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Adaptive ILC of Tracking Nonrepetitive Trajectory for Two-dimensional Nonlinear Discrete Time-varying Fornasini-Marchesini Systems with Iteration-varying Boundary States

Kai Wan and Yun-Shan Wei*
International Journal of Control, Automation, and Systems, vol. 19, no. 1, pp.417-425, 2021

Abstract : Most of adaptive iterative learning control (AILC) algorithms focus on one-dimensional (1-D) systems, rather than two-dimensional (2-D) systems. This brief is first concerned with AILC for 2-D nonlinear discrete timevarying Fornasini-Marchesini system (NDTVFMS) with nonrepetitive reference trajectory under iteration-varying boundary states. By using Lyapunov analysis method, it can guarantee that the ultimate tracking error tends to zero asymptotically, and make all identified parameters and system signals to be bounded as iteration number goes to infinity. Two illustrative examples are used to validate the effectiveness of the designed AILC approach.

Keyword : Adaptive iterative learning control (AILC), iteration-varying boundary states, nonrepetitive reference trajectory, 2-D nonlinear discrete time-varying Fornasini-Marchesini system (2-D NDTVFMS).

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