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

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Terminal Sliding Mode Control of Nonlinear Systems Using Self-Recurrent Wavelet Neural Network

자기 회귀 웨이블릿 신경망을 이용한 비선형 시스템의 터미널 슬라이딩 모드 제어

  • 이신호 (연세대학교 전기전자공학과) ;
  • 최윤호 (경기대학교 전자공학부) ;
  • 박진배 (연세대학교 전기전자공학과)
  • Published : 2007.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we design a terminal sliding mode controller based on self-recurrent wavelet neural network (SRWNN) for the second-order nonlinear systems with model uncertainties. The terminal sliding mode control (TSMC) method can drive the tracking errors to zero within finite time in comparison with the classical sliding mode control (CSMC) method. In addition, the TSMC method has advantages such as the improved performance, robustness, reliability and precision. We employ the SRWNN to approximate model uncertainties. The weights of SRWNN are trained by adaptation laws induced from Lyapunov stability theorem. Finally, we carry out simulations for Duffing system and the wing rock phenomena to illustrate the effectiveness of the proposed control scheme.

Keywords

References

  1. J. J. Slotine and W. Li, Applied Nonlinear Control, Prentice-Hall, 1991
  2. S. Tzafestas, M. Raibert, and C. Tzafestas, 'Robust sliding-mode control applied to a 5-link biped robot,' Journal of Intelligent and Robotic Systems, vol. 15, no. 1, pp. 67-133, 1996 https://doi.org/10.1007/BF00435728
  3. V. Utkin, J. Guldner, and J. Shi, Sliding Mode Control in Electromechanical Systems, Taylor & Francis, 1999
  4. S. T. Venkataraman and S. Gulati, 'Control of nonlinear systems using terminal sliding modes,' Proc. of American Control Conference, pp. 891-893, 1989
  5. S. T. Venkataraman and S. Gulati, 'Terminal slider control of robot systems,' Journal of Intelligent and Robotic Systems, vol. 7, no. 1, pp. 31-55, 1993 https://doi.org/10.1007/BF01258211
  6. Z. Man, A. P. Paplinski, and H. R Wu, 'A robust MIMO terminal sliding mode control scheme for rigid robotic manipulators,' IEEE Trans. on Automatic Control, vol. 39, no. 12, pp. 2464-2469, 1994 https://doi.org/10.1109/9.362847
  7. M. Zak, 'Terminal attractors in neural networks,' Neural Networks, vol. 2, no. 4, pp. 259-274, 1989 https://doi.org/10.1016/0893-6080(89)90036-1
  8. S. Janardhanan and B. Bandyopadhyay, 'On discretization of continuous-time terminal sliding mode,' IEEE Trans. on Automatic Control, vol. 51, no. 9, pp. 1532-1536, 2006 https://doi.org/10.1109/TAC.2006.880805
  9. Y. Wu, X. Yu, and Z. Man, 'Terminal sliding mode control design for uncertain dynamic systems,' System and Control Letter, vol. 34, no. 5, pp. 281-287, 1998 https://doi.org/10.1016/S0167-6911(98)00036-X
  10. K. H. Kyung, B. H. Lee, and M. S. Ko, 'Acceleration based learning control of robotic manipulators using a multi-layered neural network,' IEEE Trans. on Systems, Man and Cybernetics, vol. 24, no. 8, pp. 1265-1272, 1994 https://doi.org/10.1109/21.299708
  11. M. J. Lee and Y. K. Choi, 'An adaptive Neuro-controller using RBFN for robot manipulators,' IEEE Trans. on Industrial Electronics, vol. 51, no. 3, pp. 711-717, 2004 https://doi.org/10.1109/TIE.2004.824878
  12. C. H. Kim, S. J. Yoo, J. B. Park, and Y. H. Choi, 'Hybrid sliding mode control of 5-link biped robot in single support phase using a wavelet neural network,' Journal of Control, Automation and Systems Engineering, vol. 12, no. 11, pp. 1081-1087, 11, 2006 https://doi.org/10.5302/J.ICROS.2006.12.11.1081
  13. S. J. Yoo, J. B. Park, and Y. H. Choi, 'Stable predictive control of chaotic systems using self-recurrent wavelet neural network,' International Journal of Control, Automation, and Systems, vol. 3, no. 1, pp. 43-55, 2005
  14. S. J. Yoo, Y. H. Choi, and J. B. Park, 'Generalized predictive control based on self recurrent wavelet neural network for stable path tracking of mobile robots: Adaptive learning rates approach,' IEEE Trans. on Circuits and Systems I: Regular Papers, vol. 53, no. 6, pp. 1381-1394, 2006 https://doi.org/10.1109/TCSI.2006.875166
  15. J. Zhang, G. G. Walter, Y. Mao, and W. N. W. Lee, 'Wavelet neural networks for function learning,' IEEE Trans. Signal Processing, vol. 43, no. 6, pp. 1485-1497, 1995 https://doi.org/10.1109/78.388860
  16. M. Zak, 'Terminal attractors for content addressable memory in neural networks,' Physics Letters, vol. 133, pp. 218-222, 1988
  17. X. Yu and Z. Man, 'Multi-input uncertain linear systems with terminal sliding-mode control,' Automatica, vol. 34, no. 3, pp. 389-392, 1998 https://doi.org/10.1016/S0005-1098(97)00205-7
  18. J. M. Elzebda, A. H. Nayfeh, and D. T. Mook, 'Development of an analytic model of wing rock for slender delta wing,' Journal of Aircraft, vol. 26, no. 9, pp. 737-743, 1989 https://doi.org/10.2514/3.45833
  19. C. F. Hsu, C. M. Lin, and T. Y. Chen, 'Wavelet adaptive backstepping control for a class of nonlinear systems,' IEEE Trans. on Neural Networks, vol. 17, no. 5, pp. 1175-1183, 2006 https://doi.org/10.1109/TNN.2006.878122