Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance

Jules Simo, Roberto Furfaro, Joel Mueting

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Computational intelligence techniques have been used in a wide range of application
areas. This paper proposes a new learning algorithm that dynamically shapes the landing trajectories, based on potential function methods, in order to provide
computationally efficient on-board guidance and control. Extreme Learning Machine
(ELM) devises a Single Layer Forward Network (SLFN) to learn the relationship between the current spacecraft position and the optimal velocity field. The SLFN design is tested and validated on a set of data comprising data points belonging
to the training set on which the network has not been trained. Furthermore, the proposed efficient algorithm is tested in typical simulation scenarios which include
a set of Monte Carlo simulation to evaluate the guidance performances.
Original languageEnglish
Title of host publicationAdvances in the Astronautical Sciences
Place of PublicationUSA
Number of pages16
Publication statusPublished - Sep 2015
Event25th AAS/AIAA Space Flight Mechanics Meeting - Willliamsburg, VA, United States
Duration: 11 Jan 201515 Jan 2015

Publication series

NameAdvances in the Astronautical Sciences
PublisherUnivelt Inc.
ISSN (Print)0065-3438


Conference25th AAS/AIAA Space Flight Mechanics Meeting
Country/TerritoryUnited States
CityWillliamsburg, VA


  • extreme learning maching (ELM)
  • single layer forward network (SLFN)
  • learning algorithms
  • landing trajectories
  • guidance control systems


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