Intelligent attitude planning algorithm based on the characteristics of low radar cross section characteristics of microsatellites under complex constraints
| Autor: | Da-Fu Xu, Bing Hua, Liu Ruipeng, Yunhua Wu |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Radar cross-section Optimization problem Spacecraft business.industry Mechanical Engineering Control (management) Aerospace Engineering Control engineering 02 engineering and technology 020901 industrial engineering & automation Geography 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business Planning algorithms |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 233:4-21 |
| ISSN: | 2041-3025 0954-4100 |
| DOI: | 10.1177/0954410017724821 |
| Popis: | The attitude optimization problem of spacecraft under restricted conditions is an important issue of spacecraft planning control. This paper aims at on-orbit microsatellites, which is based on the directional characteristics of their own low radar cross section designs, maintaining low detection probabilities for ground, sea, and space-based detection systems, and simultaneously satisfying the constraint conditions of complex attitude constraints. In this paper, an improved pigeon-inspired optimization algorithm and a nonredundant attitude description method—modified Rodrigues parameters—are used to solve the problem of attitude optimal planning for satellites under complex constraints. This paper focuses on the core evolution mathematical model of the pigeon-inspired optimization algorithm based on the modified Rodrigues parameter, the iterative evolution process of the individual in the pigeon population, and the fitness function model of the individual at different positions. The comparison between the classical pigeon-inspired optimization algorithm and the improved pigeon-inspired optimization algorithm is made in the planned result and resource occupancy, respectively. The simulation results show that the improved algorithm has a faster convergence speed and a smoother optimization result than the classic pigeon-inspired optimization algorithm, where it greatly reduces the computational load and reduces the load of the control system, thus achieving an optimal algorithm. |
| Databáze: | OpenAIRE |
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