Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Study on Wavelet Neural Network Based Generalized Predictive Control for Path Tracking of Mobile Robots

이동 로봇의 경로 추종을 위한 웨이블릿 신경 회로망 기반 일반형 예측 제어에 관한 연구

  • Song, Yong-Tae (Technology Support Division Production Engineering Team, SAMSUNG SDI CO.,LTD) ;
  • Oh, Joon-Seop (Dept. of Electrical & Electronic Eng., Yonsei University) ;
  • Park, Jin-Bae (Dept. of Electrical & Electronic Eng., Yonsei University) ;
  • Choi, Yoon-Ho (School of Electronic Eng., Kyonggi University)
  • 송용태 (삼성 전자 SDI 기술지원본부 생산기술팀) ;
  • 오준섭 (연세대학교 전기전자 공학과) ;
  • 박진배 (연세대학교 전기전자 공학과) ;
  • 최윤호 (경기대학교 전자공학부)
  • Published : 2005.08.01
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Abstract

In this paper, we propose a wavelet neural network(WNN) based predictive control method for path tracking of mobile robots with multi-input and multi-output. In our control method, we use a WNN as a state predictor which combines the capability of artificial neural networks in learning processes and the capability of wavelet decomposition. A WNN predictor is tuned to minimize errors between the WNN outputs and the states of mobile robot using the gradient descent rule. And control signals, linear velocity and angular velocity, are calculated to minimize the predefined cost function using errors between the reference states and the predicted states. Through a computer simulation for the tracking performance according to varied track, we demonstrate the efficiency and the feasibility of our predictive control system.

본 논문에서는 다중 입$\cdot$출력을 갖는 이동 로봇의 경로 추종을 위해 웨이블깃 신경 회로망에 기반한 예측 제어 방법을 제안한다. 제안된 방법에서 상태 예측기로는 학습 능력이 뛰어난 신경 회로망의 특성 및 웨이블릿 분해의 특성을 합성한 웨이블릿 신경 회로망을 사용한다. 예측기는 경사 하강법을 사용하여 웨이블릿 신경회로망의 출력에 대한 실제 이동 로봇의 상태 오차를 최소화하도록 학습된다. 또한 이동 로봇의 제어 신호인 직진 속도 및 각속도는 추종하고자 하는 기준 경로에 대한 이동 로봇의 예측 상태 오차를 이용하여 정의된 비용 함수를 최소화하도록 구해진다. 컴퓨터 모의 실험에서 변화되는 기준 경로에 대한 경로 추종 성능을 통해 제안한 예측 제어 시스템의 적용 가능성 및 효율성을 보인다.

Keywords

References

  1. R. M. Desantis, 'Path-Tracking for Car-like Robots with Single and Double Streering,' 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 Int. Conf. on Systems, Man, and Cybernetics, Vol. 4, pp. 350-355, 1998
  3. 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
  4. J. M. Yang andJ. 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
  5. 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
  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. Celdo de sousa, Elder Moreira Hemerly, Roberto Kawakami 'Adaptive Control for Mobile Robot Using Wavelet Networks,' IEEE Trans. on Systems, Man and Cybernetics, Vol. 32, No. 42, pp. 493-504, 2002
  8. C. S. Burrus, R. A. Gopinath, H. Guo, Introduction to Wavelet and Wavelet Transforms: A Primer, Prentice Hall, 1996
  9. Y. Meyer, Wavelets: Algorithms and Applications, translated by R. D. SIAM. Philadelphia, PA: SIAM Press, 1993
  10. I. Daubechies, Ten Lectures on Wavelets, PA: SIAM Press, 1992
  11. S. Mallat, 'A Theory for Multiresolution Signal Decomposition: The Wavelet Transform,' IEEE Trans. on Pattern Anl. Machine Intell., Vol. 11, pp. 674-693, 1989 https://doi.org/10.1109/34.192463
  12. C. K. Chui, An Introduction to Wavelets. New York, Academic Press, 1992
  13. Ciftcioglu O., 'From Neural to Wavelet Network,' Proc. of NAFIPS '99, New York, pp. 894-898, 1999
  14. J. Zhang, G. G. Walter, Y. Miao, and N. W. Lee, 'Wavelet Neural Networks for Function Learning,' IEEE Trans. on Signal Processing, Vol. 43, No. 6, pp. 1485-1497, 1995 https://doi.org/10.1109/78.388860
  15. 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
  16. Y. C. Pati, P. S. Krishanaparasad, 'Analysis and Synthesis of Feedfoward Neural Networks using Discrete Affine Wavelet Transformations,' IEEE Trans. on Neural Networks, Vol. 4, No. 1, pp. 73-85, 1993 https://doi.org/10.1109/72.182697
  17. M. Cannon, J. J. E. Slotine, 'Space-Frequency Localized Basis Network for Nonlinear System estimation and Control,' Neurocomputing, Vol. 9, pp. 293-342, 1995 https://doi.org/10.1016/0925-2312(95)00036-1
  18. Y. Oussar, I. Rivals, L. Personnaz, 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
  19. Q. Zhang, A. Benvenste 'Wavelet Networks,' IEEE Trans. on Neural Networks, Vol. 3, No. 6, pp. 889-898, 1992 https://doi.org/10.1109/72.165591