Issue 3 (193), article 5

DOI:

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

S.I. Kiforenko1, DSc (Biology), Leading Researcher,

Dep. of Application Mathematical and Technical
Methods in Biology and Medicine
e-mail: skifor@ukr.net

T.M. Hontar1, PhD (Biology), Senior Researcher,
Dep. of Application Mathematical and Technical Methods in Biology and Medicine
e-mail: gtm_kiev@ukr.net

K.Yu. Ivaskiva2, PhD (Medicine), Senior Researcher,
Scientific-advisory Dep. of Ambulatory and Preventive Care for Patients with Endocrine Pathology
e-mail: _k_iva@ukr.net

A. Obelets3, Computer Systems Analyst,
e-mail: obel.tet@gmail.com

1 International Research and Training Centre for Information Technologies
and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine

2 State Institution “V.P. Komisarenko Institute of Endocrinology and Metabolism of National Academy of Medical Sciences MS of Ukraine”, 69, Vyshgorodska St., Kyiv, 04114, Ukraine

3 National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Peremogy ave 37, Kyiv, 03056, Ukraine

INFORMATIONAL DECISION SUPPORT SYSTEM FOR MONITORING AND CORRECTING SOMATIC HEALTH

Issue 3 (193), article 4

DOI:

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

L.Ya.-G. Shakhlina1, DSc. (Medicine), Professor,
Professor of Sport Medicine cafedra
e-mail: sportmedkafedra@gmail.com

N.I. Aralova2, PhD. (Engineering),Senior Researcher,
Senior Researcher of Dept. of Optimization of Controlled Processes
e-mail: aralova@ukr.net
1 National University of Physical Education and Sport of Ukraine
Fiscultury Street, 1, Kiev, 03150, Ukraine

2 Institute of Cybernetics of National Academy of Science of Ukraine, Acad.Glushkov ave., 40, Kiev, 03680, Ukraine

FORECASTING THE ORGANISM REACTION OF THE ATHLETES ON INHIBITING HYPOXIC MIXTURES ON THE MATHEMATICAL MODEL OF THE FUNCTIONAL RESPIRATION SYSTEM

Issue 3 (193), article 3

DOI:

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

М.І. Vovk, PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department
e-mail: vovk@irtc.org.ua; imvovk3940@gmail.com

Ye.B. Galyan, PhD (Engineering), Researcher,
Bioelectrical Control & Medical Cybernetics Department
e-mail: galian@irtc.org.ua

О.А. Kutsyak, PhD (Engineering), Researcher,
Bioelectrical Control & Medical Cybernetics Department
e-mail: spirotech85@ukr.net

A.D. Lauta, PhD (Medicine), Senior Researcher,
Bioelectrical Control & Medical Cybernetics Department
e-mail: dep140@irtc.org.ua

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

FORMATION OF INDIVIDUAL COMPLEX OF CONTROL ACTIONS FOR MOTOR AND SPEECH REHABILITATION AFTER A STROKE

Issue 3 (193), article 2

DOI:

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

M.Yu. ANTOMONOV, DSc (Biology), Professor,
Chief Researcher of the Laboratory of Epidemiological
Research and Medical Informatics
e-mail: antomonov@gmail.com
State Institution “O.M. Marzіeiev Institute for Public Health of the National Academy of Medical Sciences of Ukraine”, 50,  Popudrenko str.  Кyiv, 02660

INFORMATION TECHNOLOGY FOR CONSTRUCTING THE COMPOSITE INDICES FOR DATA OF DIFFERENT TYPES USED IN MEDICAL AND ENVIRONMENTAL STUDIES

Issue 3 (193), article 1

DOI:

Kibern. vyčisl. teh., 2018, Issue 2 (192), pp.

E.G. REVUNOVA, Ph.D. (Engineering),
Senior Researcher Department of Neural Information Processing Technologies
e-mail: egrevunova@gmail.com

D.A. RACHKOVSKIJ, DSc. (Engineering),
Leading Researcher Department of Neural Information Processing Technologies
e-mail: dar@infrm.kiev.ua

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. Glushkova av., 40, Kiev, 03187, Ukraine

RANDOM PROJECTION AND TRUNCATED SVD FOR ESTIMATING DIRECTION OF ARRIVAL IN ANTENNA ARRAY

Issue 3 (193)

Download Issue 3 (193) as PDF
View web version

TABLE OF CONTENTS:

Informatics and Information Technologies:
E.G. REVUNOVA, D.A. RACHKOVSKIJ
Random Projection and Truncated SVD for Estimating Direction of Arrival in Antenna Array

M.Yu. ANTOMONOV
Information Technology for Constructing the Composite Indices for Data of Different Types Used in Medical and Environmental Studies

Intellectual Control and Systems:

М.І. VOVK, Ye.B. GALYAN, О.А. KUTSYAK, A.D. LAUTA
Formation of Individual Complex of Control Actions for Motor and Speech Rehabilitation after a Stroke

Medical and Biological Cybernetics:

L.Ya.-G. SHAKHLINA, N.I. ARALOVA
Forecasting the Organism Reaction of the Athletes on Inhibiting Hypoxic Mixtures on the Mathematical Model of the Functional Respiration System

S.I. KIFORENKO, T.M. HONTAR, K.Yu. IVASKIVA, T.A. OBELETS
Informational Decision Support System for Monitoring and Correcting Somatic Health

Issue 2 (192), article 6

DOI:https://doi.org/10.15407/kvt192.02.084

Kibern. vyčisl. teh., 2018, Issue 2 (192), pp.

Zlepko S.M.1,
D.Sc. (Engineering), Professor,
Head of the Department of Biomedical Engineering
e-mail: smzlepko@ukr.net
Chernyshova T.A.2, Doctor
e-mail: tetyana.che@gmail.com
Maevsky O.E.3, Dr (Medical), Professor,
Head of the Department of Histology
e-mail: maevskyalex8@gmail.com
Krivonosov V.E.4, docent,
Department of Biomedical Engineering
e-mail: yhtverf007@ukr.net
Azarkhov O.Y., Dr (Medical), Professor,
Head of the Department of Biomedical Engineering
e-mail: azarhov55@mail.ru

1Vinnytsia National Technical University,
Khm. highway 95, 21021, Vinnytsia, Ukraine
2Medical Center of National Aviation University,
Cosmonaut Komarov ave., 1, 03058, Kyiv, Ukraine
3Nicholay Pirogov Vinnitsa National Medical University,
Pyrohova str, 56, 21000, Vinnytsia, Ukraine
4Priazovsky State Technical University,
Universytetska str, 7, 87500, Mariupol, Ukraine

INFORMATION TECHNOLOGY OF DETERMINING CIRCULAR TUMOR CELLS IN HUMAN BLOOD

Introduction. The development of information systems and technologies for the processing of medical images of cells obtained in the study of histological preparations is one of the most important and priority directions of modern medical science.
The purpose of the article is to detect the CPR at various localizations of malignant neoplasms is currently one of the topical issues in oncology.
Results. A distinctive feature of the CPR is the aggressive metastatic potential, which allows them to be considered as the main mechanism of tumor progression. The article describes the methods of detecting the CPC, the functions and operations of image processing. The modern methods and algorithms for processing and restoring biomedical images are analyzed. The work of information technology for the determination of circulating tumor cells in human blood is given step by step. A comparison of the developed technology and existing analogues is made.
Conclusions. Unlike the existing technology, it detects a 4-micromycle GPC in the study of blood samples from patients with micellar lung cancer. The doctor, thus, received an automatic technology for the determination of the CPP in peripheral or venous blood with high reliability and informativeness, with maximum preservation of the integrity and invulnerability of circulating tumor cells. The analysis of literary sources and their own clinical studies have confirmed that only technologies based on the ISET method allow the detection of very rare circulating trophoblast cells of the fetus from the mother’s blood.

Keywords: technology, circulating tumor cell, medical image, histology, treatment, definition, criterion.

Download full text!

REFERENCES

 

  1. Lukashevich M.M., Starovoytov V.V. Method of counting the number of cell nuclei on medical histological images. System analysis and applied informatics. 2016. № 2. P. 37–42. URL: https://cyberleninka.ru/article/n/metodika-podscheta-chisla-yader-kletok-na-meditsinskih-gistologicheskih-izobrazheniyah (Last accessed: 15.05.2018) (in Russian).
  2. Determination of CSC content in peripheral blood in patients with primary generalized breast cancer at the treatment stages. URL: https://www.science-education.ru/ru/article/view?id=22788 (Last accessed: 15.05.2018) (in Russian).
  3. Kagan M., Howard D., Bendele Т., 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. V. 25, N 1. P. 104–110.
  4. Vona G., Sabile A., Louha M., Sitruk V., Romana S., Schutze K., Capron F. Isolation by size of epithelial tumor cells?: a new method for the immunomorphological and molecular characterization of circulatingtumor cells. The American journal of pathology. 2000. V. 156, N 1. P. 57–63.
  5. Hayes G., Busch R., Voogt J., Siah I., Gee Т., Hellerstein M., Chiorazzi N. Isolation of malignant В cells from patients with chronic lymphocytic leukemia (CLL) for analysis of cell proliferation: validation of a simplified method suitable for multi-center clinical studies. Leukemia research. 2010. V. 34, N 6. P. 809–815.
  6. Pavlov A.Yu., Gafanov R.A., Tsibulskiy A.D., Fastovets S.V., Kravtsov I.B., Isaev T.K. The role of evaluation of circulating tumor cells in prostate cancer: diagnosis and dynamic observation. RMJ. 2016. № 8. P. 480–487 (in Russian).
  7. Сell Search. URL: https://www.cellsearchctc.com/ (Last accessed: 25.04.2018)
  8. Babyuk N.P. Method and system of estimation of dynamic changes of biomedical images in ophthalmology. Thesis, PhD (Engineering), Vinnitsia, VNTU, 2016, 24 p. (in Ukrainian
  9. Hou JM, Krebs MG, Lancashire L, Sloane R, Backen A, Swain RK, ct al. Clinical significance and molecular characteristics of circulating tumor cells and circulating tumor microemboli in patients with small-cell lung cancer. J Clin Oncol. 2012. No. 30(5). P. 525–532.
  10. Ma YC, Wang L, Yu PL. Recent Advances and Prospects in the Isolation by Size of Epithelial Tumor Ceils (ISET) Methodology. Technol Cancer Res Treat. 2012. No. f 2(4). P. 295–309.
  11. Farace F, Massard C, Vimond N, Drusch F, Jacques N, Billiot F, el al. A direct comparison of CellSearch and ISET for circulating tumour-cell detection in patients with metastatic carcinomas. Br J Cancer. 2011. No. 105(6). P. 847–853.
  12. Mouawia H, SakerA, Jais JP, Benachi A, Bussieres L, LacourB,el al. Circulating trophoblastic cells provide genetic diagnosis in 63 fetuses at risk for cystic fibrosis or spinal muscular atrophy. Reprod Blamed Online. 2012. No. 25(5). P. 503–520.
  13. Burdenyuk I.I. Information technology for decision-making support in the analysis of biomedical data. Thesis, PhD (Engineering), Vinnitsia, VNTU, 2010. — 19 p. (in Ukrainian).
  14. Ismailova G., Laget S., Paterlini-Brechot P. Diagnosis of circulatig tumor cells using ISET technology and their molecular characteristics for fluid biopsy: URL: https://cyberleninka.ru/article/n/diagnostika-tsirkuliruyuschih-opuholevyh-kletok-s-pomoschyu-tehnologii-iset-i-ih-molekulyarnaya-harakteristika-dlya-zhidkostnoy. (Last accessed: 13.05.2018) (in Russian).
  15. 15. Ledov V.K., Skrinnikova M.A., Popova O.P. Isolation of Circulating Tumor Cells by Isolated Size (ISET) (Overview). Vice versa Oncology. 2014. № 60(5). P. 548–552. (in Russian).
  16. 16. Cytological diagnosis of breast cancer URL: http://mastopatia.com/tsitologicheskaya-diagnostika-raka-molochnoy-zhelezi.html (Last accessed: 05.2018) (in Russian).
  17. Sensitivity and specificity of diagnostic research URL: http://www.ebm.org.ua/clinical-epidemiology/testing/sensitivity-specificity/ (Last accessed: 20.05.2018) (in Russian).
  18. 18. MedovyiyS., Pyatnitskiy A.M., Sokolinskiy B.Z. Innovative project Development of a complex of automated microscopy, its cloud functional, Internet resource of laboratory telemedicine for medical analysis of biomaterials (MECOS-CZ). Innovation and examination. Is. 2(9), 2012, s. 50–64. (in Russian).
  19. Ablameyko S.V., Nedzved A.M. Processing of optical images of cellular structures in medicine. Minsk, 2005. 156 p. (in Russian).
  20. Chernyshova T.A, Zlepko S.M., Timchik S.V., Krivonosov V.Ye., Zlepko O.S. Information system for obtaining and processing microscopic images of circulating tumor cells (CPC). Achievements of clinical and experimental medicine. 2017. № 4 (32). Р. 39–46.
  21. Chernishova T.A., Zlepko S.M., Azarkhov O.Yu., Danilkov S.O., Krivonosov V.Ye., Baranovskyi D.M. Medical Informatics and Engineering: Sciences. Pract. Journal 2017. № 4 (40). P. 30–35. (in Ukrainian).

Received 03.04.2018

Issue 2 (192), article 5

DOI:https://doi.org/10.15407/kvt192.02.072

Kibern. vyčisl. teh., 2018, Issue 2 (192), pp.

Rysovana L.M.1,
Assistant,
Department of Medical and Biological Physics and Medical Informatics
e-mail: rluba_24@ukr.net
Vуsotska O.V.2,
Dr (Engineering), Professor,
Professor of the Department of Information Control Systems
e-mail: evisotska@ukr.net
1Kharkov National Medical University,
Nauky ave., 4, 61022, Kharkiv, Ukraine
2Kharkov National University of Radio Electronics,
Nauky ave., 14, 61166, Kharkiv, Ukraine

INFORMATION SYSTEM OF DETECTION OF EMOTIONAL AND COGNITIVE DISORDERS IN PATIENTS WITH DISCIRCULATORY ENCEPHALOPATHY

Introduction. In modern conditions, there are topical issues of studying the mechanisms of formation and specificity of clinical manifestations of discirculatory encephalopathy in the able-bodied population. A large number of interrelated indicators that characterize emotional and cognitive disorders, the analysis of which requires the use of mathematical methods and software, determined the need to develop an information system for the detection of emotional and cognitive disorders in patients with discirculatory encephalopathy.
The purpose of the article is to develop a medical information system for the detection of emotional and cognitive disorders in patients with discirculatory encephalopathy, which increases the accuracy of diagnosis.
Materials and methods. The article uses mathematical statistics methods for processing diagnostic information; methods of mathematical modeling for constructing mathematical models for detecting the likelihood of emotional disorders and identifying and determining the severity of cognitive disorders in patients with discirculatory encephalopathy; methodical bases of construction of information technologies in medicine at construction of information system of revealing emotional and cognitive disorders in patients with discirculatory encephalopathy.
Results. During the writing of the article, a method was developed for detecting emotional and cognitive disorders in patients with discirculatory encephalopathy, including the definition of the likelihood of emotional disorders, the exposure vector for psychocorrection, the detection of cognitive disorders and determining their severity, and predicting the further development of cognitive disorders. A structural diagram of the medical information system “СognitiveDE” has been developed, which determines the composition and purpose of its main modules, and has allowed to develop a methodological basis for describing the interaction of the elements of the biological and technical subsystems. The software of the medical information system “СognitiveDE” was verified, which showed the compliance of the results of the individual stages of the system development with the requirements and restrictions formulated for them.
Conclusions. Using the developed method for detecting emotional and cognitive disorders in patients with discirculatory encephalopathy, based on developed mathematical models for determining the likelihood of emotional disorders and determining the severity of cognitive disorders, allows correctly diagnosing emotional and cognitive disorders.
The presented medical information system can be used by doctors of the neurological and psychiatric departments and medical psychologists to improve the accuracy and reduce the time of diagnosis of emotional and cognitive disorders.

Keywords: medical information system, assessment method, cognitive and emotional disorders, discirculatory encephalopathy.

Download full text!

REFERENCES

  1. Vysotskaya E.V., Kоzhina А.М., Risovanaya L.M., Chaika H.E. Application of discriminant analysis for the classification of cognitive disorders in patients with discirculatory encephalopathy. Information processing system, 2013, Vol. 9, pp. 189–193. (In Russian).
  2. Kоzhina А.М., Grigorova І.А., Korosty V.І. and others. Organic mental disorders due to somatic diseases: cognitive and emotional disorders. Kharkov: Ukraine Rarities, 2012, 120 p. (In Ukrainian).
  3. Aleksandrovsky Y.A., Shchukin B.P. Psychological disorders during and after natural disasters and disasters. Journal of Neuropathology and Psychiatry, 1991, Vol. 5, pp. 39–43. (In Russian).
  4. Bleicher V.M., Krook I.V., Bokov S.N. Clinical Pathopsychology. Moskow, 2002,
    511 pp. (In Russian).
  5. Miroshnikov S.A. Expert system Longitude. The experimental and diagnostic complex (EDC). SPb: Lema, 2010, 196 pp. (In Russian)
  6. Altamirov S.A. Application of information technology in the activities of a psychiatrist. Young Scientis, 2016, Vol. 29, pp. 200–203. (In Russian).
  7. Aimedica. General information. http://aimedica.ru/info.jsp. (In Russian).
  8. Kan L.V., Kuznetsova Y.M., Chudova N.V. Expert systems in the field of psychodiagnostics. Artificial Intelligence and Decision Making, 2010, Vol. 2, pp. 26–35. (In Russian).
  9. Rysovana L., Vysotska O., Porvan A., Alekseenko R. Family Crisis Investigation on the Basis of Regression Analysis. The problems of empirical research in psychology and humanities: Roland Barthes VIII International Scientific Conference. Europejskie Studia Humanistyczne: państwo i społeczeństwo. Krakow, 2016, Vol. 2, p. 83–91.
  10. Nechaeva G.I., Achmedov V.A., Bereznikov A.V. and others. Methodical approaches to the expert evaluation of the quality of therapeutic care for chronic cholecystitis. Therapeutic archive, 2010, Vol. 1, pp. 12–15. (In Russian).
  11. Watson A., McCabe T. Structured Testing: A Testing Methodology Using the Cyclomatic Complexity Metric. URL: http://www.mccabe.com/pdf/mccabe-nist235r.pdf. (Last accessed: 10.11.2017) 1996.

Received 26.02.2018

Issue 2 (192), article 4

DOI:https://doi.org/10.15407/kvt192.02.061

Kibern. vyčisl. teh., 2018, Issue 2 (192), pp.

Buzynovsky А.B.1,
PhD student,
e-mail: arturdoc1983@ukr.net
Kovalenko A.S.1,
D.Sci. (Medicine), Professor,
Head of Medical Information Systems Department
e-mail: alexkovalenko@yandex.ua
Bayazitov N.R.2,
D.Sci. (Mdicine),
Professor at the Surgery Department
e-mail: ics_video@ukr.net
Godlevsky L.S.2,
D.Sci. (Medicine), Professor,
Chief of the Department of Biophysics, Informatics and Medical Devices
e-mail: godlevskyleonid@yahoo.com

1International 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. Glushkova av., 40, Kiev, 03187, Ukraine
2Odessa National Medical University,
Valekhovsky Lane, 2, Odessa, 65082, Ukraine

THE EFFECTIVENESS OF SURGEON DECISION ON PAIN SYNDROME OF PELVIC ORIGIN TREATMENT IN WOMEN ESTIMATED WITH THE MODEL OF DECISION TREE

Introduction. The problem of correct diagnostics with the decision on the consequent adequate treatment of diseases which are causative for pelvic pain syndrome in women is actual for 15–24% women of fertile age.
The purpose of the work is to investigate the effectiveness of different methods of treatment women with pain syndrome originated from pelvis and lower part of abdomen on the basis of retrospective analysis of 1092 histories of diseases during 2013–2017 р.р.
Methods. Method of decision tree building up was used. The probability of different outcomes — restoration of health, recurrence of the disease along with the perioperative complications as well as duration of treatment in each case were taken into consideration as informative indices for decision tree composing. On the basis of mentioned data the index of effective period of treatment (EPT) was calculated. Period of observation was six months from the moment of disease diagnostics.
Results. It was established that the probability of complete health restoration was 0,83 after surgical treatment and 0,62 after drug treatment. In case of initial inefficiency of drug treatment the probability of restoration of health as a result of surgical intervention was 0,40. The EPT in surgically treated patients was less than EPT in patients with therapeutic treatment by 3,29 times at the moment of making decision on the method of treatment.
Conclusions. It was concluded that early decision on surgical intervention as a method of diagnostics and treatment was more effective when compared with the drug method of treatment women with pelvic pain syndrome. Dependence of the treatment effects upon perioperative complications serve as forecasting data for individual medical care delivered during postoperative period.

Keywords: tree of decision, undertaking of decision in surgery, pain syndrome, the effectiveness of treatment estimation.

Download full text!

REFERENCES

  1. Lyashenko A.V., Bayazitov N.R., Godlevsky L.S. et al. Informational-technical system for automatized laparoscopic diagnostics. Radioelectronics, computer sciences and control. 2016. № 4. P. 90–96. (in Ukrainian).
  2. Egorov А.А., Mikshina V.S. The models of surgeon decision. Letters on New Medical Technologies, 2011, Vol.. 7, №4. P. 178–81. (in Russian)
  3. Litvin А.А., Litvin V.A. Systems of decision support in surgery. News of Surgery, 2014. № 1. P. 96–100. (in Russian)
  4. Kucey D.S. Decision analys is for the surgeon. World J.Surg, 1999. Vol. 23. P. 1227–1231.
  5. Sears E.D., Chung K.C. Decision analysis in plastic surgery: A Primer. PlastReconstrSurg. 2010. Vol. 126, N4. P. 1373–1380.
  6. Оzerskaya I.А., Аgeeva М.I. Chronic pelvic pain in women of fertile age. Ultrasonic diagnostics. Мoscow 2009. 299 p. (in Russian)
  7. Kiryanov B.F., Tokmachov М.S. Mathematical models in health care: text-book. Novgorod, 2009. 279 p. (in Russian)
  8. 8. Rudenko S.V., Romanenko M.V., Katunina O.G., Kolesnikova E.V. Markov models of patients state changes in projects of delivering medical service. Control of complicated systems development. 2012. №12. P. 86–89. (in Ukrainian)
  9. 9. Detsky A.S., Nagile G., Krahnetal M.D. Primer on medical decision analysis: Part 2. Building a tree. MedDecis Making. 1997. 7. P. 126–135.
  10. Classification Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and regression trees. Monterey, CA 1984. 368 p.

Reseived 12.10.2017

Issue 2 (192), article 3

DOI:https://doi.org/10.15407/kvt192.02.044

Kibern. vyčisl. teh., 2018, Issue 2 (192), pp.

Zhiteckii L.S.,
PhD (Engineering),
Acting Head of the Department of Intelligent Automatic Systems
e-mail: leonid_zhiteckii@i.ua
Solovchuk K.Yu.,
PhD Student
e-mail: solovchuk_ok@ukr.net

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

ADAPTIVE STABILIZATION OF SOME MULTIVARIABLE SYSTEMS WITH NONSQUARE GAIN MATRICES OF FULL RANK

Introduction. The paper states and solves a new problem concerning the adaptive stabilization of a specific class of linear multivariable discrete-time memoryless systems with nonsquare gain matrices at their equilibrium states. This class includes the multivariable systems in which the number of outputs exceeds the number of control inputs. It is assumed that the unknown gain matrices have full rank.
The purpose of this paper is to answer the question of how the pseudoinverse model-based adaptive approach might be utilized to deal with the uncertain multivariable memoryless system if the number of control inputs is less than the number of outputs.
Results. It is shown that the parameter estimates generated by the standard adaptive projection recursive procedure converge always to some finite values for any initial values of system’s parameters. Based on these ultimate features, it is proved that the adaptive pseudoinverse model-based control law makes it possible to achieve the equilibrium state of the nonsquare system to be controlled. The asymptotical properties of the adaptive feedback control system derived theoretically are substantiated by a simulation experiment.
Conclusion. It is established that the ultimate behavior of the closed-loop control system utilizing the adaptive pseudoinverse model-based concept is satisfactory.

Keywords: adaptive control, multivariable system, discrete time, feedback, pseudoinversion, stability, uncertainty.

Download full text!

REFERENCES

  1. Dahleh M.A., Pearson J.B. l1 optimal-feedback controllers for MIMO discrete-time systems. IEEE Trans. Autom. Contr., 1987, vol. 32, no. 4, pp. 314–322.
  2. McDonald J.S., Pearson J.B. l1 optimal control of multivariable systems with output norm constraints. Automatica, 1991, vol. 27, no. 2, pp. 317–329.
  3. Maciejowski J. M. Multivariable Feedback Design. Wokinghan: Addison-Wesley, 1989.
  4. Skogestad S., Postlethwaite I. Multivariable Feedback Control. UK, Chichester: Wiley, 1996.
  5. Albertos P., Sala A. Multivariable Control Systems: an Engineering Approach. London: Springer , 2006.
  6. Francis B., Wonham W. The internal model principle of control theory. Automatica, 1976, vol. 12, no. 5, pp. 457–465.
  7. Brockett R. W. The invertibility of dynamic systems with application to control. Ph. D. Dissertation, Case Inst. of Technology, Cleveland, Ohio, 1963.
  8. Silverman L. M. Inversion of multivariable linear systems. IEEE Trans. Autom. Contr., 1969, vol. AC-14, no. 3, pp. 270–276..
  9. Lovass-Nagy V., Miller J. R., Powers L. D. On the application of matrix generalized inversion to the construction of inverse systems. Int. J. Control, 1976, vol. 24, no. 5, pp. 733–739.
  10. Seraji H. Minimal inversion, command tracking and disturbance decoupling in multivariable systems. Int. J. Control, 1989, vol. 49, no. 6, pp. 2093–2191.
  11. Marro G., Prattichizzo D., Zattoni E. Convolution profiles for right-inversion of multivariable non-minimum phase discrete-time systems. Automatica, 2002, vol. 38, no. 10, pp. 1695–1703.
  12. Liu C., Peng H. Inverse-dynamics based state and disturbance observers for linear time-invariant systems. ASME J. Dyn Syst., Meas. and Control, 2002, vol. 124, no. 5, pp. 376–381.
  13. Lyubchyk L. M. Disturbance rejection in linear discrete multivariable systems: inverse model approach. Prep. 18th IFAC World Congress, Milano, Italy, 2011, pp. 7921–7926.
  14. Pushkov S. G. Inversion of linear systems on the basis of state space realization. Journal of Computer and Systems Sciences International, 2018, vol. 57, vo. 1, pp. 7–17.
  15. Pukhov G. E., Zhuk K. D. Synthesis of Interconnected Control Systems via Inverse Operator Method. Kiev: Nauk. dumka, 1966 (in Russian).
  16. Skurikhin V. I., Gritsenko V. I., Zhiteckii L. S., Solovchuk K. Yu. Generalized inverse operator method in the problem of optimal controlling linear interconnected static plants. Dopovidi NAN Ukrainy, no. 8, pp. 57–66, 2014 (in Russian).
  17. Zhiteckii L. S., Azarskov V. N., Solovchuk K. Yu., Sushchenko O. A. Discrete-time robust steady-state control of nonlinear multivariable systems: a unified approach. Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 8140–8145.
  18. Zhitetskii L. S., Skurikhin V. I., Solovchuk K. Yu. Stabilization of a nonlinear multivariable discrete-time time-invariant plant with uncertainty on a linear pseudoinverse model. Journal of Computer and Systems Sciences International, 2017, vol. 56, no. 5, pp. 759–773.
  19. Zhiteckii L. S., Solovchuk K. Yu. Pseudoinversion in the problems of robust stabilizing multivariable discrete-time control systems of linear and nonlinear static objects under bounded disturbances. Journal of Automation and Information Sciences, 2017, vol. 49, no. 5, pp. 35–48.
  20. Fomin V. N., Fradkov A. L., Yakubovich V. A. Adaptive Control of Dynamic Plants. Moscow: Nauka, 1981 (in Russian).
  21. Goodwin G.C., Sin K.S. Adaptive Filtering, Prediction and Control. Engewood Cliffs, NJ.: Prentice-Hall, 1984.
  22. Landau I. D., Lozano R., M’Saad M. Adaptive Control. London: Springer, 1997.
  23. Kuntsevich V. M. Control under Uncertainty: Guaranteed Results in Control and Identification Problems. Kiev: Nauk. dumka, 2006 (in Russian).
  24. Zhiteckii L. S., Skurikhin V. I. Adaptive Control Systems with Parametric and Nonparametric Uncertainties. Kiev: Nauk. dumka, 2010 (in Russian).
  25. Narendra K. S., Annaswamy A. M. Stable Adaptive Systems. NY: Dover Publications, 2012.
  26. Ioannou P., Sun J. Robust Adaptive Control. NY: Dover Publications, 2013.
  27. Aström K. J., Wittenmark B. Adaptive Control: 2nd Edition. NY: Dover Publications, 2014.
  28. Albert A. Regression and the Moore-Penrose Pseudoinverse. New York: Academic Press, 1972.
  29. Kaczmarz S. Approximate solution of systems of linear equations. Internat. J. Control, 1993. vol. 57, no. 6. pp. 1269–1271.
  30. Marcus M., Minc H. A Survey of Matrix Theory and Matrix Inequalities. Boston: Aliyn and Bacon, 1964.
  31. Desoer C.A., Vidyasagar M. Feedback Systems: Input–Output Properties. New York: Elsevier, 1975.

Received 29.03.2018