Tensor Product Model Transformation Simplification of Takagi-Sugeno Control and Estimation Laws – An Application to a Thermoelectric Controlled Chamber

doi:10.12700/aph.15.3.2018.3.2

Cited by 4 publications

(1 citation statement)

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“…The TP model transformation and its extension, LMI and HOSVD based methods are a popular research field today, there are many publications about the investigation; [4][5][6][7][8][9][10][11][12][13]. Articles on practical applications of the TP model transformation: [14][15][16][17][18][19][20][21][22][23][24]. Additional papers used for current article: [25,26].…”

Section: Introductionmentioning

confidence: 99%

LMI feasibility analysis of 2DOF NATA model

Wéber,

Kuczmann

2024

Pollack

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Present paper shows the different types of tensor product model based linear matrix inequality controller design and feasibility analysis of two degrees of freedom aeroelastic wing section model. The tensor product models are based on reducing or removing the nonlinear behavior of the system and weighting functions. The linear matrix inequality based method results globally asymptotically stable system. The goal of the paper is to examine that selecting and varying the transformation space influences the feasibility of the linear matrix inequality based controller. The paper gives a comparison between the different tensor product models in terms of controller performance. The linear matrix inequality gives feasible solution for the controller design if the transformation space is selected adequately.

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Section: Introductionmentioning

confidence: 99%

LMI feasibility analysis of 2DOF NATA model

Wéber,

Kuczmann

2024

Pollack

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How to Vary the Input Space of a T–S Fuzzy Model: A TP Model Transformation-Based Approach

Bárányi

2022

IEEE Trans. Fuzzy Syst.

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The motivation behind 15 years of continuous development within the topic of the Tensor Product (TP) model transformation is that the greater the number of parameters or components of the Takagi Sugeno (TS) fuzzy model one can manipulate, the larger complexity reduction or control optimization one can achieve. The paper proposes a radically new type of extension to the TP model transformation: while earlier variants of the TP model transformation focused on how the antecedent-consequent fuzzy set system of a given TS fuzzy model could be varied, the present work, in contrast, focuses on how the number of inputs to a given TS fuzzy model can be manipulated. The proposed extension is capable of changing the number of inputs or transforming the non-linearity between the fuzzy rules and the input dimensions. These new features considerably increase the modeling power of the TP model transformation, allowing for further complexity reduction and more powerful control optimisation to be achieved. The paper provides two examples to show how the proposed extension can be used in a routine-like fashion.

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