RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model

Xuhong Yang and Haoxu Fang
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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

Energies, 2022, vol. 15, issue 5, 1-18

Abstract: For medium and high-powered applications, modular multilevel converters have become the most promising converter application. In this paper, a sliding mode controller based on an RBF neural network is proposed for a modular multilevel converter. The RBF neural network is designed to approximate the uncertainty mathematical model of a modular multilevel converter. The main innovation of the proposed method is that it does not require any model parameters and control parameters during the whole control process. This means that parameter changes caused by the external environment will not influence the controller performances. Finally, by comparing with a conventional PI controller, the simulation proves the feasibility and effectiveness of the proposed control method. In addition, the experimental results show that the grid-side current can become stable immediately while the active power is stabilized after 20 ms when the set value is changed.

Keywords: modular multilevel converter; sliding mode control; RBF neural network; uncertainty mathematical model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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