Tensor product transformation‐based modeling of an induction machine
Zoltán Németh, Miklós Kuczmann
Abstract
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.
Topics & Concepts
Transformation (genetics)Model transformationRepresentation (politics)Computer scienceTensor productState-space representationProcess (computing)Nonlinear systemState spaceControl theory (sociology)Control engineeringTensor (intrinsic definition)Fuzzy logicMathematicsControl (management)Artificial intelligenceEngineeringAlgorithmPhysicsQuantum mechanicsStatisticsLawBiochemistryPoliticsOperating systemChemistryConsistency (knowledge bases)Pure mathematicsGenePolitical scienceTensor decomposition and applicationsComputational Physics and Python ApplicationsReal-time simulation and control systems