An Adaptive Formulation of the Sliding Innovation Filter
Andrew Lee, S. Andrew Gadsden, Mohammad Al‐Shabi
Abstract
In this paper, an adaptive formulation of the sliding innovation filter (SIF) is presented. The SIF is a recently proposed estimation strategy that has demonstrated robustness to modeling errors and uncertainties. It utilizes a switching gain that is a function of the innovation (measurement error) and sliding boundary layer term. In this paper, a time-varying sliding boundary layer is derived based on minimizing the state error covariance. The resulting solution creates an adaptive formulation of the SIF. The adaptive SIF is applied on a linear aerospace system, and is compared with the well-known Kalman filter (KF) and the standard SIF. The results demonstrate the robustness of the new estimation strategy in the presence of modeling uncertainties and system faults.