An iterative learning control theory for a class of nonlinear dynamic systems
Automatica (Journal of IFAC)
Iterative learning control for a class of nonlinear systems
Automatica (Journal of IFAC)
Robust stability for iterative learning control
ACC'09 Proceedings of the 2009 conference on American Control Conference
On admissible pairs and equivalent feedback-Youla parameterization in iterative learning control
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Technical communique: A note on causal and CITE iterative learning control algorithms
Automatica (Journal of IFAC)
Hi-index | 22.15 |
The goal of iterative learning control (ILC) is to improve the accuracy of a system that repeatedly follows a reference trajectory. This paper proves that for each causal linear time-invariant ILC, there is an equivalent feedback that achieves the ultimate ILC error with no iterations. Remarkably, this equivalent feedback depends only on the ILC operators and hence requires no plant knowledge. This equivalence is obtained whether or not the ILC includes current-cycle feedback. If the ILC system is internally stable and converges to zero error, there exists an internally stabilizing feedback that approaches zero error at high gain. Since conventional feedback requires no iterations, there is no reason to use causal ILC.