Robust Iterative Learning Control for Interval Linear Systems

Many forms of system uncertainty result in interval description of linear systems, and numerically efficient design methods for the computation of robust iterative learning controllers with good learning transients for these systems are lacking. Using a Lyapunov framework, two design procedures that ensure robust convergence of the tracking error to zero with good learning transients are described in this study. Both methods are validated numerically for an application of position control, and robust and monotonic convergence of the tracking error to zero is demonstrated.