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Voronoi-based distributed formation control method of stratospheric aerostat for area coverage

Published online by Cambridge University Press:  21 April 2025

F. Bai
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China
X. Yang*
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China
X. Deng
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China
Z. Hou
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China
*
Corresponding author: X. Yang; Email: [email protected]

Abstract

Station-keeping control is a critical technology for stratospheric aerostats. For those aerostats that utilise wind field environments to achieve trajectory control, the station-keeping capability of a single aerostat is inherently limited. This limitation can lead to instances of the aerostat flying outside the designated task area, thereby diminishing the effectiveness of station-keeping control. To ensure continuous monitoring of the restricted area for long endurance, dynamic adjustments and cooperative coverage among multiple aerostats are necessary. This paper introduces an optimal coverage algorithm based on Voronoi diagrams and presents a formation control method for stratospheric aerostats that employs the virtual force method and the ${A^{\rm{*}}}$ algorithm, respectively. In a real wind field environment, ten aerostats are deployed to optimally cover the restricted area. Simulation results indicate that the coverage rate of the stratospheric aerostats within the restricted area can exceed 70%, while the network connectivity rate among the aerostats can reach 80% following guidance control during return flights. Furthermore, the stratospheric aerostats that flying out of the restricted area can return through path planning and optimal coverage algorithm, and the networking connectivity rate between aerostats is higher than that using the virtual force method.

Type
Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Lee, Y. and Yee, K. Numerical prediction of scientific balloon trajectories while considering various uncertainties, J. Aircraft, 2017, 54, (2), pp 768782.CrossRefGoogle Scholar
Corporation, Global Aerospace Global Constellation of Stratospheric Scientific Platforms, 1999.Google Scholar
Zuo, Z., Song, J., Zheng, Z. and Qing-Long, H. A survey on modelling, control and challenges of stratospheric airships, Control Eng. Pract., 2022, 119, p 104979.CrossRefGoogle Scholar
Kurt, G., Khoshkholgh, M.G., Alfattani, S., Ibrahim, A., Darwish, T.S.J., Alam, Md.S., Yanikomeroglu, H. and Yongacoglu, A. A vision and framework for the High Altitude Platform Station (HAPS) networks of the future, Commun. Surv. Tutorials, 2021, 2, pp 729779.CrossRefGoogle Scholar
Cathey, H.M. Development overview of the revised NASA ultra long duration balloon, Adv. Space Res., 2008, 42, (10), pp 16241632.CrossRefGoogle Scholar
Du, H., Lv, M., Li, J., Zhu, W., Zhang, L. and Wu, Yifei Station-keeping performance analysis for high altitude balloon with altitude control system, Aerospace Sci. Technol., 2019, 92, pp 644652.CrossRefGoogle Scholar
Bellemare, M.G., Candido, S.e, Castro, P.S., Gong, J., Machado, M.C., Moitra, S., Ponda, S.S. and Wang, Z. Autonomous navigation of stratospheric balloons using reinforcement learning, Nature, 2020, 588, (7836), pp 7782.CrossRefGoogle ScholarPubMed
Yoder, C.D., Gemmer, T.R. and Mazzoleni, A.P. Modelling and performance analysis of a tether and sail-based trajectory control system for extra-terrestrial scientific balloon missions, Acta Astronaut., 2019, 160, pp 527537.CrossRefGoogle Scholar
Jiang, Y., Lv, M., Qu, Z. and Zhang, L. Performance evaluation for scientific balloon station-keeping strategies considering energy management strategy, Renewable Energy, 2020, 156, pp 290302.CrossRefGoogle Scholar
Xu, Z., Liu, Y., Du, H. and Lv, M. Station-keeping for high-altitude balloon with reinforcement learning, Adv. Space Res., 2022, 70, (3), pp 733.CrossRefGoogle Scholar
Du, H., Lv, M., Zhang, L., Zhu, W., Wu, Y. and Li, J. Station-keeping for high-altitude balloon with reinforcement learning, Adv. Space Res., 2019, 93, pp 105342.Google Scholar
Zhang, Y., Zhu, M. and Chen, T. Event-triggered dynamic coverage control for multiple stratospheric airships, Sensors, 2022, 22, (7), pp 2734.CrossRefGoogle ScholarPubMed
Yu, Z., Zhang, Y., Jiang, B., Su, C.-Y., Fu, J., Jin, Y. and Chai, T. Distributed Fractional-Order Intelligent Adaptive Fault-Tolerant Formation-Containment control of two-layer networked unmanned airships for safe observation of a smart city, IEEE Trans. Cybern., 2021, 52, (9), pp 113.Google Scholar
Kim, J.H. and Yoo, S.J. Distributed event-triggered adaptive formation tracking of networked uncertain stratospheric airships using neural networks, IEEE Access, 2020, 8, pp 4997749988.CrossRefGoogle Scholar
Bicho, E., Moreira, A., Diegues, S., Carvalheira, M. and Monteiro, S. Airship Formation Control, 2006.Google Scholar
Vandermeulen, I., Guay, M. and McLellan, P. James distributed control of high-altitude balloon formation by extremum-seeking control, IEEE Trans. Control Syst. Technol., 2018, 26, (3), pp 857873.CrossRefGoogle Scholar
Chen, K., Wang, X. and Duan, D. Three-dimensional path-following adaptive control of stratospheric airship based on improved chemical reaction optimization algorithm, Aerospace Syst., 2018, 5, (1), pp 85105.CrossRefGoogle Scholar
Yang, X., Yang, X. and Deng, X. Horizontal trajectory control of stratospheric airships in wind field using Q-learning algorithm, Aerospace Sci. Technol., 2020, 106, pp 106100.CrossRefGoogle Scholar
Sniderman, A.C., Broucke, M.E. and D’Eleuterio, G.M.T. Formation control of balloons: A block circulant approach, American Automatic Control Council, 2015, pp 14631468.CrossRefGoogle Scholar
Vandermeulen, I. Distributed Extremum-Seeking Control with Applications to High-Altitude Balloons, ProQuest Dissertations Publishing, 2016.Google Scholar
Bewley, T. and Meneghello, G. Efficient coordination of swarms of sensor-laden balloons for persistent, in situ, real-time measurement of hurricane development, Phys. Rev. Fluids, 2016, 1, (6), pp 060507.CrossRefGoogle Scholar
LLC, Loon The Loon Library, 2021.Google Scholar
Zuo, L., Yan, W. and Yan, M. Efficient coverage algorithm for mobile sensor network with unknown density function, IET Control Theory Appl., 2017, 11, (6), pp 791798.CrossRefGoogle Scholar
Ma, L., He, F., Wang, L. and Yao, Y. Multi-agent coverage control design with dynamic sensing regions, Control Theory Technol., 2018, 16, (3), pp 161172.CrossRefGoogle Scholar
Du, Q., Faber, V. and Gunzburger, M. Centroidal Voronoi tessellations: Applications and algorithms, SIAM Rev., 1999, 41, (4), pp 637676.CrossRefGoogle Scholar
Lloyd, S. Least squares quantization in PCM, IEEE Trans. Inf. theory, 1982, 28, (2), pp 129137.CrossRefGoogle Scholar
Yang, J. and Xia, Y. Coverage and routing optimization of wireless sensor networks using improved cuckoo algorithm, IEEE Access, 2024, 12, pp 3956439577.CrossRefGoogle Scholar
Yao, Y., Liao, H., Liu, M. and Yang, X. Coverage optimization strategy for 3-D wireless sensor networks based on improved sparrow search algorithm, IEEE Sens. J., 2023, 23, (19), pp 2372123733.CrossRefGoogle Scholar
Auh, E., Kim, J., Joo, Y., Park, J., Lee, G., Oh, I., Pico, N. and Moon, H. Unloading sequence planning for autonomous robotic container-unloading system using A-star search algorithm, Eng. Sci. Technol. Int. J., 2024, 50, pp 101610.Google Scholar
Miyombo, M.E., Liu, Y.-k., Mulenga, C.M., Siamulonga, A., Kabanda, M.C., Shaba, P., Xi, C. and Ayodeji, A. Optimal path planning in a real-world radioactive environment: A comparative study of A-star and Dijkstra algorithms, Nuclear Eng. Des., 2024, 420, pp 113039.CrossRefGoogle Scholar