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Performance reliability and parameter sensitivity analysis of spacecraft separation system combining Monte Carlo and Adaboost regression methods

Published online by Cambridge University Press:  09 September 2025

S. Lai Open the ORCID record for S. Lai [Opens in a new window] ,
T. Wang ,
Q. Zhao ,
C. Wang Open the ORCID record for C. Wang [Opens in a new window]  and
J. Chen
S. Lai
Affiliation:
Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China National Key Laboratory of Aerospace Mechanism, Shanghai 201109, China
T. Wang
Affiliation:
Shanghai Institute of Aerospace Systems Engineering, Shanghai 201109, China
Q. Zhao
Affiliation:
Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China Shanghai Institute of Aerospace Systems Engineering, Shanghai 201109, China
C. Wang*
Affiliation:
Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
J. Chen
Affiliation:
Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
*
Corresponding author: Chen Wang; Email: nuaawangchen@nuaa.edu.cn
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Abstract

The Monte Carlo methods are frequently employed to evaluate the overall characteristics of non-monotonic, non-linear, non-superpositional performance functions. However, the multi-parameter, multi-objective spacecraft separation dynamics model is not amenable to decoupling to produce a result. This paper presents a parametric objective function that can be sampled. It combines the reliability analysis of the complex non-linear spacecraft separation model with Automated Dynamic Analysis of Mechanical Systems (ADAMS) and uses the Monte Carlo method to obtain the separation performance of the spacecraft separation system reliability profile, that is to say, the distribution of separation performance. The performance distribution of the spacecraft separation system was determined and parameters such as spring separation force, spring line of action, module mass and module centre of mass position were found to have a significant effect on the spacecraft separation dynamics by Adaboost machine learning regression.

Keywords

Adaboostaerospace mechanismMonte Carloseparation dynamicsspacecraft

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Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 22
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Wang, C., Nie, H. and Chen, J. The design and dynamic analysis of a lunar lander with semi-active control, Acta Astronaut., 2019; 157, pp 145–156.10.1016/j.actaastro.2018.12.037CrossRefGoogle Scholar
Hou, S., Li, D., Li, Q. and Tang, X. Force-transmission characteristics of a cable-driven mechanism with high initial velocity for spacecraft separation in ground tests, J. Mech. Sci. Technol., 2023; 37, (12), pp 6311–6324.10.1007/s12206-023-1108-zCrossRefGoogle Scholar
Cui, D., Zhao, J., Yan, S., Guo, X. and Li, J. Analysis of parameter sensitivity on dynamics of satellite separation, Acta Astronaut., 2015; 114, pp 22–33.10.1016/j.actaastro.2015.04.007CrossRefGoogle Scholar
Yue, H., Yang, Y., Lu, Y., et al. Research progress of space non-pyrotechnic low-shock connection and separation technology (SNLT): A review, Chin. J. Aeronaut., 2022; 35, (11), pp 113–154.10.1016/j.cja.2021.07.001CrossRefGoogle Scholar
Hu, X., Chen, X., Zhao, Y. and Yao, W. Optimization design of satellite separation systems based on Multi-Island Genetic Algorithm, Adv. Space Res., 2014; 53, (5), pp 870–876.10.1016/j.asr.2013.12.021CrossRefGoogle Scholar
Rao, B.N., Jeyakumar, D., Biswas, K.K., Swaminathan, S. and Janardhana, E. Rigid body separation dynamics for space launch vehicles, Aeronaut. J., 2016; 110, (1107), pp 289–302.10.1017/S0001924000013166CrossRefGoogle Scholar
Saki, S., Bolandi, H. and Mousavi Mashhadi, SK-e-d. Optimal direct adaptive soft switching multi-model predictive control using the gap metric for spacecraft attitude control in a wide range of operating points, Aerosp. Sci. Technol., 2018; 77, pp 235–243.10.1016/j.ast.2018.03.001CrossRefGoogle Scholar
Li, W.-J., Cheng, D.-Y., Liu, X.-G., et al. On-orbit service (OOS) of spacecraft: A review of engineering developments, Progr. Aerosp. Sci., 2019; 108, pp 32–120.10.1016/j.paerosci.2019.01.004CrossRefGoogle Scholar
Ding, J., Zhao, H., Wang, J., Sun, Y. and Chen, Z. Numerical and experimental investigation on the shock mitigation of satellite-rocket separation, Aerosp. Sci. Technol., 2020; 96, p 105538.10.1016/j.ast.2019.105538CrossRefGoogle Scholar
Fu, J., Liu, M., Cao, X. and Li, A. Robust neural-network-based quasi-sliding-mode control for spacecraft-attitude maneuvering with prescribed performance, Aerosp. Sci. Technol., 2021; 112, p 106667.10.1016/j.ast.2021.106667CrossRefGoogle Scholar
Hou, S.H., Sun, H.N., Li, Q.Z. and Tang, X.Q. Design and experimental validation of a disturbing force application unit for simulating spacecraft separation, Aerosp. Sci., Technol., 2021; 113, p 106674.10.1016/j.ast.2021.106674CrossRefGoogle Scholar
Lyu, B., Liu, C. and Yue, X. Hybrid nonfragile intermediate observer-based T-S fuzzy attitude control for flexible spacecraft with input saturation, Aerosp. Sci. Technol., 2022; 128, p 107753.10.1016/j.ast.2022.107753CrossRefGoogle Scholar
Hu, Y., Wu, M., Zhao, K., Song, J. and He, B. ADRC-based compound control strategy for spacecraft multi-body separation, Aerosp. Sci. Technol., 2023; 143, p 108686.10.1016/j.ast.2023.108686CrossRefGoogle Scholar
Luo, C., Sun, J., Wen, H. and Jin, D. Autonomous separation deployment dynamics of a space multi-rigid-body system with uncertain parameters, Mech. Mach. Theory, 2023; 180.Google Scholar
Teng, L. and Jin, Z.-h. A composite optimization method for separation parameters of large-eccentricity pico-satellites, Front. Inf. Technol. Electron. Eng., 2018; 19, (5), pp 685–698.10.1631/FITEE.1700416CrossRefGoogle Scholar
Merler, S., Caprile, B. and Furlanello, C. Parallelizing AdaBoost by weights dynamics, Comput. Statist. Data Anal., 2007; 51, (5), 2487–2498.10.1016/j.csda.2006.09.001CrossRefGoogle Scholar
Rokach, L. Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography, Comput. Statist. Data Anal., 2009; 53, (12), pp 4046–4072.10.1016/j.csda.2009.07.017CrossRefGoogle Scholar
Tsao, C.A. and Chang, Y.-cI. A stochastic approximation view of boosting, Comput. Statist. Data Anal., 2007; 52, (1), pp 325–334.10.1016/j.csda.2007.06.020CrossRefGoogle Scholar
Aslanov, V., Kruglov, G. and Yudintsev, V. Newton–Euler equations of multibody systems with changing structures for space applications, Acta Astronaut., 2011; 68, (11–12), pp 2080–2087.10.1016/j.actaastro.2010.11.013CrossRefGoogle Scholar