Name
Abstract | ||
|---|---|---|
Powered parafoils are flexible aircraft with high load capacities and low costs and are often used in long-term surveillance and airdrop missions. As a flexible aircraft, powered parafoil flights are easily affected by winds. However, wind-field identification is difficult for low-to-medium speed unmanned aerial vehicles (UAV), which makes it challenging for UAVs such as powered parafoils to perform targeted wind compensation. To observe and compensate for this, we must accurately identify the wind field for the powered parafoil. In this study, we designed a deep Q-learning iterative inversion (DQII) algorithm for wind-field identification to concretely analyze wind-field identification through model inversion. Using a deep Q-learning (DQ) inverting dynamic model, a powered parafoil can identify the wind field within 2 s. In addition, iterative chaotic particle swarm optimization was performed based on DQ inversion, which achieved a high-precision wind-field identification effect. Simulation experiments showed that, compared with the traditional dead reckoning (DR) algorithm, DQII can reduce the error by 80%–90%. In a powered parafoil homing experiment, DQII achieved a wind-field identification effect that was significantly better than that of the DR algorithm. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2022.07.185 | Information Sciences |
Keywords | DocType | Volume |
Powered parafoil,Wind-field identification,Deep Q-learning,Dynamic modeling | Journal | 610 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenping Yu | 1 | 0 | 0.34 |
| Hao Sun | 2 | 0 | 0.68 |
| Qinglin Sun | 3 | 3 | 3.77 |
| Jin Tao | 4 | 0 | 1.35 |
| Zengqiang Chen | 5 | 1 | 1.37 |