Predictive Control for Autonomous Aerial Vehicles Trajectory Tracking

Ionela Prodan 1 Sorin Olaru 2 Cristina Stoica 2 Silviu-Iulian Niculescu 3, 4
1 E3S - Supélec Sciences des Systèmes [Gif-sur-Yvette]
2 AUTO - Département Automatique
E3S - Supélec Sciences des Systèmes [Gif-sur-Yvette]
3 DISCO - Dynamical Interconnected Systems in COmplex Environments
L2S - Laboratoire des signaux et systèmes, Inria Saclay - Ile de France, SUPELEC, CNRS - Centre National de la Recherche Scientifique : UMR8506
4 Division Systèmes - L2S
L2S - Laboratoire des signaux et systèmes : 1289
Abstract : This paper addresses a predictive control strategy for Unmanned Air Vehicles in the presence of bounded disturbances. The goal is to guarantee tracking capabilities with respect to a reference trajectory which is pre-specified using the differential flatness formalism. Furthermore, an off-line linearization strategy of the nonlinear model of the vehicle along the flat trajectory is proposed. Since the reference trajectory is available beforehand, an optimization problem which minimizes the tracking error for the vehicle is formulated in a predictive control framework. The proposed method exhibits effective performance validated through software-in-the-loop simulations for the control of Unmanned Aerial Vehicles (UAVs).
Type de document :
Communication dans un congrès
Journée Envol Recherche de la Fondation d'entreprise EADS, Feb 2013, Paris, France
Liste complète des métadonnées
https://hal.inria.fr/hal-00794373
Contributeur : Josiane Dartron <>
Soumis le : lundi 25 février 2013 - 16:44:48
Dernière modification le : jeudi 5 avril 2018 - 12:30:23

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  • HAL Id : hal-00794373, version 1

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SUP_LSS | SUP_SYSTEMES | SUPELEC | SUP_E3S | SUP_AUTO | INRIA | L2S | TDS-MACS

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Ionela Prodan, Sorin Olaru, Cristina Stoica, Silviu-Iulian Niculescu. Predictive Control for Autonomous Aerial Vehicles Trajectory Tracking. Journée Envol Recherche de la Fondation d'entreprise EADS, Feb 2013, Paris, France. 〈hal-00794373〉

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