ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) FOR FRICTION MODELLING AND COMPENSATION IN MOTION CONTROL SYSTEM

Ismaila B. Tijani, Martono Wahyudi, and Hashim Talib

References

  1. [1] B. Armstrong-Helouvry, Control of machines with friction(Boston, MA: Kluwer, 1991).
  2. [2] P. Dupont & B. Armstrong-Helouvry, Compensation tech-niques for servo with friction, Proceedings of American ControlConference, San Francisco, 2, 1993, 1915–1919.
  3. [3] B. Armstrong-Helouvry, P. Dupont, & C. De Wit, A surveyof models, analysis tools and compensation method for thecontrol of machines with friction, Automatica, 30 (7), 1994,1083–1138.
  4. [4] M. Wahyudi, Friction identification and compensation for highprecision motion control system – Part 1: Friction identifica-tion, Proc. of Industrial Electronics Seminar (IES), Surabaya,Indonesia, 2003.
  5. [5] T. Tjahjowidodo, F. Al-Bender, & H.V. Brussel, Frictionidentification and compensation in a DC motor, InternationalJournal of JSPE, 32(3), 2004, 200–206.
  6. [6] C. Canudas de Wit, K.J. Astrom, & K. Braun, Adaptivefriction compensation in DC motor drives, Proc. of IEEEInternational Conference on Robotics and Automation, SanFrancisco, 3, 1986, 1556–1561.
  7. [7] C. Canudas de Wit, H. Olsson, K.J. Astrom, & P. Lischinsky,A new model for control of systems with friction, IEEETransactions on Automatic Control, 40(3), 1995, 419–425.
  8. [8] C. Makkar, W.E. Dixon, W.G. Sawyer, & G. Hu, A new con-tinuously differentiable friction model for control systems de-sign, Proc. of the 2005 IEEE/ASME International Conferenceon Advanced Intelligent Mechatronics, Monterey, California,USA, July 24–26, 2005.40
  9. [9] X.Z. Gao & S.J. Ovaska, Friction compensation in servo mo-tor systems using neural networks, Proc. of the 1999 IEEEMidnight-Sun Workshop on Soft Computing Methods in In-dustrial Applications, Kuusamo, Finland, 1999, 146–151.
  10. [10] S.N. Huang, K.K. Tan, & T.H. Lee, Adaptive friction compen-sation using neural network approximations, IEEE Transac-tions on Systems, Man, and Cybernetics – Part C: Applicationand Review, 30(4), 2000, 551–557.
  11. [11] D. Bi, Y.F. Li, T.S. Tso, & G.L. Wang, Friction modelingand compensation for haptic display based on support vectormachine, IEEE Transactions on Industrial Electronics, 51(2),2004, 491–500.
  12. [12] K.M. Cılıza & M. Tomizukab, Friction modeling and compen-sation for motion control using hybrid neural network models,Engineering Applications of Artificial Intelligence, 20 (7), 2007,898–911.
  13. [13] S.C.P. Gomes, D. Gomes, & C.M. Diniz, Neuro-fuzzy frictioncompensation to robotic actuators, Proc. of the 2005 IEEEIndustrial Conference on Mechatronics, Taipei, Taiwan, July10–12, 2005.
  14. [14] M. Wahyudi & I.B. Tijani, Friction compensation for motioncontrol system using multilayer feedforward network, Proc.of the 5th International Symposium on Mechatronics and itsApplications (ISMA08), Amman, Jordan, May 27–29, 2008.
  15. [15] J.S.R. Jang, ANFIS: Adaptive-network-based fuzzy inferencesystem, IEEE Transactions on Systems, Man and Cybernetics,23(5/6), 1993, 665–685.
  16. [16] A. Tustin, The effects of backlash and of speed-dependentfriction on the stability of closed-cycle control systems, IEEEJournal, 94(Part 2A), 1947, 143–151.
  17. [17] L. Ljung, System identification: Theory for the user, (Engle-wood Cliffs, NJ: Prentice-Hall, 1987).
  18. [18] D.Y. Ohm, A PDFF controller for tracking and regulation inmotion control, Proc. of 18th Conference, Intelligent Motion,Philadelphia, 1990.
  19. [19] J.S.R. Jang & N. Gulley, The fuzzy logic toolbox for use withMATLAB (Natick, MA: The MathWorks Inc., 1995).
  20. [20] C.C. Lee, Fuzzy logic in control systems: Fuzzy logic controller –Part I, IEEE Transactions on Systems, Man and Cybernetics,20, 1990, 404–418.
  21. [21] Z. Hou, Q. Shen, & H. Li, Nonlinear system identification basedon ANFIS, IEEE International Conference Neural Networksand Signal Processing, Nanjing, China, December 14–17, 2003.
  22. [22] W.-C. Lih, S.T.S. Bukkapatnam, P. Rao, N. Chandrasekha-ran, & R. Komanduri, Adaptive neuro-fuzzy inference systemmodeling of MRR and WIWNU in CMP process with sparseexperimental data, IEEE Transactions on Automation Scienceand Engineering, 5 (1), 2008, 71.

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