Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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A Study on Driving Control of an Autonomous Guided Vehicle using Humoral Immune Algorithm Adaptive PID Controller based on Neural Network Identifier Technique

신경회로망 동정기법에 기초한 HIA 적응 PID 제어기를 이용한 AGV의 주행제어에 관한 연구

  • 이영진 (항공기능대학 전기계측공학과) ;
  • 서진호 (동아대학교 전기전자컴퓨터공학부) ;
  • 이권순 (동아대학교 전기전자컴퓨터공학부)
  • Published : 2004.10.01
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Abstract

In this paper, we propose an adaptive mechanism based on immune algorithm and neural network identifier technique. It is also applied fur an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To solve this problem, we use the neural network identifier (NNI) technique fur modeling the plant and humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using an immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. Finally, the simulation and experimental result fur the control of steering and speed of AGV system illustrate the validity of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

Keywords

References

  1. Korea Maritime Institute, The Propriety Investigation of Automated Container Terminals -final report-, 1998
  2. Durrant-Whyte, H., 'An Autonomous Guided Vehicle for Cargo Handling Applications,' The International Journal of Robotics Research, Vol. 15, No.5, pp. 407-440, 1996 https://doi.org/10.1177/027836499601500501
  3. Kodagoda, K., Wijesoma, W., Teoh, E., 'Fuzzy Speed and Steering Control of an AGV,' IEEE Trans. on Control System Technology,' Vol. 10, No. I, pp. 112-120,2002 https://doi.org/10.1109/87.974344
  4. Park, Y. W., Kim, D. H., Kimbrough, S., 'An Inverse LQG/LTR Problem Applied to the Vehicle Steering System,' Proc. of the 11th KACC, pp. 324-327, 1996
  5. Lim, E. and Hedrick, J., 'Lateral and Longitudinal Vehicle Control Coupling for Automated Vehicle Operation,' Proc. of the American Control Conference, pp. 3676-3680, 1999 https://doi.org/10.1109/ACC.1999.782452
  6. Hwang, H. S., 'Automatic Design of Fuzzy Rule Base for Modeling and Control using Evolutionary Programming,' Proc. of Control Theory Applications, Vol. 146, No. 1, pp. 9-16, 1999 https://doi.org/10.1049/ip-cta:19990087
  7. Moore, M., Musacchio, J., Passino, K., 'Genetic Adaptive Control for an Inverted Wedge,' Proc. of American Control Conference, pp. 400-404, 1999 https://doi.org/10.1109/ACC.1999.782858
  8. Chun, J. S., Kim, M. K., Jung, H. K., 'Shape Optimization of Electromagnetic Devices using Immune algorithm,' IEEE Trans. on Magnetics, Vol. 33, No. 2, pp. 1876-1879, 1997 https://doi.org/10.1109/20.582650
  9. Lee, Y. J., Lee, K. S., Lee, J. M., Lee, M. H., 'Adaptive Control of Nonlinear System using Immune Response Algorithm,' Proc. of Asian Control Conference, pp. 1789-1793, 2000
  10. Endoh, S., Toma, N., Yamada, K., 'Immune Algorithm for n-TSP,' Proc. of ICSMC, Vol. 4, pp. 3844-3849, 1998 https://doi.org/10.1109/ICSMC.1998.726687
  11. Page, G., Gomm, J., Williams, D., Application of Neural Networks to Modeling and Control, Chapman & Hall, 1993
  12. Lin, C. T. and Lee, G., Neural Fuzzy Systems, Prentice Hall, 1996
  13. Lee, Y. J., Lee, K. S., Lee, J. M., 'An AGV Driving Control using Immune Algorithm Adaptive Controller,' Trans. on KIEE, Vol. 49D, No. 4, pp. 201-212, 2000
  14. Lee, Y. J., Kim, S. K., Suh, J. H., Lee, K. S., 'Driving Control of Autonomous Vehicle with Vision System using Cell Mediated Immune Algorithm Controller,' Proc. of International Symposium on Artificial Life and Robotics, pp. 429-432, 2004