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TP Model Transformation as a Manipulation Tool for QLPV Analysis and Design
Author(s) -
Baranyi Péter
Publication year - 2015
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.1092
Subject(s) - transformation (genetics) , model transformation , computer science , representation (politics) , identification (biology) , preprocessor , bridging (networking) , heuristic , stability (learning theory) , theoretical computer science , artificial intelligence , machine learning , consistency (knowledge bases) , computer network , biochemistry , chemistry , botany , politics , biology , political science , law , gene
The main motivation of the TP model transformation is that modern identification and control analysis, along with design methodologies, are based on various kinds of different representations that have different benefits and drawbacks in terms of identification and modeling effort and structure, however, the link between these representations is in many cases difficult to establish, especially if the model components are not given by closed formulae but rather by various soft‐computing‐based or black box models. This paper shows that the TP model transformation can serve as a gateway between different representations by bridging to a widely adopted polytopic representation. Further, it is capable of readily manipulating the resulting polytopic representation for further design requirements, which in many cases has a strong effect on the resulting control performance and the conservativeness of the solution. The paper discusses how the TP model transformation can be used as a final step of modeling and, at the same time, as a preprocessing step in polytopic model based design approaches. The paper introduces the generalized TP model transformation, which is a tractable, non‐heuristic numerical tool that can be executed on sets of functions. Different manipulation techniques are investigated to show the benefits of the generalized TP model transformation. Finally, a stability verification technique is proposed for cases where system components are available in different representations.