Skip to main content

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

  • Chapter
Intelligent Systems: Models and Applications

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Isermann, R.: Mechatronic systems: Fundamentals. Springer, Heidelberg (2005)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Landau, I.D., Zito, G.: Digital control systems: Design, identification and implementation. Springer, London (2006)

    Google Scholar 

  5. Preitl, S., Precup, R.E., Preitl, Z.: Control structures and algorithms. Editura Orizonturi Universitare, Timisoara (2009) (in Romanian)

    Google Scholar 

  6. Araki, M., Taguchi, H.: Two–degree–of–freedom PID controllers. Int. J. Control Automat. Syst. 1, 401–411 (2003)

    Google Scholar 

  7. Astrom, K.J., Hagglund, T.: PID controllers theory: Design and tuning. Instrument Society of America, Research Triangle Park (1995)

    Google Scholar 

  8. Leva, A., Bascetta, L.: On the design of the feed-forward compensator in two-degree-of-freedom controllers. Mechatronics 16, 533–546 (2006)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Preitl, S., Precup, R.E.: An extension of tuning relations after symmetrical optimum method for PI and PID controllers. Automatica 35, 1731–1736 (1999)

    Article  MATH  Google Scholar 

  12. Preitl, Z.: Model-based design methods for speed control applications. Editura Politehnica, Timisoara (2008)

    Google Scholar 

  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. 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. Visioli, A.: Fuzzy logic based set–point weight tuning of PID controllers. IEEE Trans. Syst. Man. Cybern. A Syst. Humans 29, 587–592 (1999)

    Article  Google Scholar 

  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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  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. Horowitz, I.M.: Synthesis of feedback systems. Academic Press, New York (1963)

    MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  22. Skrjanc, I., Blazic, S., Matko, D.: Direct fuzzy model–reference adaptive control. Int. J. Intell. Syst. 17, 943–963 (2002)

    Article  MATH  Google Scholar 

  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. Fodor, J., Rudas, I.J.: On continuous triangular norms that are migrative. Fuzzy Sets Systems 158, 1692–1697 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  25. Blazic, S., Skrjanc, I., Matko, D.: Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Systems 139, 3–33 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  26. Mihailovic, B., Pap, E.: Asymmetric general Choquet integrals. Acta Polytechnica Hungarica 6, 161–173 (2009)

    Google Scholar 

  27. Vascak, J., Madarasz, L.: Adaptation of fuzzy cognitive maps – a comparison study. Acta Polytechnica Hungarica 7, 109–122 (2010)

    Google Scholar 

  28. Johanyak, Z.C.: Student evaluation based on fuzzy rule interpolation. Int. J. Artif. Intell. 5, 37–55 (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Nasar, S.A., Boldea, I.: Electric drives, 2nd edn. CRC Press, Taylor and Francis, New York (2005)

    Google Scholar 

  34. Boldea, I.: Advanced electric drives. PhD courses (2010-2011), ”Politehnica” University of Timisoara, Timisoara, Romania (2011)

    Google Scholar 

  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. ECP: Industrial emulator/servo trainer model 220 system, testbed for practical control training, Bell Canyon, CA, USA. Educational Control Products (2010)

    Google Scholar 

  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. Horvath, L., Rudas, I.J.: Modelling and solving methods for engineers. Academic Press, Burlington (2004)

    Google Scholar 

  39. Vascak, J.: Navigation of mobile robots using potential fields and computational intelligence means. Acta Polytechnica Hungarica 4, 63–74 (2007)

    Google Scholar 

  40. Dankovic, B., Nikolic, S., Milojkovic, M., Jovanovic, Z.: A class of almost orthogonal filters. J. Circ. Syst. Comp. 18, 923–931 (2009)

    Article  Google Scholar 

  41. Iglesias, J.A., Angelov, P., Ledezma, A., Sanchis, A.: Evolving classification of agents’ behaviors: a general approach. Evolving Syst. 1, 161–171 (2010)

    Article  Google Scholar 

  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. Linda, O., Manic, M.: Self-organizing fuzzy haptic teleoperation of mobile robot using sparse sonar data. IEEE Trans. Ind. Electron. 58, 3187–3195 (2011)

    Article  Google Scholar 

  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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  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.2012.01.002

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexandra-Iulia Stinean .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Stinean, AI., Preitl, S., Precup, RE., Dragos, CA., Radac, MB. (2013). Classical and Fuzzy Approaches to 2–DOF Control Solutions for BLDC–m Drives. In: Pap, E. (eds) Intelligent Systems: Models and Applications. Topics in Intelligent Engineering and Informatics, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33959-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33959-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33958-5

  • Online ISBN: 978-3-642-33959-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics