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Comparative Performance Analysis of Active and Semi-active Suspensions with Road Preview Control

  • Ivan CvokEmail author
  • Joško Deur
  • H. Eric Tseng
  • Davor Hrovat
Conference paper
  • 5 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

The paper presents a comparative performance analysis of passive, active and semi-active suspensions with various optimal control system settings. The active suspension is controlled by a linear quadratic regulator (LQR) in combination with road preview control, while the semi-active suspension is controlled by a clipped-optimal LQR approach. The LQR cost function includes three conflicting criteria related to ride comfort, vehicle handling and suspension stroke limits. The trade-off among these three criteria is assessed by using covariance analysis, i.e. by comparing standard deviations of the criteria-reflected system outputs with respect to stochastic road profile input. Further comparative analyses are based on frequency responses of linear quarter-car model and time responses of nonlinear full-car suspension model. The analysis results show that for some not-too-soft settings, semi-active suspensions with road preview control can outperform active suspensions without road preview, while the best overall performance is achieved by using fully active suspension with road preview control. Time responses of a full-car model, obtained in an advanced simulation environment, demonstrate that controllers based on simple, quarter-car model can be successfully applied to nonlinear, full-car model for improving ride comfort and vehicle handling.

Keywords

Active suspension Semi-active suspension Optimal control Road preview 

Notes

Acknowledgment

It is gratefully acknowledged that this work has been supported by the Ford Motor Company. In addition, the research work of 1st author has been supported by the Croatian Science Foundation through the “Young researches’ career development project – training of new doctoral students”.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of ZagrebZagrebCroatia
  2. 2.Ford Motor CompanyDearbornUSA
  3. 3.University of CaliforniaSan DiegoUSA

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