  This article focuses on making discrete-time Adaptive Iterative Learning
Control (ILC) more effective using multiple estimation models. Existing
strategies use the tracking error to adjust the parametric estimates. Our
strategy uses the last component of the identification error to tune these
estimates of the model parameters. We prove that this strategy results in
bounded estimates of the parameters, and bounded and convergent identification
and tracking errors. We emphasize that the proof does not use the key technical
lemma. Rather, it uses the properties of square-summable sequences. We extend
this strategy to include multiple estimation models and show that all the
signals are bounded, and the errors converge. It is also shown that this works
whether we switch between the models at every instant and every iteration or at
the end of every iteration. Simulation results demonstrate the efficacy of the
proposed method with a faster convergence using multiple estimation models.
