TUNING EMPLOYING FUZZY AND ANFIS FOR A pH PROCESS

Komala S. Saji and Madhavan Sasikumar

References

  1. [1] K.J. Astrom and T. Hagglund, The future of PID control,Control Engineering Practice, 9(11), 2001, 1163–1175.
  2. [2] F.G. Shinskey, Process control system: Application, design andtuning, 4th ed. (New York: McGraw-Hill, 1996).
  3. [3] K. Yamada and K. Watanabe, A state space design method ofstable filtered inverse systems and its application to H2 sub-optimal internal model control, Proc. IFAC96, San Francisco,California, 1996, 379–382.
  4. [4] A.P. Loh, K.O. Looi, and K.F. Fong, Neural network modellingand control strategies for a pH neutralization process, Journalof Process Control, 6, 1995, 355–362.
  5. [5] T. Takagi and M. Sugeno, Fuzzy identification of systems andits applications to modelling and control, IEEE Transactionson Systems, Man, and Cybernetics, 15, 1985, 116–132.
  6. [6] S.J. Qin and G. Borders, A multiregion fuzzy logic controllerfor nonlinear process control, IEEE Transactions on FuzzySystems, 2, 1994, 74–81.
  7. [7] T. Takagi and M. Sugeno, Derivation of fuzzy control rules fromhuman operator’s control actions, Proc. IFAC Symp. FuzzyInform. Knowledge Representation and Decision Analysis,Elsevier, July 1983, 55–60.
  8. [8] S. Lee and G.G. Yen, Analysis of Takagi-Sugeno fuzzy modelsin system identification for model-based control, Control andIntelligent Systems, 32(2), 2004, 69–79.
  9. [9] E. Lughofer and C. Guardiola, On-line fault detection withdata-driven evolving fuzzy models, Control and IntelligentSystems, 36(4), 2008, 307–317.
  10. [10] R.K. Mudi, C. Dey, and Tsu-Tian Lee, An improved auto-tuning scheme for PI controllers, Journal of Science DirectISA Transactions, 47, 2008, 45–52.
  11. [11] C.C. Lee, Fuzzy logic in control systems: Fuzzy logic controller –Part I, IEEE Transactions on Systems, Man, Cybernetics, 20,1990, 404–418.
  12. [12] C.C. Lee, Fuzzy logic in control systems: Fuzzy logic controller –Part II, IEEE Transactions on Systems, Man, Cybernetics,20, 1990, 419–435.
  13. [13] P. Werbos, Beyond regression: New tools for prediction andanalysis in the behavioral sciences, Doctoral Dissertation,Harvard University, Cambridge, MA, 1974.
  14. [14] H.T. Mok, C.W. Chan, and W.K. Yeung, Neurofuzzy network-based adaptive nonlinear PI controllers, Control and IntelligentSystems, 34(3), 2006, 216–224.
  15. [15] A. Khoukhi, L. Baron, M. Balazinski, and K. Demirli, Hybridneuro-fuzzy multi-objective trajectory planning of redundantmanipulators, Control and Intelligent Systems, 37(2), 2009,87–96.
  16. [16] L.E. Zarate, P. Resende, and M. Benjamin, A fuzzy logic andvariable structure base controller for pH control, Proc. 27thAnnual Conference of IEEE, Louisiana, USA, 2001.
  17. [17] L.E. Zarate, P. Resende, and M. Benjamin, Rule extractionusing generalized neural networks, Proc. 4th IFSA WorldCongress, Brussels, July 1991.100
  18. [18] T.K. Radhakrishnan, Hybrid GA Fuzzy Controller for pHProcess, Proc. on Computational Intelligence and MultimediaApplications, Sivakasi, India, 2007.
  19. [19] J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inferencesystem, IEEE Transactions on Systems, Man, and Cybernetics,1993, 665–685.

Important Links:

Go Back