. P. 37-42.

2 Trukhin A.V. Ispol’zovanie virtual’nykh laboratoriy v obrazovanii, Otkrytoe i distantsionnoe obrazovanie. 2002. N 4 (8). P. 67-69.

3 Tzafestas C.S., Palaiologou N. Virtual and remote robotic laboratory: comparative experimental evaluation. IEEE Transactions on Education. 2006. Vol. 49. No. 3. P. 360-369.

4 Chaos D., Chac’on J., Lopez-Orozco J.A., Dormido S. Virtual and remote robotic laboratory using EJS. MATLAB and LabVIEW. Sensors. 2013. No. 13. P. 2595-2612.

5 Candelas F.A., Puente S.T., Torres F., Ortiz F.G., GIL P. Pomares J. A Virtual Laboratory for Tea-ching Robotics. International Journal of Engineering Education. 2003. Vol. 19. No. 3. P. 363-370.

6 Sukhoruchkina O.N. The structures and information processes of mobile robot intelligent control. Zbirnyk naukovykh prats Instytutu problem modelyuvannya v energetytsi im. G.Ye. Pukhova NAN Ukrainy. Kyiv, 2012. No. 62. P. 93-101.

7 Sukhoruchkina O.N., Progonnyi N.V. The intelligent mobile robot – laboratory complex with remote access. Materialy konferentsii “Avtomatyka-2011”. (28-30 of Sept, 2011, Lviv). Lviv, 2011. P. 413-414.

8 Sukhoruchkina O.N., Progonnyi N.V., Voronov M.A. Interpretation and use of the rangefinder measurements in the autonomous mobile robot control problems. USiM. 2017. No. 1. P. 26-34.

9 Website of the World Congress on Automatic Control IFAC-2017. URL: (Last accessed: 26.12.2018)

10 Sukhoruchkina O.N., Progonnyi N.V. The information technology of intelligent control of mobile robot and their use for training purposes. Materialy 19-i Mizhnar. naukovo-techn. konfer. “Systemnyi analiz ta informatsiyni technologii” SAIT-2017 (22-25th of May, 2017, Kyiv). Kyiv, 2017. P. 325-326.

Resieved 29.11.2018

Issue 1 (195), article 1


Kibern. vyčisl. teh., 2019, Issue 1 (195), pp.

Grytsenko V.I., Corresponding Member of NAS of Ukraine,
Director of International Research and Training
Center for Information Technologies and Systems
of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine

Volkov A.E., Acting Head of Department,
Intelligent Control Department,

Komar N.N., Researcher,
Intelligent Control Department,

Shepetukha Yu.M., PhD (Engineering)
Leading Researcher,
Intelligent Control Department,

Voloshenyuk D.A., Researcher,
Intelligent Control Department,

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


Introduction. At present unmanned aerial vehicles (UAVs) are successfully used in various industries in performing scientific and engineering, economical, military and a number of other missions. Effectiveness of their functioning is mainly determined by an onboard suit of hardware and software of a UAV’s control system. The process of the existing autopilot systems enhancement is intended to broaden the range of UAV’s tasks without direct human involvement and introduce additional smart functions into autopilot operation.

Purpose. The aim of research is to study the modern algorithms used in autopilots of unmanned aerial vehicles and formulation of the problem of development and usage of new intellectual methods for automatic control systems.

Results. The approach considered in the article is based on the theory of high-precision remote control of dynamic objects and on the complex interaction of methods of theory of invariance, adaptive control and intellectualization of processes of UAV control.

One of the features of the proposed method of intellectual control for unmanned aerial vehicle autopilot is the procedure of transforming a multi-dimensional system into an aggregate of virtual autonomous processes, for each of which the control algorithm is easily generated by an autonomous subsystem. Coming up next is the procedure of coordination of actions of all the autonomous systems into single functioning complex. This provides an opportunity to improved precision and sustainability of control.

Conclusion. Using the method described in the article allows creating integral and adaptive autopilots to perform complicated spatial maneuvering an unmanned aerial vehicle being based on usage of full non-linear models without simplifications and linearization.

Keywords: unmanned aerial vehicle, control system, virtual control, adaptation invariance.

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1 Fahlstrom P., Gleason T. Introduction to UAV systems. Hoboken: Wiley, 2012. 4th ed. 308 p.

2 Moiseyev V.S. Applied theory of unmanned aerial vehicles control. Kazan: GBU Republican centre for monitoring education quality, 2013.p. 768. (in Russian).

3 Beard R.W., McLain T.W. Small Unmanned Aircraft: Theory and Practice. Princeton: Princeton Univ. Press, 2012. 320 p.

4 Chao H., Cao Y., and Chen Y.Q. Autopilots for small unmanned aerial vehicles: a survey. International Journal of Control, Automation, and Systems, 2010, vol. 8, N 1. P. 36-44.

5 Feng G. A survey on analysis and design of model-based fuzzy control systems. IEEE Transactions on Fuzzy Systems, 2006, vol. 14, N 5. P. 676 – 697.

6 Shilov K.Ye. Development of unmanned air vehicle automatic control system of a rotorcraft. Works of MFTI, 2014. N 4 . P. 139-152. (in Russian).

7 Calise A., Rysdyk R. Nonlinear Adaptive Flight Control Using Neural Networks. Control Systems Magazine, 1998, vol. 18, N. 6. P. 14-25.

8 Johnson E.N., Kannan S.K. Adaptive Flight Control for an Autonomous Unmanned Helicopter. AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, California, August, 2002.

9 Lopez J., Dormido R., Gomez J.P., Dormido S., Diaz J.M. Comparison of H-infinity with QFT applied to an Altitude Command Tracker for an UAV. Proc. of the European Control Conference (2 – 5th of July, 2007, Kos, Greece) Kos, Greece, 2007. P. 46-54.

10 Lopez J., Dormido R., Dormido S., Gomez J.P. A Robust Controller for an UAV Flight Control System. The Scientific World Journal. 2015. vol. 2015. P. 15-26.

11 Ross T.J. Fuzzy Logic with Engineering Applications, 2nd Edition. NY: Wiley, 2004. 228 p.

12 Kumon M., Udo Y., Michihira H., Nagata M., Mizumoto I., Iwai Z. Autopilot system for kiteplane. IEEE/ASME Transactions on Mechatronics. 2006. vol. 11. N 5. P. 615-624.

13 Albus J.S. On intelligence and its dimensions. Technical report of the ISIS (Interdisciplinary studies of intelligent systems) group N. ISIS 94-001. University of Notre Dame, 1994. P. 11-13.

14 Antsaklis P.J. On autonomy and intelligence in control. Technical report of the ISIS (Interdisciplinary studies of intelligent systems) group N. ISIS 94-001. University of Notre Dame, 1994. P. 14-18.

15 Grytsenko V.I., Volkov O.E., Komar M.M., Bogachuk Yu.P. Intellectualization of the modern automatic control systems for unmanned aerial vehicles. Kibernetika i vycislitelnaa tehnika. 2018. N 1 (191). P. 45-59. (in Ukrainian)

16 Pavlov V.V., Pavlova S.V. Intellectual control of complex non-linear dynamic systems. Kiev: Naukova dumka. 2015. 216 p. (in Russian).

17 Kharchenko V.P., Chepizhenko V.I., Tounik A.A., Pavlova S.V. Unmanned aerial vehicles avionics. Kiev: TOV Abris-print, 2012. 464 p. (in Ukrainian).

18 Bodner V.A. Air vehicle control systems. Moscow: Mashinostroyeniye, 1973. 501 p. (in Russian).

Received 19.11.2018