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Condition Monitoring of an Electro-Hydrostatic Actuator Using the Dynamic 2nd-Order Smooth Variable Structure Filter

[+] Author Affiliations
Hamed Hossein Afshari, Saeid Habibi

McMaster University, Hamilton, ON, Canada

Stephan Andrew Gadsden

University of Maryland, Baltimore County, Baltimore, MD

Paper No. DETC2015-47436, pp. V009T07A051; 6 pages
doi:10.1115/DETC2015-47436
From:
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 9: 2015 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5719-9
  • Copyright © 2015 by ASME

abstract

This paper introduces the dynamic 2nd-order Smooth Variable Structure Filter (Dynamic 2nd-order SVSF) method for the purpose of robust state estimation. Thereafter, it presents an application of this method for condition monitoring of an electro-hydrostatic actuator system. The SVSF-type filtering is in general designed based on the sliding mode theory; whereas the sliding mode variable is equal to the innovation (measurement error). In order to formulate the dynamic 2nd-order SVSF, a dynamic sliding mode manifold is defined such that it preserves the first and second order sliding conditions. This causes that the measurement error and its first difference are pushed toward zero until reaching the existence subspace. Hence, this filter benefits from the robustness and chattering suppression properties of the second order sliding mode systems. These help the filter to suppress the undesirable chattering effects without the need for approximation or interpolation that however reduces accuracy and robustness of the SVSF-type filtering. In order to investigate the performance of the dynamic 2nd-order SVSF for state estimation, it applies to an Electro-Hydrostatic Actuator (EHA) system under the normal and uncertain scenarios. Simulation results are then compared with ones obtained by other estimation methods such as the Kalman filter and the 1st-order SVSF method.

Copyright © 2015 by ASME

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