A comparative study of FNN-based dynamic sliding mode control for DC-DC converters

In this paper, a dynamic sliding-mode variable-structure control (VSC) is proposed for the voltage tracking control for DC-DC boost converters, combining with a type of fuzzy neural networks (FNNs) based on different membership functions. In terms of the uncertainties from parameter perturbations and unmodeled dynamics in the DC-DC converter, the FNNs including, respectively, Gaussian membership functions and ellipsoidal-type membership functions are employed to approximate the uncertainties. Based on an average model for the present converter, a novel dynamic VSC that is induced by an auxiliary dynamics of the duty cycle is designed, by exploring an ideal line sliding surface based on the reference inputs and the duty cycle for the converter. Under the basis of the adaptive FNN dynamic VSC law, the tracking error system of the DC-DC boost converter is analyzed to be boundedly stable. Eventually, the validation and the superiority of the proposed dynamic VSC method are shown in some comparative simulations.

Automated summary

A dynamic sliding-mode variable-structure control (VSC) combined with fuzzy neural networks (FNNs) is proposed for voltage tracking control in DC-DC boost converters. Two types of FNNs are used to approximate uncertainties, with the control system analyzed to be boundedly stable under this method. Validation and superiority of the proposed dynamic VSC method are demonstrated through comparative simulations.