Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.automatica.2007.12.004
Title: Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition
Authors: Chi, R.
Hou, Z.
Xu, J. 
Keywords: Adaptive tuning
Iteration-varying trajectories
Iterative learning control
Random initial condition
Time-varying parameters
Issue Date: Aug-2008
Source: 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.
Source Title: Automatica
URI: http://scholarbank.nus.edu.sg/handle/10635/54900
ISSN: 00051098
DOI: 10.1016/j.automatica.2007.12.004
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