Design of Adaptive Fuzzy-Neural-Network Control for a Single-Stage Boost Inverter

This study mainly focuses on the development of an adaptive fuzzy-neural-network control (AFNNC) system for a single-stage boost inverter. First, the dynamic model of a single-stage boost inverter is analyzed and is built for the later control manipulation. Then, a total sliding-mode control (TSMC) framework without the reaching phase in conventional SMC is developed for enhancing the system robustness during the transient response of the voltage tracking 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 system is further investigated to imitate the TSMC law for the boost inverter. In the AFNNC system, 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 system can be easily supplied to the duty cycle of the power switch in the boost inverter without strict constraints on control parameters selection in conventional control strategies. In addition, the effectiveness of the proposed AFNNC scheme is verified by realistic experiments, and its advantages are indicated in comparison with a traditional double-loop proportional-integral control scheme and the TSMC framework.