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|Title:||Iterative learning control design with high-order internal model for nonlinear systems|
|Citation:||Yin, C., Xu, J.-X., Hou, Z. (2009). Iterative learning control design with high-order internal model for nonlinear systems. Proceedings of the IEEE Conference on Decision and Control : 434-439. ScholarBank@NUS Repository. https://doi.org/10.1109/CDC.2009.5399801|
|Abstract:||In this work we focus on iterative learning control (ILC) design for tracking iteration-varying reference trajectories that are generated by high-order internal models (HOIM). An HOIM can be formulated as a polynomial operator between consecutive iterations to describe the changes of desired trajectories in the iteration domain. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a unity coefficient or a special first order internal model. By inserting the HOIM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuoustime nonlinear systems. Utilizing of conventional time-weighted norm method guarantees validity of proposed algorithm in a sense of data-driven control. ©2009 IEEE.|
|Source Title:||Proceedings of the IEEE Conference on Decision and Control|
|Appears in Collections:||Staff Publications|
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