RBF Neural Network Fractional-Order Sliding Mode Control with an Application to Direct a Three Matrix Converter under an Unbalanced Grid

Xuhong Yang, Haoxu Fang, Yaxiong Wu and Wei Jia
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Xuhong Yang: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Haoxu Fang: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Yaxiong Wu: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Wei Jia: Shanghai Solar Energy Engineering Technology Research Center Co., Ltd., Shanghai 200241, China

Sustainability, 2022, vol. 14, issue 6, 1-17

Abstract: This paper presents a fractional-order sliding mode control scheme based on an RBF neural network (RBFFOSMC) for a direct three matrix converter (DTMC) operating under unbalanced grid voltages. The RBF neural network (RBF NN) is designed to approximate a nonlinear fractional-order sliding mode controller. The proposed method aims to achieve constant active power whilst maintaining a near unity input power factor. First, an opportune reference current is accurately generated according to the reference power and the RBFFOSMC is designed in a dq reference frame to achieve a perfect tracking of the input current reference. An almost constant active power, free of low-frequency ripples, is then supplied from the grid after compensating for the output voltage. Simulation and experimental studies prove the feasibility and effectiveness of the proposed control method.

Keywords: direct three matrix converter; RBF neural network; fractional-order sliding mode control; voltage compensation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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