International Journal of Fuzzy Logic and Intelligent Systems

  • 제4권1호
  • /
  • Pages.111-118
  • /
  • 2004
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Path Tracking Control Using a Wavelet Based Fuzzy Neural Network for Mobile Robots

  • Oh, Joon-Seop (Department of Electrical ＆ Electronic Engineering, Yonsei University) ;
  • Park, Yoon-Ho (School of Electronic Engineering, Kyonggi University)
  • 발행 : 2004.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

키워드

참고문헌

  1. R. M. Desantis, 'Path-tracking for carlike robots with single and double streeiing', IEEE Trans. on Vehicular Technology, Vol. 44, No. 2, pp. 366-377, 1995 https://doi.org/10.1109/25.385930
  2. X. Yang, K. He, M. Guo, and B. Zhang, 'An intelligent predictive control approach to path tracking problem of autonomous robot', Proc. of IEEE Conf. on Systems, Man, and Cybernetics. Vol. 4, pp. 350-355, 1998
  3. C. C. Wit, H. Khennouf, C. Samson and 0. J. Sordalen, 'Nonlinear control design for mobile robots', Recent Trends in Mobile Robots, World Scientific, Singapore, pp. 121-156, 1993
  4. Z, P. Jiang and H. Nijmeijer, 'Tracking control of mobile robots: a case study in backstepping', Automatica, Vol. 33. No. 7, pp. 1393-1399, 1997 https://doi.org/10.1016/S0005-1098(97)00055-1
  5. J. M. Yang and J. H. Kim, 'Sliding mode motion control of nonholonomic mobile robots', IEEE Control Systems, Vol. 19, No. 2, pp. 15-23, 1990 https://doi.org/10.1109/37.753931
  6. G. Dongbing and H. Huosheng, 'Wavelet neural work based predictive control for mobile robots', Proc. of IEEE Int. Conf, on Systems, Man, and Cybernetics, Vol. 5, pp. 3544-3549, 2000
  7. M. L. Corradini, G.. Ippoliti, S. Longhi and S. Michelini, 'Neural networks inverse model approach for the tracking problem of mobile robot', Proc. of RAAD 2000, pp. 17-22, 2000
  8. M. L. Corradini, G. Ippoliti, and S. Longhi, 'The tracking problem of mobile robots: experimental results using a neural network approach', Proc. of WAC pp. 33-37, 2000
  9. S. Horikawa, T. Furuhashi and Y. Uchikawa, 'On identification of structures in premise of a fuzzy model using a fuzzy neural networks', Proc. of the 2nd IEEE Int. Conf. on Fuzzy Systems, pp. 661-666, 1993
  10. T. Hasegawa, S. Horikawa, T. Furuhashi and Y. Uchikawa, 'On design of adaptive fuzzy neural networks and description of its dynaniical behavior', Fuuy Sets and Systems, Vol. 71, No. 1, pp. 3-23, 1995 https://doi.org/10.1016/0165-0114(94)00196-E
  11. C. K. Lin and S. D. Wang, 'Constructing a fuzzy model from wavelet transforms', Proc. of Fuzzy Systems Symposium, Soft Computing in Intelligent Systems and Information Processing, pp. 394-399, 1996
  12. C. K. Lin and S. D. Wang, 'Fuzzy modeling using wavelet transforms', Electronics Letters, Vol. 32, Issue 24, pp. 2255-2256, 1996 https://doi.org/10.1049/el:19961508
  13. T. Kugarajah and Q. Zhang, 'Multidimensional wavelet frames', IEEE Trans. on Neural Network, Vol. 6, No. 6, pp. 1552-1556, 1995 https://doi.org/10.1109/72.471353
  14. S. Mallat, 'A theory for multiresolution signal decomposition: the wavelet transform', IEEE Trans. Pattern Anal. Mach. Intelligence, Vol. 11, No. 7, 674-693, 1989 https://doi.org/10.1109/34.192463
  15. Y. C. Pati and P. S. Krishnaprasad, 'Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations', IEEE Trans. on Neural Network, Vol. 4, No. 1, pp. 73-85, 1998 https://doi.org/10.1109/72.182697
  16. Q. Zhang and A. Benveniste, 'Wavelet networks', IEEE Trans. on Neural Network, Vol. 3, No. 6, pp. 889-898, 1992 https://doi.org/10.1109/72.165591
  17. Q. Zhang, 'Using wavelet network in nonparametric estimation', IEEE Trans. on Neural Network, Vol. 8, No. 2, pp. 227-236, 1997 https://doi.org/10.1109/72.557660