Classical and Fuzzy Approaches to 2–DOF Control Solutions for BLDC–m Drives

  • Alexandra-Iulia StineanEmail author
  • Stefan Preitl
  • Radu-Emil Precup
  • Claudia-Adina Dragos
  • Mircea-Bogdan Radac
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 3)


This chapter gives two–degree–of–freedom (2–DOF) speed control solutions for brushless Direct Current motor (BLDC–m) drives with focus on design methodologies. A classical 2–DOF structure, 2–DOF proportional-integral (PI) and proportional–integral–derivative (PID) structures and 2–DOF fuzzy control solutions are presented and approaches regarding the methods are highlighted. A case study concerning a BLDC–m drive with variable moment of inertia is presented. Comparative studies based on digital simulation results are included to exemplify the design methods.


Speed control 2–DOF control brushless direct current motor PID control 


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  1. 1.
    Isermann, R.: Mechatronic systems: Fundamentals. Springer, Heidelberg (2005)Google Scholar
  2. 2.
    Akpolat, Z.H., Asher, G.M., Clare, J.C.: A practical approach to the design of robust speed controllers for machine drives. IEEE Trans. Ind. Electron 47, 315–324 (2000)CrossRefGoogle Scholar
  3. 3.
    Miklosovic, R., Gao, Z.: A robust two–degree–of–freedom control design technique and its practical application. In: Proceedings of 39th IAS Annual Meeting Conference, Seattle, WA, USA, vol. 3, pp. 1495–1502 (2004)Google Scholar
  4. 4.
    Landau, I.D., Zito, G.: Digital control systems: Design, identification and implementation. Springer, London (2006)Google Scholar
  5. 5.
    Preitl, S., Precup, R.E., Preitl, Z.: Control structures and algorithms. Editura Orizonturi Universitare, Timisoara (2009) (in Romanian) Google Scholar
  6. 6.
    Araki, M., Taguchi, H.: Two–degree–of–freedom PID controllers. Int. J. Control Automat. Syst. 1, 401–411 (2003)Google Scholar
  7. 7.
    Astrom, K.J., Hagglund, T.: PID controllers theory: Design and tuning. Instrument Society of America, Research Triangle Park (1995)Google Scholar
  8. 8.
    Leva, A., Bascetta, L.: On the design of the feed-forward compensator in two-degree-of-freedom controllers. Mechatronics 16, 533–546 (2006)CrossRefGoogle Scholar
  9. 9.
    Alfaro, V.M., Vilanova, R., Arrieta, O.: Robust tuning of Two-Degree-of-Freedom (2–DoF) PI/PID based cascade control system. J. Process Control 19, 1658–1670 (2009)CrossRefGoogle Scholar
  10. 10.
    Cheng, Z., Yamada, K., Sakanushi, T., Murakami, I., Ando, Y., Nguyen, L.T., Yamamoto, S.: A design method for two–degree–of–freedom multi–period repetitive controllers for multiple–input/multiple–output systems. In: Preprints of 18th IFAC World Congress, Milano, Italy, pp. 5753–5758 (2011)Google Scholar
  11. 11.
    Preitl, S., Precup, R.E.: An extension of tuning relations after symmetrical optimum method for PI and PID controllers. Automatica 35, 1731–1736 (1999)zbMATHCrossRefGoogle Scholar
  12. 12.
    Preitl, Z.: Model-based design methods for speed control applications. Editura Politehnica, Timisoara (2008)Google Scholar
  13. 13.
    Peng, Y.Q., Luo, J., Zhuang, J.F., Wu, C.Q.: Model reference fuzzy adaptive PID control and its applications in typical industrial processes. In: Proceedings of IEEE International Conference on Automation and Logistics (ICAL 2008), Qingdao, China, pp. 896–901 (2008)Google Scholar
  14. 14.
    Preitl, Z., Levendovszky, T.: Computer aided design of two–degree–of–freedom (2DF) controllers. Scientific Bulletin of ”Politehnica” University of Timisoara Romania. Transactions on Automatic Control and Computer Science 48(62), 70–75 (2003)Google Scholar
  15. 15.
    Visioli, A.: Fuzzy logic based set–point weight tuning of PID controllers. IEEE Trans. Syst. Man. Cybern. A Syst. Humans 29, 587–592 (1999)CrossRefGoogle Scholar
  16. 16.
    Shu, S.Q., Ding, X.Y., Wu, W., Ren, H.Y.: Application of a self–tuning two degree of freedom PID controller based on fuzzy inference for PMSM. In: Proceedings of International Conference on Electrical Machines and Systems (ICEMS 2008), Wuhan, China, pp. 1629–1632 (2008)Google Scholar
  17. 17.
    Precup, R.E., Preitl, S., Petriu, E.M., Tar, J.K., Tomescu, M.L., Pozna, C.: Generic two–degree–of–freedom linear and fuzzy controllers for integral processes. J. Franklin Inst. 346, 980–1003 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Preitl, S., Precup, R.E., Preitl, Z.: Aspects concerning the tuning of 2–DOF fuzzy controllers. In: Proceedings of Xth Triennial International SAUM Conference on Systems, Automatic Control and Measurements (SAUM 2010), Nis, Serbia, pp. 210–219 (2010)Google Scholar
  19. 19.
    Horowitz, I.M.: Synthesis of feedback systems. Academic Press, New York (1963)zbMATHGoogle Scholar
  20. 20.
    Baranyi, P., Gedeon, T.D.: Rule interpolation by spatial geometric representation. In: Proceedings of 6th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 1996), Granada, Spain, pp. 483–488 (1996)Google Scholar
  21. 21.
    Baranyi, P., Yam, Y., Varkonyi–Koczy, A.R., Patton, R.J., Michelberger, P., Sugiyama, M.: SVD based complexity reduction to TS fuzzy models. IEEE Trans. Ind. Electron 49, 433–443 (2002)CrossRefGoogle Scholar
  22. 22.
    Skrjanc, I., Blazic, S., Matko, D.: Direct fuzzy model–reference adaptive control. Int. J. Intell. Syst. 17, 943–963 (2002)zbMATHCrossRefGoogle Scholar
  23. 23.
    Johanyak, Z.C.: A brief survey and comparison on various interpolation based fuzzy reasoning methods. Acta Polytechnica Hungarica 3, 91–105 (2006)Google Scholar
  24. 24.
    Fodor, J., Rudas, I.J.: On continuous triangular norms that are migrative. Fuzzy Sets Systems 158, 1692–1697 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Blazic, S., Skrjanc, I., Matko, D.: Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Systems 139, 3–33 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Mihailovic, B., Pap, E.: Asymmetric general Choquet integrals. Acta Polytechnica Hungarica 6, 161–173 (2009)Google Scholar
  27. 27.
    Vascak, J., Madarasz, L.: Adaptation of fuzzy cognitive maps – a comparison study. Acta Polytechnica Hungarica 7, 109–122 (2010)Google Scholar
  28. 28.
    Johanyak, Z.C.: Student evaluation based on fuzzy rule interpolation. Int. J. Artif. Intell. 5, 37–55 (2010)Google Scholar
  29. 29.
    Linda, O., Manic, M.: Uncertainty-robust design of interval type–2 fuzzy logic controller for delta parallel robot. IEEE Trans. Ind. Informat. 7, 661–670 (2011)CrossRefGoogle Scholar
  30. 30.
    Stinean, A.I., Preitl, S., Precup, R.E., Pozna, C., Dragos, C.A., Radac, M.B.: Speed and position control of BLDC servo systems with low inertia. In: Proceedings of 2nd International Conference on Cognitive Infocomunications (CogInfoCom 2011), Budapest, Hungary, p. 8 (2011)Google Scholar
  31. 31.
    Stinean, A.I., Preitl, S., Precup, R.E., Dragos, C.A., Radac, M.B.: 2–DOF control solutions for BLDC–m drives. In: Proceedings of IEEE 9th International Symposium on Intelligent Systems and Informatics (SISY 2011), Subotica, Serbia, pp. 29–34 (2011)Google Scholar
  32. 32.
    Baldursson, S.: BLDC motor modelling and control – A Matlab/Simulink implementation. M.Sc. Thesis, Institutionen for Energi och Miljo, Goteborg, Sweden (2005)Google Scholar
  33. 33.
    Nasar, S.A., Boldea, I.: Electric drives, 2nd edn. CRC Press, Taylor and Francis, New York (2005)Google Scholar
  34. 34.
    Boldea, I.: Advanced electric drives. PhD courses (2010-2011), ”Politehnica” University of Timisoara, Timisoara, Romania (2011)Google Scholar
  35. 35.
    Mink, F., Bahr, A.: Adaptive speed control for drives with variable moments of inertia and natural drequencies, LTi DRIVES GmbH Entwicklung Software, Lahnau, Germany. (2011)Google Scholar
  36. 36.
    ECP: Industrial emulator/servo trainer model 220 system, testbed for practical control training, Bell Canyon, CA, USA. Educational Control Products (2010)Google Scholar
  37. 37.
    Preitl, S., Precup, R.E., Dragos, C.A., Radac, M.B.: Tuning of 2–DOF fuzzy PI (D) controllers laboratory applications. In: Proceedings of 11th International Conference on Computational Intelligence and Informatics (CINTI 2010), Budapest, Hungary, pp. 237–242 (2010)Google Scholar
  38. 38.
    Horvath, L., Rudas, I.J.: Modelling and solving methods for engineers. Academic Press, Burlington (2004)Google Scholar
  39. 39.
    Vascak, J.: Navigation of mobile robots using potential fields and computational intelligence means. Acta Polytechnica Hungarica 4, 63–74 (2007)Google Scholar
  40. 40.
    Dankovic, B., Nikolic, S., Milojkovic, M., Jovanovic, Z.: A class of almost orthogonal filters. J. Circ. Syst. Comp. 18, 923–931 (2009)CrossRefGoogle Scholar
  41. 41.
    Iglesias, J.A., Angelov, P., Ledezma, A., Sanchis, A.: Evolving classification of agents’ behaviors: a general approach. Evolving Syst. 1, 161–171 (2010)CrossRefGoogle Scholar
  42. 42.
    Garcia, A., Luviano-Juarez, A., Chairez, I., Poznyak, A., Poznyak, T.: Projectional dynamic neural network identifier for chaotic systems: Application to Chua’s circuit. Int. J. Artif. Intell. 6, 1–18 (2011)Google Scholar
  43. 43.
    Linda, O., Manic, M.: Self-organizing fuzzy haptic teleoperation of mobile robot using sparse sonar data. IEEE Trans. Ind. Electron. 58, 3187–3195 (2011)CrossRefGoogle Scholar
  44. 44.
    Kasabov, N., Abdull Hamed, N.H.: Quantum–inspired particle swarm optimisation for integrated feature and parameter optimisation of evolving spiking neural networks. Int. J. Artif. Intell. 7, 114–124 (2011)Google Scholar
  45. 45.
    Peng, C., Han, Q.L.: Delay–range–dependent robust stabilization for uncertain T–S fuzzy control systems with interval time–varying delays. Inf. Sci. 181, 4287–4299 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  46. 46.
    Obradovic, D., Konjovic, Z., Pap, E., Rudas, I.J.: Linear fuzzy space based road lane model and detection. Know. Based Syst. (2012), doi:10.1016/j.knosys.2012.01.002Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexandra-Iulia Stinean
    • 1
    Email author
  • Stefan Preitl
    • 1
  • Radu-Emil Precup
    • 1
  • Claudia-Adina Dragos
    • 1
  • Mircea-Bogdan Radac
    • 1
  1. 1.Department of Automation and Applied Informatics”Politehnica” University of TimisoaraTimisoaraRomania

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