Treffer: Stochastic algorithms for robustness of control performances

Title:
Stochastic algorithms for robustness of control performances
Authors:
PICCOLI, Benedetto, ZADARNOWSKA, Katarzyna, GAETA, Matteo
Source:
Automatica (Oxford). 45(6):1407-1414
Publisher Information:
Kidlington: Elsevier, 2009.
Publication Year:
2009
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, 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, Théorie des systèmes, System theory, Analyse des systèmes de commande, Control system analysis, Synthèse des systèmes de commande, Control system synthesis, Algorithme randomisé, Randomized algorithm, Algoritmo aleatorizado, Analyse performance, Performance analysis, Análisis eficacia, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Commande stochastique, Stochastic control, Control estocástico, Equation différentielle, Differential equation, Ecuación diferencial, Equation stochastique, Stochastic equation, Ecuación estocástica, Méthode statistique, Statistical method, Método estadístico, Robustesse commande, Control robustness, Robustez mando, Synthèse commande, Control synthesis, Síntesis control, Système incertain, Uncertain system, Sistema incierto, Control system, Randomized algorithms, Robustness, Stochastic algorithms, Stochastic differential equations
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Istituto per le Applicazioni del Calcolo Mauro Picone, Consiglio Nazionale delle Ricerche, Viale del Policlinico 137, 00161 Roma, Italy
Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, ul. Janiszewskiego 11/17, 50-372 Wrocław, Poland
Department of Information Engineering and Applied Mathematics, University of Salerno, Via Ponte don Melillo, 88084 Fisciano (SA), Italy
ISSN:
0005-1098
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=21548678
Rights:
Copyright 2009 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.21548678
Database:
PASCAL Archive

Weitere Informationen

In recent years, there has been a growing interest in developing statistical learning methods to provide approximate solutions to difficult control problems. In particular, randomized algorithms have become a very popular tool used for stability and performance analysis as well as for design of control systems. However, as randomized algorithms provide an efficient solution procedure to the intractable problems, stochastic methods bring closer to understanding the properties of the real systems. The topic of this paper is the use of stochastic methods in order to solve the problem of control robustness: the case of parametric stochastic uncertainty is considered. Necessary concepts regarding stochastic control theory and stochastic differential equations are introduced. Then a convergence analysis is provided by means of the Chernoff bounds, which guarantees robustness in mean and in probability. As an illustration, the robustness of control performances of example control systems is computed.