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Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Author

Listed:
  • Francesco Marchetti

    (Intelligent Computational Engineering Laboratory (ICE-Lab), University of Strathclyde, Glasgow G11XJ, UK)

  • Edmondo Minisci

    (Intelligent Computational Engineering Laboratory (ICE-Lab), University of Strathclyde, Glasgow G11XJ, UK)

Abstract

As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, from being just an experiment made in academia, towards a necessity. Several methods to perform this integration of AI into control systems have been considered in the past. In this work, an approach involving GP to produce, offline, a control law for a reentry vehicle in the presence of uncertainties on the environment and plant models is studied, implemented and tested. The results show the robustness of the proposed approach, which is capable of producing a control law of a complex nonlinear system in the presence of big uncertainties. This research aims to describe and analyze the effectiveness of a control approach to generate a nonlinear control law for a highly nonlinear system in an automated way. Such an approach would benefit the control practitioners by providing an alternative to classical control approaches, without having to rely on linearization techniques.

Suggested Citation

  • Francesco Marchetti & Edmondo Minisci, 2021. "Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1868-:d:609487
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    References listed on IDEAS

    as
    1. Yu Wu & Bo Yan & Xiangju Qu, 2018. "Improved Chicken Swarm Optimization Method for Reentry Trajectory Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, January.
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    Cited by:

    1. Dalue Lin & Haogan Huang & Xiaoyan Li & Yuejiao Gong, 2022. "Empirical Study of Data-Driven Evolutionary Algorithms in Noisy Environments," Mathematics, MDPI, vol. 10(6), pages 1-26, March.
    2. Askhat Diveev & Elena Sofronova, 2023. "Universal Stabilisation System for Control Object Motion along the Optimal Trajectory," Mathematics, MDPI, vol. 11(16), pages 1-20, August.

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