| Title |
Sampled-data Fuzzy Observer Design for State Estimation of Uncertain Systems Under Imperfect Premise Matching |
| Authors |
김한솔(Han Sol Kim) ; 주영훈(Young Hoon Joo) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.11.1403 |
| Keywords |
Takagi-Sugeno (T-S) fuzzy model; Sampled-data fuzzy observer; ∞; Linear matrix inequality; Immeasurable premise. |
| Abstract |
In this paper, we propose a sampled-data fuzzy observer design technique for estimating the state variables of a nonlinear system with model uncertainty. It is assumed that the IF-THEN rules of the fuzzy system contains immeasurable premise variables, which complicates the observer design. In this paper, the observer is assumed not to share the same premise part with that of the system in order to deal with the immeasurable premise condition. After then, the error between the observer and the system including model uncertainty is represented by the Takagi-Sugeno (T-S) fuzzy model. In order to minimize the impact of imperfect premise matching, model uncertainty, and disturbances on the state estimation, an ∞ performance criterion is defined. Based on the fuzzy Lyapunov function, we derive a sufficient condition in the form of linear matrix inequality to ensure that the error dynamics is asymptotically stable and satisfy the ∞ condition. Finally, a simulation example verifies the superiority of the proposed method. |