Robust-Adaptive Interval Predictive Control ...
Type de document :
Communication dans un congrès avec actes
Titre :
Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems
Auteur(s) :
Leurent, Edouard [Auteur]
RENAULT
Efimov, Denis [Auteur]
Finite-time control and estimation for distributed systems [VALSE]
Centrale Lille
Maillard, Odalric Ambrym [Auteur]
Sequential Learning [SEQUEL]
Scool [Scool]
Centrale Lille
RENAULT
Efimov, Denis [Auteur]
Finite-time control and estimation for distributed systems [VALSE]
Centrale Lille
Maillard, Odalric Ambrym [Auteur]
Sequential Learning [SEQUEL]
Scool [Scool]
Centrale Lille
Titre de la manifestation scientifique :
CDC 2020 - 59th IEEE Conference on Decision and Control
Ville :
Jeju Island / Virtual
Pays :
Corée du Sud
Date de début de la manifestation scientifique :
2020-12-10
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Automatique / Robotique
Résumé en anglais : [en]
We consider the problem of stabilization of a linear system, under state and control constraints, and subject to bounded disturbances and unknown parameters in the state matrix. First, using a simple least square solution ...
Lire la suite >We consider the problem of stabilization of a linear system, under state and control constraints, and subject to bounded disturbances and unknown parameters in the state matrix. First, using a simple least square solution and available noisy measurements, the set of admissible values for parameters is evaluated. Second, for the estimated set of parameter values and the corresponding linear interval model of the system, two interval predictors are recalled and an unconstrained stabilizing control is designed that uses the predicted intervals. Third, to guarantee the robust constraint satisfaction, a model predictive control algorithm is developed, which is based on solution of an optimization problem posed for the interval predictor. The conditions for recursive feasibility and asymptotic performance are established. Efficiency of the proposed control framework is illustrated by numeric simulations.Lire moins >
Lire la suite >We consider the problem of stabilization of a linear system, under state and control constraints, and subject to bounded disturbances and unknown parameters in the state matrix. First, using a simple least square solution and available noisy measurements, the set of admissible values for parameters is evaluated. Second, for the estimated set of parameter values and the corresponding linear interval model of the system, two interval predictors are recalled and an unconstrained stabilizing control is designed that uses the predicted intervals. Third, to guarantee the robust constraint satisfaction, a model predictive control algorithm is developed, which is based on solution of an optimization problem posed for the interval predictor. The conditions for recursive feasibility and asymptotic performance are established. Efficiency of the proposed control framework is illustrated by numeric simulations.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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