A Feature-Aided Kalman Filter Model for Electro-Mechanical Actuator Voltage Estimation

Electro-Mechanical Actuator (EMA) is increasingly utilized in More Electric Aircraft. To ensure EMA operation safety and reliability, performance degradation assessment should be effectively performed. It can provide early warning before occurrence of failures. EMA voltage is an essential parameter for EMA performance degradation assessment. However, there are gaps between voltage monitoring data and real voltage due to electromagnetic interference. To this end, as one of key technologies for performance degradation assessment, EMA voltage estimation should be focused. Besides, an accurate EMA physical model required by traditional estimation method is difficult to be obtained due to complexity of EMA. In order to solve the problem, this paper proposes a Feature-Aided Kalman Filter (FAKF) model to implement EMA voltage estimation. In FAKF, a physical model about current and voltage is utilized to obtain state data. Then, voltage estimation is conducted based on state data and voltage monitoring data. In FAKF-based voltage estimation, gaps between voltage monitoring data and real voltage are reduced. Finally, experimental results show that FAKF has better performance on EMA voltage estimation.