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Adaptive neural sliding mode control of active power filter. (English) Zbl 1266.92003
Summary: A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filters (APFs). The advantages of the adaptive control, neural network control, and sliding mode control are combined to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power systems can be improved. Sliding surface coordinate functions and sliding mode controllers are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through a gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic currents effectively and is strongly robust disturbance signals.
MSC:
92B20General theory of neural networks (mathematical biology)
93C40Adaptive control systems
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References:
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