Solving a Problem of the Lateral Dynamics Identification of a UAV using a Hyper-heuristic for Non-stationary Optimization

Evgenii Sopov

2021

Abstract

A control system of an Unmanned Aerial Vehicle (UAV) requires identification of the lateral and longitudinal dynamics. While data on the longitudinal dynamics can be accessed via precise navigation devices, the lateral dynamics is predicted using such control parameters as aileron, elevator, rudder, and throttle positions. Autoregressive neural networks (ARNN) usually demonstrate high performance when modeling dynamic systems. At the same time, the lateral dynamics identification problem is known as non-stationary because of constantly changing operating conditions and errors in control equipment buses. Thus, an optimizer for ARNN must be accurate enough and must adapt to the changes in the environment. In the study, we have proposed an evolutionary hyper-heuristic for training ARNN in the non-stationary environment. The approach is based on the combination of the algorithm portfolio and the population-level dynamic probabilities approach. The hyper-heuristic selects and controls online the interaction of five evolutionary metaheuristics for dealing with dynamic optimization problems. The experimental results have shown that the proposed approach outperforms the standard back-propagation algorithm and all single metaheuristics.

Paper Citation

in Harvard Style

Sopov E. (2021). Solving a Problem of the Lateral Dynamics Identification of a UAV using a Hyper-heuristic for Non-stationary Optimization. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: ECTA; ISBN 978-989-758-534-0, SciTePress, pages 107-114. DOI: 10.5220/0010643100003063

in Bibtex Style

@conference{ijcci21,
author={Evgenii Sopov},
title={Solving a Problem of the Lateral Dynamics Identification of a UAV using a Hyper-heuristic for Non-stationary Optimization},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: ECTA},
year={2021},
pages={107-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010643100003063},
isbn={978-989-758-534-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: ECTA
TI - Solving a Problem of the Lateral Dynamics Identification of a UAV using a Hyper-heuristic for Non-stationary Optimization
SN - 978-989-758-534-0
AU - Sopov E.
PY - 2021
SP - 107
EP - 114
DO - 10.5220/0010643100003063
PB - SciTePress