Abstract
This paper presents a design and tuning technique for a improved current predictive control based on hybrid controller for Permanent Magnet Synchronous Motor (PMSM) to overcome the problem of accuracy the PMSM is facing with, a ANFIS Controller is introduced in the Predictive Control. This novel control strategy gives the guaranteed dynamic performance with increased accuracy. The proposed method includes the Hybrid controller which comprises of ANFIS Controller in addition to the Fuzzy algorithm to get higher accuracy by eliminating the static error and simple disturbances. The main feature of this technique is to adjust the errors caused by the obsolete traditional predictive controller and PI controller, thereby enhancing the accuracy. The analysis is confirmed through comparison of various simulations using PI controller and Hybrid controller.
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References
Kim, K.-H., Baik, I.-C., Moon, G.-W., Youn, M.-J. (eds.): A Current Control for a Permanent Magnet Synchronous Motor with a Simple Disturbance Estimation Scheme (September 1999)
Morel, F., Lin-shi, X.F., Retif, J.M., et al.: A comparative study of predictive current control schemes for a permanent-magnet synchronous machine drive. IEEE Trans. Industr. Electron. 56(7), 2715–2728 (2009)
Wang, Z., Yu, A., Li, X., Zhang, G., Xia, C.: A novel current predictive control based on fuzzy algorithm for PMSM. IEEE J. (2018). https://doi.org/10.1109/JESTPE.2019.2902634
Wang, G., Yang, M., Niu, L., Gui, X., Xu, D.: Improved predictive current control with static current error elimination for permanent magnet synchronous machine. In: IECON 2014—40th Annual Conference of the IEEE In.ustrial Electronics Society, pp. 661–667, Dallas, TX (2014)
Tong, S., Sui, S., Li, Y.: Observed-based adaptive fuzzy tracking control for switched nonlinear systems with dead-zone. IEEE Trans. Cybern. 45(12), 2816–2826 (2015)
Mohamed, Y.A.R.I., El-Sadany, E.F.: A control scheme for PWM voltage-source distributed-generation inverters for fast load-voltage regulation and effective mitigation of unbalanced voltage disturbances. IEEE Trans. Industr. Electron. 55(5), 2072–2084 (2008)
Wang, H., Yang, M., Niu, L., Xu, D.: Improved deadbeat predictive current control strategy for permanent magnet motor drives. In: Proceedings of 6th IEEE Conference on Industrial Electronics and Applications, pp. 1260–1264 (2011)
Al-Aawar, N., Arkadan, A.R.A.: Optimal control strategy for hybrid electric vehicle powertrain. IEEE J. Emerg. Sel. Top. Power Electron. 3(2), 362–370 (2015)
Zeng, Q., Chang, L.: An advanced SVPWM-based predictive current controller for three-phase inverters in distributed generation systems. IEEE Trans. Industr. Electron. 55(3), 1235–1246 (2008)
Lemmens, J., Vanassche, P., Driesen, J.: PMSM drive Current and voltage limiting as a constraint optimal control problem. IEEE J. Emerg. Sel. Top. Power Electron. 3(2), 326–338 (2015)
Li, N., Ming, Y., Dianguo, X.: An adaptive robust predictive current control for PMSM with online inductance identification. In: Proceedings of International Review of Electrical Engineering (IREE), vol. 7, no. 2, pp. 3845–3856 (2012)
Serrano-Iribarnegaray, L., Martinez-Roman, J.: A Unified approach to the very fast torque control methods for DC and AC machines. IEEE Trans. Industr. Electron. 54(4), 2047–2056 (2007)
Kakosimos, P., Abu-Rub, H.: Deadbeat predictive control for PMSM drives with 3L NPC inverter accounting for saturation effects. IEEE J. Emerg. Sel. Top. Power Electron. (2018). https://doi.org/10.1109/JESTPE.2018.2796123,Jan
Fang, Y., Xing, Y.: Design and analysis of three-phase reversible high-power-factor correction based on predictive current controller. IEEE Trans. Industr. Electron. 55(12), 4391–4397 (2008)
Wipasuramonton, P., Zhu, Z.Q., Howe, D.: Predictive current control with current-error correction for PM brushless AC drives. IEEE Trans. Ind. Appl. 42(4), 1071–1079 (2006)
Moreno, J.C., Espi Huerta, J.M., Gil, R.G., Gonzalez, S.A.: A robust predictive current control for three-phase grid-connected inverters. IEEE Trans. Ind. Electron. 56(6), 1993–2004 (2009)
Niu, L., Yang, M., Xu, D.: An adaptive robust predictive current control for PMSM with online inductance identification. Proc. Int. Rev. Electr. Eng. (IREE) 7(2), 3845–3856 (2012)
Cai, X.B., Zhang, Z.B., Wang, J.X., Kennel, R.: Optimal control solutions for PMSM drives: a comparison study with experimental assessments. IEEE J. Emerg. Sel. Top. Power Electron. 6(1), 352–362 (2017)
Butt, C.B., Hoque, M.A., Rahman, M.A.: Simplified fuzzy logic-based MTPA speed control of IPMSM drive. IEEE Trans. Ind. Appl. 40(6), 1529–1533 (2004)
Uddin, M.N., Rahman, M.A.: Fuzzy logic based speed control of an IPM synchronous motor drive. IEEE Trans. Ind. Appl. 3(3), 1259–1264 (1999)
Uddin, M.N., Rahman, M.A.: High-speed control of IPMSM drives using improved fuzzy logic algorithms. IEEE Trans. Ind. Electron. 54(1), 190–199 (2007)
Nasiri, A.: Full digital current control of permanent magnet synchronous motors for vehicular applications. IEEE Trans. Veh. Technol. 56(4), 1531–1537 (2007)
Li, N., Ming, Y., Dianguo, X.: An adaptive robust predictive current control for PMSM with online inductance identification. Int. Rev. Electr. Eng. 7(2)
Yin, Z., Han, X., Du, C., Liu, J., Zhong, Y.: Research on model predictive current control for induction machine based on immune-optimized disturbance observer. IEEE J. Emerg. Sel. Top. Power Electron. (2018, March). https://doi.org/10.1109/JESTPE.2018.2820050
Li, K.: PID tuning for optimal closed-loop performance with specified gain and phase margins. IEEE Trans. Control Syst. Technol. 21(3), 1024–1030 (2013)
Kim, H., Degner, M.W., Guerrero, J.M., et al.: Discrete-time current regulator design for AC machine drives. IEEE Trans. Ind. Appl. 46(4), 1425–1435 (2010)
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Sridhar, S., Junaid, M., Yadaiah, N. (2021). Design and Tuning of Improved Current Predictive Control for PMSM. In: Gunjan, V.K., Zurada, J.M. (eds) Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol 956. Springer, Cham. https://doi.org/10.1007/978-3-030-68291-0_36
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DOI: https://doi.org/10.1007/978-3-030-68291-0_36
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