ADAPTIVE VRFT BASED ON MFAC FOR THE SPEED CONTROL OF PMDC MOTOR

Rana J. Masood, Daobo Wang, and Muhammad F. Manzoor

References

  1. [1] X. Jiang, H. Wenxin, et al. , Electric drive system of dual-winding fault-tolerant permanent-magnet motor for aerospaceapplications, IEEE Transactions on Industrial Electronics,62(12), 2015, 7322–7330. doi:10.1109/tie.2015.2454483.
  2. [2] F. Mejdr and L. Beran, The implementation of CNC con-trol using smart device controller, 2015 IEEE Int. Work-shop of Electronics, Control, Measurement, Signals andTheir Application to Mechatronics (ECMSM), June 2015,doi:10.1109/ecmsm.2015.7208687.65
  3. [3] A. NP and B. Kaliaperumal, Speed and torque control ofpermanent magnet synchronous motor using hybrid fuzzyproportional plus integral controller, Journal of Vibration andControl, 21(3), 2013, 563–579. doi:10.1177/1077546313488160.
  4. [4] Z. Qiao, S. Tingna, et al., New sliding-mode observer forposition sensorless control of permanent-magnet synchronousmotor, IEEE Transactions on Industrial Electronics, 60(2),2013, 710–719. doi:10.1109/tie.2012.2206359.
  5. [5] A. Rahimi, B. Farhad, et al., The online parameter identifica-tion of chaotic behaviour in permanent magnet synchronousmotor by self-adaptive learning bat-inspired algorithm, Inter-national Journal of Electrical Power & Energy Systems, 78,2016, 285–291. doi:10.1016/j.ijepes.2015.11.084.
  6. [6] A.d.O. Guimar˜aes, J.P. da Silva, and E.R.M. Dantas, Geneticalgorithm applied to control of dc motor with disturbance re-jection by feedforward action, Control and Intelligent Systems,43, 2015. doi:10.2316/journal.201.2015.1.201-2599.
  7. [7] A. Benmakhlouf, A. Louchene, and D. Djarah, Fuzzy logicand modified crisp logic applied to a DC motor posi-tion control, Control and Intelligent Systems, 38(3), 2010.doi:10.2316/journal.201.2010.3.201-2214.
  8. [8] H. Halmarsson, M. Gevers, S. Gunnarsson, and O. Lequin, Iter-ative feedback tuning: Theory and applications, IEEE ControlSystems Magazine, 18(4), 1998, 26–41. doi:10.1109/37.710876.
  9. [9] J. Sj¨oberg, D.B. Franky, et al., Iterative controller optimizationfor nonlinear systems, Control Engineering Practice, 11(9),2003, 1079–1086. doi:10.1016/s0967-0661(02)00231-9.
  10. [10] A. Karimi, K. Van Heusden, and D. Bonvin, Non-iterativedata-driven controller tuning using the correlation approach,2007 European Control Conference (ECC), IEEE, 2007, Kos,Greece, 5189–5195.
  11. [11] L. Miˇskovi´c, A. Karimi, and D. Bonvin, Correlation-based tun-ing of a restricted-complexity controller for an active suspen-sion system, European Journal of Control, 9(1), 2003, 77–83.doi:10.3166/ejc.9.77-83.
  12. [12] L. dos Santos Coelho and A.A.R. Coelho, Model-free adap-tive control optimization using a chaotic particle swarm ap-proach, Chaos, Solitons & Fractals, 41(4), 2009, 2001–2009.doi:10.1016/j.chaos.2008.08.004.
  13. [13] M.C. Campi, A. Lecchini, and S. M. Savaresi, Virtual referencefeedback tuning: A direct method for the design of feedbackcontrollers, Automatica, 38(8), 2002, 1337–1346. doi:10.1016/s0005-1098(02)00032-8.
  14. [14] M.C. Campi and S.M. Savaresi, Direct nonlinear control de-sign: The virtual reference feedback tuning (VRFT) approach,IEEE Transactions on Automatic Control, 51(1), 2006, 14–27.doi:10.1109/tac.2005.861689.
  15. [15] W. Ling, H. Ni, et al., Intelligent virtual reference feedbacktuning and its application to heat treatment electric furnacecontrol, Engineering Applications of Artificial Intelligence, 46,2015, 1–9. doi:10.1016/j.engappai.2015.08.008.
  16. [16] W. Xiaofeng, X. Li, et al., Data-driven model-free adap-tive sliding mode control for the multi degree-of-freedomrobotic exoskeleton, Information Sciences, 327, 2016, 246–257.doi:10.1016/ j.ins.2015.08.025.
  17. [17] L. dos Santos Coelho, M.W. Pessoa, R.R. Sumar, and A.A.R.Coelho, Model-free adaptive control design using evolutionary-neural compensator, Expert Systems with Applications, 37(1),2010), 499–508. doi:10.1016/j.eswa.2009.05.042.
  18. [18] K.K. Tan, T.H. Lee, S.N. Huang, and F.M. Leu, Adaptive-predictive control of a class of SISO nonlinear systems, Dy-namics and Control, 11(2), 2001, 151–174. doi:10.1023/a:1012583811904.
  19. [19] B. Zhang and W. Zhang, Adaptive predictive functional controlof a class of nonlinear systems, ISA Transactions, 45(2), 2006,175–183. doi:10.1016/s0019-0578(07)60188-8.
  20. [20] Y. Jinpeng, P. Shi, et al., Neural network-based adaptivedynamic surface control for permanent magnet synchronousmotors, IEEE Transactions on Neural Networks and LearningSystems, 26(3), 2015, 640–645. doi:10.1109/tnnls.2014.2316289.
  21. [21] J.-W. Jung, Q.L. Viet, et al., Adaptive PID speed controldesign for permanent magnet synchronous motor drives, IEEETransactions on Power Electronics, 30(2), 2015, 900–908.doi:10.1109/ tpel.2014.2311462.
  22. [22] Z. Hou and W. Huang, The model-free learning adaptive controlof a class of SISO nonlinear systems, Proc. 1997 AmericanControl Conference (Cat. No.97CH36041), 1997. doi:10.1109/acc.1997.611815.
  23. [23] Z. Liu, Fang L. Luo, and M.H. Ra Zuo, Nonlinear load-adaptive MIMO controller for DC motor field weakening,Electric Machines & Power Systems, 28(10), 2000, 929–943.doi:10.1080/07313560050129819.

Important Links:

Go Back