Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter
This paper presents a study on Maximum Power Point Tracking (MPPT) employing a DC-DC boost converter in MATLAB. The approach utilizes a genetic algorithm to determine optimal values for the membership functions (MFs) and the configuration of rules in a Mamdani fuzzy inference system. The fuzzy syste...
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2025
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my.uniten.dspace-369542025-03-03T15:46:05Z Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter Abdolrasol M.G.M. Tiong S.K. Ker P.J. Ansari S. Hannan M.A. Ustun T.S. 35796848700 15128307800 37461740800 57218906707 7103014445 43761679200 Adaptive boosting Boost converter Fuzzy control Fuzzy inference Fuzzy rules MOSFET devices Root loci Surface discharges BOOST converter Dc - dc boost converters DC-DC boost genetic algorithm Fuzzy controllers Fuzzy inference systems Mamdani fuzzy inferences Maximum Power Point Tracking Memberships function Optimal fuzzy logic controllers Optimal values MOS devices This paper presents a study on Maximum Power Point Tracking (MPPT) employing a DC-DC boost converter in MATLAB. The approach utilizes a genetic algorithm to determine optimal values for the membership functions (MFs) and the configuration of rules in a Mamdani fuzzy inference system. The fuzzy system is designed to manage the error signal derived from comparing the reference voltage of the Perturb and Observe (P&O) controller with the photovoltaic (PV) voltage. Inputs to the fuzzy controller include the error and its rate of change, with the output controlling the Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) pulses. The optimization aims to minimize the Root Mean Square Error (RMSE) to identify the most effective MFs and rules for the fuzzy controller, ultimately enhancing the MPPT output of the boost converter. ? 2024 IEEE. Final 2025-03-03T07:46:05Z 2025-03-03T07:46:05Z 2024 Conference paper 10.1109/ICCIGST60741.2024.10717559 2-s2.0-85208431862 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208431862&doi=10.1109%2fICCIGST60741.2024.10717559&partnerID=40&md5=507a1fada3e2168338695712dbb8b135 https://irepository.uniten.edu.my/handle/123456789/36954 Institute of Electrical and Electronics Engineers Inc. Scopus |
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Adaptive boosting Boost converter Fuzzy control Fuzzy inference Fuzzy rules MOSFET devices Root loci Surface discharges BOOST converter Dc - dc boost converters DC-DC boost genetic algorithm Fuzzy controllers Fuzzy inference systems Mamdani fuzzy inferences Maximum Power Point Tracking Memberships function Optimal fuzzy logic controllers Optimal values MOS devices |
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Adaptive boosting Boost converter Fuzzy control Fuzzy inference Fuzzy rules MOSFET devices Root loci Surface discharges BOOST converter Dc - dc boost converters DC-DC boost genetic algorithm Fuzzy controllers Fuzzy inference systems Mamdani fuzzy inferences Maximum Power Point Tracking Memberships function Optimal fuzzy logic controllers Optimal values MOS devices Abdolrasol M.G.M. Tiong S.K. Ker P.J. Ansari S. Hannan M.A. Ustun T.S. Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter |
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This paper presents a study on Maximum Power Point Tracking (MPPT) employing a DC-DC boost converter in MATLAB. The approach utilizes a genetic algorithm to determine optimal values for the membership functions (MFs) and the configuration of rules in a Mamdani fuzzy inference system. The fuzzy system is designed to manage the error signal derived from comparing the reference voltage of the Perturb and Observe (P&O) controller with the photovoltaic (PV) voltage. Inputs to the fuzzy controller include the error and its rate of change, with the output controlling the Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) pulses. The optimization aims to minimize the Root Mean Square Error (RMSE) to identify the most effective MFs and rules for the fuzzy controller, ultimately enhancing the MPPT output of the boost converter. ? 2024 IEEE. |
| author2 |
35796848700 |
| author_facet |
35796848700 Abdolrasol M.G.M. Tiong S.K. Ker P.J. Ansari S. Hannan M.A. Ustun T.S. |
| format |
Conference paper |
| author |
Abdolrasol M.G.M. Tiong S.K. Ker P.J. Ansari S. Hannan M.A. Ustun T.S. |
| author_sort |
Abdolrasol M.G.M. |
| title |
Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter |
| title_short |
Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter |
| title_full |
Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter |
| title_fullStr |
Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter |
| title_full_unstemmed |
Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter |
| title_sort |
optimal fuzzy logic controller based genetic algorithm for control maximum power point tracking for boost converter |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2025 |
| _version_ |
1825816199549681664 |
| score |
13.244109 |