Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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HGO and neural network based integral sliding mode control for PMSMs with uncertainty

  • Ge, Yang (The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University) ;
  • Yang, Lihui (The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University) ;
  • Ma, Xikui (The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University)
  • Received : 2019.12.27
  • Accepted : 2020.06.08
  • Published : 2020.09.20
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Abstract

This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Since the second-order motion equation of the PMSM is used to improve the control performance, the speed derivative, which cannot be measured directly, is required. Thus, the HGO is designed to estimate the unknown state (speed derivative). In addition, the RBFNN is designed to approximate the compounded disturbance including the lumped disturbance of system and the HGO error effect. Unlike previous studies, the output of the RBFNN is compensated by both the controller and the HGO to improve the system robustness and observer accuracy. The sliding function and the HGO error are both taken into account in the RBFNN to explicitly guarantee the stability of the whole system. To demonstrate the superiority of the proposed method, comparative simulations and experiments were carried out in different cases.

Keywords

Acknowledgement

Funding was provided by Fundamental Research Funds for the Central Universities (CN) (xjj2018007).

References

  1. Kim, S.: Moment of inertia and friction torque coefficient identification in a servo drive system. IEEE Trans. Ind. Electron. 66(1), 60-70 (2019) https://doi.org/10.1109/tie.2018.2826456
  2. Thounthong, P., Sikkabut, S., Poonnoi, N., et al.: Nonlinear differential fatness based speed/torque control with state-observers of permanent magnet synchronous motor drives. IEEE Trans. Ind. Appl. 54(3), 2874-2884 (2018) https://doi.org/10.1109/tia.2018.2800678
  3. Chaoui, H., Khayamy, M., Okoye, O., et al.: Simplified speed control of permanent magnet synchronous motors using genetic algorithms. IEEE Trans. Power Electron. 34(4), 3563-3574 (2018) https://doi.org/10.1109/tpel.2018.2851923
  4. Xu, Y., Hou, Y., Li, Z.: Robust predictive speed control for SPMSM drives based on extended state observers. J. Power Electron. 19(2), 497-508 (2019) https://doi.org/10.6113/JPE.2019.19.2.497
  5. Chen, J., Yao, W., Ren, Y., et al.: Nonlinear adaptive speed control of a permanent magnet synchronous motor: a perturbation estimation approach. Control Eng. Pract. 85, 163-175 (2019) https://doi.org/10.1016/j.conengprac.2019.01.019
  6. Aghili, F.: Optimal feedback linearization control of interior PM synchronous motors subject to time-varying operation conditions minimizing power loss. IEEE Trans. Ind. Electron. 65(7), 5414-5421 (2018) https://doi.org/10.1109/tie.2017.2784348
  7. Xu, B., Shen, X., Ji, W., et al.: Adaptive nonsingular terminal sliding model control for permanent magnet synchronous motor based on disturbance observer. IEEE Access. 6, 48913-48920 (2018) https://doi.org/10.1109/access.2018.2867463
  8. Preindl, M., Bolognani, S.: Model predictive direct speed control with finite control set of PMSM drive systems. IEEE Trans Power Electron. 28(2), 1007-1015 (2013) https://doi.org/10.1109/TPEL.2012.2204277
  9. Sousy, E., Fayez, F.: Robust recurrent wavelet interval type-2 fuzzy-neural-network control for DSP-based PMSM servo drive systems. J. Power Electron 13(1), 139-160 (2013) https://doi.org/10.6113/JPE.2013.13.1.139
  10. Lin, F., Hung, Y., Ruan, K.: An intelligent second -order sliding-mode control for an electric power steering system using a wavelet fuzzy neural network. IEEE Trans. Fuzzy Syst. 22(6), 1598-1611 (2014) https://doi.org/10.1109/TFUZZ.2014.2300168
  11. Zhang, X., Sun, L., Zhao, K.: Nonlinear speed control for PMSM system using sliding-mode control and disturbance compensation techniques. IEEE Trans. Power Electron. 28(3), 1358-1365 (2013) https://doi.org/10.1109/TPEL.2012.2206610
  12. Li, S., Zong, K., Liu, H.: A composite speed controller based on a second-order model of permanent magnet synchronous motor system. Trans. Inst. Meas. Control 33(5), 522-541 (2011) https://doi.org/10.1177/0142331210371814
  13. Bu, X., Wu, X., Zhang, R., et al.: Tracking differentiator design for the robust backstepping control of a flexible air-breathing hypersonic vehicle. J. Frankl. Inst. Eng. Appl. Math. 352(4), 1739-1765 (2015) https://doi.org/10.1016/j.jfranklin.2015.01.014
  14. Dai, X., Gao, Z., Breikin, T., et al.: High-gain observer-based estimation of parameter variations with delay alignment. IEEE Trans Autom. Control 57(3), 726-732 (2012) https://doi.org/10.1109/TAC.2011.2169635
  15. Liu, J., Vazquez, S., Wu, L., et al.: Extended state observer based sliding mode control for three-phase power converters. IEEE Trans. Ind. Electron. 64(1), 22-31 (2016) https://doi.org/10.1109/TIE.2016.2610400
  16. Mercorelli, P.: A two-stage sliding-mode high-Gain observer to reduce uncertainties and disturbances effects for sensorless control in automotive applications. IEEE Trans. Ind. Electron. 62(9), 5929-5940 (2015) https://doi.org/10.1109/TIE.2015.2450725
  17. Cunha, J., Costa, R., Lizarralde, F.: Peaking free variable structure control of uncertain linear systems based on a high-gain observer. Automatica 45(5), 1156-1164 (2009) https://doi.org/10.1016/j.automatica.2008.12.018
  18. Khalil, H., Praly, L.: High-gain observers in nonlinear feedback control. Int. J. Robust Nonlinear Control 24(6), 993-1015 (2014) https://doi.org/10.1002/rnc.3051
  19. Prasov, A., Khalil, H.: A nonlinear high-gain observer for systems with measurement noise in a feedback control framework. IEEE Trans. Autom. Control 58(3), 569-580 (2013) https://doi.org/10.1109/TAC.2012.2218063
  20. Niu, X., Zhang, C., Li, H.: Active disturbance attenuation control for permanent magnet synchronous motor via feedback domination and disturbance observer. IET Control Theory Appl. 11(6), 807-815 (2017) https://doi.org/10.1049/iet-cta.2016.1429
  21. Zhao, L., Huang, J., Liu, H., et al.: Second-order sliding-mode observer with online parameter identification for sensorless induction motor drives. IEEE Trans. Ind. Electron. 61(10), 5280-5289 (2014) https://doi.org/10.1109/TIE.2014.2301730
  22. Wang, H., Ge, X., Liu, Y.: Second-order sliding-mode MRAS observer based sensorless vector control of linear induction motor drives for medium-low speed maglev applications. IEEE Trans. Ind. Electron. 65(12), 9938-9952 (2018) https://doi.org/10.1109/tie.2018.2818664
  23. Biricik, S., Komurcugil, H.: Optimized sliding mode control to maximize existence region for single-phase dynamic voltage restorers. IEEE Trans. Ind. Informat. 12(4), 1486-1497 (2016) https://doi.org/10.1109/TII.2016.2587769
  24. Sira-Ramirez, H., Linares-Flores, J., Garcia-Rodriguez, C., et al.: On the control of the permanent magnet synchronous motor: an active disturbance rejection control approach. IEEE Trans. Control Syst Technol. 22(5), 2056-2063 (2014) https://doi.org/10.1109/TCST.2014.2298238
  25. Liu, X., Shan, Z.B., Li, Y.C.: Dynamic boundary layer based neural network quasi-sliding mode control for soft touching down on asteroid. Adv. Space Res. 59(8), 2173-2185 (2017) https://doi.org/10.1016/j.asr.2017.01.046
  26. He, W., Huang, B., Dong, Y., et al.: Adaptive neural network control for robotic manipulators with unknown deadzone. IEEE Trans. Cybern. 48(9), 2670-2682 (2018) https://doi.org/10.1109/tcyb.2017.2748418
  27. Kenne, G., Fotso, A., Lamnabhi, L.: A new adaptive control strategy for a class of nonlinear system using RBF neuro-sliding-mode technique: application to SEIG wind turbine control system". Int. J. Control 90(4), 855-872 (2017) https://doi.org/10.1080/00207179.2016.1213423
  28. Liu, L., Wang, D., Peng, Z., et al.: Bounded neural network control for target tracking of underactuated autonomous surface vehicles in the presence of uncertain target dynamics. IEEE Trans. Neural Netw. Learn. Syst. 30(4), 1241-1249 (2019) https://doi.org/10.1109/tnnls.2018.2868978
  29. Xu, B., Wang, D., Zhang, Y., et al.: DOB based neural control of flexible hypersonic flight vehicle considering wind effects. IEEE Trans. Ind. Electron. 64(11), 8676-8685 (2017) https://doi.org/10.1109/TIE.2017.2703678
  30. Yin, Y., Liu, J., Sanchez, J., et al.: Observer-based adaptive sliding mode control of NPC converters: an RBF neural network approach. IEEE Trans. Power Electron. 34(4), 3831-3841 (2019) https://doi.org/10.1109/tpel.2018.2853093
  31. Kommuri, S., Defoort, M., Karimi, H., et al.: A robust observer based sensor fault-tolerant control for PMSM in electric vehicles. IEEE Trans. Ind. Electron. 63(12), 7671-7681 (2016) https://doi.org/10.1109/TIE.2016.2590993
  32. Golubev, A., Krishchenko, A., Tkachev, S.: Stabilization of nonlinear dynamic systems using the system state estimates made by the asymptotic observer. Autom. Remote Control 66(7), 1021-1058 (2005) https://doi.org/10.1007/s10513-005-0147-5