Skip to main content
SpringerLink
Log in

Integrated Optimal Guidance for Reentry and Landing of a Rocket Using Multi-Phase Pseudo-Spectral Convex Optimization

  • Original Paper
  • Published:
International Journal of Aeronautical and Space Sciences Aims and scope Submit manuscript

Abstract

This paper proposes integrated optimal guidance for the reentry and landing of a rocket using the multi-phase pseudo-spectral convex optimization method. For a successful return of the launch vehicle, it is crucial to generate the onboard real-time trajectory of the rocket from stage separation to landing, considering vehicle/ground safety and precision landing. Recently, improved computation capability enables online trajectory optimization in real time, especially using the convex optimization method. This paper formulates a multi-phase optimal control problem involving the reentry and landing phases. Then, the original problem is converted to a convex optimization problem based on pseudo-spectral discretization and successive convexification. A case study on the reentry/landing guidance of an actual reusable launch vehicle is conducted to demonstrate the effectiveness of the proposed procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Lu P (2017) Introducing computational guidance and control. J Guid Control Dyn 40:193. https://doi.org/10.2514/1.G002745

    Article  Google Scholar 

  2. Açikmeşe B et al (2013) Flight testing of trajectories computed by G-FOLD: Fuel optimal large divert guidance algorithm for planetary landing. AAS/AIAA spaceflight mechanics meeting

  3. Scharf DP et al (2014) ADAPT demonstrations of onboard large-divert Guidance with a VTVL rocket. 2014 IEEE Aerospace Conference. https://doi.org/10.1109/AERO.2014.6836462

  4. Açikmeşe B, Ploen S (2007) Convex programming approach to powered descent guidance for mars landing. J Guid Control Dyn 30:1353–1366. https://doi.org/10.2514/1.27553

    Article  Google Scholar 

  5. Simplício P, Marcos A (2019) Guidance of reusable launchers: improving descent and landing performance. J Guid Control Dyn 42:2206–2219. https://doi.org/10.2514/1.G004155

    Article  Google Scholar 

  6. Szmuk M et al (2016) Successive convexification for fuel-optimal powered landing with aerodynamic drag and non-convex constraints. AIAA SciTech Forum. https://doi.org/10.2514/6.2016-0378

    Article  Google Scholar 

  7. Liu X (2019) Fuel-optimal rocket landing with aerodynamic controls. J Guid Control Dyn 42:65–77. https://doi.org/10.2514/1.G003537

    Article  Google Scholar 

  8. Jo B-U, Ahn J (2018) Near time-optimal feedback instantaneous impact point (IIP) guidance law for rocket. Aerosp Sci Technol 76:523–529. https://doi.org/10.1016/j.ast.2018.02.024

    Article  Google Scholar 

  9. Jo B-U, Ahn J, Roh W (2020) Instantaneous impact point guidance considering uncertainty in engine cut-off time. J Guid Control Dyn 43:373–377. https://doi.org/10.2514/1.G004703

    Article  Google Scholar 

  10. Sagliano M (2018) Pseudospectral convex optimization for powered descent and landing. J Guid Control Dyn 41:320–334. https://doi.org/10.2514/1.G002818

    Article  Google Scholar 

  11. Wang J, Cui N (2018) A pseudospectral-convex optimization algorithm for rocket landing guidance. AIAA SciTech Forum. https://doi.org/10.2514/6.2018-1871

    Article  Google Scholar 

  12. Ahn J, Roh W (2012) Noniterative instantaneous impact point prediction algorithm for launch operations. J Guid Control Dyn 35:645–648. https://doi.org/10.2514/1.56395

    Article  Google Scholar 

  13. Ahn J, Roh W (2014) Analytic time derivatives of instantaneous impact point. J Guid Control Dyn 37:383–390. https://doi.org/10.2514/1.61681

    Article  Google Scholar 

  14. Lee J-Y, Jo B-U, Moon G-H, Tahk M-J, Ahn J (2020) Intercept point prediction of ballistic missile defense using neural network learning. Int J Aeronaut Space Sci 21:1092–1104. https://doi.org/10.1007/s42405-020-00292-5

    Article  Google Scholar 

  15. Garg D (2011) Advances in global pseudospectral methods for optimal control. University of Florida

  16. Fitcher R (2013) Practical methods of optimization. Wiley, Amsterdam

    Google Scholar 

  17. Mao Y et al (2017) Successive convexification of non-convex optimal control problems with state constraints. In: 20th IFAC World Congress 50:4063–4069. https://doi.org/10.1016/j.ifacol.2017.08.789

  18. Rao AV et al (2010) Algorithm 902: GPOPS, a MATLAB software for solving multiple-phase optimal control problems using the Gauss pseudospectral method. ACM Trans Math Softw 37:29–36. https://doi.org/10.1145/1731022.1731032

    Article  MATH  Google Scholar 

  19. Grant M, Boyd S (2014) CVX: Matlab software for disciplined convex programming version 2.1. http://cvxr.com/cvx Accessed 9 Dec 2021

  20. Space X (2017) CRS-10 dragon resupply mission. Press release

Download references

Acknowledgements

This work was prepared at the Korea Advanced Institute of Science and Technology, Department of Aerospace Engineering, under a research grant from the National Research Foundation of Korea (NRF-2019M1A3A1A0207696514). The authors thank the National Research Foundation of Korea for the support of this work.

Author information

Authors and Affiliations

  1. Department of Aerospace Engineering, KAIST, Daejeon, Korea

    Junsub Hwang & Jaemyung Ahn

Authors
  1. Junsub Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaemyung Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaemyung Ahn.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

An earlier version of this paper was presented at APISAT 2021, Jeju, South Korea, in November 2021.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hwang, J., Ahn, J. Integrated Optimal Guidance for Reentry and Landing of a Rocket Using Multi-Phase Pseudo-Spectral Convex Optimization. Int. J. Aeronaut. Space Sci. 23, 766–774 (2022). https://doi.org/10.1007/s42405-022-00456-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42405-022-00456-5

Keywords

Search

Navigation