Nonlinear state estimation in mobile robot using fuzzy set membership filter

In this paper, we present a fuzzy set membership (FSM) filter for state estimation of nonlinear discrete-time systems with unknown but bounded disturbances. First, the Takagi-Sugeno (TS) fuzzy model is used to represent the nonlinear systems. Based on this model, FSM filter is proposed to further improve the numerical accuracy and stability of the nonlinear system estimation. In comparison with the existing fuzzy Kalman (FK) filter, the proposed FSM filter using the efficient and stable updating recursions provides much more accurate estimation results. Simulation results show that the proposed algorithm can improve the performance (accuracy and reliability) of the state estimation in mobile robot localization.