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Research Papers

Static Friction in a Robot Joint—Modeling and Identification of Load and Temperature Effects

[+] Author and Article Information
André Carvalho Bittencourt

Svante Gunnarsson

Division of Automatic Control,Department of Electrical Engineering,  Linköping University, Linköping, Sweden, SE 581-83svante@isy.liu.se

Fs is commonly called static friction. An alternative nomenclature was adopted to make a distinction between the dynamic and the static friction phenomena.

It is known that using the torque reference from the servo as a measure of the joint torque might not always hold because of the temperature dependence of the torque constant of the motors. The deviations are, however, considered to be small and are neglected during the experiments.

Throughout the paper, all torques are normalized to the maximum manipulation torque at low speed.

Similar results have been experienced with sampling rates down to 220 Hz.

An ABB internal tool was used for simulation purposes.

In this study, the robot gearbox was lubricated with oil, not grease, which gave an opportunity to obtain well defined temperature readings by having a temperature sensor in the circulating lubricant oil.

In fact, a full joint load description would require three torque and three force components.

J. Dyn. Sys., Meas., Control 134(5), 051013 (Jul 27, 2012) (10 pages) doi:10.1115/1.4006589 History: Received April 27, 2011; Accepted January 18, 2012; Revised January 18, 2012; Published July 26, 2012; Online July 27, 2012

Friction is the result of complex interactions between contacting surfaces in down to a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be affected by other factors than speed. In this paper, the typical friction phenomena and models used in robotics are reviewed. It is shown how such models can be represented as a sum of functions of relevant states which are linear and nonlinear in the parameters, and how the identification method described in Ref. [1] can be used to identify them when all states are measured. The discussion follows with a detailed experimental study of friction in a robot joint under changes of joint angle, load torque, and temperature. Justified by their significance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, considerably improving the prediction performance compared to a standard model.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

The experiments were made on joint 2 of the ABBi robot IRBi 6620. ϕa is the joint angle at arm side, T the joint temperature, τl the manipulation load torque and τp the perpendicular load torque

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Figure 2

Static friction curve with lubrication regimes and model-based predictions. Circles indicate friction levels achieved using Eq. (4).

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Figure 3

Excitation signals used for the static friction estimation at ϕ·=42 rad/s

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Figure 4

Illustration of effects in the velocity-weakening regime caused by ϕ·s and α. (a) and (b) with ϕ·s=[1, 50] rad/s. (c) and (d) with α = [0.02, 3.00].

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Figure 5

Relative cost increase as a function of α for the model structure M0.

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Figure 6

Simulated load torques at joint 2 caused by angle variations of joints 2 and 4 at ϕa2 and ϕa4, respectively. Notice the larger absolute values for τl when compared with τp .

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Figure 7

Static friction curves for experiments related to ϕa and τp

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Figure 8

The dependence of the static friction curves on the manipulation torque, τl , at T = 34 °C

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Figure 9

The temperature dependence of the static friction curve

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Figure 10

Indication of independence between effects caused by T and τl

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Figure 11

Validation data set. Notice the large variations of T and τl values in (b) when registering the static friction curves in (a).

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Figure 12

Distribution of the prediction errors for the models M0 and M* achieved using the validation data set. Notice the considerable better performance of M*.

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