Nonlinear model predictive control with constraint satisfactions for a quadcopter

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/99393

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor	Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.contributor.author	Wang, Ye
dc.contributor.author	Ramírez Jaime, Andrés Felipe
dc.contributor.author	Xu, Feng
dc.contributor.author	Puig Cayuela, Vicenç
dc.date	2016
dc.identifier.citation	Wang, Y., Ramírez, A., Xu, F., Puig, V. Nonlinear model predictive control with constraint satisfactions for a quadcopter. A: European Workshop on Advanced Control and Diagnosis. "ACD'16-13th European Workshop on Advanced Control and Diagnosis 17 - 18 November 2016, Lille, France". Lille: 2016, p. 1-12.
dc.identifier.uri	http://hdl.handle.net/2117/99393
dc.language.iso	eng
dc.relation	info:eu-repo/grantAgreement/ES/1PE/DPI2013-48243-C2-1-R
dc.relation	info:eu-repo/grantAgreement/ES/1PE/DPI2014-58104-R
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Informàtica
dc.subject	Predictive control
dc.subject	Control predictiu
dc.title	Nonlinear model predictive control with constraint satisfactions for a quadcopter
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the e ectiveness of he proposed strategy.

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