Towards intelligent control via genetic programming

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Marchetti, Francesco and Minisci, Edmondo and Riccardi, Annalisa; (2020) Towards intelligent control via genetic programming. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, GBR. ISBN 9781728169279 (https://doi.org/10.1109/IJCNN48605.2020.9207694)

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Abstract

In this paper an initial approach to Intelligent Control (IC) using Genetic Programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex systems, where classical controllers fail because of the high nonlinearity of the systems. The main property of GP, that is its ability to autonomously create explicit mathematical equations starting from a very poor knowledge of the considered plant, or just data, can be exploited for a vast range of applications. Here, GP has been used to design the control law in an Intelligent Control framework for a modified version of the Goddard Rocket problem in 3 different failure scenarios, where the approach to IC consists in an online re-evaluation of the control law using GP when a considerably big change in the environment or in the plant happens. The presented results are then used to highlight the potential benefits of the method, as well as aspects that will need further developments.

ORCID iDs

Marchetti, Francesco ORCID logoORCID: https://orcid.org/0000-0003-4552-0467, Minisci, Edmondo ORCID logoORCID: https://orcid.org/0000-0001-9951-8528 and Riccardi, Annalisa ORCID logoORCID: https://orcid.org/0000-0001-5305-9450;
  • Item type:Book Section
    ID code:73537
    Dates:
    Date
    Event
    28 September 2020
    Published
    20 July 2020
    Published Online
    15 March 2020
    Accepted
    Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Mechanical engineering and machinery
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Depositing user: Pure Administrator
    Date deposited:11 Aug 2020 13:08
    Last modified:11 Nov 2024 15:23
    URI:https://strathprints.strath.ac.uk/id/eprint/73537
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