Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors
In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles (UAV) dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are cons...
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| Published in | Complexity (New York, N.Y.) Vol. 2019; no. 2019; pp. 1 - 20 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2019
Hindawi John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1076-2787 1099-0526 1099-0526 |
| DOI | 10.1155/2019/6460156 |
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| Abstract | In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles (UAV) dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are considered. An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively tested with different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort’s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor. |
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| AbstractList | In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles (UAV) dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are considered. An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively tested with different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort’s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor. |
| Author | Sierra, Jesús Enrique Santos, Matilde |
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| CitedBy_id | crossref_primary_10_3390_app13116553 crossref_primary_10_1016_j_ifacsc_2021_100156 crossref_primary_10_3390_drones6100300 crossref_primary_10_1016_j_ijepes_2020_105862 crossref_primary_10_1109_TIE_2020_3026302 crossref_primary_10_1109_TAES_2020_3046087 crossref_primary_10_1016_j_asr_2023_02_030 crossref_primary_10_1109_ACCESS_2021_3061598 crossref_primary_10_1155_2019_8525397 crossref_primary_10_1155_2020_4189272 crossref_primary_10_1016_j_neucom_2019_12_139 crossref_primary_10_1016_j_ifacol_2021_11_160 crossref_primary_10_1002_jnm_2884 crossref_primary_10_1051_e3sconf_202448203004 crossref_primary_10_1016_j_eswa_2021_115380 crossref_primary_10_1080_23307706_2025_2469887 crossref_primary_10_1002_rnc_6159 crossref_primary_10_1177_10775463211050169 crossref_primary_10_1002_rnc_5760 crossref_primary_10_1007_s10846_020_01182_4 crossref_primary_10_3390_s24227299 |
| Cites_doi | 10.1016/j.riai.2011.09.016 10.1155/2014/320526 10.1016/j.conengprac.2011.06.010 10.15607/RSS.2013.IX.011 10.1109/TCST.2014.2326820 10.1016/j.riai.2017.01.001 10.1016/j.riai.2016.09.004 10.1016/j.jfranklin.2011.06.031 10.1016/j.neucom.2016.11.099 10.1007/s00521-014-1807-6 10.1109/CCECE.2008.4564736 10.1155/2018/6879419 10.3182/20110828-6-IT-1002.02564 10.1109/TLA.2016.7785921 10.1016/j.ifacol.2016.03.070 10.1109/CDC.2016.7798978 10.1109/ISKE.2010.5680812 10.23919/ACC.2017.7963553 10.1080/01691864.2014.913498 10.1016/j.ifacol.2017.08.2010 10.1109/ICUAS.2014.6842339 10.1016/j.engappai.2005.09.002 10.1007/s10846-016-0333-4 10.1109/URAI.2017.7992893 10.1109/ICRA.2013.6631275 10.1515/kbo-2015-0126 10.1007/978-3-319-61276-8_56 10.1155/2018/8124861 10.1155/2017/2750172 10.1109/AIM.2014.6878116 10.1007/978-3-319-94120-2_28 10.1007/s10846-012-9734-1 10.1109/ICRA.2012.6225213 10.1109/ISCO.2016.7727123 |
| ContentType | Journal Article |
| Copyright | Copyright © 2019 Jesús Enrique Sierra and Matilde Santos. Copyright © 2019 Jesús Enrique Sierra and Matilde Santos. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Adaptive control Computer simulation Distance learning International conferences Machine learning Neural networks Parameter estimation Proportional integral derivative Robotics Robust control Rotary wing aircraft Unmanned aerial vehicles Unmanned helicopters Vehicles |
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| Title | Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors |
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