Adaptive estimation and control of MR damper for semi-active suspension systems

Qianqian Pei, Jing Na*, Yingbo Huang, Guanbin Gao, Xing Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
245 Downloads (Pure)

Abstract

This paper proposes adaptive estimation and control methods for vehicle semi-active suspension systems with magneto-rheological (MR) damper. To incorporate MR damper into the control design, a hyperbolic model is adopted to describe its dynamics, and then adaptive parameter estimation is firstly studied to estimate the unknown parameters of the hyperbolic model. This estimation method requires the measured piston variable and damper force, and can be taken as a further extension of our recently proposed parameter estimation error based algorithms. Moreover, an adaptive control is designed to stabilize the vertical vehicle displacement to improve the ride comfort, where an alternative leakage term is introduced in the adaptive law to guarantee simultaneously the precise estimation of several essential parameters (e.g. mass of vehicle body and MR damper parameters) and the control convergence. The closed-loop system stability is proved and relevant suspension performance requirements are analyzed. Finally, simulations based on a quarter-car model are provided to validate the proposed method.

Original languageEnglish
Title of host publication35th Chinese Control Conference (CCC)
PublisherIEEE Computer Society
Pages3111-3116
Number of pages6
ISBN (Print)9789881563910
DOIs
Publication statusPublished - 26 Aug 2016
Event35th Chinese Control Conference, CCC 2016 - Chengdu, China
Duration: 27 Jul 201629 Jul 2016

Publication series

NameProceedings of the Chinese Control Conference
ISSN (Electronic)1934-1768

Conference

Conference35th Chinese Control Conference, CCC 2016
CountryChina
CityChengdu
Period27/07/1629/07/16

Keywords

  • adaptive control
  • magneto-rheological damper
  • parameter estimation
  • Semi-active suspension systems

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