A COMPARATIVE STUDY OF A SMOOTH VARIABLE STRUCTURE FILTER AND THE EXTENDED KALMAN FILTER

A new method of filtering strategy, referred to as the Smooth Variable Structure Filter (SVSF) is applied to the problem of state estimation on a class of nonlinear system. The SVSF is revised to reach the better estimation resolution. A comparative study is presented in which the Extended Kalman Filter (EKF) is applied to the same nonlinear system model. The estimation convergence and accuracy of the SVSF and EKF are comparable. The robustness of the SVSF to parameter variations is established through simulation results. This study is important because it allows the new SVSF to be critically compared to a standard technique such as the EKF.