Please use this identifier to cite or link to this item:
Title: On reference governor in iterative learning control for dynamic systems with input saturation
Authors: Tan, Y.
Xu, J.X. 
Norrlöf, M.
Freeman, C.
Keywords: Input saturation
Iterative learning control
Reference governor
Issue Date: Nov-2011
Citation: Tan, Y., Xu, J.X., Norrlöf, M., Freeman, C. (2011-11). On reference governor in iterative learning control for dynamic systems with input saturation. Automatica 47 (11) : 2412-2419. ScholarBank@NUS Repository.
Abstract: Input saturation is inevitable in many engineering applications. Most existing iterative learning control (ILC) algorithms that can deal with input saturation require that the reference signal is realizable within the saturation bound. For engineering systems without precise models, it is hard to verify this requirement. In this note, a "reference governor" (RG) is introduced and is incorporated with the available ILC algorithms (primary ILC algorithms). The role of the RG is to re-design the reference signal so that the modified reference signal is realizable. Two types of the RG are proposed: one modifies the amplitude of the reference signal and the other modifies the frequency. Our main results provide design guidelines for two RGs. Moreover, a design trade-off between the convergence speed and tracking performance is also discussed. A simple simulation result verifies the effectiveness of the proposed methods. © 2011 Elsevier Ltd. All rights reserved.
Source Title: Automatica
ISSN: 00051098
DOI: 10.1016/j.automatica.2011.08.024
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Apr 17, 2019


checked on Apr 17, 2019

Page view(s)

checked on Apr 22, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.