Decentralized sampled-data H∞ fuzzy filtering with exponential time-varying gains for nonlinear interconnected systems
This study proposes a technique to design a decentralized sampled-data H∞ fuzzy filter for nonlinear interconnected systems. Unlike previous studies, a novel sampled-data fuzzy filter design includes exponential time-varying gains, which leads to enlargement of the sampling period and improvement in...
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Published in | Information sciences Vol. 609; pp. 1518 - 1538 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Inc
01.09.2022
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Online Access | Get full text |
ISSN | 0020-0255 |
DOI | 10.1016/j.ins.2022.07.078 |
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Abstract | This study proposes a technique to design a decentralized sampled-data H∞ fuzzy filter for nonlinear interconnected systems. Unlike previous studies, a novel sampled-data fuzzy filter design includes exponential time-varying gains, which leads to enlargement of the sampling period and improvement in the decay rate performance of the filter. The filter design technique includes both measurable and immeasurable premise variable cases. In the latter case, the H∞ performance criterion is newly defined to minimize the effect of immeasurability on filtering performance. This study also focuses on deriving less conservative design conditions of the filter. First, a novel two-sided looped Lyapunov–Krasovskii functional (LKF) is constructed to make full use of the actual sampling pattern. Then, by utilizing an extended reciprocally convex matrix inequality along with Writinger-based integral inequality, less conservative design conditions are derived as linear matrix inequalities (LMIs)-based optimal problem. Finally, the effectiveness and superior performance of the proposed filtering design techniques are demonstrated through two simulation examples. |
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AbstractList | This study proposes a technique to design a decentralized sampled-data H∞ fuzzy filter for nonlinear interconnected systems. Unlike previous studies, a novel sampled-data fuzzy filter design includes exponential time-varying gains, which leads to enlargement of the sampling period and improvement in the decay rate performance of the filter. The filter design technique includes both measurable and immeasurable premise variable cases. In the latter case, the H∞ performance criterion is newly defined to minimize the effect of immeasurability on filtering performance. This study also focuses on deriving less conservative design conditions of the filter. First, a novel two-sided looped Lyapunov–Krasovskii functional (LKF) is constructed to make full use of the actual sampling pattern. Then, by utilizing an extended reciprocally convex matrix inequality along with Writinger-based integral inequality, less conservative design conditions are derived as linear matrix inequalities (LMIs)-based optimal problem. Finally, the effectiveness and superior performance of the proposed filtering design techniques are demonstrated through two simulation examples. |
Author | Kim, Euntai Kim, Han Sol Joo, Young Hoon Jang, Yong Hoon |
Author_xml | – sequence: 1 givenname: Yong Hoon surname: Jang fullname: Jang, Yong Hoon organization: School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea – sequence: 2 givenname: Han Sol surname: Kim fullname: Kim, Han Sol organization: Department of Control and Automation Engineering, National Korea Maritime and Ocean University, Busan 49112, South Korea – sequence: 3 givenname: Euntai surname: Kim fullname: Kim, Euntai organization: School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea – sequence: 4 givenname: Young Hoon surname: Joo fullname: Joo, Young Hoon email: yhjoo@kunsan.ac.kr organization: School of IT Information and Control Engineering, Kunsan National University, Kunsan 54150, South Korea |
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Keywords | Decentralized fuzzy filtering Takagi–Sugeno (T–S) fuzzy model Immeasurable premise variable Time-varying gain Sampled-data fuzzy filtering |
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SubjectTerms | Decentralized fuzzy filtering Immeasurable premise variable Sampled-data fuzzy filtering Takagi–Sugeno (T–S) fuzzy model Time-varying gain |
Title | Decentralized sampled-data H∞ fuzzy filtering with exponential time-varying gains for nonlinear interconnected systems |
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