An Adaptive Approximate Model Neural Network Controller for EAF Electrode Regulator System

Hong Ge Zhao

doi:10.4028/www.scientific.net/AMM.373-375.1432

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 373-375An Adaptive Approximate Model Neural Network...

An Adaptive Approximate Model Neural Network Controller for EAF Electrode Regulator System

Abstract:

This paper proposes a robust adaptive neural network controller (RANNC) for electrode regulator system. An equivalent model in affine-like is derived for electrode regulator system. Then, the nonlinear control law is derived directly based on the affine-like equivalent model identified with neural networks, which avoids complex control development and intensive computation. Pretraining is not required and the weights of the neural networks used in adaptive control are directly updated online based on the input-output measurement. The proposed nonlinear controller is verified by computer simulations.

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Periodical:

Applied Mechanics and Materials (Volumes 373-375)

Pages:

1432-1436

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.373-375.1432

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Online since:

August 2013

Authors:

Hong Ge Zhao

Keywords:

Approximate Model, EAF, Model Identification, Nonlinear Control

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