Application of Artificial Neural Network State Feedback Controller to Torque Ripple Minimization of PMSM

L. Niewiara, T. Tarczewski, L. M. Grzesiak

Abstract

This paper deals with the problem of torque ripple minimization of permanent magnet synchronous motor. The novelty of the presented approach lays in precisely maintain the level of the voltage source inverter DC voltage demanded for proper operation of the motor. An additional voltage matching circuit with state feedback controller is introduced in order to control of the inverter DC voltage. In the proposed solution model of a plant (i.e. permanent magnet synchronous motor fed by voltage source inverter with additional voltage matching circuit) is non-linear and non-stationary. An adaptive state feedback controller is developed by using an artificial neural network, which approximates non-linear control gain surfaces. A simple adaptation algorithm based on 2 low-order low-pass filters is used. Simulation results illustrate the proposed approach in comparison to typical drive with voltage source inverter and stationary state feedback controller.

References

  1. Ferrari, S. and Stengel, R. F. (2005). Smooth function approximation using neural networks. IEEE Transactions on Neural Networks, 16(1):24-38.
  2. Grzesiak, L. M. and Tarczewski, T. (2011). Permanent magnet synchronous motor discrete linear quadratic speed controller. In IEEE International Symposium on Industrial Electronics, ISIE 2011, pp. 667-672. IEEE.
  3. Grzesiak, L. M. and Tarczewski, T. (2013). PMSM servodrive control system with a state feedback and a load torque feedforward compensation. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 32(1):364-382.
  4. Gulez, K., Adam, A. A., and Pastaci, H. (2008). Torque ripple and EMI noise minimization in PMSM using active filter topology and field-oriented control. IEEE Transactions on Industrial Electronics, 55(1):251- 257.
  5. Hasanien, H. M. (2010). Torque ripple minimization of permanent magnet synchronous motor using digital observer controller. Energy Conversion and Management, 51(1):98-104.
  6. Huang, S. and Tan, K. K. (2012). Intelligent friction modeling and compensation using neural network approximations. IEEE Transactions on Industrial Electronics, 59(8):3342-3349.
  7. Hung, J. Y. and Ding, Z. (1993). Design of currents to reduce torque ripple in brushless permanent magnet motors. IEE Proceedings B Electric Power Applications, 140(4):260-266.
  8. Jahns, T. M. and Soong, W. L. (1996). Pulsating Torque Minimization Techniques for Permanent Magnet AC Motor Drives - A Review. IEEE Transactions on Industrial Electronics, 43(2):321-330.
  9. Kojima, M., Hirabayashi, K., Kawabata, Y., Ejiogu, E. C., and Kawabata, T. (2004). Novel vector control system using deadbeat-controlled PWM inverter with output LC filter. IEEE Transactions on Industry Applications, 40(1):162-169.
  10. Krishnan, R. (2010). Permanent Magnet Synchronous and Brushless DC Motor Drives. CRC Press, New York.
  11. Petrovic, V., Ortega, R., Stankovic, A. M., and Tadmor, G. (2000). Design and implementation of an adaptive controller for torque ripple minimization in PM synchronous motors. IEEE Transactions on Power Electronics, 15(5):871-880.
  12. Qian, W., Panda, S. K., and Xu, J.-X. (2004). Torque ripple minimization in PM synchronous motors using iterative learning control. IEEE Transactions on Power Electronics, 19(2):272-279.
  13. Steinke, J. K. (1999). Use of an LC filter to achieve a motorfriendly performance of the PWM voltage source inverter. IEEE Transactions on Energy Conversion, 14(3):649-654.
  14. Tarczewski, T. and Grzesiak, L. M. (2013). PMSM fed by 3-level NPC sinusoidal inverter with discrete state feedback controller. In 15th European Conference on Power Electronics and Applications, EPE 2013, pp. 1-9. IEEE.
  15. Tarczewski, T., Niewiara, L., and Grzesiak, L. M. (2014). Torque ripple minimization for PMSM using voltage matching circuit and neural network based adaptive state feedback control. In 16th European Conference on Power Electronics and Applications, EPE 2014. IEEE.
  16. Tewari, A. (2002). Modern Control Design: with MATLAB and SIMULINK. Wiley, Chichester.
Download


Paper Citation


in Harvard Style

Niewiara L., Tarczewski T. and Grzesiak L. (2014). Application of Artificial Neural Network State Feedback Controller to Torque Ripple Minimization of PMSM . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 363-369. DOI: 10.5220/0005086603630369


in Bibtex Style

@conference{icinco14,
author={L. Niewiara and T. Tarczewski and L. M. Grzesiak},
title={Application of Artificial Neural Network State Feedback Controller to Torque Ripple Minimization of PMSM},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={363-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005086603630369},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Application of Artificial Neural Network State Feedback Controller to Torque Ripple Minimization of PMSM
SN - 978-989-758-039-0
AU - Niewiara L.
AU - Tarczewski T.
AU - Grzesiak L.
PY - 2014
SP - 363
EP - 369
DO - 10.5220/0005086603630369