Adaptive TSK-type neural fuzzy controller for boost DC-DC converter

Abstract

The application of DC-DC Boost converter is growing day-by-day (eg: Telecommunication application) and it is always fall to attain a regulated output voltage against load and line variations. In order to regulate the output voltage, conventional PID controllers are normally used, which experiences the effect of sensitivity to disturbances and system non-linearity. In this paper, an intelligent Adaptive TSK-type Neural fuzzy Controller (ATNC) is designed for the control of DC-DC Boost converter. First, the description of the circuit frame work of a DC-DC Boost converter and system modeling is introduced. Then an Adaptive TSK-type Neural Fuzzy Controller (ATNC) system is proposed. This ATNC system is the integrated form of both Fuzzy logic and TSK-type Neural network, thereby incorporating the abilities for learning, optimization and adaptation of neural networks with Fuzzy system. In this method, the error between output of converter and its reference value are used to tune and optimize the ATNC's input membership function parameters; then propagating the same back into the controller. Finally, the simulation results show that the proposed ATNC scheme provides better output voltage tracking with minimal overshoot and settling time over conventional PD controllers and fuzzy controllers.