Metrika članka

  • citati u SCindeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[=>]
  • posete u poslednjih 30 dana:0
  • preuzimanja u poslednjih 30 dana:0
članak: 10 od 10  
Back povratak na rezultate
Facta universitatis - series: Electronics and Energetics
2005, vol. 18, br. 3, str. 379-394
jezik rada: engleski
vrsta rada: članak
doi:10.2298/FUEE0503379P
Control solutions in mechatronics systems
(naslov ne postoji na srpskom)
Department of Automation and Applied Informatics, 'Politehnica' University of Timisoara, Romania

e-adresa: radu.precup@aut.upt.ro

Sažetak

(ne postoji na srpskom)
This paper presents control solutions dedicated to a class of controlled plants widely used in mechatronics systems, characterized by simplified mathematical models of second-order and third-order plus integral type. The conventional control solution is focused on the Extended Symmetrical Optimum method proposed by the authors in 1996. There are proposed six fuzzy control solutions employing PI-fuzzy controllers. These solutions are based on the approximate equivalence in certain conditions between fuzzy control systems and linear ones, on the application of the modal equivalence principle, and on the transfer of results from the continuous-time conventional solution to the fuzzy solutions via a discrete-time expression of the controller where Prof. Milić R. Stojić's book [1] is used. There is performed the sensitivity analysis of the fuzzy control systems with respect to the parametric variations of the controlled plant, which enables the development of the fuzzy controllers. In addition, the paper presents aspects concerning Iterative Feedback Tuning and Iterative Learning Control in the framework of fuzzy control systems. The theoretical results are validated by considering a real-world application.

Ključne reči

fuzzy controllers; mechatronics systems; extended symmetrical optimum method; sensitivity analysis; iterative feedback tuning; iterative learning control

Reference

*** (1999) An extension of tuning relations after symmetrical optimum method for pi and pid controllers. Automatica, vol. 35, str. 1731-1736, Oct
Akerblad, M., Hansson, A., Wahlberg, B. (2000) Automatic tuning for classical step- response specifications using iterative feedback tuning. u: IEEE Conference on Decision and Control (39th), Sydney, Australia, Dec, str. 3347-3348
Arimoto, S., Kawamura, S., Miyazaki, F. (1984) Bettering operation of robots by learning. Journal of Robotic Systems, vol. 1, str. 123-140, Feb
Astrom, K.J., Hagglund, T. (1995) PID controllers: Theory, design, and tuning. Research Triangle Park, NC: Instrument Society of America
Astrom, K.J., Hagglund, T. (2000) Benchmark systems for pid control. u: Preprints IFAC PID Workshop, Terrassa, Spain, Apr, str. 181-182
Boverie, S., Demaya, B., Ketata, R., Titli, A. (1992) Performance evaluation of fuzzy controllers. u: SICICA '92 Symposium, Malaga, Spain, May, str. 105-110
Driankov, D., Hellendoorn, H., Reinfrank, M. (1993) An introduction to fuzzy control. Berlin, itd: Springer Verlag
Galichet, S., Foulloy, L. (1995) Fuzzy controllers: Synthesis and equivalences. IEEE Transactions on Fuzzy Systems, vol. 3, str. 140-148, May
Garcia-Cerezo, A., Ollero, A., Aracil, J. (1992) Stability of fuzzy control systems by using nonlinear systems theory. u: IFAC/IFIP/IMACS Symposium on AI in Real-Time Control, Delft, The Netherlands, Preprints, str. 171-176
Hjalmarsson, H., Gevers, M., Gunnarson, S., Lequin, O. (1998) Iterative feedback tuning: Theory and applications. IEEE Control Systems Magazine, vol. 18, str. 26-41, Aug
Hjalmarsson, H. (2002) Iterative feedback tuning: An overview. International Journal of Adaptive Control and Signal Processing, vol. 16, str. 373-395, May
Lee, T., Xu, J.X., Zhang, H.W. (2004) Analysis and comparison of two practical iterative learning control schemes. u: IEEE International Conference on Control Applications, Taipei, Taiwan, Sept, str. 382-387
Moore, K. (1993) Iterative learning control for deterministic systems. Berlin, itd: Springer Verlag
Precup, R.E., Preitl, S. (2004) Optimization criteria in development of fuzzy controllers with dynamics. Engineering Applications of Artificial Intelligence, vol. 17, str. 661- 674, Sept
Preitl, S., Precup, R.E. (1996) On the algorithmic design of a class of control systems based on providing the symmetry of open-loop bode plots. Buletinul Stiintific al U P T Transactions on Automatic Control and Computer Science, vol. 41 (55), str. 47-55, May
Ratcliffe, J., van Drinkerken, L., Lewin, P., Rogers, E., Hatonen, J., Harte, T., Owens, D. (2005) Fast norm-optimal iterative learning control for industrial applications. u: American Control Conference, Portland OR, USA, June, str. 1951-1956
Rosenwasser, E., Yusupov, R. (2000) Sensitivity of automatic control systems. Boca Raton, FL, itd: CRC Press
Stojić, M.R. (1994) Digitalni sistemi upravljanja. Beograd: Nauka
Tang, K.L., Mulholland, R.J. (1987) Analysis of direct action fuzzy pid controller structures. IEEE Transactions on Systems Man and Cybernetics, vol. 17, str. 1085-1087, Nov./Dec
Tharayil, M., Alleyne, A. (2004) A time-varying iterative learning control scheme. u: American Control Conference, Boston MA, USA, June, str. 3782- 3787
Villagran, V., Sbarbaro, D. (2000) Tuning fuzzy pi controllers by iterative learning. u: Preprints IFAC PID '00 Workshop, Terrassa, Spain, Apr, str. 660-665
Xu, J.X., Xu, J. (2003) A new fuzzy logic learning control scheme for repetitive trajectory tracking problems. Fuzzy Sets and Systems, 133, 1, 57-75