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

Jules Simo, Roberto Furfaro, Joel Mueting

Research output: Contribution to conferencePaper

74 Downloads (Pure)

Abstract

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
PagesAAS 15-356
Number of pages19
Publication statusPublished - 11 Jan 2015
Event25th AAS/AIAA Space Flight Mechanics Meeting - Willliamsburg, VA, United States
Duration: 11 Jan 201515 Jan 2015

Conference

Conference25th AAS/AIAA Space Flight Mechanics Meeting
CountryUnited States
CityWillliamsburg, VA
Period11/01/1515/01/15

Keywords

  • performance evaluation
  • artificial neural network
  • algorithm design and analysis
  • guidance control systems
  • ELMS

Fingerprint Dive into the research topics of 'Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance'. Together they form a unique fingerprint.

  • Cite this

    Simo, J., Furfaro, R., & Mueting, J. (2015). Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance. AAS 15-356. Paper presented at 25th AAS/AIAA Space Flight Mechanics Meeting, Willliamsburg, VA, United States.