Enhancing Error Convergence for Repetitive Process Based Iterative Learning Control Designs

In iterative learning control, the aim is to exploit the repeated executions, termed trials, of the same task over a finite duration by designing a control input sequence that forces the sequence of errors formed by the difference on each trial between the output and the supplied reference trajectory to converge as the number of trials increases. This paper uses the stability theory for repetitive processes to develop new design algorithms that can enhance trial‐to‐trial error convergence while simultaneously regulating any trial's dynamics.