Optimal Control Architecture for Rotor Position Estimation and Torque Ripple Minimization in 8/6 Switched Reluctance Motor
Fuente:
Wiley Open Access
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Artículo Open Access |
| Pubblicazione: |
Wiley
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1867005573016322049 |
|---|---|
| author | Rathikrinda Yedukondalu Balapanur Mouli Chandra Rayapudi Srinivasa Rao |
| author_facet | Rathikrinda Yedukondalu Balapanur Mouli Chandra Rayapudi Srinivasa Rao Rathikrinda Yedukondalu Balapanur Mouli Chandra Rayapudi Srinivasa Rao |
| collection | Wiley Open Access |
| contents | Optimal Control Architecture for Rotor Position Estimation and Torque Ripple Minimization in 8/6 Switched Reluctance Motor Rathikrinda Yedukondalu Balapanur Mouli Chandra Rayapudi Srinivasa Rao Optimal Control Applications and Methods ABSTRACT In recent years, advancements in control methods for electrical machines have increasingly focused on reducing torque and current fluctuations in power generation systems. However, external factors such as temperature, humidity, and pressure, along with uncertainties like friction, torque fluctuations, and disturbances, can negatively impact system stability and reliability. In order to overcome all of these challenges and improve the effectiveness of switched reluctance motor systems, this research introduced an Adaptive Neuro‐Fuzzy Inference System (ANFIS) optimized by the Border Collie Optimization (BCO) algorithm. The primary goal is to accurately determine the location of the rotor of an 8/6 switched reluctance motor by leveraging the connection between phase current and flux links. The two main loops of the control system are the fractional‐order proportional‐integral‐derivative speed controller and the current controller. The current controller fine‐tunes control to guarantee the best possible performance from the switching reluctance motor, while the speed controller processes the reference signal to provide drive signals for the power converter. Feedback is provided via rotor position detection and current sensors to enable precise speed regulation. In MATLAB Simulink, an 8/6 four‐phase system is used to depict a switching reluctance motor with eight stator poles and six rotor poles. Both with and without torque ripple mitigation, the system's performance is examined. In the torque ripple minimization scenario with a rotor position angle of 60°, the proposed method reduces the Integral of Squared Error to 0.9e‐04 and torque ripple to 2.1%, outperforming existing approaches. 10.1002/oca.70049 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/oca.70049 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_oca_70049 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Optimal Control Architecture for Rotor Position Estimation and Torque Ripple Minimization in 8/6 Switched Reluctance Motor Rathikrinda Yedukondalu Balapanur Mouli Chandra Rayapudi Srinivasa Rao Optimal Control Applications and Methods Optimal Control Architecture for Rotor Position Estimation and Torque Ripple Minimization in 8/6 Switched Reluctance Motor Rathikrinda Yedukondalu Balapanur Mouli Chandra Rayapudi Srinivasa Rao Optimal Control Applications and Methods ABSTRACT In recent years, advancements in control methods for electrical machines have increasingly focused on reducing torque and current fluctuations in power generation systems. However, external factors such as temperature, humidity, and pressure, along with uncertainties like friction, torque fluctuations, and disturbances, can negatively impact system stability and reliability. In order to overcome all of these challenges and improve the effectiveness of switched reluctance motor systems, this research introduced an Adaptive Neuro‐Fuzzy Inference System (ANFIS) optimized by the Border Collie Optimization (BCO) algorithm. The primary goal is to accurately determine the location of the rotor of an 8/6 switched reluctance motor by leveraging the connection between phase current and flux links. The two main loops of the control system are the fractional‐order proportional‐integral‐derivative speed controller and the current controller. The current controller fine‐tunes control to guarantee the best possible performance from the switching reluctance motor, while the speed controller processes the reference signal to provide drive signals for the power converter. Feedback is provided via rotor position detection and current sensors to enable precise speed regulation. In MATLAB Simulink, an 8/6 four‐phase system is used to depict a switching reluctance motor with eight stator poles and six rotor poles. Both with and without torque ripple mitigation, the system's performance is examined. In the torque ripple minimization scenario with a rotor position angle of 60°, the proposed method reduces the Integral of Squared Error to 0.9e‐04 and torque ripple to 2.1%, outperforming existing approaches. 10.1002/oca.70049 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Optimal Control Architecture for Rotor Position Estimation and Torque Ripple Minimization in 8/6 Switched Reluctance Motor |
| topic | Optimal Control Applications and Methods |
| url | https://onlinelibrary.wiley.com/doi/10.1002/oca.70049 |