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Presliding friction identification based upon the Maxwell Slip model structure.

Demosthenis D Rizos1, Spilios D Fassois

  • 1Stochastic Mechanical Systems (SMS) Group, Department of Mechanical & Aeronautical Engineering, University of Patras, GR 265 00 Patras, Greece.

Chaos (Woodbury, N.Y.)
|June 11, 2004
PubMed
Summary

New methods, Dynamic Linear Regression (DLR) and NonLinear Regression (NLR), improve presliding friction identification. DLR provides the highest accuracy, while NLR balances accuracy and complexity for better friction modeling.

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Area of Science:

  • Mechanical Engineering
  • Tribology
  • Control Systems

Background:

  • Presliding friction, crucial in mechanical systems, exhibits hysteresis and nonlocal memory effects.
  • Accurate identification of presliding friction is essential for precise system control and performance.
  • Existing methods often struggle to capture the complex dynamics of presliding friction.

Purpose of the Study:

  • To develop and evaluate novel methods for identifying presliding friction using the Maxwell Slip model.
  • To establish conditions for model identifiability and provide guidelines for experimental signal design.
  • To compare the performance of new methods against conventional approaches.

Main Methods:

  • Analysis of Maxwell Slip model properties to establish a priori and a posteriori identifiability conditions.

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  • Formulation of guidelines for excitation signal design based on identifiability criteria.
  • Development of Dynamic Linear Regression (DLR) and NonLinear Regression (NLR) methods as extensions of Linear Regression (LR).

    • Main Results:

    • A priori identifiability of the Maxwell Slip model was established.
    • Necessary and sufficient conditions for a posteriori identifiability were derived.
    • Both DLR and NLR methods demonstrated significant improvements over the conventional LR method.
    • DLR achieved the highest accuracy, while NLR offered a favorable trade-off between accuracy and complexity.

    Conclusions:

    • The proposed DLR and NLR methods effectively identify presliding friction, outperforming traditional LR.
    • DLR is recommended for applications prioritizing maximum accuracy in friction identification.
    • NLR presents a practical alternative, balancing high accuracy with reduced parametric complexity for friction modeling.