Robust Adaptive Controller Design for a Class of Uncertain Nonlinear Systems Using Online T–S Fuzzy-Neural Modeling Approach

dc.contributor 國立臺灣師範大學電機工程學系 zh_tw
dc.contributor.author Y.-H. Chien en_US
dc.contributor.author W.-Y. Wang en_US
dc.contributor.author Y.-G. Leu en_US
dc.contributor.author T.-T. Lee en_US
dc.date.accessioned 2014-10-30T09:28:11Z
dc.date.available 2014-10-30T09:28:11Z
dc.date.issued 2011-04-01 zh_TW
dc.description.abstract This paper proposes a novel method of online modeling and control via the Takagi–Sugeno (T–S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T–S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T–S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T–S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper en_US
dc.description.uri http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05580106 zh_TW
dc.identifier ntnulib_tp_E0604_01_004 zh_TW
dc.identifier.issn 1083-4419� zh_TW
dc.identifier.uri http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31926
dc.language en zh_TW
dc.publisher IEEE Systems, Man, and Cybernetics Society en_US
dc.relation IEEE Transactions on Systems, Man, And Cybernetics-Part B, 41(2). en_US
dc.subject.other Fuzzy-neural model en_US
dc.subject.other online modeling en_US
dc.subject.other robust adaptive control en_US
dc.subject.other uncertain nonlinear systems en_US
dc.title Robust Adaptive Controller Design for a Class of Uncertain Nonlinear Systems Using Online T–S Fuzzy-Neural Modeling Approach en_US
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