Show simple item record

dc.contributor.authorRubí Perelló, Bartomeu
dc.contributor.authorMorcego Seix, Bernardo
dc.contributor.authorPérez Magrané, Ramon
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2019-12-20T09:40:38Z
dc.date.available2019-12-20T09:40:38Z
dc.date.issued2019
dc.identifier.citationRubi, B.; Morcego, B.; Perez, R. Adaptive nonlinear guidance law using neural networks applied to a quadrotor. A: IEEE International Conference on Control and Automation. "Proceedings of the IEEE 15th International Conference on Control and Automation". 2019, p. 1626-1631.
dc.identifier.otherhttps://www.researchgate.net/publication/334604765_Adaptive_Nonlinear_Guidance_Law_Using_Neural_Networks_Applied_to_a_Quadrotor
dc.identifier.urihttp://hdl.handle.net/2117/174157
dc.description© 2019IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractThe NonLinear Guidance Law (NLGL) is a geometric algorithm commonly employed to solve the path following problem on different unmanned vehicles. NLGL is simple (does no depend on the model of the vehicle), effective and has only one tunning parameter. Its control parameter (L) depends on various factors, such as the velocity of the vehicle, the shape of the reference path and the dynamics of the vehicle. This paper analyses the effect of parameter L on the performance of NLGL when it is applied to a quadrotor vehicle. An Adaptive NLGL, which includes a velocity reduction term, is proposed. Stability proofs are given. Simulation results show that the proposed algorithm enhances the performance of the standard NLGL. Furthermore, it has no parameters to tune.
dc.format.extent6 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject.lcshAdaptive control systems
dc.subject.lcshRobotics
dc.subject.lcshDrone aircraft
dc.subject.otherAutomated guided vehicles
dc.subject.otherAdaptive control
dc.subject.otherIntelligent and AI based control
dc.titleAdaptive nonlinear guidance law using neural networks applied to a quadrotor
dc.typeConference report
dc.subject.lemacSistemes adaptatius
dc.subject.lemacAvions no tripulats
dc.subject.lemacRobòtica
dc.identifier.doi10.1109/icca.2019.8899601
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/8899601
dc.rights.accessOpen Access
local.identifier.drac25946903
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/DPI2017-88403-R
local.citation.authorRubi, B.; Morcego, B.; Perez, R.
local.citation.contributorIEEE International Conference on Control and Automation
local.citation.publicationNameProceedings of the IEEE 15th International Conference on Control and Automation
local.citation.startingPage1626
local.citation.endingPage1631


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder