Sustainable Energy and Artificial Intelligence

Dual-Mode Neural Network Control for Two-Switch Forward DC-DC Converters: Adaptive PI Tuning and Online Learning Strategies

Document Type : Original Article

Authors

Elahe Khwarizmi 1
Mohamadmahdi Shahbazi 2

1 Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

2 1. Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University , Hamedan, Iran

https://doi.org/10.66224/seai.2602-1050
Abstract
This paper presents a dual-mode artificial intelligence-based control approach for robust regulation of DC-DC two-switch forward converters under wide input voltage fluctuations and dynamic load conditions. Two neural network-driven strategies are explored: (1) an adaptive PI controller whose gains are optimized offline via a genetic algorithm and predicted using a feedforward neural network, and (2) an online neural network controller that directly computes the duty cycle in real time using online learning. Extensive simulation studies compare these neural approaches against classical PI and adaptive fuzzy controllers using various quantitative performance metrics, including steady-state error, overshoot, peak time, settling time, and integral error indices (IAE, ISE, ITAE). Simulation results under nominal conditions and multiple disturbance scenarios—including sudden load changes and input voltage drops—demonstrate that both AI-based approaches outperform conventional PI and fuzzy controllers. While the offline-trained adaptive PI controller excels in nominal conditions with minimal steady-state error and overshoot, the online neural network controller offers superior dynamic adaptability and robustness in real-time scenarios. Comparative performance metrics and radar-based multi-criteria evaluations confirm the suitability of these intelligent control strategies for real-time power electronics applications requiring high precision and resilience

Keywords

Two-Switch Forward Converter
Neural Network Control
Adaptive PI Controller
Online Learning
Robust Control
DC-DC Converters
Genetic Algorithm (GA)
Subjects
Sustainable Energy and Artificial Intelligence
Volume 2, Issue 3
Summer 2026
Pages 211-225

  • Receive Date 25 February 2026
  • Revise Date 30 April 2026
  • Accept Date 06 May 2026
  • First Publish Date 18 June 2026

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