DOI QR코드

DOI QR Code

A Design of the PID controller Using Wavelet Neural network

웨이브렛 신경망을 이용한 PID제어기의 설계

  • 하홍곤 (동의대학교 전기ㆍ전자ㆍ정보통신ㆍ메카트로닉스 공학부)
  • Published : 2003.01.01

Abstract

In this paper, the PID controller is constructed with a neural network and wavelet function. And the wavelet neural PID controller is adapted by choosing the values of the dilation and translation parameter of the wavelet function. Weights are adjusted by the inverse propagation algolithm. Applying this method to the position control system, its usefulness is verified from the results of experiment.

본 논문에서는 PID제어기를 웨이브렛과 신경망으로 설계하여 웨이브렛 이론에 근거하여 신축과 이동의 변수값을 선택하여 웨이브렛 신경망 PID 제어기가 적응되도록 하였다. 그리고 연결강도는 역전파 알고리듬에 의해서 조정되도록 하였다. 이 제어기를 위치 제어계에 적용하여 실험을 통해 그 유효성을 검증하였다.

Keywords

References

  1. Katsuhisa Endo, Yoshihisa Ishida and Takashi Honda “Gain Adjustment of I-PD Control system” T.IEE, Japan, Vol.113-C, No.6, pp 409-416, 1993.
  2. P.B.Schimidt and R.D.Lorentz “Design principles and Impl-ementation of DC Drives, IEEE, Trans, Ind, Apply, vol.28, NO.3 pp594-599, 1992. https://doi.org/10.1109/28.137444
  3. MoonYong Lee “Process control using a Neural Network Combinent with the Conventional PID Controller” icase, Korea, Vol2, No.3, pp196-200, 2000.
  4. Chang-Goo Lee “Nonlinear PID Controller with Neural Network based Compensator” Trans, KIEE Vol.49D, No.5, pp225-233, 2000.
  5. Sugn-Boo Chung, Hyun-Kwan Lee, and Ki-Hwan Eom "The Performance Improvement of Backpropagation Algorithm Using the Gain Variable of Activation Function" IEEK, Vol.38 C1, No.6, pp350-369, 2001.
  6. Homg-Gon Ha “The design of the expanded I-PD Controller with the Neuro-Precompensator” Kimics, Vol. 4, No.3,pp619-625, 2000.
  7. Chang-Goo Lee “Identification and Control of Fast Time-Varying Nonlinear System Using Error Recurrent Neural Network” Trans, KIEE, Vol.46. No,12. pp1793-1799. 1997.
  8. Chang-Goo Lee, Dong-Young Shin “Adaptive PID Controller Based on Error Self-Recurrent Neural Network” Journal of Control, Automation and System Engineering Vol,4. No,2.pp209-214, 1998. https://doi.org/10.1109/78.388860
  9. J.Zhang.G.G.Walter, Y.Miao and W.N.W,Lee “Wavelet Neural Networks for Function Learning” IEEE Trans. onsignal processing. Vol.43, No 6, pp1485-1497, 1995. https://doi.org/10.1109/78.388860
  10. Kyuong-Kwon Jung, Dong-Seol Son, Yong-Gu Lee, Hyun-Kwan Lee and Ki-Hwan Eom “Adaptive Control Methodusing Wavelet Neural Network” Pro. KIMICS, Vol.5, No,1. pp456-459, 2001.
  11. Y.Tan.X.Dang, F.Liang and Chun-Yi Su “Dynamic Wavelet Neural Network for Nonlinear Dynamic System Identification”IEEE International Conference on control Applications, pp214-219, 2000. https://doi.org/10.1109/CCA.2000.897426
  12. Seung-Jin Seo, Jae-Yong Seo, Kyuong-Jae Won, Jung-Heun Yon, and Hong-Tae Jeon “Wavelet Network for Stable Direct Adaptive Control of Nonlinear Systems” IEEIC, Vol.36-s, No.10, pp51-57 1999.
  13. Hyun-Dong Lee, Kwang-Sik Lee and Dong-In Lee “The Analysis of Partial Discharges Pattern using Wavelet Transform” Journal of KIEE, Vol.15, No.1, PP.84-89. 2001.