DocumentCode :
2044054
Title :
Observer-based adaptive fuzzy-neural control for a class of MIMO nonlinear systems
Author :
Leu, Yin-Guang ; Lee, Tsu-Tian
Author_Institution :
Dept. of Electron. Eng., Hwa-Hsia Coll. of Technol. & Commerce, Taipei, Taiwan
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
178
Abstract :
An observer-based adaptive fuzzy-neural controller for a class of multi-input multi-output (MIMO) nonlinear systems is developed, in which observers are used to estimate the time derivatives of the system outputs. The proposed method has the merit that no differentiation of the system output is required in order to avoid the noise amplification associated with numerical differentiation. The stability of the observer-based adaptive fuzzy-neural controller is proven by using the strictly-positive-real Lyapunov theory. The overall adaptive scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Finally, simulation results are provided to demonstrate the robustness and applicability of the proposed method
Keywords :
Lyapunov methods; MIMO systems; adaptive control; closed loop systems; fuzzy control; fuzzy neural nets; neurocontrollers; nonlinear systems; observers; stability; tracking; Lyapunov theory; MIMO systems; adaptive control; closed-loop system; fuzzy neural networks; fuzzy-neural controller; nonlinear systems; observers; stability; time derivative estimation; trajectory tracking; Adaptive control; Control systems; Fuzzy logic; MIMO; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear systems; Programmable control; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE
Conference_Location :
Nagoya
Print_ISBN :
0-7803-6456-2
Type :
conf
DOI :
10.1109/IECON.2000.973146
Filename :
973146
Link To Document :
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