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|Title:||On iterative learning control with high-order internal models|
|Keywords:||High-order internal mode|
Varying reference trajectory
|Citation:||Liu, C., Xu, J., Wu, J. (2010-09). On iterative learning control with high-order internal models. International Journal of Adaptive Control and Signal Processing 24 (9) : 731-742. ScholarBank@NUS Repository. https://doi.org/10.1002/acs.1163|
|Abstract:||In this work we focus on iterative learning control (ILC) for iteratively varying reference trajectories, which are described by a high-order internal models (HOIM) that can be formulated as a polynomials between two consecutive iterations. The classical ILC with iteratively invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a first-order internal model with a unity coefficient. By incorporating HOIM into the ILC law, and designing appropriate learning control gains, the learning convergence in the iteration axis can be guaranteed for continuous-time linear time-varying systems. The initial resetting condition, P-type and D-type ILC, and possible extension to nonlinear cases are also explored in this work. Copyright © 2010 John Wiley & Sons, Ltd.|
|Source Title:||International Journal of Adaptive Control and Signal Processing|
|Appears in Collections:||Staff Publications|
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