Abstract
This paper presents preliminary investigations into a novel approach to circumventing a classic tradeoff between robustness and performance in Iterative Learning Control. The tradeoff is usually performed by designing an appropriate filtering mechanism to compensate for non-perfect resetting and noise. Time-Frequency analysis is used to examine the frequency content of error signals over the compact time support of the reference signal. This then allows the filter to effectively change its bandwidth as a function of time thereby allowing high frequency system dynamics to enter into the learning process at the appropriate time instants. At the same time, robustness is maintained by reducing the bandwidth of the filter when the system dynamics do not exhibit high frequency characteristics so as to attenuate the effect of noise on the learning stability. This Adaptive Iterative Learning controller is needed for systems that have non-smooth nonlinearities and the advantages are demonstrated here in simulation and experiment.
