Iterative learning control with saturation constraints

We consider the problem of synthesis of iterative learning control schemes for linear systems with saturation constraints. The problem of minimizing the tracking error is formulated as a constrained convex optimization problem, namely a linearly constrained quadratic program. Due the lack of information regarding the disturbances in the process, descent directions cannot be determined without running experiments. This in turn leads to strict limitations on the number of iterations employed in any iterative optimization scheme. Motivated by this fact, we implement an interior point algorithm, specifically the barrier method. The method is demonstrated on a prototype wafer stage testbed and its performance is compared to other existing methods.