Approximation of large-scale dynamic models
Alessandro Amodio, Simone Gelmini, Luca Onesto, Gabriele Pozzato, Stefano Sabatini
DEIB PhD students
DEIB - PT1 Room
December 20th, 2017
1.00 pm
Contacts:
Sergio Savaresi
Research Line:
Control systems
DEIB PhD students
DEIB - PT1 Room
December 20th, 2017
1.00 pm
Contacts:
Sergio Savaresi
Research Line:
Control systems
Abstract
In this talk the speakers will present a report of the aspects seen during the summer school about “Approximation of large-scale dynamic models” .
Due to increasing use of computer-based modeling tools, numerical simulation turns to be more and more used to simulate a complex system and shorten both development time and cost. However, the need of enhanced model accuracy leads to an increasing number of variables and resources to manage at the price of a high numerical cost. One way to cope with this problem is to use model approximation, aiming at replacing the initial complex model by a simplified one, whose behavior remains representative of the genuine physical system.
The seminar aims at presenting some of the main mathematical tools and model approximation algorithms, in order to bridge the gap between complexity and representativeness required in control design, analysis, simulation and optimization. To this end, in the seminar it will be presented some of the cutting-edge research results and applications of the presented techniques.
Due to increasing use of computer-based modeling tools, numerical simulation turns to be more and more used to simulate a complex system and shorten both development time and cost. However, the need of enhanced model accuracy leads to an increasing number of variables and resources to manage at the price of a high numerical cost. One way to cope with this problem is to use model approximation, aiming at replacing the initial complex model by a simplified one, whose behavior remains representative of the genuine physical system.
The seminar aims at presenting some of the main mathematical tools and model approximation algorithms, in order to bridge the gap between complexity and representativeness required in control design, analysis, simulation and optimization. To this end, in the seminar it will be presented some of the cutting-edge research results and applications of the presented techniques.