Nonlinear dynamic system identification based on Fuzzy Kalman Filter
Danúbia Soares Pires, Ginalber Luiz de Oliveira Serra · 2016
A strategy to nonlinear dynamic system identification based on Fuzzy Kalman Filter, is proposed. A mathematical formulation based on fuzzy Takagi-Sugeno structure is presented: the algorithm FCM estimates the fuzzy sets; from the data input and output of a nonlinear dynamic system, the ERA/DC algorithm based on clustering, estimated by FCM algorithm, is applied to obtain the matrices A, B, C, and D (state matrix, input influence matrix, output influence matrix, and direct transmission matrix, respectively) to each rule of the fuzzy model consequent. A Fuzzy Kalman Filter is applied to estimate states and output of a nonlinear dynamic system. Computational results show the efficiency of the proposed methodology, once that the TS Fuzzy Model Based on Fuzzy Kalman Filter follow the behavior relative to output and states of a nonlinear dynamic system, and compared it to other methodology it shows a better performance related to follow the behavior of states.