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P1-TS fuzzy scheduling control system design using local pole placement and interval analysis

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The linear parameter-varying (LPV) discrete-time model based design of a fuzzy scheduling control scheme is developed through incorporating the advantages of P1-TS theory, and applying the local pole placement method and interval analysis of closed-loop system polynomial coefficients. The synthesis of fuzzy scheduling control scheme is proposed in the form of iterative procedure, which enables to find the appropriate number of intervals of a fuzzy interpolator ensuring that a family of local linear controllers places closed-loop polynomial coefficients within a desired range. The computational complexity of multidimensional fuzzy scheduling control scheme synthesis is reduced using a fundamental matrix method and recursive procedure for fuzzy rule-based interpretation. The usability of the proposed method is illustrated by an implementation example and experimental results obtained on a laboratory scaled overhead crane.
Rocznik
Strony
455--464
Opis fizyczny
Bibliogr. 37 poz., rys., fot., wykr.
Twórcy
autor
  • University of Science and Technology, Faculty of Mechanical Engineering and Robotics, 30 Mickiewicza Ave., 30-059 Kraków, Poland, smoczek@agh.edu.pl
Bibliografia
  • [1] D.J. Leith and W.E. Leithead, “Survey of gain-scheduling analysis and design”, Int. J. Control 73 (11), 1001–1025 (2000).
  • [2] W.J. Rugh and J.S. Shamma, “Research on gain scheduling”, Automatica 36 (10), 1401–1425 (2000).
  • [3] L. Wu, J. Lam, and C. Wang, “Robust H1 dynamic output feedback control for 2D linear parameter-varying systems”, IMA J. Mathematical Control and Information 26 (1), 23–44 (2009).
  • [4] V. Vesely and A. Ilka, “Gain scheduled controller design”, J. Process Control 23, 1141–1148 (2013).
  • [5] C.E. de Souza and J. Osowsky, “Gain-scheduled control of two-dimensional discrete-time linear parameter-varying systems in the Roesser model”, Automatica 49 (1), 101–110 (2013).
  • [6] J. Klamka, A. Czornik and M. Niezabitowski, “Stability and controlability of switched systems”, Bull. Pol. Ac.: Tech. 61 (3), 547–555 (2013).
  • [7] M. Krzaczek and Z. Kowalczuk, “Gain scheduling control applied to thermal barrier in systems of indirect passive heating and cooling of buildings”, Control Engineering Practice 20, 1325–1336 (2012).
  • [8] K. Srinivasan and K. Anbarasan, “Fuzzy scheduled RTDA controller design”, ISA Transactions 52, 252–267 (2013).
  • [9] T. Zubowicz and M.A. Brdyś, “Stability of softly switched multiregional dynamic output controllers with a static antiwindup filter: a discrete-time case”, Int. J. Appl. Math. Comput. Sci. 23 (1), 65–73 (2013).
  • [10] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control”, IEEE Trans. on Systems, Man and Cybernetics 15, 116–132 (1985).
  • [11] M.Warmus, “Calculus of approximations”, Bull. de l’Academie Polonaise des Sciences IV (5), 253–259 (1956).
  • [12] R. Moore, Interval Analysis, Prentice-Hall, Englewood Cliffs, NJ, 1966.
  • [13] M. Dahleh, A. Tesi, and A.Vicino, “An overview of extremal properties for robust control of interval plants”, Automatica 29 (3), 707–721 (1993).
  • [14] H. Chapellat, L.H. Keel, and S. P. Bhattacharyya, “External robustness properties of multilinear interval systems”, Automatica 30 (6), 1037–1042 (1994).
  • [15] S. Mallan, M. Milanese, and M. Taragna, “Robust analysis and design of control systems using interval arithmetic”, Automatica 33 (7), 1363–1372 (1997).
  • [16] M.S. Fadali and G. Bebis, “Control system design for LTI systems using interval analysis”, ISCA Conf. on Computers and Applications, Honolulu (1998).
  • [17] M. Dyvak, P. Stakhiv, and A. Pukas, “Algorithms of parallel calculations in task of tolerance ellipsoidal estimation of interval model parameters”, Bull. Pol. Ac.: Tech. 60 (1), 159–164 (2012).
  • [18] J. Xing, C. Chen, and P. Wu, “Calculation of interval damping ratio under uncertain load in power system”, Bull. Pol. Ac.: Tech. 60 (1), 151–158 (2012).
  • [19] M.D. Lorenzo del Casale, N. Femia, P. Lamberti, and V. Mainardi, “Selection of optimal closed-loop controllers for DC-DC regulators based on nominal and tolerance design”, IEEE Trans. on Industrial Electronics 51 (4), 840–849 (2004).
  • [20] C.-C. Hsu, S.-C. Chang, and C.-Y Yu, “Tolerance design of robust controllers for uncertain interval systems based on evolutionary algorithms”, IET Control Theory and Applications 1 (1), 244–252 (2007).
  • [21] C.-H. Lee, Y.-H. Lee, and C.-C. Teng, “A novel robust PID controllers design by fuzzy neural network”, American Control Conf. 1, 1561–1566 (2002).
  • [22] R.C. Martin and S.C. Kramer, “Gain scheduling optimization by genetic algorithms”, American Control Conf. 5, 3041–3042 (1995).
  • [23] N. Sadati and A. Hooshmand, “Design of a gain-scheduling anti-sway controller for tower cranes using fuzzy clustering techniques”, Int. Conf. on Computational Intelligence for Modeling, Control and Automation 1, CD-ROM (2006).
  • [24] J. Smoczek and J. Szpytko, “Evolutionary algorithm-based design of a fuzzy TBF predictive model and TSK fuzzy anti-sway crane control system”, Eng. Applications of Artificial Intelligence 28, 190–200 (2014).
  • [25] J. Smoczek and J. Szpytko, “Design of gain scheduling antisway controller using genetic fuzzy system”, 17th Int. Conf. on Methods and Models in Automation and Robotics 1, 573–578 (2012).
  • [26] J. Smoczek, “Evolutionary optimization of interval mathematics-based design of TSK fuzzy controller for anti-sway crane control”, Int. J. Appl. Math. Comput. Sci. 23 (4), 749–759 (2013).
  • [27] J. Smoczek, “Interval arithmetic-based fuzzy discrete-time crane control scheme design”, Bull. Pol. Ac.: Tech. 61 (4), 863–870 (2013).
  • [28] J. Smoczek, “Fuzzy crane control with sensorless payload deflection feedback for vibration reduction”, Mechanical System and Signal Processing 46 (1), 70–81 (2014).
  • [29] J. Kluska, “Transformation lemma on analytical modeling via Takagi–Sugeno fuzzy system and its applications”, 8th Int. Conf. on Artificial Intelligence and Soft Computing (ICAISC 2006) 1, 230–239 (2006).
  • [30] J. Kluska, “Analytical methods in fuzzy modeling and control”, Studies in Fuzziness and Soft Computing Springer-Verlag, Berlin, 2009.
  • [31] J. Kluska and Z. Hajduk, “Hardware implementation of P1-TS fuzzy rule-based systems on FPGA”, 12th Int. Conf. on Artificial Intelligence and Soft Computing (ICAISE 2013) 7894, 282–293 (2013).
  • [32] M. Mizumoto, “Fuzzy controls by fuzzy singleton-type reasoning method”, 5th IFSA World Congress 1, 945–948 (1993).
  • [33] Z. Smalko and J. Szpytko, “Safety in engineering practice”, 17th Eur. Safety and Reliability Conf. ESREL 1, 1231–1237 (2009).
  • [34] J. Szpytko and D.A. Wozniak, “To keep operational potential of transport device e-based on reliability indicators”, Eur. Safety and Reliability Conf. ESREL 1, 2377–2384 (2007).
  • [35] P. Hyla, “The crane control systems: a survey”, 17th Int. Conf. on Methods and Models in Automation and Robotics 1, 505–509 (2012).
  • [36] P. Hyla and J. Szpytko, “Vision method for rope angle swing measurement for overhead travelling crane – validation approach”, Activities of Transport Telematics: Communication in Computer and Information Science 395, 370–377 (2013).
  • [37] P. Hyla, “Stereovision system for overhead travelling crane workspace visualization – validation approach”, 18th Int. Conf. on Methods and Models in Automation and Robotics 1, 69–74 (2013).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c20911fd-fda9-437d-aeb4-f4589d0a201a
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