An Integrated Friction Model with Improved Presliding Behaviour

Ganseman, Chris; Swevers, Jan; Prajogo, Tutuko; Al‐Bender, Farid

doi:10.1016/s1474-6670(17)44258-3

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…An experiment that reveals much about the behavior at small displacements is to apply an input force T b + a sin ω t to the system described by Equation (26). It is interesting to have a close to b so that the velocity will be small for a long period.…”

Section: Behavior For Small Displacementsmentioning

confidence: 99%

Friction Models and Friction Compensation

Olsson

Åström

Wit

et al. 1998

European Journal of Control

1,265

654

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Section: Behavior For Small Displacementsmentioning

confidence: 99%

Friction Models and Friction Compensation

Olsson

Åström

Wit

et al. 1998

European Journal of Control

1,265

654

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“…Such a switching is physically not justified and results in insurmountable obstacles in implementation [14]. In order to overcome these shortcomings, Canudas de Wit et al [9] reformulated the model so that no switching function is needed while retaining all the other above mentioned aspects of friction.…”

Section: Introductionmentioning

confidence: 99%

An integrated friction model structure with improved presliding behavior for accurate friction compensation

Swevers

Al‐Bender

Ganseman

et al. 2000

IEEE Trans. Automat. Contr.

Self Cite

561

271

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This paper presents a new dynamical friction model structure which allows accurate modeling both in the sliding and the presliding regimes. Transition between these two regimes is accomplished without a switching function. The model incorporates a hysteresis function with nonlocal memory and arbitrary transition curves. These last aspects prove essential for modeling presliding friction that is encountered in real physical situations. The model as a whole can also handle the Stribeck effect and stick-slip behavior as has been demonstrated by validation on a KUKA IR 361 robot. In this sense, this model can be considered as more complete in comparison with others found in the literature. The general friction model allows modeling of individual friction systems through the identification of a set of parameters that determine the complete behavior of the system. In this way, the model structure has been used to identify the friction behavior of a linear slide as well as that of the above mentioned KUKA robot. The results of the latter identification have been consequently used for feedforward friction compensation to obtain the most accurate tracking.

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“…Since we intend to design the control scheme to compensate the effects of friction, this requires a suitable friction model. In this paper, we use a physics-based model, proposed by [14], that is complex enough to capture dynamic friction effects, such as Stribeck effect, hysteresis, stick-slip motion, pre-sliding displacement and stiction. Each component of the friction force is modeled by a set of two equations which depend on a state variable representing the average deformation of the asperities of the contacting surfaces.…”

Section: The Friction Modelmentioning

confidence: 99%

“…46th IEEE CDC, New Orleans, USA, Dec. [12][13][14]2007 FrA05.4 that is negative semi-definite, i.e. it's always negative unless fors = 0, ∀w.…”

Section: The Friction Modelmentioning

confidence: 99%

“…Particularly, Radial Basis Functions (RBFs) are used to compensate the effects of the unknown nonlinearly-occurring Stribeck parameter in the friction model. Motivated by the fact that better results can be obtained when more properties of the studied system are exploited, in this paper we consider a friction force as modeled by Swevers et al in [14], which corresponds to the model in [4] with an improved hysteresis part. We also present a PD control structure and an estimate of the whole friction force with a RBF Neural Network to control a bar moving and rotating in a 2-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive compensation of modeled friction using a RBF neural network approximation

Vitiello

Tornambè

2007

2007 46th IEEE Conference on Decision and Control

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We present a compensation technique for a friction model, which captures problematic friction effects such as Stribeck effect, hysteresis, pre-sliding displacement, stick-slip motion and stiction. The proposed control utilizes a PD control structure and an adaptive estimate of the friction force. Specifically, a Radial Basis Function (RBF) is used to compensate the effects of the non-linear friction model. The asymptotic convergence of parameter estimation errors is achieved for the system in adaptive observer form using Barbalat's Lemma. We also introduce a parameter estimation projection algorithm to avoid the parameter estimates drift when the condition of persistency of excitation is not verified. Finally, to support the theoretical concepts, we present dynamic simulations for the proposed control scheme

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An Integrated Friction Model with Improved Presliding Behaviour

Cited by 7 publications

References 7 publications

Friction Models and Friction Compensation

Friction Models and Friction Compensation

An integrated friction model structure with improved presliding behavior for accurate friction compensation

Adaptive compensation of modeled friction using a RBF neural network approximation

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