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

Jules Simo, Roberto Furfaro, Joel Mueting

Research output: Contribution to conference › Paper

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
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 2015 → 15 Jan 2015

Conference

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

Fingerprint

Landing
Learning algorithms
Artificial intelligence
Guidance
Artificial Neural Network
Spacecraft
Performance Evaluation
Learning systems
Trajectories
Neural networks
Extreme Learning Machine
Computational Intelligence
Network Design
Potential Function
Velocity Field
Learning Algorithm
Efficient Algorithms
Monte Carlo Simulation
Trajectory
Scenarios

Keywords

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

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.
Simo, Jules ; Furfaro, Roberto ; Mueting, Joel. / Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance. Paper presented at 25th AAS/AIAA Space Flight Mechanics Meeting, Willliamsburg, VA, United States.19 p.
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Simo, J, Furfaro, R & Mueting, J 2015, 'Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance' Paper presented at 25th AAS/AIAA Space Flight Mechanics Meeting, Willliamsburg, VA, United States, 11/01/15 - 15/01/15, pp. AAS 15-356.

Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance. / Simo, Jules; Furfaro, Roberto; Mueting, Joel.

2015. AAS 15-356 Paper presented at 25th AAS/AIAA Space Flight Mechanics Meeting, Willliamsburg, VA, United States.

Research output: Contribution to conference › Paper

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T1 - Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance

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AU - Furfaro, Roberto

AU - Mueting, Joel

PY - 2015/1/11

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N2 - 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

AB - 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

KW - performance evaluation

KW - artificial neural network

KW - algorithm design and analysis

KW - guidance control systems

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

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