Particle filtering approach to membership function adjustment in fuzzy logic systems

The fuzzy logic system has been a popular tool for modeling nonlinear systems in recent years. In the fuzzy logic system, the shape of the membership function has a significant effect on the modeling accuracy. Thus, membership function adjustment methods have been studied and developed. However, in highly nonlinear systems, the existing membership function adjustment method based on the extended Kalman filter (EKF) may exhibit poor performance due to the linearization error. In this paper, to overcome the drawback of the EKF-based membership function adjustment (EKFMFA), we propose a new membership function adjustment method based on the particle filter (PF). The proposed PF-based membership function adjustment (PFMFA) does not suffer from performance degradation due to the linearization error. We demonstrate that the PFMFA outperforms the EKFMFA through the simulation of a fuzzy cruise control system.