In systems biology, perfect adaptation (adaptation) denotes the property of a system reacting to a step input stimulus by completely (partially) restoring the pre-stimulus output value at steady state. We address the problem of predicting adaptation for uncertain dynamical systems. To this aim, we introduce a formal definition of adaptation tailored to the robust analysis of dynamical systems. Whilst the definition is more general and valid also for the step response analysis of nonlinear systems, in the linear case such a definition of adaptation reduces to the presence of a single real zero that dominates all poles. Based on this definition, we can assess robust adaptation by means of the robust real plot, which characterises the position of real zeros and poles for linear systems with parametric uncertainties.

Predicting adaptation for uncertain systems with robust real plots / Blanchini, Franco; Colaneri, Patrizio; Giordano, Giulia; Zorzan, Irene. - 2020-:(2020), pp. 5861-5866. (Intervento presentato al convegno 59th IEEE Conference on Decision and Control, CDC 2020 tenutosi a Jeju Island, South Korea nel 14th-18th December 2020) [10.1109/CDC42340.2020.9304173].

Predicting adaptation for uncertain systems with robust real plots

Giordano, Giulia;
2020-01-01

Abstract

In systems biology, perfect adaptation (adaptation) denotes the property of a system reacting to a step input stimulus by completely (partially) restoring the pre-stimulus output value at steady state. We address the problem of predicting adaptation for uncertain dynamical systems. To this aim, we introduce a formal definition of adaptation tailored to the robust analysis of dynamical systems. Whilst the definition is more general and valid also for the step response analysis of nonlinear systems, in the linear case such a definition of adaptation reduces to the presence of a single real zero that dominates all poles. Based on this definition, we can assess robust adaptation by means of the robust real plot, which characterises the position of real zeros and poles for linear systems with parametric uncertainties.
2020
2020 59th IEEE Conference on Decision and Control (CDC)
Piscataway, NJ
Institute of Electrical and Electronics Engineers Inc.
978-1-7281-7447-1
978-1-7281-7448-8
Blanchini, Franco; Colaneri, Patrizio; Giordano, Giulia; Zorzan, Irene
Predicting adaptation for uncertain systems with robust real plots / Blanchini, Franco; Colaneri, Patrizio; Giordano, Giulia; Zorzan, Irene. - 2020-:(2020), pp. 5861-5866. (Intervento presentato al convegno 59th IEEE Conference on Decision and Control, CDC 2020 tenutosi a Jeju Island, South Korea nel 14th-18th December 2020) [10.1109/CDC42340.2020.9304173].
File in questo prodotto:
File Dimensione Formato  
036_2020CDC_BCGZ.pdf

Solo gestori archivio

Descrizione: Articolo
Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.25 MB
Formato Adobe PDF
1.25 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/293851
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact