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

• Published in: Energies (Basel) Vol. 15; no. 11; p. 4163
• Main Authors: Wang, Yeqin, Yang, Yan, Liang, Rui, Geng, Tao, Zhang, Weixing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 06.06.2022
• Subjects: Adaptation; Computer applications; Control systems; Control systems design; Controllers; Design; Distributed generation; Dynamic models; dynamic recurrent fuzzy neural network (DRFNN); Electronic equipment; Fuzzy logic; global integral sliding-mode control (GISMC); grid-connected inverter; Inverters; Mathematical models; Neural networks; Parameters; Petri net; Petri nets; Robust control; robustness control; Sliding mode control; Systems stability; Uncertainty
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15114163

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.