Self-Constructing Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Grid-Connected Microgrid

This study mainly develops a self-constructing fuzzy neural network (SFNN) with the structure and parameter self-learning abilities to imitate a sliding-mode control (SMC), and implements the grid-connected current tracking control for a parallel-inverter system in a grid-connected microgrid (MG) with a master-slave current sharing strategy. In the proposed SFNN-imitating SMC (SFNNISMC) scheme, the initial nodes of the input layer are determined by the number of the grid-connected inverter units, and the rules of the membership layer are self-generated online from null online according to the instantaneous inputs based on the dynamic rule-generating scheme. Moreover, a dynamic Petri net is introduced to implement the pruning mechanism, and is utilized to recall the rules corresponding to the reconnected slave inverters. Only the parameters of favorable rules fired by the Petri net are updated online instead of all the parameters, which can significantly alleviate the computational burden of parameter learning. In addition, the projection algorithm and the Lyapunov stability theorem are adopted to ensure the convergence of the parameter adaptation and the grid-connected current-tracking errors. Furthermore, the rule evolutions of the proposed SFNNISMC in the structure self-learning process are illustrated in numerical simulations. The superiority of the proposed SFNNISMC framework is further validated by experimental comparisons with a proportional-integral control (PIC) strategy, an SMC scheme and an adaptive FNN-imitating SMC (AFNNISMC) framework with a fixed network structure from the previous research to be carried out on a parallel-inverter system with two single inverters.