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http://dx.doi.org/10.6113/JPE.2018.18.5.1380

Nonlinear Backstepping Control of SynRM Drive Systems Using Reformed Recurrent Hermite Polynomial Neural Networks with Adaptive Law and Error Estimated Law  

Ting, Jung-Chu (Dept. of Industrial Education and Technology, National Changhua University of Education)
Chen, Der-Fa (Dept. of Industrial Education and Technology, National Changhua University of Education)
Publication Information
Journal of Power Electronics / v.18, no.5, 2018 , pp. 1380-1397 More about this Journal
Abstract
The synchronous reluctance motor (SynRM) servo-drive system has highly nonlinear uncertainties owing to a convex construction effect. It is difficult for the linear control method to achieve good performance for the SynRM drive system. The nonlinear backstepping control system using upper bound with switching function is proposed to inhibit uncertainty action for controlling the SynRM drive system. However, this method uses a large upper bound with a switching function, which results in a large chattering. In order to reduce this chattering, a nonlinear backstepping control system using an adaptive law is proposed to estimate the lumped uncertainty. Since this method uses an adaptive law, it cannot achiever satisfactory performance. Therefore, a nonlinear backstepping control system using a reformed recurrent Hermite polynomial neural network with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SynRM drive system. Further, the reformed recurrent Hermite polynomial neural network with two learning rates is derived according to an increment type Lyapunov function to speed-up the parameter convergence. Finally, some experimental results and a comparative analysis are presented to verify that the proposed control system has better control performance for controlling SynRM drive systems.
Keywords
Backstepping control; Recurrent Hermite polynomial neural network; Synchronous reluctance motor;
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1 I. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse, “Systematic design of adaptive controller for feedback linearizable system,” IEEE Trans. Autom. Contr., Vol. 36, No. 11, pp. 1241-1253, Nov. 1991.   DOI
2 Bartolini, A. Ferrara, L. Giacomini and E. Usai, “Peoperties of a combined adaptive/second-order sliding mode control algorithm for some classes of uncertain nonlinear systems,” IEEE Trans. Autom. Contr., Vol. 45, No. 7, pp. 1334-1341, Jul. 2000.   DOI
3 S. I. Amer and M. M. Salem, “A comparison of different intelligent control techniques for a PM dc motor,” J. Power Electron., Vol. 5, No. 1, pp. 1-10, Jan. 2005.
4 C. H. Lin, “A backstepping control of LSM drive systems using adaptive modified recurrent Laguerre OPNNUO,” J. Power Electron., Vol. 16, No. 2, pp. 598-609, Mar. 2016.   DOI
5 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,” J. Power Electron., Vol. 11, No. 5, pp. 719-725, Sep. 2011.   DOI
6 C. H. Lin "Hybrid recurrent wavelet neural network control of PMSM servo-drive system for electric scooter," Int. J. Contr., Autom. Syst., Vol. 12, No. 1, pp. 177-187, Feb. 2014.   DOI
7 C. H. Lin, "A PMSM driven electric scooter system with V-belt continuously variable transmission using novel hybrid modified recurrent Legendre neural network control," J. Power Electron., Vol. 14, No. 5, pp.1008-1027, Sep. 2014.   DOI
8 C. H. Lin, "Novel adaptive recurrent Legendre neural network control for PMSM servo-drive electric scooter," J. Dynamic Syst., Meas., Contr.- Trans. ASME, Vol. 137, 011010-1, Jan. 2015.
9 C. H. Lin, “Nonlinear backstepping control design of LSM drive system using adaptive modified recurrent Laguerre othognal polynomial network,” Int. J. Contr., Autom. Syst., Vol. 15, No. 2, pp. 905-917, Apr. 2017.   DOI
10 L. Ma and K. Khorasani, “Constructive feedforward neural networks using Hermite polynomial activation functions,” IEEE Trans. Neural Netw., Vol. 16, No. 4, pp. 821-833, Jul. 2005.   DOI
11 L. Ma and K. Khorasani, "Adaptive constructive neural networks using Hermite polynomials for image compression," in Intl. Symp. on Neural Networks, pp 713-722, 2005.
12 G. G. Rigatos and S. G. Tzafestas, "Feed-forward neural networks using Hermite polynomial activation functions," 4th Helenic Conference on Advances in Artificial Intelligence, SETN 2006, pp. 323-333, 2006.
13 C. H. Lin, "Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive," Int. J. Electr. Power Energy Syst., Vol. 52, pp. 143-160, Nov. 2013.   DOI
14 V. Dmitrievskii, V. Prakht, V. Kazakbaev, A. Pozdeev, and S. Oshurbekov, "Development of a high efficient electric drive with synchronous reluctance motor," in Intl. Conf. on Electrical Machines and Systems (ICEMS), pp. 876-881, 2015.
15 S. M. Siniscalchi, J. Li, and C. H. Lee, "Hermitian polynomial for speaker adaptation of connectionist speech recognition systems," IEEE Trans. Audio, Speech, Language Process., Vol. 21, No. 10, pp. 2152-2161, Oct. 2013.   DOI
16 J. J. E. Slotine and W. Li, Applied Nonlinear Control, Englewood Cliffs, NJ: Prentice-Hall, 1991.
17 J. Astrom and B. Wittenmark, Adaptive Control, New York: Addison-Wesley, 1995.
18 C. C. Ku and K. Y. Lee, "Diagonal recurrent neural networks for dynamic system control," IEEE Trans. Neural Netw., Vol. 6, No. 1, pp.144-156, Jan. 1995.   DOI
19 F. L. Lewis, J. Campos, and R. Selmic, Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities. SIAM Frontiers in Applied Mathematics, 2002.
20 K. J. Astrom and T. Hagglund, PID Controller: Theory, Design, and Tuning, North Carolina: Instrument Society of America, Research Triangle Park, 1995.
21 W. Chai, W. Zhao, and B. Kwon, "Optimal design of wound field synchronous reluctance machines to improve torque by increasing the saliency ratio," IEEE Trans. Magn., Vol. 53, No. 11, Nov. 2017.
22 V. Kazakbaev, V. Prakht, V. Dmitrievskii, and I. Sokolov, "The feasibility study of the application of a synchronous reluctance motor in a pump drive," in Intl. Conf. on Power Drives Systems (ICPDS), 2016.
23 K. C. Kim, J. S. Ahn, S. H. Won, J. P. Hong, and J. Lee , "A study on the optimal design of SynRM for the high torque and power factor," IEEE Trans. Magn., Vol. 43, No. 6, pp. 2543-2545, Jun. 2007.   DOI
24 Y. H. Kim and J. H. Lee, "Optimum design criteria of an ALA-SynRM for the maximum torque density and power factor improvement," Int. J. Applied Electromagn. Mech., Vol. 53, No. S2, pp. S279-S288, 2017.   DOI
25 T. Matsuo, A. E. Antably, and T. A. Lipo, “A new control strategy for optimum-efficiency operation of a synchronous reluctance Motor,” IEEE Trans. Ind. Electron., Vol. 33, No. 5, pp. 1146-1153, Sep./Oct. 1997.
26 E. M. Rashad, T. S. Radwan, and M. A. Rahman, "A maximum torque per ampere vector control strategy for synchronous reluctance motors considering saturation and iron losses," in IEEE Industry Applications Annual Meeting, pp. 2411-2417, 2004.
27 C. H. Lin, “Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive,” IEE Proc. Electric Power Appl., Vol. 151, No. 6, pp. 712-724, Nov. 2004.
28 T. Hagglund and K. J. Astrom, "Revisiting the Ziegler-Nichols tuning rules for PI control," Asian J. Contr., Vol. 4, No. 4, pp. 364-380, Dec. 2002.
29 M. Y. Wei and T. H. Liu, “Design and implementation of an online tuning adaptive controller for synchronous reluctance motor drives,” IEEE Trans. Ind. Electron., Vol. 60, No. 9, pp. 3644-3657, Sep. 2013.   DOI
30 H. K. Chiang and C.T. Chu, “Reference model with an adaptive Hermite fuzzy neural network controller for tracking a synchronous reluctance motor,” Electric Power Compon. Syst., Vol. 43, No. 7, pp. 770-780, Apr. 2015.   DOI
31 T. Hagglund and K. J. Astrom, “Revisiting the Ziegler-Nichols tuning rules for PI control-part II: The frequency response method,” Asian J. Contr., Vol. 6, No. 4, pp. 469-482, Dec. 2004.
32 F. J. Lin and C. H. Lin, "On-line gain-tuning IP controller using RFNN," IEEE Trans. Aerosp. Electron. Syst., Vol. 37, No. 2, pp. 655-670, Apr. 2001.   DOI