JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Adaptive PI Controller Design Based on CTRNN for Permanent Magnet Synchronous Motors
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Adaptive PI Controller Design Based on CTRNN for Permanent Magnet Synchronous Motors
Kim, Il-Hwan;
  PDF(new window)
 Abstract
In many industrial applications that use the electric motors robust controllers are needed. The method using a neural network in order to design a robust controller when a disturbance occurs is studied. Backpropagation algorithm, which is used in a conventional neural network controller is used in many areas, but when the number of neurons in the input layer, hidden layer and output layer of the neural network increases the processing speed of the learning process is slow. In this paper an adaptive PI(Proportional and Integral) controller based on CTRNN(Continuous Time Recurrent Neural Network) for permanent magnet synchronous motors is presented. By varying the load and the speed the validity of the proposed method is verified through simulation and experiments.
 Keywords
LPF;PMSM;Current control;Permanent magnet Synchronous motor;VCF;Cutoff frequency;
 Language
Korean
 Cited by
 References
1.
M. H. Shin, D. S. Hyen, S. B. Cho and S. Y. Choe, "An improved stator flux estimation for speed sensorless stator flux orientation control of induction motors", IEEE PESC, pp. 1581-1586, 1998.

2.
K. Y. Nam, W. T. Lee, C. Lee, and J-Pyo Hong, "Reducing torque of DC motor by varying input voltage", IEEE Trans. Magn. vol. 42, pp. 1307-1311, 2006. crossref(new window)

3.
L. Zhang and W. L. Qu, "Commutation torque ripple restraint in BLDC motor over whole speed range", in Proc. IEEE ICEMS, pp. 1501-1507. 2005.

4.
Y. Liu, Z. Q. Zhu, and D. Howe, "Direct torque control of brushless DC drives with reduced torque ripple", IEEE Trans. Ind. Appl., vol. 41, no. 2, pp. 599-608, 2005. crossref(new window)

5.
Y. Liu, Z. Q. Zhu, and D. Howe, "Instantaneous torque estimation in sensorless direct torque controlled brushless DC motors", IEEE Trans. Ind. Appl., vol. 42, no. 5, pp. 1275-1283, 2006. crossref(new window)

6.
Z. Q. Zhu, Y. Liu, and D. Howe, "Steady-state dynamic performance of a direct torque controlled PM brushless DC drive accounting for influence of PWM chopping and cogging torque", in Proc. IEE Int. Conf. Power Electron., Mach. and Drives, Dublin, pp. 556-560, 2006.

7.
Tae-Sung Kim, Sung-Chan Ahn, and Dong-Seok Hyun, "A new current control algorithm for torque ripple reduction of BLDC motors", IECON '01. The 27th Annual Conf. of the IEEE, vol. 2, pp. 1521-1526, 2001.

8.
K. Friston, "The free-energy principle: a unified brain theory?", Nature, vol. 11, no. 2, pp. 127-138, 2010.

9.
K. Friston, "The history of the future of the Bayesian brain", Neuro Image, vol. 62, no. 2, pp. 1230-1233, 2012.

10.
Y. Yamashita and J. Tani, "Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment", PLoS Computational Biology, vol. 4, no. 11, 2008.