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

作者：Xu, Qing Yuan; Li, Xiao Dong^*; Lv, Mang Mang

来源：International Journal of Control Automation and Systems, 2016, 14(2): 478-485.

DOI：10.1007/s12555-015-0005-3

• Adaptive iterative learning control
• non-repetitive reference trajectory
• random boundary condition
• 2-D Fornasini Marchesina Model

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.

• 出版日期2016-4
• 单位中山大学