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\cal H\infty Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems.

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Název: \cal H\infty Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems.
Autoři: Su, Xiaojie, Wu, Ligang, Shi, Peng, Song, Yong-Duan
Zdroj: IEEE Transactions on Systems, Man & Cybernetics: Part B; Dec2012, Vol. 42 Issue 6, p1574-1585, 12p
Témata: FUZZY systems, STOCHASTIC systems, MEAN square algorithms, MATHEMATICAL optimization, ALGORITHMS
Abstrakt: This paper is concerned with the problem of \cal H\infty model reduction for Takagi–Sugeno (T–S) fuzzy stochastic systems. For a given mean-square stable T–S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an \cal H\infty performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]
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Databáze: Complementary Index
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Abstrakt:This paper is concerned with the problem of \cal H\infty model reduction for Takagi–Sugeno (T–S) fuzzy stochastic systems. For a given mean-square stable T–S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an \cal H\infty performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]
ISSN:10834419
DOI:10.1109/TSMCB.2012.2195723