Speed control of permanent magnet synchronous motor using neural network model predictive control

Abstract

Model predictive control has been widely used in the industry. This can control the multivariable system with constraints on input and output variables but it needs online computation solver, and creates the non-convex solution in nonlinear plant due to the parameter uncertainties. The online computational problem and non-convex solution of the model predictive control are achieved via neural network model predictive control. The paper explores the speed control of permanent magnet synchronous motor (PMSM) by using neural network model predictive control (NNMPC) technique. The multi-layer artificial neural network is used to identify the dynamics of PMSM. The set point speed tracking control of PMSM is identified by using neural network model predictive control strategy. By using the set of input and output data obtained from the system, the multi input-output feed-forward neural network model is created. Levenberg-Marquardt algorithm is used to train the process models of the PMSM. That provides future plant output for control optimization of the predictive control. The overall system is developed and tested in the MATLAB/Simulink. To evaluate the efficiency of the controller proposed, it is compared with a constrained model predictive controller through the studies of simulation. The overshoot and settling time of the speed response of the PMSM are measured and analyzed for NNMPC and constrained MPC.

Keywords

• Neural network
• Permanent magnet synchronous motor
• Predictive controller

References

1. [1] Cunha, G., Rossa, A.J., Alves, J.A., Cardoso, E., Control of permanent magnet synchronous machines for subsea applications, IEEE Transactions on Industry Applications, 2018, 54 (2), 1899–1905.
2. [2] Demir, Y., Aydin, M.,A novel dual three-phase permanent magnet synchronous motor with asymmetric stator winding, IEEE Transactions on Magnetics, 2016, 52(7), 1-5.
3. [3] Onsal, M., Demir, Y., Aydin, M., A new nine-phase permanent magnet synchronous motor with consequent pole rotor for high-power traction applications, IEEE Transactions on Magnetics, 2017, 53(11), 1-6. [4] Wang, Z., Chen, J., Cheng, M., Chau, K.T., Field-oriented control and direct torque control for paralleled VSIs fed PMSM drives with variable switching frequencies, IEEE Transactions on Power Electronics, 2016, 31(3), 2417–2428, DOI:10.1109/TPEL.2015.2437893.
4. [5] Premkumar, K., Manikandan, B.V., Stability and performance analysis of anfis tuned pid based speed controller for brushless dc motor, Current Signal Transduction Therapy, 2018, 13(1), 19-30.
5. [6] Mendoza - Mondragón, F., Hernández - Guzmán. V.M., Rodríguez-Reséndiz, J., Robust speed control of permanent magnet synchronous motors using two – degrees – of – freedom control, IEEE Transactions on Industrial Electronics, 2018, 65(8), 6099–6108.
6. [7] Chang, Y.C., Chen, C.H., Zhu, Z.C., Huang Y.W., Speed control of the surface-mounted permanent-magnet synchronous motor based on takagi–sugeno fuzzy models, IEEE Transactions on Power Electronics, 2016, 31(9), 6504–6510.
7. [8] Zaihidee, F.M., Mekhilef, S., Mubin, M., Application of fractional order sliding mode control for speed control of permanent magnet synchronous motor, IEEE Access, 2019, 7, 101765-101774.
8. [9] Kim, S.K., Lee, J.S., Lee, K.B., Offset-free robust adaptive back-stepping speed control for uncertain permanent magnet synchronous motor, IEEE Transactions on Power Electronics, 2016, 31(10), 7065-7076.

9. [10] Chai, S., Wang, L., Rogers, E., Model predictive control of a permanent magnet synchronous motor with experimental validation, Control Engineering Practice, 2013, 21 (11), 1584–1593.
10. [11] Arehpanahi, M., Fazli, M., Position control improvement of permanent magnet motor using model predictive control, International Journal of Engineering transactions, 2018, 31(7), 1044-1049.
11. [12] Nguyen, A.T., Rafaq, M.S., Choi, H.H., Jung, J.W., A model reference adaptive control based speed controller for a surface-mounted permanent magnet synchronous motor drive, IEEE Transactions on Industrial Electronics ,2018 ,65(12), 9399-9409.
12. [13] Wu, B.F., Adaptive neural predictive control for permanent magnet synchronous motor systems with long delay time, IEEE Access, 2019, 7, 108061-108069. DOI10.1109/ACCESS.2019.2932746.
13. [14] Aguilar-Mejía, O., Tapia-Olvera, R., Valderrabano-González, A., Cambero, I.R., Adaptive neural network control of chaos in permanent magnet synchronous motor, Intelligent Automation and Soft Computing, 2016, 2(3), 499-507.
14. [15] Hadian, M., Mehrshadian, M., Karami, M., Makvand, A.B., Event-based neural network predictive controller application for a distillation column, Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd, 2019, 1–13. doi.org/10.1002/asjc.2265.
15. [16] Wang, L., Chai, S., Yoo, D., Gan, L., Ng, K., Discrete-time model predictive control (DMPC) of electrical drives and power converter, PID and Predictive Control of Electrical Drives and Power Converters using Matlab®/Simulink®, 2015.
16. [17] Wang, L., Chai, S., Yoo, D., Gan, L., Ng, K., MATLAB®/Simulink® Tutorials on physical modeling and test-bed setup, Wiley-IEEEPress, 2015, 315–337, DOI:10.1002/9781118339459.
17. [18] Fasil, M., Antaloae, C., Mijatovic, N., Jensen, B.B., Holbolll, J., Improved dq-axes model of PMSM considering air gap flux harmonics and saturation, IEEE Transactions on Applied Super conductivity, 2016, 26(4), 1-5.
18. [19] Tarczewski, T., Grzesiak, L.M., Constrained state feedback speed control of PMSM based on model predictive approach, IEEE Transactions on Industrial Electronics,2016, 63(6), 3867–3875, DOI:10.1109/TIE.2015.2497302.
19. [20] Wang, W.C., Liu, T.H., Syaifudin, Y., Model predictive controller for a micro - PMSM – based five-finger control system, IEEE Transactions on Industrial Electronics, 2016, 63(6), 3666–3676, DOI:10.1109/TIE.2016.2543179.
20. [21] Formentini, A., AndrewTrentin, A., Marchesoni, M., Zanchetta, P., Speed finite control set model predictive control of a PMSM fed by matrix converter, IEEE Transactions on Industrial Electronics, 2015, 62(11), 6786–6796, DOI:10.1109/TIE.2015.2442526.
21. [22] Luo, Y., Liu, C., PMSM motor with a reduced-dimension cost function, IEEE Transactions on Industrial Electronics, 2019, 67(2), 969–979, DOI:10.1109/TIE.2019.2901636.
22. [23] Wang, F., Mei, X, Kennel, J.R.R., Model predictive control for electrical drive systems-an overview, Ces Transactions on Electrical Machines and Systems, 2017, 1(3), 219-230.
23. [24] Arashloo, R.S., Salehifar, M., Martinez, J.L.R., Andrade, F., Predictive dead beat current control of five-phase BLDC machines, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, Dallas, TX, 2014, 3049-3053, doi: 10.1109/IECON.2014.7048944.
24. [25] Bolognani, S., Bolognani, S., Peretti, L., Zigliotto, M., Design and implementation of model predictive control for electrical motor drives, IEEE Transactions on Industrial Electronics, 2009, 56(6), 1925-1936.
25. [26] Ahmed, A.A., Koh, B.K. Lee, Y., A comparison of finite control set and continuous control set model predictive control schemes for speed control of induction motors, IEEE Transactions on Industrial Informatics, 2018, 14(4), 1334-1346, DOI:10.1109/TII.2017.2758393.
26. [27] Veksler, A., Johansen, T.A., Borrelli, F., Dynamic positioning with model predictive control, IEEE Transactions on Control Systems Technology, 2016, 24(4), 1340–1353, DOI:10.1109/TCST.2015.2497280.
27. [28] Tavernini, D., Metzler, M., Gruber, P., Sorniotti, A., Explicit nonlinear model predictive control for electric vehicle traction control, IEEE Transactions on Control Systems Technology, 2019, 27(4), 1438–1451, DOI:10.1109/TCST.2018.2837097.
28. [29] Zbede, Y.B., Gadoue, S.M., Atkinson, D.J., Model predictive MRAS estimator for sensorless induction motor drives, IEEE Transactions on Industrial Electronics, 2016, 63(6), 3511-3521, DOI:10.1109/TIE.2016.2521721.
29. [30] Wang, G., Qi, J., Xu, J., Zhang, X., Xu, D., Anti roll back control for gearless elevator traction machines adopting offset-free model predictive control strategy, IEEE Transactions on Industrial Electronics, 2015, 62(10), 6194–6203, DOI:10.1109/TIE.2015.2431635.
30. [31] Arefi, M.M., Montazeri, A., Poshtan, J., Jahed-Motlagh, M.R., Wiener-neural identification and predictive control of a more realistic plug-flow tubular reactor, Chemical Engineering Journal, 2008, 138(1-3), 274–282.
31. [32] Kamaraj, P., Manikandan, B.V., Kumar, C.A., Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor, Electric Power Components and Systems, 2017, 45(20), 2304-2317.
32. [33] Csekő, L.H., Kvasnica, M., Lantos, B., Explicit MPC-based RBF neural network controller design with discrete-time actual kalman filter for semi active suspension, IEEE Transactions on Control Systems Technology, 2015, 23(5), 1736–1753, DOI:10.1109/TCST.2014.2382571.
33. [34] Mohamed, I.S., Rovetta, S., Do, T.D., Dragicević, T.,Diab, A.A.Z., A neural – network – based model predictive control of three – phase inverter with an output LCfilter, IEEE Access, 2019, 7, 124737–124749, DOI:10.1109/ACCESS.2019.2938220.
34. [35] Wang, T., Gao, H., Qiu, J., A combined adaptive neural network and nonlinear model predictive control for multi rate networked industrial process control, IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(2), 416–425, DOI:10.1109/TNNLS.2015.2411671.
35. [36] Cheng, L., Liu, W., Hou, Z.G., Yu, J., Tan, M., Neural – network –based nonlinear model predictive control for piezo electric actuators, IEEE Transactions on Industrial Electronics, 2015, 62(12), 7717–7727, DOI:10.1109/TIE.2015.2455026.
36. [37] Li, Z., Xiao, H., Yang, C., Zhao, Y., Model predictive control of nonholonomic chained systems using general projection neural networks optimization, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(10), 1313-1321, DOI:10.1109/TSMC.2015.2398833.
37. [38] Liu, W., Cheng, L., Hou, Z.G., Yu, J., Tan, M., An inversion-free predictive controller for piezo electric actuators based on a dynamic linearized neural network model, IEEE/ASME Transactions on Mechatronics, 2016, 21(1), 214–226, DOI:10.1109/TMECH.2015.2431819.
38. [39] Xu, X., Chen, H., Lian, C., Li, D., Learning – Based predictive control for discrete –time nonlinear systems with stochastic disturbances, IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(12), 6202–6213, DOI:10.1109/TNNLS.2018.2820019.
39. [40] Yoon, S., Jeon, H., Kum, D., Predictive cruise control using radial basis function network –based vehicle motion prediction and chance constrained model predictive control, IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10), 3832–3843, DOI:10.1109/TITS.2019.2928217.
40. [41] Akesson, B.M., Toivonen, H.T., A neural network model predictive controller, Journal of Process Control, Elsevier, 2006, 16(9), 937–946.
41. [42] San, O., Maulik, R., Neural network closures for nonlinear model order reduction, Advances in Computational Mathematics, 2018, 1717-1750.
42. [43] Kassem, A.M., Neural predictive controller of a two-area load frequency control for inter connected power system, Ain Shams Engineering Journal, 2010, 1, 49-58.