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RBF-NN based Upper-bound Adaptive learning Sliding Mode Control for Grid-Connected Converter

The current control strategy of grid-connected inverter has always been a core question about generating systems. This paper studied the upper bound adaptive learning method applying sliding mode control based on RBF neural network. Utilizing the characteristics of radial basis function to approximate non-linear objects to control the uncertain part of system. Simulation comparison results from the proposed RBF-SMC and conventional QPR method are demonstrated, which show the proposed method has the merits of excellent current tracking performance and fast response speed.