TY - JOUR
T1 - Adaptive fuzzy-neural observer for a class of nonlinear systems
AU - Leu, Yih Guang
AU - Lee, Tsu Tian
PY - 2000/4
Y1 - 2000/4
N2 - Based on the H∞ control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach, an adaptive fuzzy-neural observer tuned on-line for a class of uncertain (unknown) nonlinear systems is developed. Unlike previous results, the assumption that the uncertain system nonlinearities only are restricted to the system output is not required. Moreover, the adaptive fuzzy-neural observer provides the modeling error (and the external bounded disturbance) attenuation with H∞ performance, obtained by a Riccati-like equation. Finally, simulation results demonstrate that the proposed observer yields satisfactory performance.
AB - Based on the H∞ control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach, an adaptive fuzzy-neural observer tuned on-line for a class of uncertain (unknown) nonlinear systems is developed. Unlike previous results, the assumption that the uncertain system nonlinearities only are restricted to the system output is not required. Moreover, the adaptive fuzzy-neural observer provides the modeling error (and the external bounded disturbance) attenuation with H∞ performance, obtained by a Riccati-like equation. Finally, simulation results demonstrate that the proposed observer yields satisfactory performance.
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U2 - 10.1109/ROBOT.2000.846344
DO - 10.1109/ROBOT.2000.846344
M3 - Article
AN - SCOPUS:0033724068
SN - 1050-4729
VL - 3
SP - 2130
EP - 2135
JO - Proceedings-IEEE International Conference on Robotics and Automation
JF - Proceedings-IEEE International Conference on Robotics and Automation
ER -