Neural Network Approaches For Feedback Linearization
- Authors
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Yiannis Boutalis
Democritus University of Thrace, Automatic Control and Systems Laboratory
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- Abstract
- In this paper a recent approach is reported, which performs feedback linearization of uncertain nonlinear systems using Artificial Neural Networks (ANNs). Also, a new ANN approach is presented, which tackles a special case of feedback linearization using ANNs. Instead of using the ANN as an estimator of the uncertain system dynamics the ANN is used as a compensator to the effects of the model uncertainties, which appear in the linearizing control law. The updating of the neural weights is carried out on-line using a conventional back propagation scheme, where the error to be minimized is chosen such that it ensures the stability of the tracking error system. The proposed method is tested on a well known nonlinear system and its application on a fermentation process is reported.
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- Published
- 2006-03-31
- Issue
- Vol. 6 No. 1 (2004)
- Section
- Articles