Robust adaptive fuzzy-neural control of nonlinear dynamical systems using generalized projection update law and variable structure controller

In this paper, a robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance, and modeling errors. A generalized projection update law, which generalizes the projection algorithm modification and the switc...

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Veröffentlicht in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Jg. 31; H. 1; S. 140 - 147
Hauptverfasser: Wang, W Y, Leu, T G, Hsu, C C
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2001
Schlagworte:
Adaptive control
Control systems
Disturbances
Dynamical systems
Error correction
Law
Mathematical models
Neural networks
Nonlinear control systems
Nonlinear dynamical systems
Nonlinear dynamics
Nonlinear systems
Nonlinearity
Programmable control
Projection
Robust control
Robust stability
ISSN:1083-4419
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Zusammenfassung:In this paper, a robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance, and modeling errors. A generalized projection update law, which generalizes the projection algorithm modification and the switching-/spl sigma/ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system to the specified regions. Moreover, a variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances, and modeling errors. To demonstrate the effectiveness of the proposed method, several examples are illustrated in this paper.
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ISSN:1083-4419
DOI:10.1109/3477.907573