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http://dx.doi.org/10.5391/IJFIS.2004.4.1.111

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)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.4, no.1, 2004 , pp. 111-118 More about this Journal
Abstract
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.
Keywords
Wavelet Based Fuzzy Neural Network; Direct Adaptive control; Mobile Robot; Gradient Descent Method;
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1 T. Kugarajah and Q. Zhang, 'Multidimensional wavelet frames', IEEE Trans. on Neural Network, Vol. 6, No. 6, pp. 1552-1556, 1995   DOI   ScienceOn
2 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   DOI   ScienceOn
3 Q. Zhang and A. Benveniste, 'Wavelet networks', IEEE Trans. on Neural Network, Vol. 3, No. 6, pp. 889-898, 1992   DOI   ScienceOn
4 Q. Zhang, 'Using wavelet network in nonparametric estimation', IEEE Trans. on Neural Network, Vol. 8, No. 2, pp. 227-236, 1997   DOI   ScienceOn
5 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   DOI   ScienceOn
6 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
7 S. Mallat, 'A theory for multiresolution signal decomposition: the wavelet transform', IEEE Trans. Pattern Anal. Mach. Intelligence, Vol. 11, No. 7, 674-693, 1989   DOI   ScienceOn
8 C. K. Lin and S. D. Wang, 'Fuzzy modeling using wavelet transforms', Electronics Letters, Vol. 32, Issue 24, pp. 2255-2256, 1996   DOI   ScienceOn
9 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
10 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
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 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
13 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
14 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   DOI   ScienceOn
15 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   DOI   ScienceOn
16 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
17 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   DOI   ScienceOn