Semi active damping force estimation using LPV−H estimators with different sensing configurations

Juan C. Tudon-Martinez, Luis Amezquita-Brooks, Diana Hernandez-Alcantara*, Olivier Sename

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Semi-active suspension systems have become a widespread tool to improve the handling and comfort of vehicles. These systems require adjustable dampers as well as supplementary sensing elements. In addition to displacements and accelerations, some of the best performing approaches require knowledge of the semi active damper force. Since this variable can be difficult and expensive to measure, several estimation methods have been proposed. In this article, two Linear-Parameter-Varying H (LPV-H) filters are developed to estimate the Semi-Active (SA) damper force, considering two different combinations of sensing elements: the first configuration is more expensive, but potentially more accurate and reliable; whereas the second configuration is cheaper and arguably less reliable. Thanks to the use of LPV-H theory, both filters are designed to account for the main nonlinear phenomena of SA dampers (i.e. saturation, hysteresis, etc.), as well as being quadratically stable, robust to the road disturbances and optimized to reduce the estimation error in a specified frequency band. Simulations and experimental data are used to assess the proposed estimators as well as a typical inverse-dynamics estimation approach. The results show that while both of the proposed estimators yield a good degree of accuracy, there are indeed fundamental differences depending on the available sensing elements; a conclusion which could be crucial to appropriately define the instrumentation of semi-active suspension systems.

Original languageEnglish
Pages (from-to)928-951
Number of pages24
JournalJournal of the Franklin Institute
Issue number2
Publication statusPublished - Jan 2022

Bibliographical note

Publisher Copyright:
© 2021 The Franklin Institute

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics


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