Robustness improvement for permanent synchronous motor drive using neural network
The report suggests using an active disturbance rejection control (ADRC) system to decrease torque ripple in PMSM motor drives. The system combines an extended state observer (ESO) with a neural network-based torque compensator, which reduces torque harmonic distortion caused by cogging and back EMF...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/167326 |
| _version_ | 1826123060619509760 |
|---|---|
| author | Tan, Eugene Yan Hao |
| author2 | Christopher H. T. Lee |
| author_facet | Christopher H. T. Lee Tan, Eugene Yan Hao |
| author_sort | Tan, Eugene Yan Hao |
| collection | NTU |
| description | The report suggests using an active disturbance rejection control (ADRC) system to decrease torque ripple in PMSM motor drives. The system combines an extended state observer (ESO) with a neural network-based torque compensator, which reduces torque harmonic distortion caused by cogging and back EMF. The ESO detects and rejects low-frequency disturbances, while the NN compensator has two layers that output backpropagation torque compensation to further reduce torque ripple. Simulations and experiments on the Simulink Model show that this approach is effective in suppressing torque ripple. |
| first_indexed | 2024-10-01T05:58:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167326 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:58:27Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1673262023-07-07T15:46:33Z Robustness improvement for permanent synchronous motor drive using neural network Tan, Eugene Yan Hao Christopher H. T. Lee School of Electrical and Electronic Engineering chtlee@ntu.edu.sg Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering The report suggests using an active disturbance rejection control (ADRC) system to decrease torque ripple in PMSM motor drives. The system combines an extended state observer (ESO) with a neural network-based torque compensator, which reduces torque harmonic distortion caused by cogging and back EMF. The ESO detects and rejects low-frequency disturbances, while the NN compensator has two layers that output backpropagation torque compensation to further reduce torque ripple. Simulations and experiments on the Simulink Model show that this approach is effective in suppressing torque ripple. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-25T07:16:16Z 2023-05-25T07:16:16Z 2023 Final Year Project (FYP) Tan, E. Y. H. (2023). Robustness improvement for permanent synchronous motor drive using neural network. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167326 https://hdl.handle.net/10356/167326 en A1057-221 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Tan, Eugene Yan Hao Robustness improvement for permanent synchronous motor drive using neural network |
| title | Robustness improvement for permanent synchronous motor drive using neural network |
| title_full | Robustness improvement for permanent synchronous motor drive using neural network |
| title_fullStr | Robustness improvement for permanent synchronous motor drive using neural network |
| title_full_unstemmed | Robustness improvement for permanent synchronous motor drive using neural network |
| title_short | Robustness improvement for permanent synchronous motor drive using neural network |
| title_sort | robustness improvement for permanent synchronous motor drive using neural network |
| topic | Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
| url | https://hdl.handle.net/10356/167326 |
| work_keys_str_mv | AT taneugeneyanhao robustnessimprovementforpermanentsynchronousmotordriveusingneuralnetwork |