Control of multiple non-holonomic air vehicles under wind uncertainty using model predictive control and decentralized navigation functions

Roussos, GP; Chaloulos, G; Kyriakopoulos, KJ; Lygeros, J

dc.contributor.author	Roussos, GP	en
dc.contributor.author	Chaloulos, G	en
dc.contributor.author	Kyriakopoulos, KJ	en
dc.contributor.author	Lygeros, J	en
dc.date.accessioned	2014-03-01T02:45:12Z
dc.date.available	2014-03-01T02:45:12Z
dc.date.issued	2008	en
dc.identifier.issn	01912216	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32201
dc.subject	Collision Avoidance	en
dc.subject	Control Strategy	en
dc.subject	Formation Control	en
dc.subject	Input Constraint	en
dc.subject	Local Minima	en
dc.subject	Model Predictive Control	en
dc.subject	multirobot systems	en
dc.subject	Potential Field	en
dc.subject	Time Constraint	en
dc.subject	3 dimensional	en
dc.subject	Air Traffic Control	en
dc.subject.other	Air vehicles	en
dc.subject.other	Conflict-free	en
dc.subject.other	Control strategies	en
dc.subject.other	Decentralized navigations	en
dc.subject.other	Navigation functions	en
dc.subject.other	Non-holonomic	en
dc.subject.other	Non-holonomic vehicles	en
dc.subject.other	Novel control schemes	en
dc.subject.other	Performance objectives	en
dc.subject.other	Realistic simulations	en
dc.subject.other	Ad hoc networks	en
dc.subject.other	Air navigation	en
dc.subject.other	Air traffic control	en
dc.subject.other	Predictive control systems	en
dc.subject.other	Model predictive control	en
dc.title	Control of multiple non-holonomic air vehicles under wind uncertainty using model predictive control and decentralized navigation functions	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CDC.2008.4738792	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CDC.2008.4738792	en
heal.identifier.secondary	4738792	en
heal.publicationDate	2008	en
heal.abstract	We present a novel control scheme for multiple non-holonomic vehicles under uncertainty, which can guarantee collision avoidance while complying with constraints imposed on the vehicles. Dipolar Navigation Functions are used for decentralized conflict-free control, while Model Predictive Control is used in a centralized manner in order to ensure that the resulting trajectories remain feasible with respect to the constraints present and to optimize the performance objectives. The model used is chosen to resemble air traffic control problems, with some uncertainty introduced in the system. The efficiency of the control strategy is demonstrated by realistic simulations. © 2008 IEEE.	en
heal.journalName	Proceedings of the IEEE Conference on Decision and Control	en
dc.identifier.doi	10.1109/CDC.2008.4738792	en
dc.identifier.spage	1225	en
dc.identifier.epage	1230	en