An investigation of the Control Quality of the Automatic Control System for Fixed-wing UAVs During Landing Process-Reference-Cited by-同舟云学术

An investigation of the Control Quality of the Automatic Control System for Fixed-wing UAVs During Landing Process

Published:2022-12-29 Issue:is02 Volume:vm03 Page:61-69
ISSN:2687-525X
Container-title:Volume 03 Issue 02
language:en
Short-container-title:IJAST

Author:

Anh Trung Vuong¹^ORCID,Tran Hong Son²^ORCID,Nguyen Dinh-dung³^ORCID,Nguyen Truong-thanh⁴^ORCID,Phan Trong-son⁵^ORCID,Nguyen Hong Tien¹^ORCID

Affiliation:

1. Faculty of Aviation Technical, Air defense-Air Force Academy, 100000 Hanoi, Vietnam

2. Faculty of Control Engineering, Le Quy Don Technical University, 100000 Hanoi, Vietnam

3. Department. of Aircraft System Design, Faculty of Aerospace Engineering, Le Quy Don Technical University, 100000 Hanoi, Vietnam

4. Department of Military Science, Air Force Officer’s College, 650000 Khanh Hoa, Vietnam

5. Department of Aircraft-Engines, Air Force Officer’s College, 650000 Khanh Hoa, Vietnam

Abstract

This study presents an investigation and evaluation of the control quality of the automatic control system for UAVs in the vertical plane under windy conditions. For the operational stages of UAVs in general, the landing stage is one of the high-probability stages that pose a threat to flight safety, especially at the time of landing. Therefore, to evaluate the control quality of the system, the authors investigated the parameters during UAV landing. The automatic control system uses a PID controller with optimal parameters selected by the Signal Constraint tool in Matlab Simulink. The predetermined wind model was used to verify at the most extreme times. The programs proposed in the paper are simulated on Matlab Simulink software.

Publisher

International Sustainable Aviation Research Society

Subject

General Medicine,General Chemistry

Reference27 articles.

1. Ambati, P.R. and Padhi, R. (2017) ‘Robust auto-landing of fixed-wing UAVs using neuro-adaptive design’, Control Engineering Practice, 60, pp. 218–232. doi:10.1016/j.conengprac.2016.03.017.

2. Bayerlein, H., Kerret, P. De and Gesbert, D. (2018) ‘Trajectory Optimization for Autonomous Flying Base Station via Reinforcement Learning’, in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5. doi:10.1109/SPAWC.2018.8445768.

3. Brukarczyk, B. et al. (2021) ‘Fixed Wing Aircraft Automatic Landing with the Use of a Dedicated Ground Sign System’, Aerospace, 8(6). doi:10.3390/aerospace8060167.

4. Dang, C.V. et al. (2016) ‘Application of adaptive controller improves flight safety for small UAVs in wind turbulence conditions’, Journal of Military Science and Technology [Preprint].

5. Do, V.T., Do, Q.T. and Ngo, V.T. (2016) ‘Identify drone landing data on image processing database’, Journal of Science and Engineering [Preprint].