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EKF-Based PI-/PD-Like Fuzzy-Neural-Network Controller for Brushless Drives

EKF-Based PI-/PD-Like Fuzzy-Neural-Network Controller for Brushless Drives. Student : Tz -Han Jung. Rubaai , A.; Young, P.  Industry Applications, IEEE Transactions on Volume: 47 , Issue : 6 Digital Object Identifier:  10.1109/TIA.2011.2168799 Publication Year: 2011 , Page(s): 2391 – 2401

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EKF-Based PI-/PD-Like Fuzzy-Neural-Network Controller for Brushless Drives

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  1. EKF-Based PI-/PD-Like Fuzzy-Neural-NetworkController for Brushless Drives Student : Tz-Han Jung Rubaai, A.; Young, P. Industry Applications, IEEE Transactions onVolume: 47 ,Issue: 6Digital Object Identifier: 10.1109/TIA.2011.2168799Publication Year: 2011 , Page(s): 2391 – 2401 IEEE JOURNALS & MAGAZINES

  2. Abstract This paper presents the development of a fuzzyneural-network (FNN) proportional–integral (PI)-/proportional–derivative (PD)-like controller with online learning for speedtrajectory tracking of a brushless drive system. The design implementsthe novel use of the extended Kalman filter (EKF) totrain FNN structures as part of the PI-/PD-like fuzzy design. The objective is to replace the conventional PI–derivative (PID) controller with the proposed FNN PI-/PD-like controller with EKF learning mechanism.

  3. Proposed PI-/PD-like FNN controller structure

  4. FNN PI.PD structure

  5. Block diagram of the hardware apparatus

  6. under normal condition

  7. under zero speed

  8. under disturbance

  9. under load

  10. under constant speed

  11. poor initial condition&training improvement

  12. REFERENCES [1] A. Sant and K. R. Rajagopal, “PM synchronous motor speed control using hybrid fuzzy-PI with novel switching functions,” IEEE Trans. Magn.,vol. 45, no. 10, pp. 4672–4675, Oct. 2009. [2] B. M. Hohan and A. Sinha, “Analytical structure and stability analysis of a fuzzy PID controller,” Appl. Soft Comput., vol. 8, no. 1, pp. 749–758,Jan. 2008. [3] A. Rubaai, M. J. Castro-Sitiriche, and A. R. Ofoli, “DSP-based laboratory implementation of hybrid fuzzy-PID controller using genetic optimization for high performance motor drives,” IEEE Trans. Ind. Appl., vol. 44, no. 6, pp. 1977–1986, Nov./Dec. 2008. [4] Y.-P. Kuo and T.-H. S. Li, “GA-based Fuzzy PI/PD controller for automotive active suspension system,” IEEE Trans. Ind. Electron., vol. 46, no. 6, pp. 1051–1056, Dec. 1999. [5] B.-G. Hu, G. K. I.Mann, and R. Gosine, “A systematic study of fuzzy PID controllers—Function-based evaluation approach,” IEEE Trans. Fuzzy Syst., vol. 9, no. 5, pp. 699–712, Oct. 2001. [6] H.-X. Li, L. Zhang, K.-Y. Cai, and G. Chen, “An improved robust fuzzy-PID controller with optimal fuzzy reasoning,” IEEE Trans. Syst., Man,Cybern. B, Cybern., vol. 35, no. 6, pp. 1283–1294, Dec. 2005. [7] M.Masiala, B. Vafakhah, J. Salmon, and A.M. Knight, “Fuzzy self-tuning speed control of an indirect field-oriented control induction motor drive,” IEEE Trans. Ind. Appl., vol. 44, no. 6, pp. 1732–1740, Nov./Dec. 2008. [8] M. Barut, S. Bogosyan, and M. Gokasan, “Speed sensorlessestimation for induction motors using extended Kalman filters,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 272–280, Feb. 2007. [9] K. Szabat and T. Orlowska-Kowalska, “Performance improvement of industrial drives with mechanical elasticity using nonlinear adaptive Kalmanfilter,” IEEE Trans. Ind. Electron., vol. 55, no. 3, pp. 1075–1084, Mar. 2008. [10] K. K. Ahn and D. Q. Truong, “Online tuning fuzzy PID controller using robust extended Kalman filter,” J. Process Control, vol. 19, no. 6, pp. 1011–1023, Jun. 2009. [11] D. J. Lary and H. Y. Mussa, “Using an extended Kalman filter learning algorithm for feed-forward neural networks to describe tracer correlations,” Atmos. Chem. Phys. Discuss., vol. 4, pp. 3653–3657, 2004. [12] S. Singhal and L. Wu, “Training feedforward networks with extended Kalmanfilter algorithm,” in Proc. Int. Conf. ASSP, 1989, pp. 1187–1190.

  13. REFERENCES [13] S.-J. Ho, L.-S. Shu, and S.-Y. Ho, “Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA,” IEEE Trans. Fuzzy Syst., vol. 14, no. 3, pp. 421–434, Jun. 2006. [14] I. del Campo, J. Echanobe, G. Bosque, and J. M. Tarela, “Efficient hardware/ software implementation of an adaptive neuro–fuzzy system,” IEEE Trans. Fuzzy Syst., vol. 16, no. 3, pp. 761–778, Jun. 2008. [15] M. N. Uddin and M. A. Rahman, “Development and implementation of a hybrid intelligent controller for interior permanent-magnet synchronous motor drives,” IEEE Trans. Ind. Appl., vol. 40, no. 1, pp. 68–76, Jan./Feb. 2004. [16] dSPACE User’s Guide, Digital Signal Processing and Control Engineering, dSPACE, Paderborn, Germany, 2003. [17] G413-817 Technical Data Manual, Moog Aerospace, East Aurora, New York, 2000. [18] T200-410 Technical Data Manual, Moog Aerospace, East Aurora, New York, 2000.

  14. Thank you for your attention.

  15. Q & A.

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