Model-free iterative learning control for LTI systems and experimental validation on a linear motor test setup

This paper presents a novel model-free iterative learning control algorithm for linear time-invariant systems with actuator constraints. At every trial, a finite impulse response filter to update the system input is computed by solving a convex optimization problem that minimizes the next trial´s tracking error while accounting for actuator constraints. The presented iterative learning control algorithm is validated on a linear motor positioning system. Experimental results show the ability of the proposed model-free algorithm to learn the optimal system input in the presence of cogging forces and actuator input constraints.