The uncertainty learning filter: A revised smooth variable structure filter

In this contribution the problem of state estimation of systems with imprecise description is addressed. A revised version of smooth variable structure filter (SVSF) called uncertainty learning filter (ULF) is proposed. By introducing an uncertainty learning parameter a novel strategy to minimize a pseudo mean squared estimation error is developed. The introduced uncertainty learning parameter acts as a tuning factor controlling the influence of the imprecise process model on the estimations. Parameter dependencies can be avoided by adapting the uncertainty learning parameter from the innovation process. Consequently the proposed adaptive uncertainty learning filter (A-ULF) has the ability to tune itself according to the uncertainties of the process model. The boundedness of estimation error of A-ULF approach is proven. Simulation results are given to compare the estimation performance of the proposed method with SVSF, and the extended Kalman filter (EKF).