Premium
Tensor product transformation‐based modeling of an induction machine
Author(s) -
Németh Zoltán,
Kuczmann Miklós
Publication year - 2021
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.2468
Subject(s) - transformation (genetics) , model transformation , representation (politics) , computer science , tensor product , state space representation , process (computing) , nonlinear system , state space , control theory (sociology) , control engineering , tensor (intrinsic definition) , fuzzy logic , mathematics , control (management) , artificial intelligence , engineering , algorithm , physics , law , operating system , biochemistry , chemistry , statistics , consistency (knowledge bases) , quantum mechanics , politics , political science , pure mathematics , gene
Abstract The paper demonstrates a tensor product (TP) model transformation‐based framework for an induction machine (IM). The state space model of an IM is highly nonlinear, thus the Takagi–Sugeno (TS) fuzzy model‐based quasi‐linear parameter‐varying (qLPV) representation can be a good alternative approach of machines modeling. The paper presents the basics of IM state space modeling, how the TP transformation can be applied in details. The control of IM is always a pivotal point; hence, options of feedback control are discussed. The main goal of this paper is to present the whole process of IM TP transformation‐based modeling including a control system.