SELF-LEARNING DISTURBANCE COMPENSATION FOR ACTIVE SUSPENSION SYSTEMS

Eckehard Münch, Henner Vöcking, Thorsten Hestermeyer

Abstract

Ride comfort and safety of vehicles can be increased by active suspension systems. A problem is the detection of disturbances which can generally not be measured until they impact the chassis. Provided guidance and disturbance are known in advance, a controller can use this information to achieve considerably improved behavior. This paper presents an approach in which railway vehicles coupled in a network, in repeated runs over the same track section, learn a disturbance compensation that can almost entirely compensate for stationary disturbances, i.e., disturbances that occur at the same spot in equal measure. Here information on the respective track section is sampled, stored locally at the track, and retrieved by the succeeding vehicle which will use them for an improved compensation for the occurring disturbances and again store information there. This iterative procedure results in an optimal compensation. The algorithm is described and criteria for its design are derived from digital control theory. The procedure was implemented on a testbed for a semi-vehicle with three degrees of freedom. The results of the measurements are displayed and evaluated in this paper.

References

  1. , E., and Oberschelp, O. (2004). Sollbahn-Planung f ür schienengebundene Fahrzeuge. In Numerical Analysis and Simulation in Vehicle Engineering, VDI-Berichte 1846, pages 137-158, Würzburg.
  2. Ioannou, P. (1998). Evaluation and analysis of automated highway system concepts and architectures. California PATH Research Report UCB-ITS-PRR-98-12, University of Southern California.
  3. M ünch, E., Hestermeyer, T., Oberschelp, O., Scheideler, P., and Schmidt, A. (2004). Distributed optimization of reference trajectories for active suspension with multi-agent systems. In European Simulation Multiconference 2004 - Networked Simulations and Simulated Networks, pages 343-350, Magdeburg, Germany. SCS.
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Paper Citation


in Harvard Style

Münch E., Vöcking H. and Hestermeyer T. (2005). SELF-LEARNING DISTURBANCE COMPENSATION FOR ACTIVE SUSPENSION SYSTEMS . In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 972-8865-29-5, pages 32-38. DOI: 10.5220/0001185700320038


in Bibtex Style

@conference{icinco05,
author={Eckehard Münch and Henner Vöcking and Thorsten Hestermeyer},
title={SELF-LEARNING DISTURBANCE COMPENSATION FOR ACTIVE SUSPENSION SYSTEMS},
booktitle={Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2005},
pages={32-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001185700320038},
isbn={972-8865-29-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - SELF-LEARNING DISTURBANCE COMPENSATION FOR ACTIVE SUSPENSION SYSTEMS
SN - 972-8865-29-5
AU - Münch E.
AU - Vöcking H.
AU - Hestermeyer T.
PY - 2005
SP - 32
EP - 38
DO - 10.5220/0001185700320038