Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Francesco Marchetti and Edmondo Minisci
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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

Mathematics, 2021, vol. 9, issue 16, 1-19

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.

Keywords: evolutionary optimization; genetic programming; control; differential evolution; reusable launch vehicle (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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