Finite-Time Adaptive Neural Network Observer-Based Output Voltage-Tracking Control for DC-DC Boost Converters

This paper investigates the problem of accurate voltage tracking control for direct current-direct current (DC-DC) boost converter under unknown system parameters and load. Firstly, uncertainties caused by the perturbation of the inductor, capacitor, input voltage and load are approximated by neural networks. Meanwhile, a finite-time observer is proposed to obtain the estimates of lumped uncertainty without any true parameters of the system. Finally, to improve the convergence of output voltage, a finite-time control scheme is proposed for the DC-DC boost converter. It is proven that all signals of the closed-loop system are bounded and both the estimate errors and tracking errors can converge to a small neighborhood of the origin in finite time. Numerical simulations and real-time experiments are presented to demonstrate the effectiveness and superiority of the proposed controller.

Automated summary

This paper investigates the problem of accurate voltage tracking control for DC-DC boost converter under unknown system parameters and load. Uncertainties caused by the perturbation of the inductor, capacitor, input voltage and load are approximated by neural networks. A finite-time observer is proposed to obtain the estimates of lumped uncertainty without any true parameters of the system. Finally, a finite-time control scheme is proposed for the DC-DC boost converter to improve the convergence of output voltage.