Fuzzy Hammerstein Model Based States Space Identification Approach of Nonlinear Dynamics Systems

摘要

This paper presents a novel methodology for evolving fuzzy identification of nonlinear systems in state space based on Hammerstein models. The nonlinear static characteristic is approximated by an evolving Takagi-Sugeno fuzzy model and the linear dynamics by a state space model. The recursive estimation of the linear model in state space is performed based on the system Markov parameters applied to the algorithm of minimum realization ERA. Computational results illustrate the effectiveness of the proposed method in the online identification of nonlinear systems.