Data-Weighting Based Discrete-Time Adaptive Iterative Learning Control for Nonsector Nonlinear Systems With Iteration-Varying Trajectory and Random Initial Condition

In this paper, a new discrete-time adaptive iterative learning control (AILC) approach is presented to deal with nonsector nonlinearities by incorporating a recursive least-squares algorithm with a nonlinear data weighted coefficient. This scheme is also extended as a d-iteration-ahead adaptive iterative learning predictive control to address for multiple inputs multiple outputs (MIMO) nonlinear systems with unknown input gains. A major distinct feature of the presented methods is that the global stability result is obtained through Lyapunov analysis without assuming any linear growth condition on the nonlinearities. Another distinct feature is that the pointwise convergence of the presented methods is achieved over a finite interval without requiring any identical conditions on the initial states and reference trajectory.