EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels
This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a singl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/19/4174 |
| _version_ | 1797999041185841152 |
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| author | Rodrigo Munguía Sarquis Urzua Antoni Grau |
| author_facet | Rodrigo Munguía Sarquis Urzua Antoni Grau |
| author_sort | Rodrigo Munguía |
| collection | DOAJ |
| description | This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied. |
| first_indexed | 2024-04-11T10:58:21Z |
| format | Article |
| id | doaj.art-2006029cb7d342468303aefd15f645de |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T10:58:21Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2006029cb7d342468303aefd15f645de2022-12-22T04:28:41ZengMDPI AGSensors1424-82202019-09-011919417410.3390/s19194174s19194174EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModelsRodrigo Munguía0Sarquis Urzua1Antoni Grau2Department of Computer Science, CUCEI, University of Guadalajara, 44430 Guadalajara, MexicoDepartment of Mechanical Engineering, CUCEI, University of Guadalajara, 44430 Guadalajara, MexicoDepartment of Automatic Control, Technical University of Catalonia UPC, 08034 Barcelona, SpainThis work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.https://www.mdpi.com/1424-8220/19/19/4174unmanned aerial vehiclesmulti-rotorparameter identificationobservability analysiskalman filter |
| spellingShingle | Rodrigo Munguía Sarquis Urzua Antoni Grau EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels Sensors unmanned aerial vehicles multi-rotor parameter identification observability analysis kalman filter |
| title | EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels |
| title_full | EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels |
| title_fullStr | EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels |
| title_full_unstemmed | EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels |
| title_short | EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels |
| title_sort | ekf based parameter identification of multi rotor unmanned aerial vehiclesmodels |
| topic | unmanned aerial vehicles multi-rotor parameter identification observability analysis kalman filter |
| url | https://www.mdpi.com/1424-8220/19/19/4174 |
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