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.
- Dynamic Systems and Control Division
A Wavelet-Based Smooth Variable Structure Filter
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Zhang, W, Gadsden, SA, & Habibi, SR. "A Wavelet-Based Smooth Variable Structure Filter." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 649-655. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8838
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