Design of adaptive fuzzy-neural-network control for 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, a total sliding-mode control (TSMC) strategy without the reaching pahse in the conventional SMC is developed for enhancing the system robustness during the transient response of the voltage control. In order to alleviate chattering phenomena caused by the sign function in TSMC design and reduce the dependence on detailed system dynamics, it further designs an AFNNC scheme to imitate the TSMC law for the boost converter. In the AFNNC scheme, on-line 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 numerical simulations, and its advantages are indicated in comparison with the TSMC strategy.