International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Stable Path Tracking Control of a Mobile Robot Using a Wavelet Based Fuzzy Neural Network

  • Oh, Joon-Seop (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Park, Jin-Bae (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Choi, Yoon-Ho (School of Electronic Engineering, Kyonggi University)
  • Published : 2005.12.01
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Abstract

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the 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. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

Keywords

References

  1. R. M. Desantis, 'Path-tracking for car-like robots with single and double steering,' 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 O. J. Sordalen, 'Nonlinear control design for mobile robots,' Recent Trends in Mobile Robots, World Scientific, 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 network,' Proc. of 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 dynamical behavior,' Fuzzy Sets and Systems, vol. 71, no. 1, pp. 3-23, 1995 https://doi.org/10.1016/0165-0114(94)00196-E
  11. J. T. Choi and Y. H. Choi, 'Fuzzy neural network based predictive control of chaotic nonlinear systems,' IEICE Trans. on Fundamentals, vol. E87-A, no. 5, pp. 1270-1279, 2004
  12. Y. Oussar, I. Rivals, L. Personnaz, and G. Dreyfus, 'Training wavelet networks for nonlinear dynamic input-output modeling,' Neurocomputing, vol. 20. pp. 173-188, 1998 https://doi.org/10.1016/S0925-2312(98)00010-1
  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. on 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. C. K. Lin and S. D. Wang, 'Fuzzy modeling using wavelet transforms,' Electronics Letters, vol. 32, pp. 2255-2256, 1996 https://doi.org/10.1049/el:19961508
  17. 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
  18. 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
  19. 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
  20. J. S. Oh, S. J. You, J. B. Park, and Y. H. Choi, 'The modeling of chaotic nonlinear system using wavelet based fuzzy neural network,' Proc. of Int. Conf. on Control, Automation and Systems, pp. 635-639, 2004