Fuzzy Kalman Filter Black Box Modeling Approach for Dynamic System with Partial Knowledge of States

A strategy to Fuzzy Kalman Filter identification, is proposed. A mathematical formulation applied to fuzzy Takagi-Sugeno structure is presented: the algorithm FCM estimates the antecedent parameters; from the input and output data of dynamic system, the ERA/DC algorithm based on FCM clustering algorithm, is applied to obtain the state matrix, input influence matrix, output influence matrix, and direct transmission matrix (the matrices A, B, C, and D, respectively) to each rule of the consequent parameters. The Fuzzy Kalman Filter is applied to estimate states and output of a dynamic system with partial knowledge of states and the efficiency of the proposed methodology is shown in computational results, once that the Fuzzy Kalman Filter follows the dynamic behavior related to output and states of the dynamic system.