AILC for 2-D systems with unknown control directions and iteration-varying trail lengths

In most adaptive iterative learning control (AILC) researches, there are many achievements in one-dimensional (1-D) dynamic systems, but few in two-dimensional dynamic systems. Considering the unknown control direction, variable trajectory length, iteration-dependent boundary condition and reference trajectory, this paper designs an AILC law for two-dimensional Fornasini-Marchesini model (2-D FMM). Theoretical studies demonstrate that the proposed 2-D AILC algorithm can accurately accomplish the tracking task.