Robust Output Tracking Control of an Uncertain Linear System via a Modified Optimal Linear-Quadratic Method
N.C. Shieh,
K.Z. Liang and
C.J. Mao
Additional contact information
N.C. Shieh: Chung-Shan Institute of Science and Technology
K.Z. Liang: Ching-Yun Institute of Technology
C.J. Mao: Southern Taiwan University of Technology
Journal of Optimization Theory and Applications, 2003, vol. 117, issue 3, No 13, 649-659
Abstract:
Abstract This study investigates the robust output tracking problem for a class of uncertain linear systems. The uncertainties are assumed to be time invariant and to satisfy the matching conditions. According to the selected nominal parameters, an optimal solution with a prescribed degree of stability is determined. Then, an auxiliary input via the use of an adapting factor, connected to the nominal optimal control, is introduced to guarantee the robustness and prescribed degree of stability for the output tracking control of the uncertain linear systems. This method is very simple and effective and can reject bounded uncertainties imposed on the states. A maglev vehicle model example is given to show its effectiveness.
Keywords: Optimal linear-quadratic control; uncertain linear systems; output tracking control; matching conditions; maglev vehicle model (search for similar items in EconPapers)
Date: 2003
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1023/A:1023910008156 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:117:y:2003:i:3:d:10.1023_a:1023910008156
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2
DOI: 10.1023/A:1023910008156
Access Statistics for this article
Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull
More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().