Fuzzy Reset-Based <italic>H</italic><sub>∞</sub> Unknown Input Observer Design for Uncertain Nonlinear Systems With Unmeasurable Premise Variables
This paper proposes an <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> reset unknown input observer (R-UIO) based on the Takagi-Sugeno (T-S) fuzzy model for the state estimation of nonlinear uncertain systems. Firstly, <inli...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/4211727a19e84605a45bc426488ad6df |
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| Sumario: | This paper proposes an <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> reset unknown input observer (R-UIO) based on the Takagi-Sugeno (T-S) fuzzy model for the state estimation of nonlinear uncertain systems. Firstly, <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> unknown input observer (UIO) is designed for TSFM-based nonlinear systems with measurable and unmeasurable premise variables. Then, according to the importance of observers based on unmeasurable premise variables, the results on UIO is modified to propose R-UIO. The sufficient conditions for the stabilization of the estimation error are derived in terms of linear matrix inequalities (LMIs). The proposed R-UIO benefits from less computation complexity to find the feasible parameters, improvement of the estimation process in viewpoints of convergence speed and overshoot. To verify the effectiveness of the recommended approaches, the methods are applied to a practical system. |
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