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2013 Adaptive Neural Sliding Mode Control of Active Power Filter
Juntao Fei, Zhe Wang
J. Appl. Math. 2013: 1-8 (2013). DOI: 10.1155/2013/341831

Abstract

A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals.

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Juntao Fei. Zhe Wang. "Adaptive Neural Sliding Mode Control of Active Power Filter." J. Appl. Math. 2013 1 - 8, 2013. https://doi.org/10.1155/2013/341831

Information

Published: 2013
First available in Project Euclid: 14 March 2014

zbMATH: 1266.92003
MathSciNet: MR3056210
Digital Object Identifier: 10.1155/2013/341831

Rights: Copyright © 2013 Hindawi

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