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

Simo, Jules and Furfaro, Roberto and Mueting, Joel; (2015) Performance evaluation of artificial neural network-based shaping algorithm for planetary pinpoint guidance. In: Advances in the Astronautical Sciences. Advances in the Astronautical Sciences, 155 . Univelt Inc, USA, pp. 2233-2248.

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    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.

    Creators(s): Simo, Jules ORCID logoORCID: https://orcid.org/0000-0002-1489-5920, Furfaro, Roberto and Mueting, Joel;
    Item type:Book Section
    ID code:54409
    Keywords:extreme learning maching (ELM), single layer forward network (SLFN), learning algorithms, landing trajectories, guidance control systems, Mechanical engineering and machinery, Motor vehicles. Aeronautics. Astronautics, Mechanical Engineering, Aerospace Engineering, Control and Systems Engineering
    Subjects:Technology > Mechanical engineering and machinery
    Technology > Motor vehicles. Aeronautics. Astronautics
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Depositing user: Pure Administrator
    Date deposited:30 Sep 2015 15:55
    Last modified:24 Feb 2021 09:00
    URI:https://strathprints.strath.ac.uk/id/eprint/54409
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