Decentralized Adaptive Voltage Control and Equal Current Sharing of Parallel- Connected Buck Converters via Wavelet Neural Network Approximators

A parallel connection of Buck converters improves system reliability and efficiency. However, the open circuit fault, load, and supply voltage uncertainties, and interactions among the converters increase the complexity of output voltage control and balanced current sharing. Thus, in this paper, first, a decentralized backstepping sliding mode control strategy is designed to meet such challenges. However, this controller is quite conservative since the uncertainties and interaction bounds are not known. Moreover, the sliding mode based controllers suffer from chattering phenomena which limits the practical applications. Therefore, a decentralized adaptive backstepping control strategy with wavelet neural network approximators is proposed. This strategy reduces the chattering and approximates the uncertainties and interactions by replacing the switching terms with wavelet neural networks. To show the effectiveness of the proposed controller, different numerical simulations have been performed in the presence of reference voltage changes, load, supply voltage variations, and open circuit faults.