Abstarct: This thesis is concerned with the design and implementation of H∞ state estimator of a dynamical system baxsed on the adaptive fuzzy modeling. At first, a universal linear model is considered with some unknown variables. Since the variables are not time invariant in modeling of a real nonlinear system so they are modeled by fuzzy approximates with some free parameters. The system states constitute input variables of the fuzzy system. The novelty of this research is design of stable state and parameter estimator baxsed on H∞ criterion in wich the system states and adaptive fuzzy systems' parameters are determined simultaneously. It will be proved that the parameter estimator is just modified normalized least mean squares (NLMS) algorithm which follows H∞ optimality. In this way, it reduces required tuning parameters of the filter and has lower computational complexity. The proposed model can be used in signal processing such as time series and speech processing or in nonlinear circuit modeling. Finally, suggested algorithm is implemented on a practical data gathered from battery for state of charge estimation. The results show stability, good performance, fast convergence time, and robustness to far initial conditions of the suggested estimator. For comparison, the proposed method is compared with the one in which the identified free parameters are not adjusting during estimation process.