# The Voltage Regulation of a Buck Converter Using a Neural Network Predictive Controller

arXiv: https://arxiv.org/abs/2002.02544 · HTML: https://mlsanity.com/paper/2002.02544/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Artificial Neural Network-Based Voltage Control of DC/DC Converter for DC Microgrid Applications](https://mlsanity.com/paper/2111.03207/) (2021)

## Abstract (arXiv)

In this paper, a neural network predictive controller (NNPC) is proposed to control a buck converter. Conventional controllers such as proportional integral (PI) or proportional integral derivative (PID) are designed based on the linearized small signal model near the operating point. Therefore, the performance of the controller in the start up, load change, or reference change is not optimal since the system model changes by changing the operating point. The neural network predictive controller optimally controls the buck converter by following the concept of the traditional model predictive controller. The advantage of the NNPC is that the neural network system identification decreases the inaccuracy of the system model with inaccurate parameters. A NNPC with a well trained neural network can perform as an optimal controller for the buck converter. To compare the effectiveness of the traditional buck converter and the NNPC, the simulation results are provided.