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Application of an inverse data-driven model for reconstructing wheel movement signals

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Języki publikacji
EN
Abstrakty
EN
This paper considers a method for indirect measuring the vertical displacement of wheels resulting from the road profile, using an inverse parametric data-driven model. Wheel movement is required in variable damping suspension systems, which use an onboard electronic control system that improves ride quality and vehicle handling in typical maneuvres. This paper presents a feasibility study of such an approach which was performed in laboratory conditions. Experimental validation tests were conducted on a setup consisting of a servo-hydraulic test rig equipped with displacement, force and acceleration transducers and a data-acquisition system. The fidelity and adequacy of various parametric SISO model structures were evaluated in the time domain based on correlation coefficient, FPE and AIC criteria. The experimental test results showed that inverse models provide accuracy of inversion, ranging from more than 70% for the ARX model structure to over 90% for the OE model structure.
Rocznik
Strony
491--500
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
autor
  • AGH University of Science and Technology, Department of Robotics and Mechatronics, Al. Mickiewicza 30, 30-059 Kraków, Poland, piotr.czop@labmod.com
Bibliografia
  • [1] Dixon, J.C. (2007). The Shock Absorber Handbook, Wiley, England.
  • [2] Garcia, C.E., Morari, M. (1982). Internal Model Control-1. A unifying review and some new results. Ind. Eng. Chem. Process Des. & Dev., 21, 308-323.
  • [3] Oppenheim, A.V., Schafer, R.W. (1989). Discrete-Time Signal Processing, Prentice-Hall, Englewood Cliffs, NJ.
  • [4] Piazzi. A., Visioli, A. (2001). Robust set-point constrained regulation via dynamic inversion. Int. J. Robust Nonlinear Control, 11, 1-22.
  • [5] Moscinski, J., Ogonowski, Z. (eds). (1995). Advanced control with MATLAB & SIMULINK. Ellis Horwood Ltd, UK.
  • [6] Piegat. A. (2001). Fuzzy Modeling and Control (Studies in Fuzziness and Soft Computing). Springer-Verlag.
  • [7] Elster, C., Link, A., Bruns, T. (2007). Analysis of dynamic measurements and determination of measurement uncertainty using a second-order model. Meas. Sci. Technol, 18, 3682-7.
  • [8] Mroczka, J., Szczuczyński, D. (2009). Inverse problems formulated in terms of first-kind Fredholm integral equations in indirect measurements. Metrol. Meas. Syst., XVI(3), 333-357.
  • [9] Kammer, D.C. (1998). Input force reconstruction using a time domain technique. ASME Journal of Vibration and Acoustics, 120(4), 868-874.
  • [10] Mendrok, K., Uhl, T. (2004). Overview of modal model based damage detection methods. Proceedings of the 29th International Conference on Noise and Vibration Engineering (ISMA), Leuven, Belgium, 561-576.
  • [11] Uhl, T. (2007). The inverse identification problem and its technical application. Archive of Applied Mechanics, 77(5), 325-337.
  • [12] Czop, P. (November 12, 2010). Application of Advanced Data-Driven Parametric Models to Load Reconstruction in Mechanical Structures and Systems. Journal of Vibration and Control, Published online before print. DOI: 10.1177/1077546310369688.
  • [13] Bracciali, A., Cascini, G. (1998). High-frequency mobile input reconstruction algorithm (HF-MIRA) applied to forces acting on a damped linear mechanical system. Mechanical Systems and Signal Processing, 21(2), 255-268.
  • [14] Bojko, T. (2005). Smart sensors solutions for mechanical measurement and diagnostics. Metrology and Measurement Systems, XII(1), 95-103.
  • [15] Raath, A.D., Van Waveren, C.C. (1998). A time domain approach to load reconstruction for durability testing. Engineering Failure Analysis, 4(1), 113-119.
  • [16] Chen, T.C., Lee, M.H. (2008). Research on Moving Force Estimation of the Bridge Structure using the Adaptive Input Estimation Method. Journal of Structural Engineering, (8).
  • [17] Hundhausen, R.J., Adams, D.E., Derriso, M., Kukuchek, P., Alloway, R. (2005). Transient Loads Identification for a Standoff Metallic Thermal Protection System Panel. In 23rd International, Modal Analysis Conference (IMAC XXIII), Orlando, Florida.
  • [18] Hollandsworth, P.E., Busby, H.R. (1989). Impact force identification using the general inverse technique. International Journal of Impact Engineering, 8, 315-322.
  • [19] Allena, M.S., Carne, T.G. (2008). Multi-step inverse structural filter for robust force identification. Mechanical Systems and Signal Processing, 22(5), 1036-1054.
  • [20] Chan, T.H.T., Law, S.S., Zeng, Q.H. (1997). Moving force identification: a time domain method. Journal of Sound and Vibration, 201, 1-22.
  • [21] Markusson, O. (2002). Model and system inversion with application in nonlinear system identification and control. PhD Thesis, Royal Institute of Technology, Stockholm, Sweden.
  • [22] MATHWORKS Inc. (2007). Matlab System Identification Toolbox Guide, Natick, MA: The Mathworks Inc.
  • [23] Ljung, L. (1999). System Identification - Theory for the User, Prentice-Hall.
  • [24] Lee, J.E., Fassois, S.D., Lee, J.E. (1993). On the Problem of Stochastic Experimental Modal Analysis Based on Multiple-Excitation Multiple-Response Data - Parts I and II. Journal of Sound and Vibration, 161, 33-87.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0083-0014
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