Robustness comparison of some state estimation methods with an explicit consideration of modeling uncertainties

This paper presents a comparative analysis of well-known state estimation methods that are commonly used in real systems. The aim of this research is to measure and then evaluate the robustness (i.e., a measure of performance when a small and deliberate changes are made to the method conditions) of these methods against modeling uncertainties. The state estimation methods include the Kalman Filter, the 1^st-Order Smooth Variable Structure Filter (1^st-order SVSF), and the new developed Dynamic 2^nd-Order SVSF. A relatively new performance robustness criterion (so-called robustness index) is adopted in this work to first measure the robustness of the estimation methods, and then, evaluate their performance. The robustness index is calculated for each method when modeling uncertainties are explicitly considered. Simulation analysis is performed using the linear model of an Electro-Hydrostatic Actuator (EHA) setup under the normal and uncertain conditions. Simulation results showed the superior performance of the Dynamic 2^nd-Order SVSF over other methods in terms of robustness against modeling uncertainties.