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Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor

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Języki publikacji
EN
Abstrakty
EN
This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear inductance characteristics. The augmented model of the drive with additional state variables is introduced to assure precise control of selected state variables (i.e. angular speed and d-axis current). Optimal, non-constant coefficients of the controller are calculated using a linear-quadratic optimisation method. Non-constant coefficients are approximated using an artificial neural network (ANN) to assure superior accuracy and relatively low usage of resources during implementation. To the best of our knowledge, this is the first time when ANN-based gain-scheduled state feedback controller (G-S SFC) is applied for speed control of SynRM. Based on numerous simulation tests, including a comparison with a signum-based SFC, it is shown that the proposed solution assures good dynamical behaviour of SynRM drive and robustness against q-axis inductance, the moment of inertia and viscous friction fluctuations.
Wydawca
Rocznik
Strony
276--288
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • Institute of Engineering and Technology, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Toruń, Poland
  • Institute of Engineering and Technology, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Toruń, Poland
  • Institute of Control and Industrial Electronics, Warsaw University of Technology, Warsaw, Poland
Bibliografia
  • Awan, H. A., Saarakkala, S. E. and Hinkkanen, M. (2019). Flux-Linkage-Based Current Control of Saturated Synchronous Motors. IEEE Transactions on Industry Applications, 55(5), pp. 4762–4769.
  • Bianchi, N., Bolognani, S., Carraro, E., Castiello, M. and Fornasiero, E. (2016). Electric Vehicle Traction Based on Synchronous Reluctance Motors. IEEE Transactions on Industry Applications, 52(6), pp. 4762–4769.
  • Boldea, I. and Tutelea, L. (2018). Reluctance Electric Machines: Design and Control. CRC Press.
  • Brasel, M. (2014). A Gain-scheduled Multivariable LQR Controller for Permanent Magnet Synchronous Motor. In: Proceedings of the 19th International Conference on Methods and Models in Automation and Robotics MMAR, Miedzyzdroje, 2 – 5 September 2014.
  • Credo, A., Fabri, G., Villani, M., and Popescu, M. (2020). Adopting the Topology Optimization in the Design of High-speed Synchronous Reluctance Motors for Electric Vehicles. IEEE Transactions on Industry Applications, 56(5), pp. 5429–5438.
  • Cvetkovski, G. V. and Petkovska, L. (2021). Selected Nature Inspired Algorithms in Function of PM Synchronous Motor Cogging Torque Minimization. Power Electronics and Drives, 6(41), pp. 209–222.
  • Ewert, P. (2019). Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor. Power Electronics and Drives, 4(39), pp. 203–215.
  • Grzesiak, L. M. and Tarczewski, T. (2015). State Feedback Control with ANN Based Load Torque Feedforward for PMSM Fed by 3-Level NPC Inverter with Sinusoidal Output Voltage Waveform. In: Ferrier J. L., Gusikhin O., Madani K., Sasiadek J., eds., Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, 325, Springer, Cham, pp. 73–90.
  • Hadla, H. and Cruz, S. (2016). Active Flux Based Finite Control Set Model Predictive Control of Synchronous Reluctance Motor Drives. In: Proceedings of the 18th European Conference on Power Electronics and Applications EPE’16 ECCE Europe, Karlsruhe, 6 – 8 September 2016.
  • Hannoun, H., Hilairet, M. and Marchand, C. (2011). High Performance Current Control of a Switched Reluctance Machine Based on a Gain-Scheduling PI Controller. Control Engineering Practice, 19(11), pp. 1377–1386.
  • Farhan, A., Abdelrahem, M., Saleh, A., Shaltout, A. and Kennel, R. (2020). Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using On-line Parameter Estimation. Energies, 13(2), pp. 1–18.
  • Kaźmierkowski, M. P., Blaabjerg, F. and Krishnan, R. (2001). Control in Power Electronics – Selected Problems. London: Academic Press.
  • Li, J. C., Xin, M., Fan, Z. N. and Liu, R. (2020). Design and Experimental Evaluation of a 12 kW Large Synchronous Reluctance Motor and Control System for Elevator Traction. IEEE Access, 8, pp. 34256–34264.
  • Lin, F. J., Chen, S. G. and Hsu, C. W. (2018). Intelligent Backstepping Control Using Recurrent Feature Selection Fuzzy Neural Network for Synchronous Reluctance Motor Position Servo Drive System. IEEE Transactions on Fuzzy Systems, 27(3), pp. 413–427.
  • Lin, F. J., Huang, M. S., Chen, S. G., Hsu, C. W. and Liang, C. H. (2019). Adaptive Backstepping Control for Synchronous Reluctance Motor Based on Intelligent Current Angle Control. IEEE Transactions on Power Electronics, 35(7), pp. 7465–7479.
  • Oliveira, F. and Ukil, A. (2019). Comparative Performance Analysis of Induction and Synchronous Reluctance Motors in Chiller Systems for Energy Efficient Buildings. IEEE Transactions on Industrial Informatics, 15(8), pp. 4384–4393.
  • Safonov, M. and Athans, M. (1977). Gain and Phase Margin for Multiloop LQG Regulators. IEEE Transactions on Automatic Control, 22(2), pp. 173–179.
  • Scalcon, F. P., Osório, C. R., Koch, G. G., Gabbi, T. S., Vieira, R. P., Gründling, H. A., and Montagner, V. F. (2020). Robust Control of Synchronous Reluctance Motors by Means of Linear Matrix Inequalities. IEEE Transactions on Energy Conversion, 36(2), pp. 779–788.
  • Senjyu, T., Kinjo, K., Urasaki, N. and Uezato, K. (2003). High Efficiency Control of Synchronous Reluctance Motors Using Extended Kalman Filter. IEEE Transactions on Industrial Electronics, 50(4), pp. 726–732.
  • Shyu, K. K., Lai, C. K. and Hung, J. Y. (2001). Totally Invariant State Feedback Controller for Position Control of Synchronous Reluctance Motor. IEEE Transactions on Industrial Electronics, 48(3), pp. 615–624.
  • Tarczewski, T. and Grzesiak, L. M. (2009). High Precision Permanent Magnet Synchronous Servo-drive with Lqr Position Controller. Przeglad Elektrotechniczny, 85(8), pp. 42–47.
  • Tarczewski, T., Niewiara, L. J. and Grzesiak, L. M. (2021). Gain-Scheduled State Feedback Speed Control of Synchronous Reluctance Motor. In: Proceedings of the 19th International Power Electronics and Motion Control Conference PEMC, Gliwice, 25–29 April 2021.
  • Tarczewski, T., Skiwski, M. and Grzesiak, L. M. (2017). Constrained Non-stationary State Feedback Speed Control of PMSM. In: Proceedings of the 19th European Conference on Power Electronics and Applications EPE’17 ECCE Europe, Warsaw, 11–14 September 2017.
  • Truong, P. H., Flieller, D., Nguyen, N. K., Mercklé J. and Sturtzer G. (2016). Torque Ripple Minimization in Non-sinusoidal Synchronous Reluctance Motors Based on Artificial Neural Networks. Electric Power Systems Research, 140, pp. 37–45.
  • Yousefi-Talouki, A., Pescetto, P., Pellegrino, G. and Boldea, I. (2017). Combined Active Flux and High-frequency Injection Methods for Sensorless Direct-flux Vector Control of Synchronous Reluctance Machines. IEEE Transactions on Power Electronics, 33(3), pp. 2447–2457.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f6669c85-46b3-4c6f-9fa5-ffca85a74911
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