Abstract
Boost converters play a crucial role in power electronics but present control challenges due to their non-minimum phase behavior and nonlinear dynamics at high switching frequencies. To address these issues, this work proposes a Fractional-order adaptive Model Predictive Control (FO-MPC) framework incorporating Exponential Regressive Least Squares (ERLS) for system identification. Traditional MPC frameworks often rely on accurate mathematical models, which are difficult to obtain in real-world scenarios. This adaptive modelling approach based on ERLS identification method eliminates the need for precise system models, improving robustness and adaptability under parameter variations. Additionally, a FO derivative term enhances damping, stability, and noise resistance, overcoming conventional MPC limitations. To optimize controller performance, Grey Wolf Optimization (GWO) is employed for fine-tuning FO-MPC parameters, ensuring improved tracking accuracy and disturbance rejection. The proposed FO-MPC is validated through simulations and experimental evaluations using an Arduino DUE-based setup. Comparative studies against PID and FO-PID controllers, also optimized with GWO, confirm the superior performance, stability, and adaptability of the FO-MPC, making it a practical and effective solution for high-performance Boost converter applications.
Document Type
Journal Article
Date of Publication
12-1-2025
Volume
15
Issue
1
PubMed ID
40730603
Publication Title
Scientific Reports
Publisher
Nature
School
School of Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Peng, C., Ghamari, S. M., Mollaee, H., & Rezaei, O. (2025). Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-10125-8