Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs
Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperatively intercepting a maneuvering target, this paper proposes a cooperative guidance law with less energy consumption and a newly accurate time-to-go estimation algorithm in the two-dimensional (2D) plane. Firstly, based on the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/11/657 |
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| author | Hanqiao Huang Yue Dong Haoran Cui Huan Zhou Bo Du |
| author_facet | Hanqiao Huang Yue Dong Haoran Cui Huan Zhou Bo Du |
| author_sort | Hanqiao Huang |
| collection | DOAJ |
| description | Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperatively intercepting a maneuvering target, this paper proposes a cooperative guidance law with less energy consumption and a newly accurate time-to-go estimation algorithm in the two-dimensional (2D) plane. Firstly, based on the relative motion equations between UAVs and the target on the 2D plane, the line-of-sight (LOS) direction and the LOS normal direction models are established. Then, based on the distributed model predictive control (DMPC) theory, DMPC cooperative guidance laws are designed in two directions. This guidance law can ensure that all UAVs intercept the maneuvering target at the expected LOS angle at the same time and reduce the energy consumption during the guidance process. Then, a new time-to-go estimation algorithm is designed, which can reduce the time-to-go estimation error and improve the cooperative accuracy. Finally, the simulation results show that the DMPC cooperative guidance law reduces energy consumption by more than 50% compared to other guidance laws and the proposed time-to-go estimation algorithm improves the accuracy by 200% compared to traditional methods. |
| format | Article |
| id | doaj-art-59df00cd585d4980b152213e30d790b5 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-59df00cd585d4980b152213e30d790b52024-11-26T18:00:44ZengMDPI AGDrones2504-446X2024-11-0181165710.3390/drones8110657Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVsHanqiao Huang0Yue Dong1Haoran Cui2Huan Zhou3Bo Du4Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaAviation Engineering School, Air Force Engineering University, Xi’an 710043, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaAiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperatively intercepting a maneuvering target, this paper proposes a cooperative guidance law with less energy consumption and a newly accurate time-to-go estimation algorithm in the two-dimensional (2D) plane. Firstly, based on the relative motion equations between UAVs and the target on the 2D plane, the line-of-sight (LOS) direction and the LOS normal direction models are established. Then, based on the distributed model predictive control (DMPC) theory, DMPC cooperative guidance laws are designed in two directions. This guidance law can ensure that all UAVs intercept the maneuvering target at the expected LOS angle at the same time and reduce the energy consumption during the guidance process. Then, a new time-to-go estimation algorithm is designed, which can reduce the time-to-go estimation error and improve the cooperative accuracy. Finally, the simulation results show that the DMPC cooperative guidance law reduces energy consumption by more than 50% compared to other guidance laws and the proposed time-to-go estimation algorithm improves the accuracy by 200% compared to traditional methods.https://www.mdpi.com/2504-446X/8/11/657multiple UAVstemporal cooperationspatial cooperationmodel predictive controltime-to-go estimationmaneuvering targets |
| spellingShingle | Hanqiao Huang Yue Dong Haoran Cui Huan Zhou Bo Du Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs Drones multiple UAVs temporal cooperation spatial cooperation model predictive control time-to-go estimation maneuvering targets |
| title | Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs |
| title_full | Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs |
| title_fullStr | Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs |
| title_full_unstemmed | Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs |
| title_short | Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs |
| title_sort | distributed model predictive control cooperative guidance law for multiple uavs |
| topic | multiple UAVs temporal cooperation spatial cooperation model predictive control time-to-go estimation maneuvering targets |
| url | https://www.mdpi.com/2504-446X/8/11/657 |
| work_keys_str_mv | AT hanqiaohuang distributedmodelpredictivecontrolcooperativeguidancelawformultipleuavs AT yuedong distributedmodelpredictivecontrolcooperativeguidancelawformultipleuavs AT haorancui distributedmodelpredictivecontrolcooperativeguidancelawformultipleuavs AT huanzhou distributedmodelpredictivecontrolcooperativeguidancelawformultipleuavs AT bodu distributedmodelpredictivecontrolcooperativeguidancelawformultipleuavs |