Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems
An essential aspect to achieving safety with a UAV is that it operates within the limits of its capabilities, the available flight time being a key aspect when planning and executing a mission. The flight time will depend on the relationship between the available energy and the energy required by th...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Technologies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7080/11/1/12 |
| _version_ | 1797618014067097600 |
|---|---|
| author | Luis H. Manjarrez Julio C. Ramos-Fernández Eduardo S. Espinoza Rogelio Lozano |
| author_facet | Luis H. Manjarrez Julio C. Ramos-Fernández Eduardo S. Espinoza Rogelio Lozano |
| author_sort | Luis H. Manjarrez |
| collection | DOAJ |
| description | An essential aspect to achieving safety with a UAV is that it operates within the limits of its capabilities, the available flight time being a key aspect when planning and executing a mission. The flight time will depend on the relationship between the available energy and the energy required by the UAV to complete the mission. This paper addresses the problem of estimating the energy required to perform a mission, for which a fuzzy Takagi–Sugeno system was implemented, whose premises were developed using fuzzy C-means to estimate the power required in the different stages of the mission. The parameters used in the fuzzy C-means algorithm were optimized using particle swarm optimization. On the other hand, an equivalent circuit model of a battery was used, for which fuzzy modeling was employed to determine the relationship between the open-circuit voltage and the state of charge of the battery, which in conjunction with an extended Kalman filter allows determining the battery charge. In addition, we developed a methodology to determine the minimum allowable battery charge level. From this, it is possible to determine the available flight time at the end of a mission defined as the flight time margin. In order to evaluate the developed methodology, a physical experiment was performed using an hexarotor UAV obtaining a maximum prediction error equivalent to the energy required to operate for 7 s, which corresponds to 2% of the total mission time. |
| first_indexed | 2024-03-11T08:04:09Z |
| format | Article |
| id | doaj.art-b949667a4b384fb29e2ef7d116d9d4fa |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-11T08:04:09Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-b949667a4b384fb29e2ef7d116d9d4fa2023-11-16T23:35:59ZengMDPI AGTechnologies2227-70802023-01-011111210.3390/technologies11010012Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy SystemsLuis H. Manjarrez0Julio C. Ramos-Fernández1Eduardo S. Espinoza2Rogelio Lozano3Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, MexicoDepartment of Mechatronics Engineering, Polytechnic University of Pachuca, Hidalgo 43830, MexicoCenter for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, MexicoCenter for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, MexicoAn essential aspect to achieving safety with a UAV is that it operates within the limits of its capabilities, the available flight time being a key aspect when planning and executing a mission. The flight time will depend on the relationship between the available energy and the energy required by the UAV to complete the mission. This paper addresses the problem of estimating the energy required to perform a mission, for which a fuzzy Takagi–Sugeno system was implemented, whose premises were developed using fuzzy C-means to estimate the power required in the different stages of the mission. The parameters used in the fuzzy C-means algorithm were optimized using particle swarm optimization. On the other hand, an equivalent circuit model of a battery was used, for which fuzzy modeling was employed to determine the relationship between the open-circuit voltage and the state of charge of the battery, which in conjunction with an extended Kalman filter allows determining the battery charge. In addition, we developed a methodology to determine the minimum allowable battery charge level. From this, it is possible to determine the available flight time at the end of a mission defined as the flight time margin. In order to evaluate the developed methodology, a physical experiment was performed using an hexarotor UAV obtaining a maximum prediction error equivalent to the energy required to operate for 7 s, which corresponds to 2% of the total mission time.https://www.mdpi.com/2227-7080/11/1/12SoC estimationfuzzy clusteringmultirotor UAV |
| spellingShingle | Luis H. Manjarrez Julio C. Ramos-Fernández Eduardo S. Espinoza Rogelio Lozano Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems Technologies SoC estimation fuzzy clustering multirotor UAV |
| title | Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems |
| title_full | Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems |
| title_fullStr | Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems |
| title_full_unstemmed | Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems |
| title_short | Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems |
| title_sort | estimation of energy consumption and flight time margin for a uav mission based on fuzzy systems |
| topic | SoC estimation fuzzy clustering multirotor UAV |
| url | https://www.mdpi.com/2227-7080/11/1/12 |
| work_keys_str_mv | AT luishmanjarrez estimationofenergyconsumptionandflighttimemarginforauavmissionbasedonfuzzysystems AT juliocramosfernandez estimationofenergyconsumptionandflighttimemarginforauavmissionbasedonfuzzysystems AT eduardosespinoza estimationofenergyconsumptionandflighttimemarginforauavmissionbasedonfuzzysystems AT rogeliolozano estimationofenergyconsumptionandflighttimemarginforauavmissionbasedonfuzzysystems |