#
An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter^{ †}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Relevant Theories

#### 2.1. Kalman Filter

**Step 1:**Prediction step:

**Step 2:**Update step:

#### 2.2. Feature-Aided Kalman Filter

## 3. The Framework of FAKF-Based Voltage Estimation

**Step 1 (Data Acquisition):**the monitoring data of feature to be estimated and the related feature is required to be obtained. In this study, voltage is the feature to be estimated while current is chosen to be the related feature.

**Step 2 (Model construction):**the model structure of the feature to be estimated and the related feature needs to be determined. Generally, the transfer function of the two features can be easily obtained. The model structure of voltage and current utilized in this study is presented in Equation (15).

**Step 3 (Model Identification):**model parameters are derived from the monitoring data based on model identification algorithm. The Least Squares Criterion identification algorithm is adopted to identify the model parameters.

**Step 4 (KF-based Estimation):**the model is determined after model construction and model identification, which can be utilized as a KF transition equation. Based on voltage monitoring data and estimation data with the constructed model, KF-based voltage estimation can be implemented.

**Step 5 (Estimation data Output):**the estimated feature is the output of FAKF. In this study, the estimated voltage is set to be the output of FAKF-based voltage estimation which can be further utilized in EMA PHM.

## 4. Experiment Results and Analysis

#### 4.1. FLEA Introduction

#### 4.2. Data Description of EMA

#### 4.3. Voltage Estimation Based on FAKF

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Parameters | Identified Results with −40 lbs Load | Identified Results with +40 lbs Load |
---|---|---|

${a}_{0}$ | −0.4998 | −0.4424 |

${a}_{1}$ | −0.0343 | −0.0512 |

${b}_{0}$ | −0.7889 | −1.2471 |

${b}_{1}$ | 5.9267 | 6.7634 |

${b}_{2}$ | −3.5857 | −3.2269 |

FAKF-Based Estimation | Model-Based Estimation | |
---|---|---|

MAE | 3.4361 | 6.2292 |

RMSE | 5.1754 | 8.0186 |

FAKF-Based Estimation | Model-Based Estimation | |
---|---|---|

MAE | 3.7047 | 6.5225 |

RMSE | 5.1613 | 8.4893 |

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**MDPI and ACS Style**

Zhang, Y.; Liu, L.; Peng, Y.; Liu, D.
An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter. *Sensors* **2018**, *18*, 4190.
https://doi.org/10.3390/s18124190

**AMA Style**

Zhang Y, Liu L, Peng Y, Liu D.
An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter. *Sensors*. 2018; 18(12):4190.
https://doi.org/10.3390/s18124190

**Chicago/Turabian Style**

Zhang, Yujie, Liansheng Liu, Yu Peng, and Datong Liu.
2018. "An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter" *Sensors* 18, no. 12: 4190.
https://doi.org/10.3390/s18124190