The Voltage Regulation of Boost Converters Using Dual Heuristic Programming

TL;DR

This paper introduces a dual heuristic programming controller for boost converters, leveraging neural networks to adaptively manage large disturbances and improve performance over traditional linear controllers.

Contribution

The paper proposes a neural network-based dual heuristic programming controller that optimally manages boost converters under varying conditions, outperforming traditional PI controllers.

Findings

DHP controller effectively handles large disturbances.

Simulation shows improved performance over PI controllers.

Neural network-based control adapts to changing operating points.

Abstract

In this paper, a dual heuristic programming controller is proposed to control a boost converter. Conventional controllers such as proportional integral derivative (PID) or proportional integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during start up, load change, or input voltage variation is not optimal since the system model changes by varying the operating point. The dual heuristic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the DHP is that the neural network based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. A DHP with a well trained critic and action networks can perform as an optimal controller for the boost converter. To compare the effectiveness of the traditional PI based and the DHP boost converter, the simulation results are provided.