標題: Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems
作者: Leu, YG
Wang, WY
Lee, TT
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: direct adaptive control;fuzzy-neural control;nonaffine nonlinear systems;output feedback control
公開日期: 1-Jul-2005
摘要: In this paper, an observer-based direct adaptive fuzzy-neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities. A direct adaptive fuzzy-neural controller and a class of generalized nonlinear systems, which are called nonaffine nonlinear systems, are instead of the indirect one and affine nonlinear systems given by Leu et al. By using implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the fuzzy-neural controller are derived for the nonaffine nonlinear systems. Based on strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. Moreover, the overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.
URI: http://dx.doi.org/10.1109/TNN.2005.849824
http://hdl.handle.net/11536/13544
ISSN: 1045-9227
DOI: 10.1109/TNN.2005.849824
期刊: IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume: 16
Issue: 4
起始頁: 853
結束頁: 861
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