Browse > Article
http://dx.doi.org/10.5207/JIEIE.2006.20.7.065

Speed Control of IPMSM Drive using NNPI Controller  

Jung, Dong-Wha (순천대 공대 전기공학과)
Choi, Jung-Sik (순천대학교 대학원 전기공학과)
Ko, Jae-Sub (순천대학교 대학원 전기공학과)
Publication Information
Journal of the Korean Institute of Illuminating and Electrical Installation Engineers / v.20, no.7, 2006 , pp. 65-73 More about this Journal
Abstract
This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.
Keywords
IPMSM drive; Neural network; NNPI controller; ANN;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Y. Tzou, 'DSP-based robust control of an AC induction servo drive for motion control,' IEEE Trans. Contr. Syst. Technol., vol. 4, pp. 614-626, 1996   DOI   ScienceOn
2 M. Ali Unar, D. J. Murray-Smith and S. F. Ali Shah, 'Design and tuning of fixed structure PID controller - A survey,' Technical Report CSC-96016, Faculty of Engineering, Glasgow University, Scotland, 1996
3 Z. Ibrahim and E. Levi, 'Comparative analysis of fuzzy logic and PI speed control in high performance AC drives using experimental approach,' Proc. of IEEE IAS'2000, Rome, Italy, CD-ROM paper 46-3, 2000
4 M. A. Rahman and M. A, Hoque, 'On-line adaptive artificial neural network based vector control of permanent magnet synchronous motors,' IEEE Trans. EC, vol. 13, pp. 311-318, 1998
5 J. C. Lee and D. H. Chung, 'MRAC fuzzy control for high performance of induction motor drive,' The Trans. of KIPE, vol. 7, no. 3, pp. 215-223, 2002
6 H. G. Lee, J. C. Lee and D. H. Chung, 'New fuzzy controller for high performance of induction motor drive,' The journal of KIIS, vol. 17, no. 4, pp. 87-93, 2002
7 H. G. Lee, J. C. Lee and D. H. Chung, 'Adaptive FNN controller for speed control of IPMSM drive,' The Trans. of KIEE, vol. 41-SC, no. 3, pp. 39-46, 2004
8 J. C. Lee, H. G. Lee, Y. S. Lee and S. M. Nam, D. H. Chung, 'Speed estimation and control of induction motor drive using hybrid intelligent control,' International Conference ICPE'04, no. 3, pp. 181-185, 2004
9 J. C. Lee, H. G. Lee and S. M. Nam, D. H. Chung, ' Speed control of induction motor drive using adaptive FNN controller,' International Conference ICEMS'04, Conference no. PI-5(430-M09-052), 2004. [CD no. 2]
10 K.J. Astron and B. Wittenmark, 'Adaptive control,' Addison-Wesley, 1989
11 C. M. Ong, 'Dynamic simulation of elecctric machinery using Matlab/simulink,' Upper Saddle River, NJ: Prentice-Hall, 1998
12 H. G. Lee, J. C. Lee and D. H. Chung, 'Design of fuzzy controller induction drive considering parameter change,' The Trans. of KIEE, vol. 51P, no. 3, pp. 111-119, 2002
13 M. Santos and J. M. de la Cruz, 'Between fuzzy PID and PID conventional controllers,' NARPS'96, Berkley, USA, June 1996