Artificial Neural Network Control of DC–DC Buck Converters: Improved Transient Response and Robustness under Variable Loads

Walid El Fezzani; Abdulla Mohsen Yusuf; Amal Naser Emes; Ambi Rachel Alex

doi:10.51173/jt.v8i2.2983

Authors

Walid El Fezzani Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain
Abdulla Mohsen Yusuf Engineering Management Department, Mantech Commercial Services W.L.L., Bader Ahmed Kaiksow Group (BAK Group), Road 469, Sitra, Capital Governorate, Kingdom of Bahrain
Amal Naser Emes Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain
Ambi Rachel Alex Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain

DOI:

https://doi.org/10.51173/jt.v8i2.2983

Keywords:

Buck Converter, Artificial Neural Networks (ANN), DC-DC Conversion, Intelligent Control, Dynamic Response

Abstract

In this work, a novel voltage control method for DC–DC buck converters using an artificial neural network (ANN) is proposed under nonlinear, time-varying load conditions. Traditional PID controllers typically exhibit poor performance in response to abrupt load variations and parameter uncertainties. To overcome these limitations, a lightweight feedforward ANN controller is formulated and trained with data from the MATLAB/Simulink buck-converter model under diverse operating scenarios. For the model itself, the proposed controller provides direct estimation of the optimal pulse-width modulation (PWM) duty cycle, enabling accurate voltage regulation and rapid transient response without an explicit mathematical model. The simulation results show that the ANN-based controller exhibits lower overshoot, shorter settling time, and better load and line regulation than a conventional PID controller. Also, the controller is very resilient to component variations or external disturbances. In contrast to ANN–model predictive control (ANN–MPC) hybrid architectures described in the literature, the current proposed approach does not require predictive optimization or expert-based training. It is thus less computationally demanding and more suitable for real-time embedded and FPGA-based applications. The proposed method yields rapid, stable voltage regulation during transient simulations under both start-up and load disturbance conditions.

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Author Biographies

Walid El Fezzani, Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain

Abdulla Mohsen Yusuf, Engineering Management Department, Mantech Commercial Services W.L.L., Bader Ahmed Kaiksow Group (BAK Group), Road 469, Sitra, Capital Governorate, Kingdom of Bahrain

Amal Naser Emes, Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain

Ambi Rachel Alex, Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain

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