Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter
Abstract DC/ DC Boost converter has a right half‐plane zero structure called a non‐minimum phase system, which presents several challenging constraints for designing well‐behaved control techniques. The Fractional‐Order concept as a beneficial scheme provides several advantages, such as lower sensit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | The Journal of Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/tje2.12255 |
| _version_ | 1797768681810296832 |
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| author | Mohammad Abdollahzadeh Hasan Mollaee Seyyed Morteza Ghamari Fatemeh Khavari |
| author_facet | Mohammad Abdollahzadeh Hasan Mollaee Seyyed Morteza Ghamari Fatemeh Khavari |
| author_sort | Mohammad Abdollahzadeh |
| collection | DOAJ |
| description | Abstract DC/ DC Boost converter has a right half‐plane zero structure called a non‐minimum phase system, which presents several challenging constraints for designing well‐behaved control techniques. The Fractional‐Order concept as a beneficial scheme provides several advantages, such as lower sensitivity to noise and parametric variation. For this purpose, a Fractional‐order Proportional‐Integrated‐Derivative (FOPID) controller is designed for the Boost converter. On the other hand, for wider ranges of disturbances, including parametric variations, load uncertainty, supply voltage variation, and noise, this approach shows an unsuitable practical application based on its fixed gain values; therefore, the control parameters need to be optimized again to provide ideal operations. An Artificial Neural Network structure (ANNs) is adopted here to optimize the gains of the FOPID in challenging conditions. This method suffers from higher complexity and slower dynamics in practical applications. A single‐layer ANNs is designed to reduce the complexity; also, particle swarm optimization (PSO) algorithm is utilized in this decision‐making structure to provide better results with its online mechanism. Furthermore, the Black‐box technique is applied for the proposed system, which does not require an accurate mathematical model resulting in a lower computational burden, easy implementation, and lower dependency on the states of the model. The Artificial Neural Network structure can optimize the FOPID gains real‐time, even in severe challenging conditions, efficiently. To better examine the superiority of the proposed method, conventional Proportional‐Integrated‐Derivative (PID) and the FOPID controllers are proposed to drive a comparison with this work, which are tuned by the PSO optimization algorithm. The evaluation of simulation results demonstrates that the proposed control scheme is suitable not only for preserving stability but also for compensating for the disturbances and uncertainties in the Boost converter, properly. |
| first_indexed | 2024-03-12T20:57:49Z |
| format | Article |
| id | doaj.art-34b29a864db646ca9efa45bbede91870 |
| institution | Directory Open Access Journal |
| issn | 2051-3305 |
| language | English |
| last_indexed | 2024-03-12T20:57:49Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Engineering |
| spelling | doaj.art-34b29a864db646ca9efa45bbede918702023-07-31T13:40:31ZengWileyThe Journal of Engineering2051-33052023-04-0120234n/an/a10.1049/tje2.12255Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converterMohammad Abdollahzadeh0Hasan Mollaee1Seyyed Morteza Ghamari2Fatemeh Khavari3Department of Control Engineering Shahid Beheshti University Tehran IranDepartment of Electrical And Robotic Engineering Shahrood University of Technology Shahrood IranDepartment of Electrical And Robotic Engineering Shahrood University of Technology Shahrood IranDepartment of Electrical And Robotic Engineering Shahrood University of Technology Shahrood IranAbstract DC/ DC Boost converter has a right half‐plane zero structure called a non‐minimum phase system, which presents several challenging constraints for designing well‐behaved control techniques. The Fractional‐Order concept as a beneficial scheme provides several advantages, such as lower sensitivity to noise and parametric variation. For this purpose, a Fractional‐order Proportional‐Integrated‐Derivative (FOPID) controller is designed for the Boost converter. On the other hand, for wider ranges of disturbances, including parametric variations, load uncertainty, supply voltage variation, and noise, this approach shows an unsuitable practical application based on its fixed gain values; therefore, the control parameters need to be optimized again to provide ideal operations. An Artificial Neural Network structure (ANNs) is adopted here to optimize the gains of the FOPID in challenging conditions. This method suffers from higher complexity and slower dynamics in practical applications. A single‐layer ANNs is designed to reduce the complexity; also, particle swarm optimization (PSO) algorithm is utilized in this decision‐making structure to provide better results with its online mechanism. Furthermore, the Black‐box technique is applied for the proposed system, which does not require an accurate mathematical model resulting in a lower computational burden, easy implementation, and lower dependency on the states of the model. The Artificial Neural Network structure can optimize the FOPID gains real‐time, even in severe challenging conditions, efficiently. To better examine the superiority of the proposed method, conventional Proportional‐Integrated‐Derivative (PID) and the FOPID controllers are proposed to drive a comparison with this work, which are tuned by the PSO optimization algorithm. The evaluation of simulation results demonstrates that the proposed control scheme is suitable not only for preserving stability but also for compensating for the disturbances and uncertainties in the Boost converter, properly.https://doi.org/10.1049/tje2.12255boost converterdisturbancefractional‐order proportional‐integrated‐derivativeneural network methodparticle swarm optimization (PSO) algorithmproportional‐integrated‐derivative |
| spellingShingle | Mohammad Abdollahzadeh Hasan Mollaee Seyyed Morteza Ghamari Fatemeh Khavari Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter The Journal of Engineering boost converter disturbance fractional‐order proportional‐integrated‐derivative neural network method particle swarm optimization (PSO) algorithm proportional‐integrated‐derivative |
| title | Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter |
| title_full | Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter |
| title_fullStr | Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter |
| title_full_unstemmed | Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter |
| title_short | Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter |
| title_sort | design of a novel robust adaptive neural network based fractional order proportional integrated derivative controller on dc dc boost converter |
| topic | boost converter disturbance fractional‐order proportional‐integrated‐derivative neural network method particle swarm optimization (PSO) algorithm proportional‐integrated‐derivative |
| url | https://doi.org/10.1049/tje2.12255 |
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