An Adaptive Type-2 Fuzzy Neural Controller for Nonlinear Uncertain Systems

C.-H. Lee and Y.-C. Lin

References

  1. [1] Y.C. Chen & C.C. Teng, A model reference control structure using a fuzzy neural network, Fuzzy Sets and Systems, 73, 291–312, 1995. doi:10.1016/0165-0114(94)00319-3
  2. [2] C.H. Lee & C.C. Teng, Identification and control of dynamic systems using recurrent fuzzy neural networks, IEEE Trans. on Fuzzy Systems, 8 (4), 2000, 349–366. doi:10.1109/91.868943
  3. [3] C.T. Lin & C.S.G. Lee, Neural fuzzy systems (Englewood Cliffs, NJ: Prentice-Hall, 1996).
  4. [4] C.H. Lee & C.C. Teng, Tuning PID controller of unstable processes: A fuzzy neural network approach, Fuzzy Sets and Systems, 128 (1), 2002, 95–106. doi:10.1016/S0165-0114(01)00051-3
  5. [5] C.H. Lee, Y.C. Lin, & W.Y. Lai, System identification using type-2 fuzzy neural network (Type-2 FNN) systems, IEEE Conf. on Computer, Intelligent, Robotics, and Automation (CIRA03), Japan, 2003, 1264–1269.
  6. [6] C.H. Lee, Stabilization of nonlinear nonminimum phase systems: An adaptive parallel approach using recurrent fuzzy neural network, IEEE Trans. on Systems, Man, and Cybernetics, Part B, 34 (2), 2004, 1075–1088. doi:10.1109/TSMCB.2003.820592
  7. [7] C.H. Lee, W.Y. Lai, & Y.C. Lin, A TSK-type fuzzy neural network (TFNN) system for dynamic systems identification, 42nd IEEE Conf. on Decision and Control (CDC03), Maui, HI, December 9–12, 2003, 4002–4007.
  8. [8] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Information Sciences, 8, 1975, 199–249. doi:10.1016/0020-0255(75)90036-5
  9. [9] Q. Liang & J. Mendel, Interval type-2 fuzzy logic systems: Theory and design, IEEE Trans. on Fuzzy Systems, 8 (5), 2000, 535–550. doi:10.1109/91.873577
  10. [10] N. Karnik, J. Mendel, & Q. Liang, Type-2 fuzzy logic systems, IEEE Trans. on Fuzzy Systems, 7 (6), 1999, 643–658. doi:10.1109/91.811231
  11. [11] J. Mendel & R. John, Type-2 fuzzy sets made simple, IEEE Trans. on Fuzzy Systems, 10 (2), 2002, 117–127. doi:10.1109/91.995115
  12. [12] J.M. Mendel, Uncertain rule-based fuzzy logic systems: Introduction and new directions (Englewood Cliffs, NJ: Prentice-Hall, 2001).
  13. [13] M. Mizumoto & K. Tanaka, Some properties of fuzzy sets of type 2, Information and Control, 31, 1976, 312–340. doi:10.1016/S0019-9958(76)80011-3
  14. [14] A. Lotfi & A.C. Tsoi, Learning fuzzy inference systems using an adaptive membership function scheme, IEEE Trans. on Systems, Man, and Cybernetic, Part B: Cybernetics, 26 (2), 1996, 326–331. doi:10.1109/3477.485884
  15. [15] R. Hecht-Nielsen, Theory of the backuppropagation neural network, Proc. IEEE IJCNN, 1, 1989, 593–605. doi:10.1109/IJCNN.1989.118638
  16. [16] P.J. Werbos, Back propagation through time: What it does and how to do it, Proc. IEEE, 78 (10), 1990, 1550–1560. doi:10.1109/5.58337
  17. [17] T. Yabuta & T. Yamada, Learning control using neural networks, Proc. IEEE Int. Conf. Robotics and Automation, Sacramento, CA, 1991, 740–745. doi:10.1109/ROBOT.1991.131673
  18. [18] W.Y. Wang, C.Y. Cheng, & Y.G. Leu, An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems, IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics, 34 (1), 2004, 334–345. doi:10.1109/TSMCB.2003.816995
  19. [19] J.D. Morningred, B.E. Paden, D.E. Seborg, & D.A. Mellichamp, An adaptive nonlinear predictive controller, Chemical Engineering Science, 47 (4), 1992, 755–762. doi:10.1016/0009-2509(92)80266-F
  20. [20] C.T. Chen & S.T. Peng, Learning control of process system with hard input constraints, Journal of Process Control, 9, 1999, 151–160. doi:10.1016/S0959-1524(98)00038-9
  21. [21] H. Zhang & L. Cai, Nonlinear adaptive control using the Fourier integral and its application to CSTR systems, IEEE Trans. on Systems, Man and Cybernetics, part B: Cybernetics, 32(3), 2002, 367-372. doi:10.1109/TSMCB.2002.999812

Important Links:

Go Back