Optimized Exponential Square Root Unscented Kalman Filter for State Estimation of Hydraulic Systems

This paper presents a new version of Archimedes' optimization algorithm. The basic algorithm is changed in a way that uses information from previous iteration to update the candidate solutions to find the best solution for the proposed optimization problem. The proposed algorithm can be used to find the best solution for different optimization problems. As an engineering problem, the new algorithm is applied to find the parameters of a nonlinear version of Kalman filter, named exponential square root unscented Kalman filter. The optimized filter is applied to a servo-hydraulic system to estimate its states. The state estimation method works without a priori knowledge. The filter tries to estimate states of a hydraulic system and statistics of the noises which affect states and measurements. The presented results show the efficiency of the optimized filter in comparison with the basic filter.