Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art
A novel Artificial Neural Network (ANN) Based Optimal Feedforward Torque Control (OFTC) strategy is proposed which, after proper ANN design, training and validation, allows to analytically compute the optimal reference currents (minimizing copper and iron losses) for Interior Permanent Magnet Synchr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/5/1838 |
| _version_ | 1797475229093593088 |
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| author | Max A. Buettner Niklas Monzen Christoph M. Hackl |
| author_facet | Max A. Buettner Niklas Monzen Christoph M. Hackl |
| author_sort | Max A. Buettner |
| collection | DOAJ |
| description | A novel Artificial Neural Network (ANN) Based Optimal Feedforward Torque Control (OFTC) strategy is proposed which, after proper ANN design, training and validation, allows to analytically compute the optimal reference currents (minimizing copper and iron losses) for Interior Permanent Magnet Synchronous Machines (IPMSMs) with highly operating point dependent nonlinear electric and magnetic characteristics. In contrast to conventional OFTC, which either utilizes large look-up tables (LUTs; with more than three input parameters) or computes the optimal reference currents numerically or analytically but iteratively (due to the necessary online linearization), the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal operation strategy such as e.g., Maximum Torque per Losses (MTPL), Maximum Current (MC) or Field Weakening (FW). Therefore, it is (much) faster and easier to implement while (i) still machine nonlinearities and nonidealities such as e.g., magnetic cross-coupling and saturation and speed-dependent iron losses can be considered and (ii) very accurate optimal reference currents are obtained. Comprehensive simulation results for a real and highly nonlinear IPMSM clearly show these benefits of the proposed ANN-based OFTC approach compared to conventional OFTC strategies using LUT-based, numerical or analytical computation of the reference currents. |
| first_indexed | 2024-03-09T20:41:11Z |
| format | Article |
| id | doaj.art-cfc2ae665bdc4dc598fa3620bb28fbbc |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T20:41:11Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-cfc2ae665bdc4dc598fa3620bb28fbbc2023-11-23T22:58:12ZengMDPI AGEnergies1996-10732022-03-01155183810.3390/en15051838Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-ArtMax A. Buettner0Niklas Monzen1Christoph M. Hackl2Department of Electrical Engineering and Information Technology, Hochschule München (HM) University of Applied Sciences, Lothstr. 64, 80335 München, GermanyDepartment of Electrical Engineering and Information Technology, Hochschule München (HM) University of Applied Sciences, Lothstr. 64, 80335 München, GermanyDepartment of Electrical Engineering and Information Technology, Hochschule München (HM) University of Applied Sciences, Lothstr. 64, 80335 München, GermanyA novel Artificial Neural Network (ANN) Based Optimal Feedforward Torque Control (OFTC) strategy is proposed which, after proper ANN design, training and validation, allows to analytically compute the optimal reference currents (minimizing copper and iron losses) for Interior Permanent Magnet Synchronous Machines (IPMSMs) with highly operating point dependent nonlinear electric and magnetic characteristics. In contrast to conventional OFTC, which either utilizes large look-up tables (LUTs; with more than three input parameters) or computes the optimal reference currents numerically or analytically but iteratively (due to the necessary online linearization), the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal operation strategy such as e.g., Maximum Torque per Losses (MTPL), Maximum Current (MC) or Field Weakening (FW). Therefore, it is (much) faster and easier to implement while (i) still machine nonlinearities and nonidealities such as e.g., magnetic cross-coupling and saturation and speed-dependent iron losses can be considered and (ii) very accurate optimal reference currents are obtained. Comprehensive simulation results for a real and highly nonlinear IPMSM clearly show these benefits of the proposed ANN-based OFTC approach compared to conventional OFTC strategies using LUT-based, numerical or analytical computation of the reference currents.https://www.mdpi.com/1996-1073/15/5/1838electrical drive control systemoperation managementoptimal feedforward torque controloptimal reference current computationtransformer-like nonlinear machine modelartificial neural network |
| spellingShingle | Max A. Buettner Niklas Monzen Christoph M. Hackl Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art Energies electrical drive control system operation management optimal feedforward torque control optimal reference current computation transformer-like nonlinear machine model artificial neural network |
| title | Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art |
| title_full | Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art |
| title_fullStr | Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art |
| title_full_unstemmed | Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art |
| title_short | Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art |
| title_sort | artificial neural network based optimal feedforward torque control of interior permanent magnet synchronous machines a feasibility study and comparison with the state of the art |
| topic | electrical drive control system operation management optimal feedforward torque control optimal reference current computation transformer-like nonlinear machine model artificial neural network |
| url | https://www.mdpi.com/1996-1073/15/5/1838 |
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