Systematic H2 /H∞ haptic shared control synthesis for cars, parameterized by sharing level
In: Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, 2020
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International audience ; This paper presents a methodology for the systematic synthesis of haptic shared control (HSC) of a car. This HSC design is based on a two-part architecture. The first part is a trajectory generator that provides a reference trajectory to the second part, which is a static output feedback. In this paper, the haptic shared control is used as an lane keeping assist system (LKA); hence, the reference trajectory is chosen to fulfill this function. The main contribution of this article is related to the combination of the H2/H∞ feedback synthesis. This, involves an H2 criterion quantifying the sharing level and quality as an objective function, and H2/H∞ constraints for lane-keeping performance, driver comfort and robustness. The control design relies on a driver cybernetic model, which decreases conflicts between the assistance and the driver. A systematic method to tune criterion and constraints is described, enabling the attainment of desired lane-following and shared-control performance. The proposed methodology facilitates the design of lateral assistance, ensuring stability and guaranteed performance regardless of the prescribed level of sharing between the actions of the driver and the automaton. The shared control between human driver and automation for the lane keeping task over the Satory test track with the sharing level adaptation is then shown for the validation of the proposed architecture. This work introduces perspectives on smooth transitioning between manual and autonomous driving modes.
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Systematic H2 /H∞ haptic shared control synthesis for cars, parameterized by sharing level
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Autor/in / Beteiligte Person: | Pano, Béatrice ; Claveau, Fabien ; Chevrel, Philippe ; Sentouh, Chouki ; Mars, Franck ; Perception, Action, Cognition pour la Conception et l’Ergonomie (LS2N - équipe PACCE) ; Laboratoire des Sciences du Numérique de Nantes (LS2N) ; Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST) ; Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique) ; Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST) ; Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) ; Commande (LS2N - équipe Commande) ; Département Automatique, Productique et Informatique (IMT Atlantique - DAPI) ; IMT Atlantique (IMT Atlantique) ; Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH) ; Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France) ; Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA) ; ANR-16-CE22-0007,AutoConduct,Adaptation de la stratégie d'automatisation des véhicules autonomes (niveaux 3-4) aux besoins et à l'état des conducteurs en conditions réelles(2016) |
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Zeitschrift: | Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, 2020 |
Veröffentlichung: | HAL CCSD, 2020 |
Medientyp: | Konferenz |
DOI: | 10.1109/SMC42975.2020.9283441 |
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