TY - JOUR
T1 - Suppressing intersample behavior in iterative learning control
AU - Oomen, T.A.E.
AU - Wijdeven, van de, J.J.M.
AU - Bosgra, O.H.
PY - 2009
Y1 - 2009
N2 - Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in realistic simulation examples.
AB - Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in realistic simulation examples.
U2 - 10.1016/j.automatica.2008.10.022
DO - 10.1016/j.automatica.2008.10.022
M3 - Article
SN - 0005-1098
VL - 45
SP - 981
EP - 988
JO - Automatica
JF - Automatica
IS - 4
ER -