Saturation and deadzone compensation of systems using neural network and fuzzy logic

A saturation and deadzone compensator is designed for systems by the fuzzy logic (FL) and the neural network (NN). The classification property of the FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and d...

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Bibliographic Details
Main Authors: Jang, Jun Oh, Chung, Hee Tae, Jeon, Gi Joon
Format: Conference Proceeding
Language:English
Subjects:
Actuators
Adaptive control
Control systems
Feedback loop
Function approximation
Fuzzy logic
Neural networks
Nonlinear control systems
Robust stability
Windup
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Description
Summary:A saturation and deadzone compensator is designed for systems by the fuzzy logic (FL) and the neural network (NN). The classification property of the FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is implemented on a system to show its efficacy.
ISSN:0743-1619
2378-5861
DOI:10.1109/ACC.2005.1470215