Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm
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) fr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10125-8 |
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| _version_ | 1849343961207930880 |
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| author | Chao Peng Seyyed Morteza Ghamari Hasan Mollaee Omid Rezaei |
| author_facet | Chao Peng Seyyed Morteza Ghamari Hasan Mollaee Omid Rezaei |
| author_sort | Chao Peng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e978a67a8040437c9438963f4741d659 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e978a67a8040437c9438963f4741d6592025-08-20T03:42:48ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-10125-8Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithmChao Peng0Seyyed Morteza Ghamari1Hasan Mollaee2Omid Rezaei3College of Electrical Engineering and New Energy, THREE GORGES UniversitySchool of Engineering, Edith Cowan UniversityShahroud University of TechnologyQazvin Islamic Azad UniversityAbstract 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.https://doi.org/10.1038/s41598-025-10125-8Boost converterModel predictive controlERLS identificationFractional calculusGrey wolf optimizationArduino DUE |
| spellingShingle | Chao Peng Seyyed Morteza Ghamari Hasan Mollaee Omid Rezaei Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm Scientific Reports Boost converter Model predictive control ERLS identification Fractional calculus Grey wolf optimization Arduino DUE |
| title | Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm |
| title_full | Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm |
| title_fullStr | Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm |
| title_full_unstemmed | Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm |
| title_short | Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf optimization algorithm |
| title_sort | design of a novel robust adaptive fractional order model predictive controller for boost converter using grey wolf optimization algorithm |
| topic | Boost converter Model predictive control ERLS identification Fractional calculus Grey wolf optimization Arduino DUE |
| url | https://doi.org/10.1038/s41598-025-10125-8 |
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