Two adaptive fuzzy logic controller (AFLC) topologies for the DC-DC converter are developed and presented in this paper. They essentially consist of combining fuzzy inference system and neural networks and implementing them within the framework of adaptive networks. The architecture of the AFLC along with the learning rule, which is used to give an adaptive and learning structure to a fuzzy controller, is also described. The emphasis here is on fuzzy-neural-network control philosophies in designing a novel controller for the DC-DC converter that allows the benefits of neural network structure to be realized without sacrificing the intuitive nature of fuzzy system. The AFLC topologies are built on Matlab environment and tested for both the buck and buck boost converter for load regulation and line regulation. The proposed AFLCs have satisfactory results for tracking the reference output voltage.
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