LMI Approach to Iterative Learning Control Design

This paper uses linear matrix inequalities to design iterative learning controller gains. Comparisons are made between Arimoto-style gains, causal gains, and non-causal gains, using the supervector approach. The results show that linear time-varying gains have better performance than linear time invariant gains and non-causal terms make the system more stable in the sense of monotonic convergence.