Measurement and Identification of Pre‐Sliding Friction Dynamics

Al‐Bender, Farid; Lampaert, Vincent; Fassois, Spilios D.; Rizos, Demosthenis D.; Worden, Keith; Engster, David; Hornstein, A; Parlitz, Ulrich

doi:10.1002/3527602585.ch20

Cited by 13 publications

(12 citation statements)

References 19 publications

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“…μ k and μ b are identified using the second data bin using the same least-squares algorithm. This approach is more suitable for haptic AR systems compared to direct nonlinear identification methods that generally require more input data and are less stable than linear identification methods (Altpeter, 1999;Al-Bender et al, 2004). …”

Section: Model and Identification Proceduresmentioning

confidence: 98%

Real Stiffness Augmentation for Haptic Augmented Reality

Jeon

2011

Presence: Teleoperators and Virtual Environments

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Haptic augmented reality (AR) mixes a real environment with computer-generated virtual haptic stimuli, enabling the system to modulate the haptic attributes of a real object to desired values. This paper reports our second study on this functionality, with stiffness as a goal modulation property. Our first study explored the potential of haptic AR by presenting an effective stiffness modulation system for simple 1D interaction. This paper extends the system so that a user can interact with a real object in any 3D exploratory pattern while perceiving its augmented stiffness. We develop a complete set of algorithms for contact detection, deformation estimation, force rendering, and force control. The core part is the deformation estimation where the magnitude and direction of real object deformation are estimated using a contact dynamics model identified in a preprocessing step. All algorithms are designed in a way that maximizes the efficiency and usability of the system while maintaining convincing perceptual quality. In particular, the need for a large amount of preprocessing such as geometry modeling is avoided to improve the usability. The physical performance of each algorithm is thoroughly evaluated with real samples. Each algorithm is experimentally verified to satisfy the physical performance requirements that need to be satisfied to achieve convincing rendering quality. The final perceptual quality of stiffness rendering is assessed in a psychophysical experiment where the difference in the perceived stiffness between augmented and virtual objects is measured. The error is less than the human discriminability of stiffness, demonstrating that our system can provide accurate stiffness modulation with perceptually insignificant errors. The limitations of our AR system are also discussed along with a plan for future work.

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Section: Model and Identification Proceduresmentioning

confidence: 98%

Real Stiffness Augmentation for Haptic Augmented Reality

Jeon

2011

Presence: Teleoperators and Virtual Environments

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“…The first question may be answered by considering the model advantages, that are its simplicity and physical interpretation, as well as its capability of reproducing rate -independent hysteresis [Oh et al, 2005] with nonlocal memory [Lampaert et al, 2002]. Due to these, the Maxwell Slip model has been employed in several applications, with friction (see Lampaert and Swevers [2001], Lampaert et al [2002], , Al-Bender et al [2003], Parlitz et al [2004], Rizos andFassois [2004, 2005a,b], Worden et al [2007], Rizos and Fassois [2009], Moerlooze et al [2010]) and piezoelectric actuator modelling (see Goldfarb and Celanovic [1997], Kuhnen [1999], Choi et al [2002], Georgiou and Mrad [2006] for a representative collection) being probably the most frequent. The common conclusion from all these applications is that the Maxwell Slip model appears capable of representing the measured hysteresis quite adequately.…”

Section: Introductionmentioning

confidence: 99%

A-Posteriori Identifiability of the Maxwell Slip Model of Hysteresis

Rizos

Fassois

2011

IFAC Proceedings Volumes

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The a-posteriori identifiability of the Maxwell Slip model of hysteresis is addressed. The necessary and sufficient conditions that guarantee that the available data are informative enough are provided. An Output Error type estimator is subsequently postulated and its consistency is established. It is specifically shown that the estimates converge in probability to their actual counterparts under easily verifiable conditions on the Maxwell Slip model structure, the excitation, and mild assumptions on the additive measurement noise. The analytical results are verified through Monte Carlo simulations.

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“…For this reason identification techniques based upon experimentally obtained data are typically used. These may be classified under two broad categories: Black-box methods, for which no prior knowledge on the presliding friction dynamics is utilized [5,6], or grey-box methods (also referred to as physics based ), for which some a priori information is used. Depending upon the form of the model structure, they may be also classified as state-independent and state-dependent.…”

Section: Introductionmentioning

confidence: 99%

Presliding friction identification based upon the Maxwell Slip model structure

Rizos

Fassois

2004

Chaos: An Interdisciplinary Journal of Nonlinear Science

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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 signal 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 preassignment 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.

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Measurement and Identification of Pre‐Sliding Friction Dynamics

Cited by 13 publications

References 19 publications

Real Stiffness Augmentation for Haptic Augmented Reality

Real Stiffness Augmentation for Haptic Augmented Reality

A-Posteriori Identifiability of the Maxwell Slip Model of Hysteresis

Presliding friction identification based upon the Maxwell Slip model structure

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