Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Experimental Studies on Decentralized Neural Networks Using Reference Compensation Technique For Controlling 2-DOF Inverted Pendulum Based on Velocity Estimation

속도추정 기반의 2자유도 도립진자의 안정화를 위한 입력보상 방식의 분산 신경망 제어기에 관한 실험적 연구

  • 조현택 (충남대학교 메카트로닉스공학과) ;
  • 정슬 (충남대학교 메카트로닉스공학과)
  • Published : 2004.04.01
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Abstract

In this paper, the decentralized neural network control of the reference compensation technique is proposed to control a 2-DOF inverted pendulum on an x-y plane. The cart with the 2-DOF inverted pendulum moves on the x-y plane and the 2-DOF inverted pendulum rotates freely on the x-y axis. Since the 2-DOF inverted pendulum is divided into two 1-DOF inverted pendulums, the decentralized neural network control is applied not only to balance the angle of pendulum, but also to control the position tracking of the cart. Especially, a circular trajectory tracking is tested for position tracking control of the cart while maintaining the angle of the pendulum. Experimental results show that position control of the inverted pendulum system is successful.

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References

  1. W T. Miller, R. S. Sutton, and P. J. Werbos, 'Neural Networks for Control', The MIT Press, 1991
  2. 정슬, '지능제어시스템,' 충남대 강의교재, 2002
  3. 정슬, 임선빈, '신경회로망을 이용한 비선형 시스템 제어의 실험적 연구,' 제어 . 자동화 . 시스템공학 논문지, 제 7 권, 제 11 호, pp. 918-926, 2001. 11
  4. M. Miyamoto, M. Kawato, T. Setoyama, and R. Suzuki, 'Feedback error learning,' IEEE Trans. on Neural Networks, vol. 1, pp. 251-265, 1988 https://doi.org/10.1109/72.80250
  5. T. H. Hung, M. F. Yeh, and H. C. Lu, 'A pi-like fuzzy controller implementation for the inverted pendulum system,' Proc. of IEEE Conference on Intelligent Processing Systems, pp. 218-222, 1997 https://doi.org/10.1109/ICIPS.1997.672769
  6. M. E. Magana, and F. Holzapfel, 'Fuzzy-logic control of an inverted pendulum with vision feedback,' IEEE Trans. on Education, voI.41,no.2,pp. 165-170,1998 https://doi.org/10.1109/13.669727
  7. S. Omatu, T. Fujinaka, and M. Yoshioka, 'Neuro-pid control for inverted single and double pendulums,' IEEE Conf., pp. 2685-2690, 2000
  8. S. Jung and H. T. Cho, 'Decentralized neural network reference compensation technique for balancing two degrees-of-freedom inverted pendulum,' ICCAS Korea-Japan Joint Workshop on Intelligent System, October 2002
  9. S. H. Lee, and J. B. Song, 'Acceleration estimator for low-velocity and low-acceleration regions based on encoder position data,' IEEE/ASME Trans. on Mechatronics, vol. 6, no. 1, pp. 58-64, 2001 https://doi.org/10.1109/3516.914392
  10. P. S. Carpenter, R. H. Brown, J. A. Heinen, and S. C. Schneider, 'On algorithms for velocity estimation using discrete position encoders,' Industrial Electronics, Control, and Instrumentation, Vol. 2, pp. 844-849, 1995
  11. R. H. Brown, S. C. Schneider, and M. G. Mulligan, 'Analysis of algorithms for velocity estimation from discrete position versus time data,' IEEE Trans. on Industrial Electronics, Vol. 39, No.1, pp. 11-19, 1992 https://doi.org/10.1109/41.121906
  12. M. F. Benkhoris and M. Nt-Ahmed, 'Discrete speed estimation from a position encoder for motor drives,' Power Electronics and Variable Speed Drives, Conf. Publ. No. 429, pp.283-287, 1996