LAND VEHICLE STATE ESTIMATION USING A MODIFIED LOLIMOT ALGORITHM

Modelling unknown nonlinear dynamic process is an essential prerequisite for model-based state estimation. Fuzzy local linearization (FLL) is a useful divide-and-conquer method for coping with complex problem such as data based nonlinear process modelling. In this paper first a modified local linear model tree (LOLIMOT) algorithm for nonlinear system modelling are proposed. Expectation maximization (EM) algorithm is used for local model estimation. Based on available information two commonly used state estimators, based on FLL models are presented, with an implementation on a land vehicle state estimation to validate the proposed method. The proposed method has better results for state estimation.