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Spacecraft reentry trajectory optimization by heuristic optimization methods and optimal control theory

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Abstract

In the present work, a hybrid method for trajectory optimization of reentry spacecraft was proposed. The guiding legislation on the basis of heat reduction is presented in the procedure of obtaining the optimal trajectory. The heat rate should be decreased in the early phase of reentry flight by concentrating on the angle of attack profile. To meet terminal circumstances, the second step of suggested guidance included specifying the accurate profiles of the angle of attack and bank angle. A specific cost function should be minimized in each step. As a result, many optimization strategies have been utilized and compared. Due to its long flight duration and numerical sensitivity, the optimum trajectory issue of the space reentry vehicle (SRV) has been explored as one of the toughest problems in trajectory design. In both phases, the ideal trajectory for the optimality requirements has been discovered for various cost functions such as (i) overall heat rate, (ii) maximal heat rate, and (iii) terminal conditions. It may be inferred that the unique suggested strategy can minimize heat while maintaining the final conditions.

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References

  1. Rahimi A, Dev Kumar K, Alighanbari H (2012) Particle swarm optimization applied to spacecraft reentry trajectory. J Guid Control Dyn 36(1):307. https://doi.org/10.2514/1.56387

    Article  Google Scholar 

  2. Pontryagin LS (1987) The mathematical theory of optimal processes. CRC Press

    Google Scholar 

  3. Betts JT, Kolmanovsky I (2002) Practical methods for optimal control using nonlinear programming. Appl Mech Rev 55:B68. https://doi.org/10.1115/1.1483351

    Article  Google Scholar 

  4. Hargraves CR, Paris SW (1987) Direct trajectory optimization using nonlinear programming and collocation. J Guid Control Dyn 10(4):338. https://doi.org/10.2514/3.20223

    Article  MATH  Google Scholar 

  5. Gurfil P, Kasdin NJ (2002) Niching genetic algorithms-based characterization of geocentric orbits in the 3D elliptic restricted three-body problem. Comput Methods Appl Mech Eng 191:5683–5706. https://doi.org/10.1016/S0045-7825(02)00481-4

    Article  MathSciNet  MATH  Google Scholar 

  6. Yokoyama N, Suzuki S (2005) Modified genetic algorithm for constrained trajectory optimization. J Guid Control Dyn 28(1):139. https://doi.org/10.2514/1.3042

    Article  Google Scholar 

  7. Shafieenejad I, Novinzadeh AB, Molazadeh VR (2014) Comparing and analyzing min-time and min-effort criteria for free true anomaly of low-thrust orbital maneuvers with new optimal control algorithm. Aerosp Sci Technol 4(35):116–134. https://doi.org/10.1016/j.ast.2014.03.009

    Article  Google Scholar 

  8. Shafieenejad I, Novinzadeh AB, Molazadeh VR (2015) Introducing a novel algorithm for minimum-time low-thrust orbital transfers with free initial condition. Proc Inst Mech Eng G J Aerosp Eng 229(2):333–351. https://doi.org/10.1177/0954410014533311

    Article  Google Scholar 

  9. Duan H, Li S (2015) Artificial bee colony–based direct collocation for reentry trajectory optimization of hypersonic vehicle. IEEE Trans Aerosp Electron Syst 51(1):615–626. https://doi.org/10.1109/TAES.2014.120654

    Article  Google Scholar 

  10. Graichen K, Petit N (2008) Constructive methods for initialization and handling mixed state-input constraints in optimal control. J Guid Control Dyn 31(5):1334–1343. https://doi.org/10.2514/1.33870

    Article  Google Scholar 

  11. Berend N, Talbot C (2006) Overview of some optimal control methods adapted to expendable and reusable launch vehicle trajectories. Aerosp Sci Technol 10(3):222–232. https://doi.org/10.1016/j.ast.2005.11.004

    Article  MATH  Google Scholar 

  12. Peng H, Yang C, Li Y, Zhang S, Chen B (2013) Surrogate-based parameter optimization and optimal control for optimal trajectory of Halo orbit rendezvous. Aerosp Sci Technol 26(1):176–184. https://doi.org/10.1016/j.ast.2012.04.001

    Article  Google Scholar 

  13. Pan B, Lu P, Pan X, Ma Y (2016) Double-homotopy method for solving optimal control problems. J Guid Control Dyn doi 10(2514/1):G001553

    Google Scholar 

  14. Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York

    Google Scholar 

  15. Gen M, Cheng R (2000) Genetic algorithms and engineering optimization, vol 7. Wiley, Newyork

    Google Scholar 

  16. Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27(6):17–26. https://doi.org/10.1109/2.294849

    Article  Google Scholar 

  17. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471. https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  18. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687. https://doi.org/10.1016/j.asoc.2007.05.007

    Article  Google Scholar 

  19. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132. https://doi.org/10.1016/j.jfranklin.2008.11.003

    Article  MathSciNet  MATH  Google Scholar 

  20. Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014. https://doi.org/10.1007/s10845-010-0393-4

    Article  Google Scholar 

  21. Yusup N, Sarkheyli A, Zain AM, Hashim SZM, Ithnin N (2014) Estimation of optimal machining control parameters using artificial bee colony. J Intell Manuf 25(6):1463. https://doi.org/10.1007/s10845-013-0753-y

    Article  Google Scholar 

  22. Ramadan HS, Fathy A, Becherif M (2017) Optimal gain scheduling of VSC-HVDC system sliding mode control via artificial bee colony and mine blast algorithms. IET Gener Trans Distrib 12(3):661. https://doi.org/10.1049/iet-gtd.2017.0935

    Article  Google Scholar 

  23. Samani M, Tafreshi M, Shafieenejad I, Nikkhah AA (2015) Minimum-time open-loop and closed-loop optimal guidance with GA-PSO and neural-fuzzy for Samarai MAV flight. IEEE Aerosp Electron Syst Mag 30:28–37. https://doi.org/10.1109/MAES.2015.7119822

    Article  Google Scholar 

  24. Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) fo landslide spatial modelling. CATENA 157:310–324. https://doi.org/10.1016/j.catena.2017.05.034

    Article  Google Scholar 

  25. Abdulkhader HK, Jacob J, Mathew AT (2018) Fractional-order lead-lag compensator-based multi-band power system stabiliser design using a hybrid dynamic GA-PSO algorithm. IET Gener Transm Distrib 12:3248–3260. https://doi.org/10.1049/iet-gtd.2017.1087

    Article  Google Scholar 

  26. Liu Y, Li X et al (2022) Evolutionary multi-objective trajectory optimization for a redundant robot in Cartesian space considering obstacle avoidance. Mech Sci 13:41–53. https://doi.org/10.5194/ms-13-41-2022

    Article  Google Scholar 

  27. Zhang S (2022) An optimal design scheme of missile trajectory. journal of physics: conference series. In: 2022 International conference on automation and space science & technology. https://doi.org/10.1088/1742-6596/2220/1/012012

  28. Ta E, Ar V, John L (2021) Costate mapping for indirect trajectory optimization. Astrodynamics 5:359–371. https://doi.org/10.1007/s42064-021-0114-0

    Article  Google Scholar 

  29. Yang H, Li S, Bai X (2019) Fast homotopy method for asteroid landing trajectory optimization using approximate initial costates. J Guid Control Dyn 42(3):585–597. https://doi.org/10.2514/1.G003414

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Alireza Ekrami Kivaj & Farshad Pazooki

  2. Department of Aerospace Engineering K. N, Toosi University of Technology, Tehran, Iran

    Alireza Basohbat Novinzadeh

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  1. Alireza Ekrami Kivaj
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  2. Alireza Basohbat Novinzadeh
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Correspondence to Alireza Basohbat Novinzadeh.

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Ekrami Kivaj, A., Basohbat Novinzadeh, A. & Pazooki, F. Spacecraft reentry trajectory optimization by heuristic optimization methods and optimal control theory. Int. J. Dynam. Control 11, 1132–1141 (2023). https://doi.org/10.1007/s40435-022-01033-0

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  • DOI: https://doi.org/10.1007/s40435-022-01033-0

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