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Adaptive nonlinear guidance law using neural networks applied to a quadrotor
dc.contributor.author | Rubí Perelló, Bartomeu |
dc.contributor.author | Morcego Seix, Bernardo |
dc.contributor.author | Pérez Magrané, Ramon |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2019-12-20T09:40:38Z |
dc.date.available | 2019-12-20T09:40:38Z |
dc.date.issued | 2019 |
dc.identifier.citation | Rubi, 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.other | https://www.researchgate.net/publication/334604765_Adaptive_Nonlinear_Guidance_Law_Using_Neural_Networks_Applied_to_a_Quadrotor |
dc.identifier.uri | http://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.abstract | The 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.extent | 6 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.lcsh | Adaptive control systems |
dc.subject.lcsh | Robotics |
dc.subject.lcsh | Drone aircraft |
dc.subject.other | Automated guided vehicles |
dc.subject.other | Adaptive control |
dc.subject.other | Intelligent and AI based control |
dc.title | Adaptive nonlinear guidance law using neural networks applied to a quadrotor |
dc.type | Conference report |
dc.subject.lemac | Sistemes adaptatius |
dc.subject.lemac | Avions no tripulats |
dc.subject.lemac | Robòtica |
dc.identifier.doi | 10.1109/icca.2019.8899601 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/abstract/document/8899601 |
dc.rights.access | Open Access |
local.identifier.drac | 25946903 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-88403-R/ES/SEGURIDAD Y CONTROL EN VEHICULOS AUTONOMOS/ |
local.citation.author | Rubi, B.; Morcego, B.; Perez, R. |
local.citation.contributor | IEEE International Conference on Control and Automation |
local.citation.publicationName | Proceedings of the IEEE 15th International Conference on Control and Automation |
local.citation.startingPage | 1626 |
local.citation.endingPage | 1631 |