nach oben

Programming and Computer Software

Erschienen in:

01.12.2020

Fault Identification in Mobile Robot Groups Using Sliding Mode Observers

verfasst von: O. Sergiyenko, A. Zhirabok

Erschienen in: Programming and Computer Software | Ausgabe 8/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The paper studies the emerging trends in advanced computing and information technology for efficient solutions of fault identification problem for mobile robot groups under the unmatched disturbances. The sliding mode observers are considered for mentioned problem solution. It facilitates the concept of Smart everything inside the considered robotic group during its generalized control: smart surrounding sensing, communication, processing, and scanned data storing. The suggested novel approach to sliding mode observer design is based on obtaining the reduced order model of the initial system. This allows reduce the complexity of sliding mode observer and relax restrictions imposed on the initial system.

Vorheriger Artikel Mixed Integer Programming Formulations for Steiner Tree and Quality of Service Multicast Tree Problems

Nächster Artikel Classification of Depressive Episodes Using Nighttime Data; a Multivariate and Univariate Analysis

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Jaulin, L., Mobile Robotics, 1st ed., ISTE Press-Elsevies, 2015.

Klancar, G., Adezar, A., Blazic, S., and Skrjanc, I., Wheeled Mobile Robots: from Fundamentals towards Autonomous Systems, Elsevier/Butterwoth-Heinemann, 2017.

Klancar, G., Matko, D., andBlazic, S., Wheeled mobile robots control in a linear platoon, J. Intell. Rob. Syst., 2017, vol. 54, pp. 709–731.CrossRef

Ivanov, M., Sergiyenko, O., Tyrsa, V., Lindner, L., Rodriguez- Quiñonez, J., Flores-Fuentes, W., Rivas-Lopez, M., Hernández- Balbuena, D., and Nieto Hipólito, J., Software advances using n-agents wireless communication integration for optimization of surrounding recognition and robotic group dead reckoning, Program. Comput. Software, 2019, vol. 45, no. 8, pp. 557–569.CrossRef

Osipov, V., Automatic synthesis of action programs for intelligent robots, Program. Comput. Software, 2016, vol. 42, pp. 155–160.MathSciNetCrossRef

Zhdanov, D., Galaktionov, V., Voloboy, A., Zhdanov, A.D., Garbul’, A.A., Potemin, I.S., and Sokolov, V.G., Photorealistic rendering of images formed by augmented reality optical systems, Program. Comput. Software, 2018, vol. 44, pp. 213–224.CrossRef

Yarygina, A., Execution and optimization techniques for approximate queries in heterogeneous systems, Program. Comput. Software, 2013, vol. 39, pp. 309–317.CrossRef

Bezmen, G. and Kolesov, N., Program models for diagnosis of information control systems, Program. Comput. Software, 2014, vol.40, pp. 250–258.CrossRef

Lindner, L., Sergiyenko, O., Rivas-Lopez, M., Hernandez-Balbuena, D., Flores-Fuentes, W., Rodríguez-Quiñonez, J.C., Murrieta-Rico, F.N., Ivanov, M., Tyrsa, V., and Basaca, L.C., Exact laser beam positioning for measurement of vegetation vitality, Ind. Rob.: Int. J., 2017, vol. 44, no. 4, pp. 532–541.CrossRef

10.

Sergiyenko, O. Yu., Optoelectronic system for mobile robot navigation, Optoelectron., Instrum. Data Process., 2010, vol. 46, no. 5, pp. 414–428.CrossRef

11.

Sergiyenko, O.Yu., Ivanov, M.V., Tyrsa, V.V., Kartashov, V.M., Rivas-López, M., Hernández-Balbuena, D., Flores-Fuentes, W., Rodríguez-Quiñonez, J.C., Nieto-Hipólito, J.I., Hernandez, W., and Tchernykh, A., Data transferring model determination in robotic group, Rob. Auton. Syst., 2016, vol. 83, pp. 251–260.CrossRef

12.

Reyes-García, M., Sergiyenko, O., Ivanov, M., Lindner, L., Rodríguez-Quiñonez, J.C., Hernandez-Balbuena, D., Flores-Fuentes, W., Tyrsa, V., Moreno-Ahedo, L.O., and Murrieta-Rico, F.N., Defining the final angular position of DC motor shaft using a trapezoidal trajectory profile, Proc. 28th IEEE Int. Symp. on Industrial Electronics (ISIE2019), Vancouver, June 12–14, 2019, pp. 1694–1699.

13.

Sepúlveda-Valdez, C.A., Sergiyenko, O., Hernandez-Balbuena, D., Tyrsa, V., Mercorelli, P., Flores-Fuentes, W., Reyez-García, M., Lindner, L., and Melnik, V., Circular scanning resolution improvement by its velocity close loop control, Proc. 28th IEEE Int. Symp. on Industrial Electronics (ISIE2019), Vancouver, June 12–14, 2019, pp. 244–249.

14.

Ruderman, M. and Iwasaki, M., Observer of nonlinear friction dynamics for motion control, IEEE Trans. Ind. Electron., 2015, vol. 62, no. 9, pp. 5941–5949.CrossRef

15.

Sergienko, O.Yu. and Pavlov, S.P., Analysis of the natureof the atomic-molecular interaction of the friction surfaces, Vestn. Nats. Tekhn. Univ.: Kharkov. Politechn. Inst., 2002, vol. 1, no. 6, pp. 39–43.

16.

Blanke, M., Kinnaert, M., Lunze, J., and Staroswiecki, M., Diagnosis and Fault-Tolerant Control, Berlin: Springer-Verlag, 2006.MATH

17.

Ding, S., Data-Driven Design of Fault Diagnosis and Fault-Tolerant Control Systems, London: Springer-Verlag, 2014.CrossRef

18.

Samy, I., Postlethwaite, I., and Gu, D., Survey and application of sensor fault detection and isolation schemes, Control Eng. Pract., 2011, vol. 19, pp. 658–674.CrossRef

19.

Utkin, V., Sliding Modes in Control Optimiztion, Berlin: Springer, 1992.CrossRef

20.

Chandra, K., Alwi, H., and Edwards, C., Fault reconstruction for a quadrotor using an lpv sliding mode observer, Proc. IFAC Symp. Safeprocess, Paris, 2015, pp. 374–379.

21.

Edwards, C., Spurgeon, S., and Patton, R., Sliding mode observers for fault detection and isolation, Automatica, 2000, vol. 36, pp. 541–553.MathSciNetCrossRef

22.

Tan, C. and Edwards, C., Sliding mode observers for robust detection and reconstruction of actuator and sensor faults, Int. J. Robust Nonlinear Control, 2003, vol. 13, pp. 443–463.MathSciNetCrossRef

23.

Tan, C.P. and Edwards, C., Robust fault reconstruction using multiple sliding mode observers in cascade: development and design, Proc. American Control Conf., St. Louis, 2009, pp. 3411–3416.

24.

Zhirabok, A., Zuev, A., and Shumsky, A., Fault diagnosis in linear systems via sliding mode observers, Int. J. Control, 2019, no. 3, pp. 1–18. https://doi.org/10.1080/00207179.2019.1590738

25.

Brahim, A., Dhahri, S., Hmida, F., and Sellami, A., Simultaneous actuator and sensor faults reconstruction based on robust sliding mode observer for a class of nonlinear systems, Asian J. Control, 2017, vol. 19, pp. 362–371.MathSciNetCrossRef

26.

He, J. and Zhang, C., Fault reconstruction based on sliding mode observer for nonlinear systems, Math. Probl. Eng., 2012, no. 6, pp. 1–22.

27.

Yan, X. and Edwards, C., Nonlinear robust fault reconstruction and estimation using a sliding modes observer, Automatica, 2007, vol. 43, pp. 1605–1614.MathSciNetCrossRef

28.

Chan, J., Tan, C., and Trinh, H., Robust fault reconstruction for a class of infinitely unobservable descriptor systems, Int. J. Syst. Sci., 2017, no. 1, pp. 1–10.

29.

Alwi, H. and Edwards, C., Fault tolerant control using sliding modes with on-line control allocation, Automatica, 2008, vol. 44, pp. 1859–1866.MathSciNetCrossRef

30.

Edwards, C., Alwi, H., and Tan, C., Sliding mode methds for fault detection and fault tolerant control with application to aerospace systems, Int. J. Appl. Math. Comput. Sci., 2012, vol. 22, pp. 109–124.MathSciNetCrossRef

31.

Filaretov, V., Zuev, A., Zhirabok, A., and Protcenko, A., Development of fault identification system for electric servo actuators of multilink manipulators using logic-dynamic approach, J. Control Sci. Eng., 2017, no. 1, pp. 1–8.

32.

Meziane, H., Labarre, C., Lefteriu, S., Defoort, M., and Djemai, M., Fault detection and isolation for a multi-cellular converter based on sliding mode observer, Proc. 9th IFAC Symp. Safeprocess, Paris, 2015, pp. 164–170.

33.

Mohamed, M., Yan, X.-G., Spurgeon, S., and Jiang, B., Robust sliding mode observer design for interconnected systems with application to multimachine power systems, Proc. IEEE Conf. on Decision and Control, Las Vegas, 2016, pp. 6246–6251.

34.

Zhang, K., Jiang, B., Yan, X., and Mao, Z., Sliding mode observer based incipient sensor fault detection with application to high-speed railway traction device, ISA Trans., 2016, vol. 63, pp. 49–59.CrossRef

35.

Corradini, M., Orlando, G., and Parlangeli, G., A fault tolerant sliding mode controller for accommodating actuator failures, Proc. 44th IEEE Conf. on Decision and Control, and the European Control Conf., Seville, 2005, pp. 3091–3096.

36.

Edwards, C., Alwi, H., and Tan, C., Sliding mode methods for fault detection and fault tolerant control, Proc. Conf. Control and Fault Tolerant Systems, Nice, 2010, pp. 106–117.

37.

Edwards, C. and Tan, C., A comparison of sliding mode and unknown input observers for fault reconstruction, Eur. J. Control, 2006, vol. 12, pp. 245–260.MathSciNetCrossRef

38.

Zhirabok, A., Shumsky, A., and Pavlov, S., Diagnosis of linear dynamic systems by the nonparametric method, Autom. Remote Control, 2017, vol. 78, pp. 1173–1188.MathSciNetCrossRef

39.

Zhirabok, A., Shumsky, A., Solyanik, S., and Suvorov, A., Fault detection in nonlinear systems via linear methods, Int. J. Appl. Math. Comput. Sci., 2017, vol. 27, pp. 261–272.

Titel: Fault Identification in Mobile Robot Groups Using Sliding Mode Observers
verfasst von: O. Sergiyenko
A. Zhirabok
Publikationsdatum: 01.12.2020
Verlag: Pleiades Publishing
Erschienen in: Programming and Computer Software / Ausgabe 8/2020
Print ISSN: 0361-7688
Elektronische ISSN: 1608-3261
DOI: https://doi.org/10.1134/S0361768820080216

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 8/2020

Object Detection in Aerial Navigation using Wavelet Transform and Convolutional Neural Networks: A First Approach

Automated Testing of a TCG Frontend for Qemu

Analysis of Correct Synchronization of Operating System Components

Quality of Service in Software Defined Networks for Scientific Applications: Opportunities and Challenges

Mitigating Uncertainty in Developing and Applying Scientific Applications in an Integrated Computing Environment

Machine Learning Based Activity Learning for Behavioral Contexts in Internet of Things (IoT)

Premium Partner