MODELLING PREISACH-TYPE HYSTERESIS NONLINEARITY USING NEURAL NETWORK

C. Li∗ and Y. Tan∗∗

References

  1. [1] D. Crott, G. Shed, & S. Devasia, Creep, hysteresis, and vibra-tion compensation for piezoactuator: atomic force microscopyapplication, Journal of Dynamic Systems, Measurement, andControl, 123, 2001, 35–43.
  2. [2] R.B. Gorbet, K.A. Morris, & D.W.L. Wang, Passivity-basedstability and control of hysteresis in smart actuators, IEEETransaction of Control Systems Technology, 9 (1), 2001, 5–15. doi:10.1109/87.896741
  3. [3] P. Ge & M. Jouaneh, Generalized preisach model for hysteresisnonlinearity of piezoceramic actuators, Precision Engeering,20, 1997, 99–111. doi:10.1016/S0141-6359(97)00014-7
  4. [4] I.D. Mayergoyz & G. Friedman, Generalized preisach modelfor hysteresis, IEEE Transaction on Magnetics, 24 (1), 1988,212–217. doi:10.1109/20.43892
  5. [5] G.V. Webb & D.C. Lagoudas, Hysteresis modelling of SMA ac-tuators for control applications, Journal of Intelligent MaterialSystems and Structures, 9, 1998, 432–448.
  6. [6] I.D. Mayergoz & G. Friedman, Mathematical models of hys-teresis (Springer-Verlag, 1991).
  7. [7] D. Hughes & J.T. Wen, Preisach modelling of piezoceramic andshape memory alloy hysteresis, Smart Materials and Structures,6, 1997, 287–300. doi:10.1088/0964-1726/6/3/007
  8. [8] A.A. Adly, & I.D Mayergoyz, Preisach modelling of magne-tostrictive hysteresis, Journal Applied Physics, 69(8), 1991,5777–5779. doi:10.1063/1.347873
  9. [9] K. Funahashi, On the approximate realization of continuousmappings by neural networks, Neural Networks, 2, 1989, 183–192. doi:10.1016/0893-6080(89)90003-8
  10. [10] G. Cybenko, Approximation by superpositions of a sigmoidalfunction, Mathematics of Control Signals and Systems, 2 (4),1989, 303–314. doi:10.1007/BF02551274
  11. [11] A.A. Adly & S.K. Abd-Hafiz, Using neural networks in theidentification of Preisach-type hysteresis models, IEEE Trans-actions on Magnetics, 34(3), 1998, 629–635. doi:10.1109/20.668057
  12. [12] C. Serpico & C. Visone, Magnetic hysteresis modelling via feed-forward neural networks, IEEE Transactions on Magnetics,34(3), 1998, 623–628. doi:10.1109/20.668055
  13. [13] H.H. Saliah & D.A. Lowther, Modelling magnetic using artificialneural networks, IEEE Transactions on Magnetics, 34 (5),1998, 3056–3059. doi:10.1109/20.717715
  14. [14] M. Kuczmann & A. Ivanyi, A new neural-network-based scalarhysteresis model, IEEE Transactions on Magnetics, 38 (2),2002, 857–860. doi:10.1109/20.996221
  15. [15] H.H. Saliah & D.A. Lowther, A neural network model of mag-netic hysteresis for computational magnetics, IEEE Transac-tions on Magnetics, 33 (5), 1997, 4146–4148. doi:10.1109/20.619691
  16. [16] R.B. Gorbert, Control of hysteretic systems with Preisachmodel representations (Ph.D Thesis of University of Waterloo,Ontario, Canada, 1997).
  17. [17] L. Chuntao & T. Yonghong, A neural network model forhysteresis nonlinearity, Sensor and Actuator Physical: A, 112,49–54, 2004.
  18. [18] G. Webb, A. Kurdila, & D. Lagoudas, Adaptive hysteresismodel for model reference control with actuator hysteresis, J.Guidance, Control, and Dynamics, 23(3), 2000, 459–465.

Important Links:

Go Back