A Wavelet-Based Smooth Variable Structure Filter Conferences

abstract

• 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. Copyright © 2012 by ASME.

authors

• Zhang, William
• Gadsden, Stephen
• Habibi, Saeid

status

• published 

publication date

• October 17, 2012

published in

• Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperativ  Journal

presented at event

• ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference  Conference

keywords

• Automation & Control Systems
• Engineering
• Engineering, Electrical & Electronic
• Engineering, Mechanical
• KALMAN FILTERS
• MULTIRESOLUTION
• SYSTEMS
• Science & Technology
• Technology

Digital Object Identifier (DOI)

• 10.1115/dscc2012-movic2012-8838

International Standard Book Number (ISBN) 13

• 978-0-7918-4529-5

start page

• 649

end page

• 655

volume

• 1