• Sampled-data model predictive...

Result: Sampled-data model predictive control for nonlinear time-varying systems : Stability and robustness

Title:
Sampled-data model predictive control for nonlinear time-varying systems : Stability and robustness
Authors:
FONTES, Fernando A. C. C, MAGNI, Lalo, GYURKOVICS, Eva
Source:
Assessment and future directions of nonlinear model predictive control (Freudenstadt-Lauterbad, Germany, August 26-30, 2005)Lecture notes in control and information sciences. :115-129
Publisher Information:
Berlin: Springer, 2007.
Publication Year:
2007
Physical Description:
print, 17 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Documentation, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Automatique théorique. Systèmes, Control theory. Systems, Synthèse des systèmes de commande, Control system synthesis, Commande MPC, Model predictive control, Control modelo predicativo, Commande non linéaire, Non linear control, Control no lineal, Commande robuste, Robust control, Control robusta, Discontinuité, Discontinuity, Discontinuidad, Echantillonnage, Sampling, Muestreo, Modélisation, Modeling, Modelización, Programme commande, Control program, Programa mando, Robustesse, Robustness, Robustez, Rétroaction, Feedback regulation, Retroacción, Stabilité robuste, Robust stability, Estabilidad robusta, Système non holonome, Non holonomic system, Sistema no holónomo, Système paramètre variable, Time varying system, Sistema parámetro variable, Système échantillonné, Sampled system, Sistema muestreado, Temps continu, Continuous time, Tiempo continuo, Temps discret, Discrete time, Tiempo discreto
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Officina Niathematica, Departamento de Matemática para a Ciência e Tecnologia, Universidade do Minho, 4800-058 Guimaraes, Portugal
Dipartimento di Informatica e Sistimistica, Università degli Studi di Pavia, via Ferrata 1, 27100 Pavia, Italy
Budapest University of Technology and Economics, Institute of Mathematics, Budapest I521, Hungary
ISSN:
0170-8643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19161687
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19161687
Database:
PASCAL Archive

Further Information

We describe here a sampled-data Model Predictive Control framework that uses continuous-time models but the sampling of the actual state of the plant as well as the computation of the control laws, are carried out at discrete instants of time. This framework can address a very large class of systems, nonlinear, time-varying, and nonholonoinic. As in many others saed-data Model Predictive Control schemes, Barbalat's lemma lias an important role in the proof of nominal stability results. It is argued that the generalization of Barbalat's lemma, described here, can have also a similar rolle in the proof of robust stability results, allowing also to address a very general class of nonlinear, time-varying, nonholonomic systems, subject to disturbances. The possibility of the framework to accommodate discontinuous feedbacks is essential to achieve both nominal stability and robust stability for such general classes of systems.