Hybrid dynamical models are a powerful tool for describing the behaviour of many industrial processes and physical phenomena in which logical (discrete) and analog (continuous) dynamics exist and interact. Black-box identification of hybrid models from input/output observations and no information on the operating mode of the system is a challenging problem, as both the logical and the continuous dynamics must be retrieved. In this work, we consider the identification of discrete hybrid automata (DHA), which represent a mathematical abstraction of hybrid models whose logical dynamics are described by a finite state machine (FSM) and the continuous dynamics are represented through affine discrete-time dynamical models. We propose a two-stage estimation algorithm based on the joint use of clustering, multi-model recursive least-squares and linear multicategory discrimination techniques, which allows us to estimate both the affine models describing the continuous dynamics and the FSM governing the logical dynamics of the system.

Learning hybrid models with logical and continuous dynamics via multiclass linear separation

Breschi V.;
2016

Abstract

Hybrid dynamical models are a powerful tool for describing the behaviour of many industrial processes and physical phenomena in which logical (discrete) and analog (continuous) dynamics exist and interact. Black-box identification of hybrid models from input/output observations and no information on the operating mode of the system is a challenging problem, as both the logical and the continuous dynamics must be retrieved. In this work, we consider the identification of discrete hybrid automata (DHA), which represent a mathematical abstraction of hybrid models whose logical dynamics are described by a finite state machine (FSM) and the continuous dynamics are represented through affine discrete-time dynamical models. We propose a two-stage estimation algorithm based on the joint use of clustering, multi-model recursive least-squares and linear multicategory discrimination techniques, which allows us to estimate both the affine models describing the continuous dynamics and the FSM governing the logical dynamics of the system.
2016 IEEE 55th Conference on Decision and Control, CDC 2016
978-1-5090-1837-6
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/11311/1166998
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact