ABSTRACT

This chapter has introduced the basic ILC design and analysis tools for linear systems. The results and design details presented here only touch the surface of the field of ILC. In particular, a number of advanced algorithms have been developed, which may be of interest to the reader. Current-iteration-tracking-error (CITE) [6] ILC algorithms use the current iteration’s error signal in the learning algorithm, effectively adding feedback control in ILC design. Most notably, CITE algorithms can use the feedback component to compensate for trial-varying disturbances. For stochastic measurement noise, one might consider the use of optimal trial-varying algorithms [15]. For faster convergence rate and better robustness, high-order algorithms [6] use several iterations of past control and error signals in the update algorithm.