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Mathematical Problems in Engineering
Volume 2013, Article ID 712615, 12 pages
Research Article

Switched Two-Level and Robust Fuzzy Learning Control of an Overhead Crane

1Department of Mechanical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
2Department of Computer Science & Information Engineering, Asia University, Taichung 41354, Taiwan

Received 30 November 2012; Revised 9 February 2013; Accepted 25 February 2013

Academic Editor: Peng Shi

Copyright © 2013 Kao-Ting Hung et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Overhead cranes are typical dynamic systems which can be modeled as a combination of a nominal linear part and a highly nonlinear part. For such kind of systems, we propose a control scheme that deals with each part separately, yet ensures global Lyapunov stability. The former part is readily controllable by the PDC techniques, and the latter part is compensated by fuzzy mixture of affine constants, leaving the remaining unmodeled dynamics or modeling error under robust learning control using the Nelder-Mead simplex algorithm. Comparison with the adaptive fuzzy control method is given via simulation studies, and the validity of the proposed control scheme is demonstrated by experiments on a prototype crane system.