AN OPTIMAL EXTENDED KALMAN FILTER DESIGNED BY GENETIC ALGORITHMS

A Geno-Kalman filter is utilized for state estimation of a bench-scale batch reactor that handles an exothermic reaction between H2O2 and Na2S2O3. This reaction system includes three different states including the concentration of reactants as well as the temperature of the reactor. All of the states are measured during the process. The proposed procedure is to run an optimal extended Kalman filter by which the Kalman design parameters, Q and R, are obtained by genetic algorithms. The extended Kalman filter is initially designed by trial and error and used as a baseline in this study. Then an optimal white-bound extended Kalman filter design is obtained through an optimization on the baseline estimator, using genetic algorithms. The results show a significant improvement in the performance of the estimator. Moreover, a color-bound extended Kalman filter was also designed to allow a dynamic linear trend for the change in nonzero elements of the process noise covariance matrix.