The problem of presliding friction identification based upon the Maxwell Slip model structure, which is capable of accounting for the presliding hysteresis with nonlocal memory, is considered. The model structure's basic properties are examined, based upon which a priori identifiability is established, the role of initial conditions on identification is investigated, and the necessary and sufficient conditions for a posteriori identifiability are derived. Using them, guidelines for excitation

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... gnal design are also formulated. Building upon these results, two new methods, referred to as Dynamic Linear Regression (DLR) and NonLinear Regression (NLR), are postulated for presliding friction identification. Both may be thought of as different extensions of the conventional Linear Regression (LR) method that uses threshold preassignment: The DLR by introducing extra dynamics in the form of a vector finite impulse response filter, and the NLR by relaxing threshold preassignemnt through a special nonlinear regression procedure. The effectiveness of both methods is assessed via Monte Carlo experiments and identification based upon laboratory signals. The results indicate that both methods achieve significant improvements over the LR. The DLR offers the highest accuracy, with the NLR striking a very good balance between accuracy and parametric complexity. Presliding Friction Identification Based Upon the Maxwell Slip Model Structure 2 Leading Paragraph Presliding friction is the ascendant friction phenomenon that dominates the behavior of positioning systems at velocity reversals or near the goal position. Its precise identification is thus important for purposes such as dynamic analysis, automatic control, and fault diagnosis. This study examines the problem of presliding friction identification based upon the Maxwell Slip model structure, which is capable of accounting for the presliding hysteresis with nonlocal memory phenomena present. The model structure's necessary and sufficient conditions for identifiability are derived, and the role of initial conditions on identification is investigated. Two new identification methods are then postulated: A Dynamic Linear Regression (DLR) method and a Nonlinear Regression (NLR) method. Both may be thought of as different extensions of the conventional Linear Regression (LR) method that uses threshold preassignment. The effectiveness of the methods is assessed via Monte Carlo experiments and identification based upon laboratory signals. The results indicate that both methods achieve significant improvements over the LR. The DLR offers the highest accuracy, with the NLR striking a very good balance between accuracy and parametric complexity. Presliding Friction Identification Based Upon the Maxwell Slip Model Structure 3