A Wavelet-Based Smooth Variable Structure Filter

For linear and well-defined estimation problems with Gaussian noise, the Kalman filter (KF) yields the best result in terms of estimation accuracy. However, the KF performance degrades and can fail in cases involving large uncertainties such as modeling errors in the estimation process. The smooth variable structure filter (SVSF) is a model-based estimation method built on sliding mode theory with excellent robustness to modeling uncertainties. Wavelet theory has attracted interest as a powerful tool for signal and image processing, and can be used to further improve estimation accuracy. In this paper, a new filtering strategy based on stationary wavelet theory and the smooth variable structure filter is proposed. This strategy, referred to as W-SVSF, is applied on an electrohydrostatic actuator (EHA) for the purposes of state estimation. The results of the W-SVSF are compared with the standard KF, SVSF, and combined W-KF.