Tuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach

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dc.authoridKIRNAP, Ahmet/0000-0002-7685-5297
dc.authoridSavas, Sertac/0000-0001-8096-1140
dc.authoridULU, BURAK/0000-0002-2526-2483
dc.authoridYILDIRIM, SAHIN/0000-0002-7149-3274
dc.contributor.authorUlu, Burak
dc.contributor.authorSavas, Sertac
dc.contributor.authorErgin, Oemer Faruk
dc.contributor.authorUlu, Banu
dc.contributor.authorKirnap, Ahmet
dc.contributor.authorBingoel, Mehmet Safa
dc.contributor.authorYildirim, Sahin
dc.date.accessioned2024-11-07T13:32:37Z
dc.date.available2024-11-07T13:32:37Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractNowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional-integral-derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 x 10-5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.
dc.identifier.doi10.3390/s24092752
dc.identifier.issn1424-8220
dc.identifier.issue9
dc.identifier.pmid38732858
dc.identifier.scopus2-s2.0-85192701333
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/s24092752
dc.identifier.urihttps://hdl.handle.net/11480/15519
dc.identifier.volume24
dc.identifier.wosWOS:001220042500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSensors
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectneural networks
dc.subjecttrajectory control
dc.subjectunmanned aerial vehicles
dc.subjectautonomous navigation
dc.subjectagricultural technologies
dc.titleTuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach
dc.typeArticle

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