Relevant bibliographies by topics / Friction identification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Friction identification'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Friction identification"

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Elhami, M. R., and D. J. Brookfield. "Identification of Coulomb and Viscous Friction in Robot Drives: An Experimental Comparison of Methods." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 210, no. 6 (November 1996): 529–40. http://dx.doi.org/10.1243/pime_proc_1996_210_228_02.

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This paper presents an experimental comparison of five methods of identifying friction in robot drives. The methods considered are direct plotting of velocity versus armature voltage, plotting velocity versus armature current, third harmonic estimation, batch least squares and sequential least squares. These methods were implemented on a d.c. servo motor robot drive system to identify Coulomb and viscous friction parameters. It is shown that an asymmetric Coulomb and viscous model properly identifies the frictional torque due to the combined sliding and rolling friction in the motor. Furthermore, although each of the identification methods is shown to be capable of giving reasonable estimates of the frictional coefficients, the plotting of velocity versus armature current is shown to be most suitable for off-line frictional identification and the sequential least-squares method most suitable for on-line identification, particularly when coefficients may change with time.
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Nouri, Bashir M. Y. "Friction identification in mechatronic systems." ISA Transactions 43, no. 2 (April 2004): 205–16. http://dx.doi.org/10.1016/s0019-0578(07)60031-7.

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Vergé, M. "FRICTION IDENTIFICATION WITH GENETIC ALGORITHMS." IFAC Proceedings Volumes 38, no. 1 (2005): 560–65. http://dx.doi.org/10.3182/20050703-6-cz-1902.00094.

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Gaudin, H., and G. Bessonnet. "From identification to motion optimization of a planar manipulator." Robotica 13, no. 2 (March 1995): 123–32. http://dx.doi.org/10.1017/s0263574700017628.

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SummaryIdentification of inertia constants and joint frictions of a robot manipulator is achieved in situ, without dismantling operations, by means of specific test motions. The necessary estimation of actuating torques is carried out by measuring, with Hall effect transducers, the current absorbed by the motors which power the system. This identification is accomplished by using a precise methodological order adapted to a planar SCARA type manipulator with two degrees of freedom. The identification of friction laws underscores a hysteresis phenomenon of the dissipative torques. This indicates that friction doesn't result from a simple superposition of a dry friction law and a viscous damping law. The identification results were applied with success to implementation of optimized trajectories computed on the basis of a dynamic criterion. The effective minimization of the performance criterion along the optimized trajectories, according to the corresponding standard trajectories, was verified experimentally by evaluating the motor work and actuator torques.
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Keck, Alexander, Jan Zimmermann, and Oliver Sawodny. "Friction parameter identification and compensation using the ElastoPlastic friction model." Mechatronics 47 (November 2017): 168–82. http://dx.doi.org/10.1016/j.mechatronics.2017.02.009.

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Prada, Erik, Michal Kelemen, Tatiana Kelemenova, Lubica Miková, Ivan Virgala, Peter Frankovský, and Milan Lörinc. "Friction Force Identification for Machine Locomotion." Applied Mechanics and Materials 816 (November 2015): 276–81. http://dx.doi.org/10.4028/www.scientific.net/amm.816.276.

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This paper describes the basic characteristic of the in-pipe micromachine. Themicromachine is based on two masses impact principle. Its diameter is 10 mm, length is 45 mm andweight is 10g. The micromachine is composed of a electromagnet, a permanent magnet, a adjustingunit, a guide rod, a damping spring and bristles. The paper represents the methods of friction forcesidentification
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Tao, Chen, and Yan Chen. "Fabric Identification Based on Friction Wave." Advanced Materials Research 796 (September 2013): 211–14. http://dx.doi.org/10.4028/www.scientific.net/amr.796.211.

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This paper presents an approach to identify the fabric of styles by wave of friction. First, the friction waves of typical cotton, wool, silk and hemp styled fabrics are collected under controlled conditions with a self-made device. In allusion to the non-stationary and attenuation of the signals, differences based on scopes are proposed to represent the features of wave signal in various aspects, which forms into a feature impulse. Pairwise comparisons are made among feature impulses of the four typical fabrics and it is revealed that their features are unfolded gradually from microcosmic to macrocosmic. Then, considerable amount of fabrics of each style are included to extract feature impulses, which constructed a feature library. Finally, the indeterminate fabrics are identified by the feature library, and the results are reflected in a ocular way into a discrimination chart.
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Guo, Kejian, Xingang Zhang, Hongguang Li, and Guang Meng. "Non-reversible friction modeling and identification." Archive of Applied Mechanics 78, no. 10 (January 8, 2008): 795–809. http://dx.doi.org/10.1007/s00419-007-0200-7.

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Parlitz, U., A. Hornstein, D. Engster, F. Al-Bender, V. Lampaert, T. Tjahjowidodo, S. D. Fassois, et al. "Identification of pre-sliding friction dynamics." Chaos: An Interdisciplinary Journal of Nonlinear Science 14, no. 2 (June 2004): 420–30. http://dx.doi.org/10.1063/1.1737818.

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YU Wei, 于伟, 马佳光 MA Jia-guang, 李锦英 LI Jin-ying, and 肖靖 XIAO Jing. "Friction parameter identification and friction compensation for precision servo turning table." Optics and Precision Engineering 19, no. 11 (2011): 2736–43. http://dx.doi.org/10.3788/ope.20111911.2736.

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Dissertations / Theses on the topic "Friction identification"

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Thimmalapura, Satish Voddina. "Identification and compensation of friction for a dual stage positioning system." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2571.

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Motion control systems are usually designed to track trajectories and/or regulate about a desired point. Most of the other objectives, like minimizing the tracking time or minimizing the energy expended, are secondary which quantify the above described objectives. The control problem in hard disk drives is tracking and seeking the desired tracks. Recent increase in the storage capacity demands higher accuracy of the read/write head. Dual stage actuators as compared to conventional single actuator increases the accuracy of the read/write head in hard disk drives. A scaled up version of the dual stage actuator is considered as the test bed for this thesis. Friction is present in all electromechanical systems. This thesis deals with modelling of the dual stage actuator test bed. A linear model predicts the behavior of the fine stage. Friction is significant in the coarse stage. Considerable time has been spent to model the coarse stage as a friction based model. Initially, static friction models were considered to model the friction. Dynamic models, which describe friction better when crossing zero velocity were considered. By analyzing several experimental data it was concluded that the friction was dependent on position and velocity as compared to conventional friction models which are dependent on the direction of motion. Static and Coulomb friction were modelled as functions of velocity and position. This model was able to predict the behavior of the coarse stage satisfactorily for various initial conditions. A friction compensation scheme based on the modelled friction is used to linearize the system based on feedback linearization techniques.
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Wong, Chian Xng. "Nonlinear system identification with emphasis on dry friction." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425213.

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Längkvist, Martin. "Online Identification of Friction Coefficients in an Industrial Robot." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-19269.

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All mechanical systems with moving parts are affected by friction, including industrial robots. Being able to design an accurate friction model would further increase the performance of todays robots. Friction is a complex dynamic phenomena that is constantly changing depending on the state and environment of the robot. It is therefore beneficial to update the parameters of the friction model online. An estimate of the friction will be made using the feedback control signal with the help of a feedforward control scheme in a two axis simulation setup. The friction estimate is then used for an offline identification of three friction model parameters in a static Lugre friction model. Improvements on the identification will be done by introducing some shut-off rules that will improve the estimate. The normalized least mean square method (NLMS) will then be used to update the parameters online. A simulation of friction compensation with a fixed friction model, and with an adaptive friction model will be studied. The method will also be simulated using experimental data taken from a real industrial robot.

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Marshall, Dustin John. "An Alternative System Identification Method for Friction Stir Processing." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/4061.

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Temperature control has been implemented in friction stir processing and has demonstrated the ability to give improved process control. In order to have optimal control of the process, the parameters of the system to be controlled must be accurately identified. The system parameters change with tool geometry and materials, workpiece materials, and temperature. This thesis presents the use of the relay feedback test to determine the system parameters. The relay feedback test is easy to use and promotes system stability during its use. The results from the relay feedback test can be used to determine controller gains for a PID controller. The use of this method, as well as the quality of the resulting control is demonstrated in this paper.
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Elhami, Mohammad Reza. "Modelling, identification and compensation of friction in robot control systems." Thesis, University of Liverpool, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243266.

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Khatibi, Rahman Haghi. "Mathematical open channel flow models and identification of their friction parameters." Thesis, University of London, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263145.

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This thesis l concerned with the mathematical modelling of open channel flows governed by the Saint-Venant equations, which are used as a prediction or identification tools. A survey of the literature in these fields identified the problems in need of Immediate research. Numerical test runs were then devised which led to projecting a clear picture as follows. The performance of twn widely used Implicit finite difference schemes, the 4-point box and 6-point staggered schemes were compared In a wide range of circumstances. it is concluded that both schemes produce 'very close results, but the staggered scheme is prone to convergence problems In some extreme cases. It was also noted that a sharp change in geometric configuration of compound channels produced discontinuous features on the aim ulated depth and discharge hydrographs. The inability of the staggered scheme In handling a head-discharge relationship as a downstream boundary condition was tackled by proposing and implementing a scheme of second order accuracy. As model data are generally corrupted withh errors and noise, their effects together with that of other factors on the Identified friction parameters we Investigated. The results demonstte the paramount Importance of the effect of a choice of objective function on the Identified parameters. While the individual values of the identified M2nning n may vary from one flood event to another, their mean is shown both numerically and rigorously to be dependent upon the choice of objective function. It is shown that an objective function formulated by using absolute errors performs ideally and produces reliable results even in the presence of autocorrelated Gaucian noise samples. The mean of the Identified parameters is also found to be adversely affected if the observation station is affected by localized disturbances. Sensitivity of objective functions to the variation In the value of the friction parameter Is also found to be an Important factor, as Insensitivity leads to ill-conditioning.
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Ren, Ying. "The analysis and identification of friction joint parameters in the dynamic response of structures." Thesis, Imperial College London, 1992. http://hdl.handle.net/10044/1/8904.

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Piroti, Shwana, and Jesper Eriksson. "Friction Modeling in FE Simulation : Identification of Friction Model Parameters in Airbag and Crash Dummy Head Contact through Simulation and Experimental Data Response Correlation." Thesis, KTH, Hållfasthetslära (Avd.), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232473.

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Motor vehicle-traffic accidents are a common cause of traumatic brain injuries, resulting in severe and sustaining disabilities, or even fatality. In an effort to mitigate injuries related to vehicle crashes, various safety systems such as the occupant airbag has been implemented. In angled impacts, occupant interaction with the airbags can lead to head rotation, and during recent years head rotation has been emphasized as an important contributor to head injury risk. Therefore, for prediction of head injury risk in crash simulations it is important to correctly model the friction force which arises in the contact between occupants and the car interior. The aim of this thesis is therefore to study the friction within such a system. More specifically, the analysis is focusing on dummy head to airbag interaction and to correlate a three parameter friction model for this contact pair, as well as a one parameter model currently used by Volvo Car Group, with measured laboratory test data in the software LS-DYNA.A preliminary study in LS-DYNA was conducted to determine the configuration of the laboratory setup consisting of a statically inflated customized driver airbag and a crash dummy head being launched to impact the airbag. The laboratory test data was analyzed using linear regression and Students T-test to identify the influence of parameters on the measured responses. The simulation model was then modified to represent the laboratory setup, prior to an optimization study performed to correlate simulation and laboratory test data responses. Lastly, an evaluation study was made to test whether or not the proposed friction model could improve occupant crash simulations.It was found in the thesis study that the friction force had a large effect on the rotation of the head around the vertical axis (z−axis in the anatomical coordinate system of the head). The experimental data showed that the internal pressure of the airbag had little effect on the response. This was likely due to the studied pressures being large enough for the airbag to be so stiff that no plowing effect of the dummy head moving through the airbag fabric could be seen. Furthermore, results from the optimization study indicated that the model correlation was improved when a three parameter friction model with velocity dependence was used. This implies that the friction coefficient is dependent on the velocity. It was also shown that material properties affecting friction behavior vary between different crash dummy heads, as well as different surface coating. Both dummy T-shirt fabric and grease paint resulted in significantly lower surface friction.Due to the difference in friction for different dummy heads, a single set of friction model parameter values that describes the friction behavior of all crash dummy heads does not exist. The study finds that when sliding is present in a contact, a three parameter model for describing the friction improves the correlation, as it can account for the velocity dependence of the friction in the contact. In contrast, when sliding is not present the one parameter and the three parameter model give similar results.Keywords: friction, velocity dependent friction coefficient, finite element analysis, car crash simulation, Volvo Cars, crash test dummy head, driver airbag, LS-DYNA, laboratory testing, optimization study.
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Wondimu, Nahom Abebe. "SIMULATED AND EXPERIMENTAL SLIDING MODE CONTROL OF A HYDRAULIC POSITIONING SYSTEM." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1145419922.

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Cho, Hyunjoong. "Development of advanced techniques for identification of flow stress and friction parameters for metal forming analysis." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1167699938.

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Books on the topic "Friction identification"

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Friction ridge skin: Comparison and identification of fingerprints. Boca Raton, Fla: CRC Press, 1993.

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Armstrong, Brian Stewart Randall. Dynamics for robot control: Friction modeling and ensuring excitation during parameter identification. Stanford, CA: Dept. of Computer Science, Stanford University, 1988.

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Keith, Chapman. Structure identification within a transitioning swept-wing boundary layer. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Friction ridge skin identification training manual. [s.l.]: [s.n.], 2004.

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Cowger, James F. Friction Ridge Skin: Comparison and Identification of Fingerprints (Practical Aspects of Criminal & Forensic Investigations). CRC, 1992.

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Ashbaugh, David R. Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. Taylor & Francis Group, 1999.

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Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology (Crc Series in Practical Aspects of Criminal and Forensic Investigations). CRC, 1999.

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P.S.I. guide: Prefix/suffix identification guide for bearings : world's most complete history of prefix/suffix designations, a computer generated identification guide for various types of anti-friction ball and roller bearings. St. Louis Park, MN, USA: Interchange, 1992.

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Book chapters on the topic "Friction identification"

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Frenz, Thomas. "Identification and Compensation of Friction." In Intelligent Observer and Control Design for Nonlinear Systems, 149–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04117-8_7.

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Savaresi, Sergio M., and Mara Tanelli. "Identification of Tyre–road Friction Conditions." In Advances in Industrial Control, 159–200. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-350-3_8.

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Marshall, Dustin, and Carl Sorensen. "System Parameter Identification for Friction Stir Processing." In Friction Stir Welding and Processing VII, 289–99. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-48108-1_30.

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Marshall, Dustin, and Carl Sorensen. "System Parameter Identification for Friction Stir Processing." In Friction Stir Welding and Processing VII, 289–99. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118658345.ch30.

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Dominguez, M., J. M. Michelin, and J. M. Martinez. "Recurrent neural networks for identification of friction." In Lecture Notes in Computer Science, 1060–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59497-3_285.

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Ma, Guoliang, Zhonghua Wang, and Fangfang Wang. "Friction Measurement, Identification, and Compensation for Servomechanisms." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 1469–75. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_189.

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Al-Bender, F., V. Lampaert, S. D. Fassois, D. C. Rizos, K. Worden, D. Engster, A. Hornstein, and U. Parlitz. "Measurement and Identification of Pre-Sliding Friction Dynamics." In Nonlinear Dynamics of Production Systems, 349–67. Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527602585.ch20.

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Yong-Quan, Gu. "Identification of Friction Modes and Analys is of Friction Characteristics of Mechanical Face Seals." In Fluid Sealing, 289–300. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2412-6_19.

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Ivlev, Vladimir I., Sergey Yu Misyurin, and Andrey P. Nelyubin. "Friction Model Identification for Dynamic Modeling of Pneumatic Cylinder." In Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020, 127–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65596-9_16.

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Idrissi, Mekki, and Azzeddine Soulaimani. "Identification of the Friction Coefficient in Shallow Water Flows." In Computational Mechanics ’95, 1007–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79654-8_164.

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Conference papers on the topic "Friction identification"

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Eglite, Irina, and Andrei A. Kolyshkin. "Spatial Stability Analysis of Shallow Mixing Layers with Variable Friction Coefficient." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.809-036.

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Baur, Joerg, Sebastian Dendorfer, Julian Pfaff, Christoph Schutz, Thomas Buschmann, and Heinz Ulbrich. "Experimental friction identification in robot drives." In 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. http://dx.doi.org/10.1109/icra.2014.6907744.

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Dominguez, M., J. M. Martinez, and J. M. Michelin. "Dynamic friction identification using neural networks." In 1997 European Control Conference (ECC). IEEE, 1997. http://dx.doi.org/10.23919/ecc.1997.7082622.

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Romeo, Rocco A., Marco Maggiali, Daniele Pucci, and Luca Fiorio. "Friction Identification in a Pneumatic Gripper." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341593.

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Indri, Marina, Ivan Lazzero, Alessandro Antoniazza, and Aldo Maria Bottero. "Friction modeling and identification for industrial manipulators." In 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 2013. http://dx.doi.org/10.1109/etfa.2013.6647958.

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Moucka, Michal. "Identification of friction in pneumatic linear drive." In 2012 13th International Carpathian Control Conference (ICCC). IEEE, 2012. http://dx.doi.org/10.1109/carpathiancc.2012.6228694.

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Bruzzone, Luca, and Giorgio Bozzini. "Modified LuGre Friction Model for the Simulation of Stick-Slip Phenomena." In Artificial Intelligence and Applications / Modelling, Identification, and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-057.

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Lian, Yufeng, Yantao Tian, Leilei Hu, and Cheng Yin. "Development of identification of tire-road friction conditions." In 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV 2012). IEEE, 2012. http://dx.doi.org/10.1109/icarcv.2012.6485425.

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Tanelli, Mara, Luigi Piroddi, Marco Piuri, and Sergio M. Savaresi. "Real-time identification of tire-road friction conditions." In 2008 IEEE International Conference on Control Applications (CCA) part of the IEEE Multi-Conference on Systems and Control. IEEE, 2008. http://dx.doi.org/10.1109/cca.2008.4629601.

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Hongliu Du and S. S. Nair. "Identification of friction for control at low velocities." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.609023.

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Reports on the topic "Friction identification"

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Kowalski, Karol, Rebecca McDaniel, and Jan Olek. Identification of Laboratory Technique to Optimize Superpave HMA Surface Friction Characteristics. West Lafayette, Indiana: Purdue University, 2010. http://dx.doi.org/10.5703/1288284314265.

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Feliu, Vicente, Kuldip S. Rattan, Jr Brown, and H. B. A New Approach to Control Single-Link Flexible Arms. Part 1. Modelling and Identification in the Presence of Joint Friction. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada210590.

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