Please use this identifier to cite or link to this item:
|Title:||A high-order internal model based iterative learning control scheme for nonlinear systems with time-iteration-varying parameters|
|Keywords:||High-order internal model|
iterative learning control (ILC)
|Citation:||Yin, C., Xu, J.-X., Hou, Z. (2010-11). A high-order internal model based iterative learning control scheme for nonlinear systems with time-iteration-varying parameters. IEEE Transactions on Automatic Control 55 (11) : 2665-2670. ScholarBank@NUS Repository. https://doi.org/10.1109/TAC.2010.2069372|
|Abstract:||In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored. © 2006 IEEE.|
|Source Title:||IEEE Transactions on Automatic Control|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 22, 2018
WEB OF SCIENCETM
checked on May 15, 2018
checked on May 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.