Integrated guidance for mars entry and powered descent using reinforcement learning and gauss pseudospectral method

Xiuqiang Jiang, Roberto Furfaro, Shuang Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Traditional studies on Mars entry, descent, and landing (EDL) usually take a divide- and-conquer approach where each phase is investigated separately. This paper proposes a new approach to achieve integrated guidance for mid-lift highmass human Mars entry and powered descent. The Gauss pseudospectral method is adopted to shape optimal entry trajectories and propellant-optimal powered descent guidance, respectively. According to the interaction between the entry trajectory and the propellant usage in the powered descent, reinforcement learning algorithm is employed to obtain the optimal integrated guidance. It is shown in this paper that appropriately integrating the working of the entry and powered descent guidance can bring great reduction in propellant consumption during high-mass Mars powered descent. Each of these two components and how they work together to reach the least propellant usage and pin-point landing are investigated in this paper. Numerical results are presented to demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publicationDynamics and Control of Space Systems
EditorsJeng-Shing Chern, Ya-Zhong Luo, Xiao-Qian Chen, Lei Chen
PublisherUnivelt Inc.
Pages761-774
Number of pages14
Volume165
ISBN (Print)9780877036531
StatePublished - Jan 1 2018
Event4th IAA Conference on Dynamics and Control of Space Systems, DYCOSS 2018 - Changsha, China
Duration: May 21 2018May 23 2018

Other

Other4th IAA Conference on Dynamics and Control of Space Systems, DYCOSS 2018
CountryChina
CityChangsha
Period5/21/185/23/18

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

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  • Cite this

    Jiang, X., Furfaro, R., & Li, S. (2018). Integrated guidance for mars entry and powered descent using reinforcement learning and gauss pseudospectral method. In J-S. Chern, Y-Z. Luo, X-Q. Chen, & L. Chen (Eds.), Dynamics and Control of Space Systems (Vol. 165, pp. 761-774). Univelt Inc..