RBF ANN Nonlinear Prediction Model Based Adaptive PID Control of Switched Reluctance Motor Drive

  • Chang-Liang Xia
  • Jie Xiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


The inherent nonlinear of switched reluctance motor (SRM) makes it hard to get a good performance by using the conventional PID controller to the speed control of SRM. This paper develops a radial basis function (RBF) artificial neural network (ANN) nonlinear prediction model based adaptive PID controller for SRM. ANN, under certain condition, can approximate any nonlinear function with arbitrary precision. It also has a strong ability of adaptive, self-learning and self-organization. So, combining it with the conventional PID controller, a neural network based adaptive PID controller can be developed. Appling it to the speed control of SRM, a good control performance can be gotten. At the same time, the nonlinear mapping property and high parallel operation ability of ANN make it suitable to be applied to establish nonlinear prediction model performing parameter prediction. In this paper, two ANN – NNC and NNI are employed. The former is a back propagation (BP) ANN with sigmoid activation function. The later is an ANN using RBF as activation function. The former is used to adaptively adjust the parameters of the PID controller on line. The later is used to establish nonlinear prediction model performing parameter prediction. Compared with BP ANN with sigmoid activation function, the RBF ANN has a more fast convergence speed and can avoid getting stuck in a local optimum. Through parameter prediction, response speed of the system can be improved. To increase the convergence speed of ANN, an adaptive learning algorithm is adopted in this paper that is to adjust the learning rate according to the error. This can increase the convergence speed of ANN and make the system response quick. The experimental results demonstrate that a high control performance is achieved. The system responds quickly with little overshoot. The steady state error is zero. The system shows robust performance to the load torque disturbance.


Hide Layer Radial Basis Function Convergence Speed Step Response Steady State Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Garside, J.J., Brown, R.H., Arkadan, A.A.: Switched reluctance motor control with artificial neural networks. IEEE International Electric Machines and Drives Conference Record, TB1/2.1–TB1/2.3 (1997)Google Scholar
  2. 2.
    Trifa, V., Gaura, E., Moldovan, L.: Neuro-control approach of switched reluctance motor drives. In: MELECON 1996, 8th Mediterranean Electrotechnical Conference, vol. 3, pp. 1461–1464 (1996)Google Scholar
  3. 3.
    Jian, L., Hui, W.: Neural networks indentification for switched reluctance motor drive system. Journal of Shenyang Arch. and Civ. Eng. Inst. 16(2), 146–149 (2000)Google Scholar
  4. 4.
    Rahman, K.M., Rajarathnam, A.V., Ehsani, M.: Optimized instantaneous torque control of switched reluctance motor by neural network. In: Industry Applications Conference, 1997. Thirty-Second IAS Annual Meeting, IAS 1997., Conference Record of the 1997 IEEE, vol. 1, pp. 556–563 (1997)Google Scholar
  5. 5.
    Yilmaz, K., Mese, E., Cengiz, A.: Minimum inductance estimation in switched reluctance motors by using artificial neural networks. In: 11th Mediterranean Electrotechnical Conference, 2002, MELECON 2002, pp. 152–156 (2002)Google Scholar
  6. 6.
    Fahimi, B., Suresh, G., Johnson, J.P., Ehsani, M., Arefeen, M., Panahi, I.: Self-tuning control of switched reluctance motors for optimized torque per ampere at all operating points. In: Applied Power Electronics Conference and Exposition, 1998. Conference Proceedings 1998, Thirteenth Annual, vol. 2, pp. 778–783 (1998)Google Scholar
  7. 7.
    Senjyu, T., Shingaki, T., Omoda, A., et al.: High efficiency drives for synchronous reluctance motors using neural network. In: IECON 2000, vol. 2, pp. 777–782 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chang-Liang Xia
    • 1
  • Jie Xiu
    • 1
  1. 1.School of Electrical Engineering and AutomationTianjin UniversityTianjinChina

Personalised recommendations