Hardware implementation of an adaptive network-based fuzzy controller for DC-DC converters

Two control topologies of adaptive network-based fuzzy inference system (ANFIS) for control of the DC-DC converter are developed and presented in this paper. They essentially consist of combining fuzzy inference system and neural networks and implementing within the framework of adaptive networks. The architecture of the ANFIS 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. Specifically, it permits this type of setup to simultaneously share the benefits of both fuzzy control and neural network capabilities. Furthermore, the membership functions and rules can be retrieved from the ANFIS after training. A commensurate number of components is designed and built. The components are tested individually and in various combinations of hardware and software segments. Two categories of tests, namely, load regulation, and line regulation will be carried out to evaluate the performance of the proposed control system. Test results demonstrate the advantages and flexibilities of the ANFIS for the DC-DC converter.