Adaptive Neural Sliding Mode Control of Active Power Filter | Zendy

Juntao Fei | Zendy; Zhe Wang | Zendy

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Adaptive Neural Sliding Mode Control of Active Power Filter

Author(s) -

Juntao Fei,

Zhe Wang

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/341831

Subject(s) - control theory (sociology) , sliding mode control , artificial neural network , robustness (evolution) , computer science , adaptive control , controller (irrigation) , nonlinear system , control (management) , artificial intelligence , physics , biochemistry , chemistry , quantum mechanics , biology , agronomy , gene

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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