A comparative study of Extended Kalman Filter and H∞ filtering for state estimation of stewart platform manipulator

This paper presents the estimation of both position and velocity of Stewart Manipulator by means of limbs potentiometer measurements and MEMS inertial sensors. The estimation used the Extended Kalman Filter (EKF) and H∞ optimal filtering technique based on the combination of these sensors. The two types of filters are used as nonlinear state estimators to the Stewart platform which is modeled as a stochastic differential equations due to measurement noise in case of EKF and as a continuous time system model in case of H∞ filtering technique. The results of the both filters are compared with each other on the Stewart platform DELTALAB EX800 using MATLAB SimMechanics toolbox. The simulation results show that Kalman filters are not the best choice for parallel manipulator state estimation as they bear from the hypothesis of statistical noise with zero mean as well as known noise covariance, which may reduce its performance. For these reasons, H∞ filter may be the alternative of Kalman filter.

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