A Robust State Estimator for T-S Fuzzy System - Ziqiang Li; Jing Wang; Huabo Liu (IEEE Access, 2020)
In this paper, a robust filter is derived to estimate the state of a nonlinear system which is described by T-S fuzzy model. Firstly, this paper studies a robust state estimation algorithm based on the relationship between Kalman filter and regularized least square, and the algorithm considers the influence of model error caused by system parameter uncertainty of the system in any way. The form of the algorithm is similar to Kalman filter, and the computational complexity is similar to that of Kalman filter. Secondly, the robust filter is combined with the fuzzy model, and the fuzzy rule is used to approximate the nonlinear system. The new algorithm is used to estimate the state of the system. Finally, the new algorithm is compared with the fuzzy Kalman filter based on actual and nominal parameters by simulation experiment, and the simulation experimental results are given to illustrate the effectiveness of the proposed method, which proves that the proposed robust state estimator is better than the fuzzy Kalman filter based on nominal parameters.