An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter
Electro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. How...
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4190 |
| _version_ | 1817989794015215616 |
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| author | Yujie Zhang Liansheng Liu Yu Peng Datong Liu |
| author_facet | Yujie Zhang Liansheng Liu Yu Peng Datong Liu |
| author_sort | Yujie Zhang |
| collection | DOAJ |
| description | Electro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. However, deviation exists between motor voltage monitoring data and real motor voltage due to electromagnetic interference. To reduce the deviation, EMA motor voltage estimation generally requires an accurate voltage state equation which is difficult to obtain due to the complexity of EMA. To address this problem, a Feature-aided Kalman Filter (FAKF) method is proposed, in which the state equation is substituted by a physical model of current and voltage. Consequently, voltage state data can be obtained through current monitoring data and a current⁻voltage model. Furthermore, voltage estimation can be implemented by utilizing voltage state data and voltage monitoring data. To validate the effectiveness of the FAKF-based estimation method, experiments have been conducted based on the published data set from NASA’s Flyable Electro-Mechanical Actuator (FLEA) test stand. The experiment results demonstrate that the proposed method has good performance in EMA motor voltage estimation. |
| first_indexed | 2024-04-14T00:51:32Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:51:32Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-33b7eb7eb53c4630a3a42b586e63a5812022-12-22T02:21:47ZengMDPI AGSensors1424-82202018-11-011812419010.3390/s18124190s18124190An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman FilterYujie Zhang0Liansheng Liu1Yu Peng2Datong Liu3Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150001, ChinaDepartment of Automatic Test and Control, Harbin Institute of Technology, Harbin 150001, ChinaDepartment of Automatic Test and Control, Harbin Institute of Technology, Harbin 150001, ChinaDepartment of Automatic Test and Control, Harbin Institute of Technology, Harbin 150001, ChinaElectro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. However, deviation exists between motor voltage monitoring data and real motor voltage due to electromagnetic interference. To reduce the deviation, EMA motor voltage estimation generally requires an accurate voltage state equation which is difficult to obtain due to the complexity of EMA. To address this problem, a Feature-aided Kalman Filter (FAKF) method is proposed, in which the state equation is substituted by a physical model of current and voltage. Consequently, voltage state data can be obtained through current monitoring data and a current⁻voltage model. Furthermore, voltage estimation can be implemented by utilizing voltage state data and voltage monitoring data. To validate the effectiveness of the FAKF-based estimation method, experiments have been conducted based on the published data set from NASA’s Flyable Electro-Mechanical Actuator (FLEA) test stand. The experiment results demonstrate that the proposed method has good performance in EMA motor voltage estimation.https://www.mdpi.com/1424-8220/18/12/4190electro-mechanical actuatorperformance degradationvoltage estimationfeature-aided Kalman filter |
| spellingShingle | Yujie Zhang Liansheng Liu Yu Peng Datong Liu An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter Sensors electro-mechanical actuator performance degradation voltage estimation feature-aided Kalman filter |
| title | An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter |
| title_full | An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter |
| title_fullStr | An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter |
| title_full_unstemmed | An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter |
| title_short | An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter |
| title_sort | electro mechanical actuator motor voltage estimation method with a feature aided kalman filter |
| topic | electro-mechanical actuator performance degradation voltage estimation feature-aided Kalman filter |
| url | https://www.mdpi.com/1424-8220/18/12/4190 |
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