Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Motion Control of an Uncertain robotic Manipulator System via Neural Network Disturbance Observer

신경회로망 외란 관측기를 이용한 불확실한 로봇 시스템의 운동 제어

  • Kim, Eun-Tai (Yonsei University, School of Electrical and Electronic Engr.) ;
  • Kim, Han-Jung (Hankyong National University, dept. of Control and Instrumentation Engr.)
  • 김은태 (聯世大學校 電氣電子工學部) ;
  • 김한정 (國立 韓京大學校, 制御計測工學科)
  • Published : 2002.07.01
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Abstract

A neural network disturbance observer for a robotic manipulator is derived in this paper. The neural network used as the disturbance observer is a feedforward MLP(multiple-layered perceptron) network. The uniform ultimate boundness(UUB) of the proposed neural disturbance observer and the control error within a sufficiently small compact set is guaranteed. This neural disturbance observer method overcomes the disadvantages of the existing adaptive control methods which require the tedious analysis of the regressor matrix of the given manipulator. The effectiveness of the proposed neural disturbance observer is demonstrated by the application to the three-link robotic manipulator.

본 논문에서는 로봇 매니퓰레이터의 제어에 사용할 수 있는 신경망 외란 관측기를 제안하도록 한다. 제안한 신경망 외란 관측기는 다층신경망의 구조로 신경망 외란관측기의 오차와 제어 오차가 충분히 작은 콤팩트 집합에 절대 상시 유계된다. 본 논문에서 제안하는 신경망 외란 관측기는 기존의 적응 제어기의 단점을 해결한 방식으로 복잡한 회귀 모델을 필요로 하지 않는다. 끝으로 제안한 방식을 3관절 로봇에 적용하여 그 타당성을 확인한다.

Keywords

References

  1. R. J. Schilling, Fundamentals of Robotics, Englewood Cliffs, NJ: Prentice Hall, 1990
  2. F. L. Lewis, C. T. Abdallah, and D. M. Dawson, Control of Robot Manipulators, New York, MacMillan Publishing Company, 1993
  3. Spong and M. Vidyasagar, Robot Dynamics and Control, John Wiley & Sons, Inc., 1989
  4. L. R. Hunt, R. Su and G. Meyer, 'Global transformations of nonlinear systems,' IEEE Trans. Autom. Control, vol. AC-28, no. 1, pp. 24-31, 1983 https://doi.org/10.1109/TAC.1983.1103137
  5. R. Ortega and M. W. Spong, 'Adaptive motion control of rigid robots: A tutorial,' Automatica, vol. 25, pp. 977-888, 1989 https://doi.org/10.1016/0005-1098(89)90054-X
  6. J. J. E. Slotine and W. Li, 'Adaptive manipulator control: A case study,' IEEE Trans. Automatic Control, vol. 33, no. 11, pp. 995-1003, 1988 https://doi.org/10.1109/9.14411
  7. J. J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall 1991
  8. C. Abdallah, D. Dawson, P. Dorato and M. Jamshidi, 'Survey of robust control of rigid robots,' IEEE Contr. Syst. Mag., vol. 11, pp. 24-30, 1991 https://doi.org/10.1109/37.67672
  9. Y. Stepanenko and C. Y. Su, 'Variable structure control of robust maniplators with nonlinear sliding manifolds,' Int. Jour. Contr., vol. 58, pp. 265-300, 1993
  10. C. Y. Su and Y. Stepanenko, 'Adaptive variable structure tracking control for constrained robots,' IEEE Trans. Aerospace and Electronic. Systems, vol. 30, no. 2, pp. 493-503, 1994 https://doi.org/10.1109/7.272271
  11. H. Miyamoto, M. Kawato, T. Setoyama and R. Suzuki, 'Feedback error learning neural networks for trajectory control of a robotic manipulator,' Neural Networks, vol. 1, pp. 251-265, 1988 https://doi.org/10.1016/0893-6080(88)90030-5
  12. M. Kawato, Y, Uno, M. Isobe and R. Suzuki, 'Hierarchical neural network model for voluntary movement with application to robotics,' IEEE Control Systems Mag., pp. 8-16, 1988 https://doi.org/10.1109/37.1867
  13. F. L. Lewis, A. Yesildirek and K. L. Liu, 'Multilayer neural-net robot controller with guaranteed tracking performance,' IEEE Trans. Neural Networks, vol. 7, no. 2, pp. 388-399, 1996 https://doi.org/10.1109/72.485674
  14. V. Yen and T. -Z, Liu, 'A neural network assisted computed torque method for manipulator tracking control problems,' Int. Jour. of Systems Science, vol. 27, no. 11, pp. 1133-1141, 1996 https://doi.org/10.1080/00207729608929320
  15. R. Carelli, E. F. Camacho and D. Patino, 'A neural network based feedforward adaptive controller for robots,' IEEE Trans. Sys., Man, and Cyb., vol. 25, no. 9, pp. 1281-1288, 1995 https://doi.org/10.1109/21.400506
  16. V. Etxebarria and M. de La Sen, 'An approach to adaptive neural control of robot manipu-lators,' Int. Jour. of Systems Science, vol. 27, no. 11, pp. 1143-1152, 1996 https://doi.org/10.1080/00207729608929321
  17. W. H. Chen, D. J. Ballance, P. J. Gawthrop and J. O'Reilly, 'A nonlinear disturbance observer for robotic manipulator,' IEEE Trans. Ind. Electron., vol. 47, no. 4, pp. 932-938, 2000 https://doi.org/10.1109/41.857974
  18. K. Funahashi, 'On the approximate realization of continuous mappings by neural networks,' Neural Networks, vol. 2, pp. 183-192, 1989 https://doi.org/10.1016/0893-6080(89)90003-8
  19. K. Hornik, M. Stinchcombe and H. White, 'Multilayer feedforward networks are universal approximators,' Neural Networks, vol. 2, pp. 359-366, 1989 https://doi.org/10.1016/0893-6080(89)90020-8
  20. L. X. Wang, A Course in Fuzzy Systems and Control, NJ: Prentice-Hall, 1997