The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

Neuro-Adaptive Control of Robot Manipulator Using RBFN

RBFN를 이용한 로봇 매니퓰레이터의 신경망 적응 제어

  • 김정대 ((주)코웰 시스넷 개발부) ;
  • 이민중 (부산대 전기공학과) ;
  • 최영규 (부산대 전자전기정보컴퓨터공학부) ;
  • 김성신 (부산대 전기전자정보컴퓨터공학부)
  • Published : 2001.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the parameters of RBFN is developed based on the Lyapunov stability theory to guarantee the stability of the overall control system. The filtered tracking error between the system output and the desired output is shown to be UUB(uniformly ultimately bounded). To evaluate the performance of the controller, the proposed method is applied to the trajectory contro of the two-link manipulator.

Keywords

References

  1. J.-J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, 1991
  2. K. S. Narendra and K. Parthasarathy, 'Identification and control of dynamical systems using neural networks,' IEEE Trans. on Neural Networks, vol. 1, no. 1, pp. 4-27, March 1990 https://doi.org/10.1109/72.80202
  3. A. S. Morris and S. Khemaissia, 'A Neural network based adaptive robot controller,' Journal of Intelligent and Robotics Systems, vol. 15, pp. 3-10, 1996 https://doi.org/10.1007/BF00435721
  4. D. Y. Meddah and A. Benallegue, 'A stable neuro-adaptive controller for rigid robot manipulators,' Journal of Intelligent and Robotics Systems, vol. 20, pp. 181-193, 1997 https://doi.org/10.1023/A:1007904210780
  5. R. Carelli and E. F. Camacho, 'A neural network based feedforward adaptive controller for robots,' IEEE Trans. on Systems, Man and Cybernetics, vol. 25, no. 9, pp. 1281-1288, Sep. 1995 https://doi.org/10.1109/21.400506
  6. M. Zhihong, H. R. Wu, and M. Palaniswame, 'An adaptive tracking controller using neural networks for a class of nonlinear systems,' IEEE Trans. on Neural Networks, vol. 9, no. 5, Sep. 1998 https://doi.org/10.1109/72.712168
  7. V. Etxebarria and M. D. L. Sen, 'An approach to adaptive neural control of robot manipulator,' Journal of Systems Science, vol. 27, no. 11, 1996
  8. S. Yildirim, 'New neural networks for adaptive control of robot manipulators,' IEEE Trans. on Neural Networks, 1997 https://doi.org/10.1109/ICNN.1997.614156
  9. M. A. Abido and Y. Abdel-Magid, 'On-line identification of synchronous machines using radial basis function neural networks,' IEEE Trans. on Power Systems, vol. 12, no. 4, Nov. 1997 https://doi.org/10.1109/59.627848
  10. Y. Li and J. M. Deng, 'WAV - a weight adaptation algorithm for normalized radial basis function networks,' IEEE Trans. on Neural networks, 1998
  11. L. Yingwei, N. Sundararajan and P. Saratchandran, 'Identification of Time-varying nonlinear systems using minimal radial basis function neural networks,' IEE Proc.- Control Theory Appl., vol. 144, no.2, March 1997 https://doi.org/10.1049/ip-cta:19970891
  12. L. Behera, S. Chandhury and M. Gopal, 'Neuro-adaptive hybrid controller for robot- manipulator tracking control,' IEE Proc.-Control Theory Appl., vol. 143, no. 3, May 1996 https://doi.org/10.1049/ip-cta:19960121
  13. G. P. Liu, V. Kadirkamanathan and S. A. Billings, 'Variable neural networks for adaptive control of nonlinear systems,' IEEE Trans. on Systems, Man and Cybernetics, vol. 29, no. 1, Feb. 1999 https://doi.org/10.1109/5326.740668
  14. K. S. Fu, R. C. Gonzalez, and C. S. G. Lee, Robotics, McGraw-Hill International Editions, 1987
  15. M. W. Spong and M. Vidyasagar, Robot Dynamics and Controls, John Wiley & Sons, 1989
  16. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall, 1997
  17. Li-Xin Wang, Adaptive Fuzzy Systems and Control : Design and Stability Analysis, Prentice Hall, 1994
  18. Frank L. Lewis, Kai Liu, and Aydin Yesildirek, 'Neural net robot controller with guaranteed tracking performance,' IEEE Trans. Neural Networks, vol. 6, no. 3, May 1995 https://doi.org/10.1109/72.377975