Journal of Advanced Marine Engineering and Technology

  • 제38권10호
  • /
  • Pages.1303-1309
  • /
  • 2014
  • /
  • 2234-7925(pISSN)
  • /
  • 2234-8352(eISSN)
한국마린엔지니어링학회 (The Korean Society of Marine Engineering)
권호 표지
DOI QR코드

DOI QR Code

A study on the optimal tracking problems with predefined data by using iterative learning control

  • Le, Dang-Khanh (Department of Marine Engineering, Mokpo National Maritime University) ;
  • Le, Dang-Phuong (Department of Marine Engineering, Mokpo National Maritime University) ;
  • Nam, Taek-Kun (Division of Control Engineering, Mokpo National Maritime University)
  • 투고 : 2014.10.27
  • 심사 : 2014.12.12
  • 발행 : 2014.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.

키워드

참고문헌

  1. D. A. Bristow, M. Tharayil, and A. G. Alleyne, "A survey of iterative learning control: A learning-based method for high-performance tracking control," IEEE Control Systems Magazine, vol. 26, no. 3, pp. 96-114, 2006. https://doi.org/10.1109/MCS.2006.1636313
  2. H. S. Ahn, Y. Q. Chen, and K. L. Moore, "Iterative learning control: Brief survey and categorization," IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 37, no. 6, pp. 1099-1121, 2007.
  3. K. L. Moore, "Iterative learning control: An expository overview," Journal of Applied and Computational Control, Signals, and Circuits, vol. 1, no. 1, pp. 151-214, 1999.
  4. C. T. Freeman, Z. Cai, E. Rogers, and P. L. Lewin, "Iterative learning control for multiple point-to-point tracking application," IEEE Transactions on Control Systems Technology,vol. 19, no. 3, pp. 590-600, 2011. https://doi.org/10.1109/TCST.2010.2051670
  5. G. Gauthier and B. Boulet, "Terminal iterative learning control design with singular value decomposition decoupling for thermoforming ovens," Proceedings of the American Control Conference, pp. 1640-1645, 2009.
  6. J. X. Xu and D. Huang, "Initial state iterative learning for final state control in motion systems,"Journal of Automatica, vol. 44, no. 12, pp. 3162-3169, 2008. https://doi.org/10.1016/j.automatica.2008.05.017
  7. C. T. Freeman andY. Tan, "Point-to-point iterative learning control with mixed constraints," Proceedings of American Control Conference, pp. 3657-3662, 2011.
  8. C. T. Freeman, C. Zhonglun, P. L. Lewin, and E. Rogers, "Iterative learning control for multiple point-to-point tracking," Proceedings of the 48th IEEE Conference on Decision and Control, pp. 3288-3293, 2009a.
  9. C. T. Freeman, C. Zhonglun, P. L. Lewin, and E. Rogers,"Objective-driven ILC for point-to-point movement tasks," Proceedings of American Control Conference, pp. 252-257, 2009b.
  10. C. T. Freeman, E.Rogers, A. M. Hughes, J. H. Burridge, and K. L.Meadmore, "Iterative learning control in healthcare electrical stimulation and robotic-assisted upper limb stroke rehabilitation," IEEE Control Systems Magazine, vol. 32, no. 1, pp. 18-43, 2012.
  11. D. K. Le and T. K. Nam,"Performance improvement of trajectory tracking control by using ILC,"Proceedings of the 38th KOSME Spring Conference, pp. 194, 2014.
  12. A. D. Luca, L. Lanari, and G. A. Oriolo, "Sensitivity approach to optimal spline robot trajectories,"Journal of Automatica, vol. 27, no. 3, pp. 535-539, 1991. https://doi.org/10.1016/0005-1098(91)90111-E
  13. S. Sun, M. Egerstedt, and C. F. Martin, "Control theoretic smoothing spline," IEEE Transactions on Automatic Control, vol. 45, no. 12, pp. 2271-2279, 2000. https://doi.org/10.1109/9.895563
  14. N. Amann, D. H. Owens, and E. Rogers,"Iterative learning control using optimal feedback and feed forward actions," International Journal of Control, vol. 65, no. 2, pp. 277-293, 1996. https://doi.org/10.1080/00207179608921697
  15. D. K. Le and T. K. Nam,"Optimaliterative learning control with model uncertainty," Journal of the Korean Society of Marine Engineering, vol. 37, no. 7, pp. 743-751, 2013. https://doi.org/10.5916/jkosme.2013.37.7.743
  16. J. X. Xu and J. Xu,"Iterative learning control for nonuniform trajectory tracking problems," Proceedings of the 15th IFAC World Congress, pp. 1048-1048, 2002.