A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

- Journal title : Journal of Power Electronics
- Volume 16, Issue 4, 2016, pp.1438-1454
- Publisher : The Korean Institute of Power Electronics
- DOI : 10.6113/JPE.2016.16.4.1438

Title & Authors

A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

Lin, Chih-Hong;

Lin, Chih-Hong;

Abstract

Because the nonlinear and time-varying characteristics of continuously variable transmission (CVT) systems driven by means of a six-phase copper rotor induction motor (CRIM) are unconscious, the control performance obtained for classical linear controllers is disappointing, when compared to more complex, nonlinear control methods. A blend modified recurrent Gegenbauer orthogonal polynomial neural network (OPNN) control system which has the online learning capability to come back to a nonlinear time-varying system, was complied to overcome difficulty in the design of a linear controller for six-phase CRIM driving CVT systems with lumped nonlinear load disturbances. The blend modified recurrent Gegenbauer OPNN control system can carry out examiner control, modified recurrent Gegenbauer OPNN control, and reimbursed control. Additionally, the adaptation law of the online parameters in the modified recurrent Gegenbauer OPNN is established on the Lyapunov stability theorem. The use of an amended artificial bee colony (ABC) optimization technique brought about two optimal learning rates for the parameters, which helped reform convergence. Finally, a comparison of the experimental results of the present study with those of previous studies demonstrates the high control performance of the proposed control scheme.

Keywords

Artificial bee colony optimization;Lyapunov stability theorem;Modified recurrent Gegenbauer orthogonal polynomial neural network;Six-phase copper rotor induction motor;

Language

English

References

1.

K. K. Mahopatra, K. Gopalkumar, V.T. Somasekhar L. Umanand, "A novel scheme for six phase induction motor with open end windings," 28th Annual Conference of IEEE Industrial Electronics Society, Spain, 5-8 Nov. 2002, pp. 810-815.

2.

C. H. Lin. "Modelling and control of six-phase induction motor servo-driven continuously variable transmission system using blend modified recurrent Gegenbauer orthogonal polynomial neural network control system and amended artificial bee colony optimization," International Journal of Numerical Modelling Electronic Networks Devices and Fields, DOI: 10.1002/jnm.2154, Record online: 17 Mar. 2016.

3.

E. F. Brush, J. L. Kirtley, and D. T. Peters, “Die-cast copper rotors as strategy for improving induction motor efficiency,” Electrical Insulation Conference and Electrical Manufacturing Expo., Nashville, Tennessee, USA, 2007, pp. 322-327.

4.

D. Liang, X. Yang, J. Yu, and V. Zhou, “Experience in China on the die-casting of copper rotors for induction motors,” IEEE Intl. Conference on Electrical Machines, Marseille, France, 2012, pp. 254-258.

5.

A. R. Munoz and T. A. Lipo, “Dual stator winding induction machine drive,” IEEE Trans. Ind. Appl., Vol. 36, No. 5, pp. 1369-1379, Sep./Oct. 2000.

6.

R. Bojoi, M. Lazzari, F. Profumo, and A. Tenconi, “Digital field-oriented control for dual three-phase induction motor drives,” IEEE Trans. Ind. Appl., Vol. 39, No. 3, pp. 752-760, May/Jun. 2013.

7.

G. K. Singh, K. Nam, and S. K. Lim, “A simple indirect field-oriented control scheme for multiphase induction machine,” IEEE Trans. Ind. Electron., Vol. 52, No.12, pp. 1177-1184, Aug. 2005.

8.

O. Ojo and I. E. Davidson, “PWM-VSI inverter assisted stand-alone dual stator winding induction generator,” IEEE Trans. Industry Applications, Vol. 36, No. 6, pp. 1604-1611, Nov./Dec. 2000.

9.

C. Y. Tseng, L. W. Chen, Y. T. Lin, and J. Y. Li, “A hybrid dynamic simulation model for urban scooters with a mechanical-type CVT,” IEEE Intl. Conference on Automation and Logistics, Qingdao, China, 2008, pp. 519-519.

10.

C. Y. Tseng, Y. F. Lue, Y. T. Lin, J. C. Siao, C. H. Tsai, and L. M. Fu, “Dynamic simulation model for hybrid electric scooters,” IEEE Intl. Symposium on Industrial Electronics, Seoul, Korea, 2009, pp. 1464-1469.

11.

L. Guzzella, and A. M. Schmid, “Feedback linearization of spark-ignition engines with continuously variable transmissions,” IEEE Trans. Control Systems Technology, Vol. 3, No.1, pp. 54-58, Mar. 1995.

12.

W. Kim and G. Vachtsevanos, “Fuzzy logic ratio control for a CVT hydraulic module,” Proceedings of the IEEE Symposium on Intelligent Control, Rio, Greece, 2000, pp 151-156.

13.

G. Carbone, L. Mangialardi, B. Bonsen, C. Tursi, and P. A. Veenhuizen, “CVT dynamics: Theory and experiments,” Mechanism and Machine Theory, Vol. 42, No. 4, pp. 409-428, Apr. 2007.

14.

N. Srivastava and I. Haque, “Transient dynamics of metal V-belt CVT: Effects of bandpack slip and friction characteristic,” Mechanism and Machine Theory, Vol. 43, No. 4, pp. 457-479, Apr. 2008.

15.

N. Srivastava and I. Haque, “A review on belt and chain continuously variable transmissions (CVT): dynamics and control,” Mechanism and Machine Theory, Vol. 44, No. 1, pp. 19-41, Jan. 2009.

16.

C. H. Lin, ‘Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization,” Nonlinear Dynamics, Vol. 81, pp. 1219-1245, Aug. 2015.

17.

K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical system using neural networks,” IEEE Trans. Neural Networks, Vol. 1, No. 1, pp. 4-27, Mar. 1990.

18.

P. S. Sastry, G. Santharam and K. P. Unnikrishnan, “Memory neural networks for identification and control of dynamical systems,” IEEE Trans. Neural Networks, Vol. 5, No. 3, pp. 306-319, Mar. 1994.

19.

M. N. Eskander, “Minimization of losses in permanent magnet synchronous motors using neural network,” Journal of Power Electronics, Vol. 2, No. 3, pp 220-229, Jul. 2002.

20.

A. F. Payam, M. N. Hashemnia and J. Faiz, “Robust DTC control of doubly-fed induction machines based on input-output feedback linearization using recurrent neural networks,” Journal of Power Electronics, Vol. 11, No. 5, pp. 719-725, Sep. 2011.

21.

C. H. Lin, "Novel adaptive recurrent Legendre neural network control for PMSM servo-drive electric scooter," J. Dynamic Systems, Measurement, and Control-Transactions of the ASME, Vol. 137, No. 1, 12 pages, Jan. 2015.

22.

C. H. Lin, “A backstepping control of LSM drive systems using adaptive modified recurrent Laguerre OPNNUO,” Journal of Power Electronics, Vol. 16, No. 2, pp. 598-609, Mar. 2016.

23.

C. H. Lin, “Design of a composite recurrent Laguerre orthogonal polynomial neural network control system with ameliorated particle swarm optimization for a continuously variable transmission system,” Control Engineering Practice, Vol. 49, pp. 42-59, Apr. 2016.

24.

S. Belmehdi, “Generalized Gegenbauer orthogonal polynomials,” Intl. J. Computational and Applied Mathematics, Vol. 133, No. 1-2, pp. 195-205, Aug. 2001.

25.

C. Wu, H. Zhang, and T. Fang, “Flutter analysis of an airfoil with bounded random parameters in incompressible flow via Gegenbauer polynomial approximation,” Aerospace Science and Technology, Vol. 11, No. 7-8, pp. 518-526, Nov./Dec.2007.

26.

Y. Zhang and W. Li, “Gegenbauer neural network and its weights-direct determination method,” IET Electronics Letters, Vol. 45, No. 23, pp. 1184-1185, Nov. 2009.

27.

T. W. S. Chow and Y. Fang, “A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics,” IEEE Trans. Ind. Electron., Vol. 45, No.1, pp. 151-161, Feb. 1998.

28.

X. D. Li, J. K. L. Ho, and T. W. S. Chow, “Approximation of dynamical time-variant systems by continuous-time recurrent neural networks,” IEEE Trans. Circuits Syst. II, Exp. Briefs, Vol. 52, No. 10, pp. 656-660, Oct. 2005.

29.

C. H. Lin, “Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive,” Intl. J. Electrical Power and Energy Systems, Vol. 52, pp. 143-160, Nov. 2013.

30.

C. H. Lin, “Dynamic control for permanent magnet synchronous generator system using novel modified recurrent wavelet neural network,” Nonlinear Dynamics Vol. 77, No. 4, pp. 1261-1284, Sep. 2014.

31.

C. H. Lin, “PMSM servo drive for V-belt continuously variable transmission system using hybrid recurrent Chebyshev NN control system,” J. Electrical Engineering and Technology, Vol. 10, No.1, pp. 408-421, Feb. 2015.

32.

D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey, 2005.

33.

D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” J. Global Optimization, Vol. 39, No. 3, pp. 459-471, Nov. 2007.

34.

D. Karaboga and B. Basturk, “Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems,” Lecture Notes in Artificial Intelligence, Vol. 4529, pp. 789-798, Jun. 2007.

35.

D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, Vol. 8, No. 1, pp. 687-697, Jan. 2008.

36.

W. Xiang and M. An, “An efficient and robust artificial bee colony algorithm for numerical optimization,” Computers and Operations Research, Vol. 40, No. 5, pp. 1256-1265, May 2013.

37.

S. Biswas, S. Das, S. Debchoudhury, and S. Kundu, “Coevolving bee colonies by forager migration: A multi-swarm based artificial bee colony algorithm for global search space,” Applied Mathematics and Computation, Vol. 232, pp. 216-234, Apr. 2014.

38.

A. Ahrari and A. A. Atai, “Grenade explosion method-a novel tool for optimization of multimodal functions,” Applied Soft Computing, Vol. 10, No. 4, pp. 1132-140, Sep. 2010.

39.

C. Zhang, J. Zheng, and Y. Zhou, “Two modified artificial bee colony algorithms inspired by Grenade explosion method,” Neurocomputing, Vol. 151, No. 3, pp. 1198-1207, Mar. 2015.

40.

A. Alizadegan, B. Asady, and M. Ahmadpour, “Two modified versions of artificial bee colony algorithm,” Applied Mathematics and Computation, Vol. 225, pp. 601-609, Dec. 2013.

41.

P. Mansouri, B. Asady, and N. Gupta, “The Bisection–artificial bee colony algorithm to solve fixed point problems,” Applied Soft Computing, Vol. 26 pp. 143-148, Jan. 2015.

42.

C. H. Lin, “Hybrid recurrent wavelet neural network control of PMSM servo-drive system for electric scooter,” Intl. J. Control, Automation, and Systems, Vol. 12, No. 1, pp. 77-187, Feb. 2014.

43.

C. H. Lin, “A PMSM driven electric scooter system with V-belt continuously variable transmission using novel hybrid modified recurrent Legendre neural network control,” Journal of Power Electronics, Vol. 14, No. 5, pp. 1008-1027, Sep. 2014.

44.

C. H. Lin, “Dynamic control of V-belt continuously variable transmission driven electric scooter using hybrid modified recurrent Legendre neural network control system,” Nonlinear Dynamics, Vol. 79, pp. 787-808, Sep. 2015.

45.

C. H. Lin, "Application of V-belt continuously variable transmission system using hybrid recurrent Laguerre orthogonal polynomials NN control system and modified particle swarm optimization," J. Computational and Nonlinear Dynamics-Transactions of the ASME, Vol. 10, No. 5, 16 pages, Sep. 2015.

46.

K. J. Astrom, T. Hagglund, PID Controller: Theory, Design, and Tuning, Instrument Society of America, Research Triangle Park, North Carolina, USA, 1995.

47.

T. Hagglund, K. J. Astrom, “Revisiting the Ziegler-Nichols tuning rules for PI control,” Asian J. Control, Vol. 4, No. 4, pp. 364-380, Dec. 2002.

48.

T. Hagglund and K. J. Astrom, “Revisiting the Ziegler-Nichols tuning rules for PI control-part II: the frequency response method,” Asian J. Control, Vol. 6, No. 4, pp. 469-482, Dec. 2004.

49.

J. J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, Englewood Cliffs, New Jersey, USA, 1991.

50.

K. J. Astrom and B. Wittenmark, Adaptive Control, Addison-Wesley, New York, USA, 1995.