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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115949

    Title: New Time-Efficient Structure for Observer-Based Adaptive Fuzzy-Neural Controllers for Nonaffine Nonlinear Systems
    Authors: Wang, Wei Yen;Li, I. Hsum;Chen, Ming Chang;Su, Shun Feng;Leu, Yih Guang
    Keywords: Software;Theoretical Computer Science;Information Systems;Computational Theory and Mathematics
    Date: 2010-03
    Issue Date: 2019-03-13 12:10:16 (UTC+8)
    Abstract: This paper proposes an observer-based adaptive controller with a merged fuzzy-neural network for nonaffine nonlinear systems under the constraint that only the system output is available for measurement. Using a conventional fuzzy-neural network leads to rule explosion which leads to huge computation time. Our proposed merged-FNN does not have this problem, and can take the place of the conventional fuzzy-neural networks under some assumptions while maintaining the property of stability. Moreover, the adaptive scheme using the merged-FNN guarantees that all signals involved are bounded and the output of the closed-loop system asymptotically tracks the desired output trajectory. Finally, this paper gives examples of the proposed controller for nonaffine nonlinear systems, and is shown to provide good effectiveness.
    Relation: International Journal of Innovative Computing, Information and Control 6(3), p.963-978
    Appears in Collections:[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Journal Article

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