Adaptive Fuzzy-Neural-Network Design for Voltage Tracking Control of a DC–DC Boost Converter

In this study, an adaptive fuzzy-neural-network control (AFNNC) scheme is designed for the voltage tracking control of a conventional dc–dc boost converter. First, the description of the circuit framework of a conventional boost converter and system modeling is introduced. Then, a total sliding-mode control (TSMC) strategy without the reaching phase in the conventional SMC is developed for enhancing system robustness during the transient response of the voltage control. In order to alleviate the control chattering phenomena caused by the sign function in the TSMC design and relax the requirement of detailed system dynamics, an AFNNC scheme is further investigated to imitate the TSMC law for the boost converter. In the AFNNC scheme, online learning algorithms are derived in the sense of Lyapunov stability theorem and projection algorithm to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The output of the AFNNC scheme can be easily supplied to the duty cycle of the power switch in the boost converter without strict constraints on control parameters selection in conventional control strategies. In addition, the effectiveness of the proposed AFNNC scheme is verified by realistic experimentations, and its advantages are indicated in comparison with the TSMC strategy.