Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives
A diverse group of so-called multi-vector techniques has recently appeared to enhance the control performance of multiphase drives when a direct control strategy is implemented. With different numbers of switching states and approaches for estimating the application times, each multi-vector solution...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/2/115 |
| _version_ | 1827343294868226048 |
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| author | Juan Jose Aciego Ignacio Gonzalez-Prieto Mario Javier Duran Angel Gonzalez-Prieto Juan Carrillo-Rios |
| author_facet | Juan Jose Aciego Ignacio Gonzalez-Prieto Mario Javier Duran Angel Gonzalez-Prieto Juan Carrillo-Rios |
| author_sort | Juan Jose Aciego |
| collection | DOAJ |
| description | A diverse group of so-called multi-vector techniques has recently appeared to enhance the control performance of multiphase drives when a direct control strategy is implemented. With different numbers of switching states and approaches for estimating the application times, each multi-vector solution has its own nature and merits. Previous studies have individually tested each version of the proposed finite-control-set model predictive control (FCS-MPC) strategies using a single experimental setup with specific parameters and, in some cases, using a limited range of operating conditions and focusing exclusively on some control aspects. Although such works provide partial contributions, the control performance is highly affected by the test and rig conditions, being dependent on the machine parameters, the switching frequency and the range of operation. Consequently, it becomes difficult to extract some universal conclusions that guide the control designer on the best alternative for each application. Aiming to enrich the knowledge in this field and provide a broader picture, this work performs a global analysis with different multi-vector techniques, various machine parameters, multiple operating points and a complete set of indices. Experimental results confirm that the selection of the most adequate control strategy is not a trivial task because the degree to which multi-vector techniques are affected by the test conditions is variable and complex. Some tables with a qualitative analysis, based on the extensive empirical tests, contribute with a more complete insight and guide eventual control designers on the decision about the optimal regulation approach to be chosen. |
| first_indexed | 2024-03-07T22:24:09Z |
| format | Article |
| id | doaj.art-dc609ed4402f41f68f90112779a53486 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-07T22:24:09Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-dc609ed4402f41f68f90112779a534862024-02-23T15:25:03ZengMDPI AGMachines2075-17022024-02-0112211510.3390/machines12020115Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase DrivesJuan Jose Aciego0Ignacio Gonzalez-Prieto1Mario Javier Duran2Angel Gonzalez-Prieto3Juan Carrillo-Rios4Electrical Engineering Departament, Industrial Engineering School, University of Malaga, 29010 Malaga, SpainElectrical Engineering Departament, Industrial Engineering School, University of Malaga, 29010 Malaga, SpainElectrical Engineering Departament, Industrial Engineering School, University of Malaga, 29010 Malaga, SpainElectrical Engineering Departament, Industrial Engineering School, University of Malaga, 29010 Malaga, SpainElectrical Engineering Departament, Industrial Engineering School, University of Malaga, 29010 Malaga, SpainA diverse group of so-called multi-vector techniques has recently appeared to enhance the control performance of multiphase drives when a direct control strategy is implemented. With different numbers of switching states and approaches for estimating the application times, each multi-vector solution has its own nature and merits. Previous studies have individually tested each version of the proposed finite-control-set model predictive control (FCS-MPC) strategies using a single experimental setup with specific parameters and, in some cases, using a limited range of operating conditions and focusing exclusively on some control aspects. Although such works provide partial contributions, the control performance is highly affected by the test and rig conditions, being dependent on the machine parameters, the switching frequency and the range of operation. Consequently, it becomes difficult to extract some universal conclusions that guide the control designer on the best alternative for each application. Aiming to enrich the knowledge in this field and provide a broader picture, this work performs a global analysis with different multi-vector techniques, various machine parameters, multiple operating points and a complete set of indices. Experimental results confirm that the selection of the most adequate control strategy is not a trivial task because the degree to which multi-vector techniques are affected by the test conditions is variable and complex. Some tables with a qualitative analysis, based on the extensive empirical tests, contribute with a more complete insight and guide eventual control designers on the decision about the optimal regulation approach to be chosen.https://www.mdpi.com/2075-1702/12/2/115finite-control-set model predictive controlmultiphase electric drivesmulti-vector assessmentmulti-vector guiding selection |
| spellingShingle | Juan Jose Aciego Ignacio Gonzalez-Prieto Mario Javier Duran Angel Gonzalez-Prieto Juan Carrillo-Rios Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives Machines finite-control-set model predictive control multiphase electric drives multi-vector assessment multi-vector guiding selection |
| title | Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives |
| title_full | Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives |
| title_fullStr | Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives |
| title_full_unstemmed | Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives |
| title_short | Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives |
| title_sort | guiding the selection of multi vector model predictive control techniques for multiphase drives |
| topic | finite-control-set model predictive control multiphase electric drives multi-vector assessment multi-vector guiding selection |
| url | https://www.mdpi.com/2075-1702/12/2/115 |
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