Optimal Segmentation of LPV Systems for Control Applications via Genetic Algorithms

Zoltán Téczely, Bálint Kiss

2024

Abstract

The paper presents an automatic method for subdividing parameter regions in a Linear Parameter-Varying (LPV) controlled system based on global optimization. A known limitation of the LPV framework is the conservatism originating from excessive parameter regions. This conservatism can be relaxed if the controller design is performed in a collection of subregions of the parameter bounding box wherein local controllers are synthesized yielding an increased performance level. The choice of subregion boundaries, however, is usually based on heuristics. This, combined with the recurring issue of scheduling variable selection motivates an automated LPV parameter space description. The paper suggests genetic algorithms to automate parameter space subdivision where the problem is posed in terms of global optimization, considering closed-loop performance, computational complexity and parameter-dependent performance constraints. The benefits of the proposed approach are demonstrated on a pitch-axis missile autopilot, which is formulated as a quasi-LPV model but generally does not admit the polytopic framework. Hence, the necessary simplifications and selection criteria are introduced to effectively employ polytopic LPV methods in the vertical acceleration control for such a missile.

Paper Citation

in Harvard Style

Téczely Z. and Kiss B. (2024). Optimal Segmentation of LPV Systems for Control Applications via Genetic Algorithms. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 503-510. DOI: 10.5220/0013067200003822

in Bibtex Style

@conference{icinco24,
author={Zoltán Téczely and Bálint Kiss},
title={Optimal Segmentation of LPV Systems for Control Applications via Genetic Algorithms},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={503-510},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013067200003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Optimal Segmentation of LPV Systems for Control Applications via Genetic Algorithms
SN - 978-989-758-717-7
AU - Téczely Z.
AU - Kiss B.
PY - 2024
SP - 503
EP - 510
DO - 10.5220/0013067200003822
PB - SciTePress