Iteration-dependent High-order Internal Model based Iterative Learning Control for Continuous-time Nonlinear Systems

摘要

In this paper, an adaptive iterative learning control (AILC) scheme based on high-order internal model (HOIM) is presented for a class of nonlinear continuous-time systems with unknown time-iteration-varying parameter. The time-iteration-varying parameter is generated by a general iteration-dependent HOIM with iteration-varying order and coefficients. Compared with the existing works based on iteration-invariant HOIM with fixed order and coefficients, our work significantly expands the application scope of HOIM-based ILC. Using the designed HOIM-based iterative learning controller, the learning convergence along the iteration axis is guaranteed through rigorous theoretical analysis under Lyapunov theory. Furthermore, the effectiveness of the proposed method is demonstrated according to the simulation results.