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The Loewner framework for nonlinear identification and reduction of Hammerstein cascaded dynamical systems
Author(s) -
Karachalios Dimitrios S.,
Gosea Ion Victor,
Antoulas Athanasios C.
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000337
Subject(s) - nonlinear system , control theory (sociology) , reduction (mathematics) , transfer function , nonlinear system identification , a priori and a posteriori , lti system theory , system identification , mathematics , dynamical systems theory , identification (biology) , linear system , computer science , actuator , algorithm , control (management) , engineering , artificial intelligence , measure (data warehouse) , physics , mathematical analysis , philosophy , geometry , botany , epistemology , quantum mechanics , database , electrical engineering , biology
We present an algorithm for data‐driven identification and reduction of nonlinear cascaded systems with Hammerstein structure. The proposed algorithm relies on the Loewner framework (LF) which constitutes a non‐intrusive algorithm for identification and reduction of dynamical systems based on interpolation. We address the following problem: the actuator (control input) enters a static nonlinear block. Then, this processed signal is used as an input for a linear time‐invariant system (LTI). Additionally, it is considered that the orders of the linear transfer function and of the static nonlinearity are not a priori known.