Adaptive Current Control for Grid-Connected Inverter with Dynamic Recurrent Fuzzy-Neural-Network

Yeqin Wang, Yan Yang, Rui Liang, Tao Geng and Weixing Zhang
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Yeqin Wang: School of Automation, Huaiyin Institute of Technology, Huaian 223003, China
Yan Yang: School of Automation, Huaiyin Institute of Technology, Huaian 223003, China
Rui Liang: School of Automation, Huaiyin Institute of Technology, Huaian 223003, China
Tao Geng: School of Automation, Huaiyin Institute of Technology, Huaian 223003, China
Weixing Zhang: School of Automation, Huaiyin Institute of Technology, Huaian 223003, China

Energies, 2022, vol. 15, issue 11, 1-20

Abstract: The grid-connected inverter is a vital power electronic equipment connecting distributed generation (DG) systems to the utility grid. The quality of the grid-connected current is directly related to the safe and stable operation of the grid-connected system. This study successfully constructed a robust control system for a grid-connected inverter through a dynamic recurrent fuzzy-neural-network imitating sliding-mode control (DRFNNISMC) framework. Firstly, the dynamic model considering system uncertainties of the grid-connected inverter is described for the global integral sliding-mode control (GISMC) design. In order to overcome the chattering phenomena and the dependence of the dynamic information in the GISMC, a model-free dynamic recurrent fuzzy-neural-network (DRFNN) is proposed as a major controller to approximate the GISMC law without the extra compensator. In the DRFNN, a Petri net with varied threshold is incorporated to fire the rules, and only the parameters of the fired rules are adapted to alleviate the computational workload. Moreover, the network is designed with internal recurrent loops to improve the dynamic mapping capability considering the uncertainties in the control system. In addition, to assure the parameter convergence in the adaptation and the stability of the designed control system, the adaptation laws for the parameters of the DRFNN are deduced by the projection theorem and Lyapunov stability theory. Finally, the experimental comparisons with the GISMC scheme are performed in an inverter prototype to verify the superior performance of the proposed DRFNNISMC framework for the grid-connected current control.

Keywords: grid-connected inverter; global integral sliding-mode control (GISMC); dynamic recurrent fuzzy neural network (DRFNN); Petri net; robustness control (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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