Please use this identifier to cite or link to this item:
|Title:||Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition|
Iterative learning control
Random initial condition
|Citation:||Chi, R., Hou, Z., Xu, J. (2008-08). Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition. Automatica 44 (8) : 2207-2213. ScholarBank@NUS Repository. https://doi.org/10.1016/j.automatica.2007.12.004|
|Abstract:||In this work we present a discrete-time adaptive iterative learning control (AILC) scheme to deal with systems with time-varying parametric uncertainties. Using the analogy between the discrete-time axis and the iterative learning axis, the new adaptive ILC can incorporate a Recursive Least Squares (RLS) algorithm, hence the learning gain can be tuned iteratively along the learning axis and pointwisely along the time axis. When the initial states are random and the reference trajectory is iteration-varying, the new AILC can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis. © 2008 Elsevier Ltd. All rights reserved.|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 13, 2018
WEB OF SCIENCETM
checked on Jun 12, 2018
checked on Jun 30, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.