Conference article

Modelling and parameter identification of a semi-active vehicle damper

Michael Fleps-Dezaße
German Aerospace Center (DLR), Institute of System Dynamics and Control, Wessling, Germany

Jakub Tobolá r
German Aerospace Center (DLR), Institute of System Dynamics and Control, Wessling, Germany

Johannes Pitzer
German Aerospace Center (DLR), Institute of System Dynamics and Control, Wessling, Germany

Download articlehttp://dx.doi.org/10.3384/ecp14096283

Published in: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden

Linköping Electronic Conference Proceedings 96:29, p. 283-292

Show more +

Published: 2014-03-10

ISBN: 978-91-7519-380-9

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

In this paper two semi-physical models of the semi-active dampers of the DLR robotic electric vehicle ROboMObil (ROMO) are described and their implementation in Modelica is presented. Besides the damper characteristics and hysteresis; the models additionally consider the gas force and cover the differences of the damper characteristics for compression and rebound. A procedure to identify the damper model parameters was implemented using the DLR Optimization library. The measurement data used for parameter identification was recorded during experiments on a damper test bench. The simulation results of the damper models are compared to the experiment data of the semi-active damper and the suitability of the damper models with respect to accuracy and real-time simulation is discussed.

Keywords

Semi-active damper; model identification; Bouc-Wen model; vehicle dynamics

References

[1] Savaresi S. M.: Semi-active suspension control design for vehicles. Amsterdam, Boston: Butterworth-Heinemann/Elsevier, 2010.

[2] Guglielmino E.: Semi-active suspension control: Improved vehicle ride and road friendliness. London: Springer, 2008.

[3] ValášekM. and KortümW.: EU-Project COPERNICUS: Development of Semi-Active Road-Friendly Truck Suspension: Choice of Control Law, Experimental Verification, Implementation, Results. Aachen, 2000.

[4] Brembeck J., Ho L. M., Schaub A., C. Satzger, Tobolar J., Bals J. and Hirzinger G.: ROMO – the robotic electric vehicle. In: International Association for Vehicle System Dynamics (IAVSD), 2011.

[5] Kortüm W., Valášek M., Šika Z., Schwartz W., Steinbauer P., and Vaculín O.: Semi-active damping in automotive systems: Design-bysimulation. International Journal of Vehicle Design, vol. 28, no. 1, pp. 103–120, 2002. DOI: 10.1504/IJVD.2002.001981

[6] Fleps-Dezasse M. and Brembeck J.: Model Based Vertical Dynamics Estimation with Modelica and FMI. In: Advances in Automotive Control, Elsevier, IFAC, pp. 341–346, 2013.

[7] Koch G., Kloiber T. and Lohmann B.: Nonlinear and filter based estimation for vehicle suspension control. In: 49th IEEE Conference on Decision and Control (CDC), pp. 5592–5597, 2010. DOI: 10.1109/CDC.2010.5718052

[8] Duym S.W., Stiens R., and Reybrouk K.: Evaluation of Shock Absorber Models. Vehicle System Dynamics, vol. 27, no. 2, pp. 109–127, 1997. DOI: 10.1080/00423119708969325

[9] Duym S. W.: Simulation Tools, Modelling and Identification, for an Automotive Shock Absorber in the Context of Vehicle Dynamics. Vehicle System Dynamics, vol. 33, no. 4, pp. 261–285, 2000. DOI: 10.1076/0042-3114(200004)33:4;1-U;FT261

[10] Pellegrini E., Koch G., and Lohmann B.: Physical Modeling of a Nonlinear Semi-Active Vehicle Damper. In: Advances in Automotive Control: IFAC, Elsevier, pp. 324–329, 2010.

[11] Spencer Jr. B. F., Dyke S. J., SainM. K. and Carlson J. D.: Phenomenological Model for Magnetorheological Dampers. J. Eng. Mech, vol. 123, no. 3, pp. 230–238, 1997. DOI: 10.1061/(ASCE)0733-9399(1997)123:3(230)

[12] Butz T. and von Stryk O.: Modelling and Simulation of Electro- and Magnetorheological Fluid Dampers. ZAMM – Journal of Applied Mathematics and Mechanics, vol. 82, no. 1, pp. 320, 2002.

[13] Altmann F.: Identifizierung eines magnetorheologischen Dämpfers: Vorbereitung des Modells eines leichten individuellen Stadt-Autos (LISA) zur Simulation unter Verwendung des semiaktiven Dämpfers. Diploma thesis, 2004.

[14] Savaresi S. M., Bittanti S. and Montiglio M.: Identification of semi-physical and black-box non-linear models: the case of MR-dampers for vehicles control. Automatica, vol. 41, no. 1, pp. 113–127, 2005.

[15] Pfeiffer A.: Optimization library for interactive multi-criteria optimization tasks. In: Modelica 2012 Conference, 2012.

Citations in Crossref