Adaptive Iterative Learning Control for Linear Systems With Binary-Valued Observations

Abstract

This brief presents a novel adaptive iterative learning control (ILC) algorithm for a class of single parameter systems with binary-valued observations. Using the certainty equivalence principle, the adaptive ILC algorithm is designed by employing a projection identification algorithm along the iteration axis. It is shown that, even though the available system information is very limited and the desired trajectory is iteration-varying, the proposed adaptive ILC algorithm can guarantee the convergence of parameter estimation over a finite-time interval along the iterative axis; meanwhile, the tracking error is pointwise convergence asymptotically. Two examples are given to validate the effectiveness of the algorithm.