TY - JOUR
T1 - Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems
AU - Leu, Yih Guang
AU - Wang, Wei Yen
AU - Lee, Tsu Tian
N1 - Funding Information:
Manuscript received December 9, 2003; revised May 27, 2004. This work was supported by the National Science Council, Taiwan, R.O.C., under Grant NSC 92-2213-E-030-001.
PY - 2005/7
Y1 - 2005/7
N2 - 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.
AB - 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.
KW - Direct adaptive control
KW - Fuzzy-neural control
KW - Nonaffine nonlinear systems
KW - Output feedback control
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U2 - 10.1109/TNN.2005.849824
DO - 10.1109/TNN.2005.849824
M3 - Article
C2 - 16121727
AN - SCOPUS:23044495263
SN - 1045-9227
VL - 16
SP - 853
EP - 861
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 4
ER -