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.
The paper discusses the theoretical background of the state space modeling of induction machines. The main goal is to present the necessary equations of the induction machine and the topic of the state space modeling. Although the induction machine is a highly non-linear system, LPV/qLPV model can be formulated from these equations.
The modeling and drive control of electric machines are still actively researched scientific topics. Most of the existing models contain parameters that have no physical content or cannot be measured at all. For this reason, the use of observers in modern drive control algorithms is necessary. The main goal of this paper is to present the mathematical formalism of a linear matrix inequality (LMI)-based controller-observer design for a tensor product (TP) transformation-based model, including its implementation in a simulation environment. Based solely upon simulation results, the designed observer can provide a stable and accurate state space variable, regardless of the highly nonlinear induction machine model.
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