This paper proposes a generalised inverse learning structure to control the LCL
converter. A feedforward neural network is trained to act as an inverse model of the LCL
converter then both are cascaded such that the composed system results in an identity
mapping between desired response and the LCL output voltage. Using the large signal model,
we analyse the transient output response of the controlled LCL converter in the case of large
variation of the load. The simulation results show the efficiency of using neural networks to
regulate the LCL converter.