Journal of Navigation and Port Research (한국항해항만학회지)

Korean Institute of Navigation and Port Research (한국항해항만학회)
권호 표지
DOI QR코드

DOI QR Code

Position Control of Linear Motor-based Container Transfer System using DR-FNNs

DR-FNNs를 이용한 리니어 모터 기반 컨테이너 이송시스템의 위치제어

  • 이진우 (동아대학교 전기공학과) ;
  • 서진호 (동아대학교 전기공학) ;
  • 이영진 (항공기능대학 전기계측제어) ;
  • 이권순 (동아대학교 전기전자컴퓨터공학부)
  • Published : 2004.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

In the maritime container terminal. LMCTS (Linear Motor-based Container Transfer System) is horizontal transfer system for the yard automation, which In., been proposed to take the place of AGV (Automated Guided Vehicle). The system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car (mover). Because of large variant of mover's weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's trouble etc. LMCTS is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the softcomputing method of a multi-step prediction control for LMCTS using DR- FNN (Dynamically-constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi step prediction Consequently, the system has an ability to adapt for external disturbance, detent force, force ripple, and sudden changes by loading and unloading the container.

본 논문에서는 항만 자동화를 위해 새로이 제안된 리니어 모터 기반 컨테이너 이송시스템에 지능제어기법을 이용하여 그 정밀도를 향상시키고자 한다. LMCTS(Linear Motor-based Container Transfer System)는 스케일의 거대함 때문에 일반 리니어 모터에서 중요시 되지 않는 정지마찰력과 디텐트럭(detent force)이 정밀제어에 큰 문제가 된다. 특히, 컨테이너 적제유무에 따라 시스템 자체가 급격히 변하므로 기존의 PID형 제어기로는 좋은 성능을 얻기 어렵다. 따라서 본 논문에서는 같은 구조를 갖는 두 개의 DR-FNN(Dynamically- constructed Recurrent Fuzzy Neural Network)를 제어기와 에뮬레이터로 구성하여 이러한 문제를 해결하고자 하였다.

Keywords

References

  1. Franke, K. P.(2001), 'Boosting Efficiency of Split Mahne Container Terninals by Innovative Technology,' IEEE Intelligent Transportation Systems Conference Proc., Oakland, USA, August, pp. 774-779
  2. Tan, K. K. Huang, S. N. and Lee, T. H.(2002), 'Robust Adaptive Numerical Compensation for Friction and Force Ripple in Permanent-Magnet Linear Motors,' IEEE Trans. on Magnetics, Vol. 38, No. 1, pp.221-228 https://doi.org/10.1109/20.990111
  3. Lin, F. J., Wai, R. J, and Hong, C. M.(2002), 'Hybrid Supervisory Control Usins Recurrent Fuzzy Neural Network for Tracking Periodic Inputs,' IEEE Trans. on Neural Networks, Vol. 12, No. 1, January.
  4. Wai, R. J., Lin, F. J.(2001), 'Adaptive Recurrent Neural-Network Control for Linear Induction Motor,' IEEE Trans. on Aerospace and Electronic Systems, Vol. 37, No. 4, pp. 1176-1192 https://doi.org/10.1109/7.976958
  5. Yoshida, K., Takarni, H., Kong, X. and Sonoda, A.(2001), 'Mass Reduction and Propulsion Control for a Permanent-Magnet Linear Synchronous Motor Vehicle,' IEEE Trans. on Industry Applications, Vol.37, No. 1, pp. 67-72 https://doi.org/10.1109/28.903128
  6. Yoshida, K., Kong, X. and Takami, H.(2000), 'Network Transport Automation Study of PM LSM Vehicle on Orthogonally Switching-Guideway,' Industry Applications Conference, Vol. 2, pp. 1109-1114
  7. Frayman, Y. and Wang, L.(2002), 'A Dynamically constructed Fuzzy Neural Controller for Direct Model Reference Adaptive Control of Multi-input-multi output Nonlinear,' Soft Computing, Vol. 6, pp. 244-253 https://doi.org/10.1007/s005000100150
  8. Widrow B. and Walach, E.(1996), Adaptive Inverse Control, Upper Saddle River, Prentice Hall
  9. Kecman, V. Vlacic, L. and Salman, R.(1999), 'Learning in and performance of the new neural network based adaptive backthrough control structure,' Proceedings of the 14-th IFAC Trienniad World Congress, Beijing, PR Chaina, Vol. K, pp. 133-140
  10. Seng, T. L. Khalid, M. B. and Yusof, R. Y.(1999),'Tuning of a Neuro Fuzzy Controller by GeneticAlgohthm,' IEEE Trans. on Systerns, Man, and Cybernetics, Part B: Cybernetics, Vol. 29, No. 2, pp.226-236 https://doi.org/10.1109/3477.752795