Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm

Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target trac...

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Veröffentlicht in:Aerospace science and technology Jg. 70; S. 497 - 510
Hauptverfasser: Wu, Jianfa, Wang, Honglun, Li, Na, Yao, Peng, Huang, Yu, Su, Zikang, Yu, Yue
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Masson SAS 01.11.2017
Schlagworte:
Adaptive Grasshopper Optimization Algorithm (AGOA)
Distributed model predictive control (DMPC)
Sight occlusions
Solar-powered UAVs (SUAVs)
Target tracking
Urban environment
Target tracking
Solar-powered UAVs (SUAVs)
Urban environment
Adaptive Grasshopper Optimization Algorithm (AGOA)
Sight occlusions
Distributed model predictive control (DMPC)
ISSN:1270-9638, 1626-3219
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Zusammenfassung:Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target tracking problem in urban environment is modeled by formulating the SUAVs kinematic and target models, the urban environment constraints, solar power harvesting and consumption models for SUAV, and sight occlusions by constructions. Especially, the sight occlusions in urban environment for SUAV are taken into consideration for the first time in this paper. A judgment method of sight occlusions for SUAV is proposed to make the calculation of the energy index more precise. Second, based on the precise modeling, the DMPC method is adopted as the framework for trajectory optimization in real time. Third, AGOA, a novel intelligent algorithm to mimic the behaviors of grasshoppers, is proposed to be the DMPC solver. The proposed AGOA has a better searching ability than the traditional GOA and some other intelligent algorithms by introducing some improvement measures e.g. the natural selection strategy, the democratic decision-making mechanism, and the dynamic feedback mechanism based on the 1/5 Principle. Finally, the effectiveness of the proposed method is demonstrated by the simulations.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2017.08.037