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Adaptive ILC for Tracking Non-repetitive Reference Trajectory of 2-D FMM under Random Boundary Condition

Qing-Yuan Xu, Xiao-Dong Li*, and Mang-Mang Lv
International Journal of Control, Automation, and Systems, vol. 14, no. 2, pp.478-485, 2016

Abstract : Almost all of the existing research achievements in Iterative Learning Control (ILC) hitherto have been focused on One-Dimensional (1-D) dynamical systems. Few ILC researches are related to Two-Dimensional Fornasini Marchesina Model (2-D FMM). In this paper, an adaptive ILC approach is proposed for 2-D FMM system with non-repetitive reference trajectory under random boundary condition. The proposed adaptive ILC algorithm learns the coefficient matrices of the system and updates the control input iteratively. As the times of iteration goes to infinity, the ILC tracking error outside the boundary tends to zero and all system signals keep bounded in the whole ILC process. Illustrative examples are provided to verify the validity of the proposed adaptive ILC algorithm.

Keyword : Adaptive iterative learning control, non-repetitive reference trajectory, random boundary condition, 2-D Fornasini Marchesina Model.

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