Discrete-time Adaptive ILC for Non-parametric Uncertain Nonlinear Systems with Iteration-Varying Trajectory and Random Initial Condition

This paper presents a new discrete-time adaptive iterative learning control approach (AILC) for a class of time-varying nonlinear systems with nonparametric uncertainties and non-repeatable external disturbances by incorporating a novel iterative estimate scheme. A major distinct feature of the presented approach is that uncertainties can be completely compensated for, using only I/O data. Another distinct feature is that the pointwise convergence is achieved over a finite time interval without requiring the matching condition on initial states and reference trajectory. Rigorous mathematical analysis is developed, and simulation results illustrate the effectiveness of the proposed approach.