Monotonic convergence of iterative learning control for uncertain systems using a time-varying Q-filter

Time-varying Q-filtering in Iterative Learning Control (ILC) has demonstrated potential performance benefits over time-invariant Q-filtering. In this paper, LTV Q-filtering of ILC is considered for uncertain systems. Sufficient conditions for stability and the important monotonic convergence property are developed for the uncertain system. A class of LTV Q-filters that has particular benefit for rapid motion trajectories is presented, and monotonic convergence conditions are developed. The developed conditions highlight a relationship that the bandwidth can be increased locally and decreased elsewhere to localize high performance at specific times. These conditions are also iteration-length invariant and allow for significant design freedom after analysis enabling online modification of the LTV Q-filter.