Computer-controlled systems: theory and design (2nd ed.)
Computer-controlled systems: theory and design (2nd ed.)
A lifting technique for linear periodic systems with applications to sampled-data control
Systems & Control Letters
Multirate systems and filter banks
Multirate systems and filter banks
Robust and optimal control
On the iterative learning control of sampled-data systems
Iterative learning control
Optimal Sampled-Data Control Systems
Optimal Sampled-Data Control Systems
Iterative Learning Control for Deterministic Systems
Iterative Learning Control for Deterministic Systems
Paper: Zeros of sampled systems
Automatica (Journal of IFAC)
Brief On the design of ILC algorithms using optimization
Automatica (Journal of IFAC)
Brief Sampled-data iterative learning control for nonlinear systems with arbitrary relative degree
Automatica (Journal of IFAC)
Reading of cracked optical discs using iterative learning control
ACC'09 Proceedings of the 2009 conference on American Control Conference
Low-order system identification and optimal control of intersample behavior in ILC
ACC'09 Proceedings of the 2009 conference on American Control Conference
| Hi-index | 22.14 |
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.