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Experimental identification of the dynamics model for 6-DOF parallel manipulators

Published online by Cambridge University Press:  22 May 2009

Houssem Abdellatif  and
Bodo Heimann
Houssem Abdellatif*
Affiliation:
Institute of Mechatronic Systems (former Institute of Robotics), Appelstr. 11, D-30167 Hannover, Germany
Bodo Heimann
Affiliation:
Institute of Mechatronic Systems (former Institute of Robotics), Appelstr. 11, D-30167 Hannover, Germany
*
*Corresponding author. E-mail: [email protected]
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Summary

The paper presents a self-contained approach for the dynamics identification of six degrees of freedom (DOF) parallel robots. Major feature is the consequent consideration of structural properties of such machines to provide an experimentally adequate identification method. The known periodic excitation is modified and enhanced to take the actuator coupling as well as the numerical solution of the direct kinematics into account. The benefits of explicit frequency-domain data filtering are demonstrated. Additionally, a new implementation of the maximum-likelihood estimator allows for automatic tuning of the data filter. The issue of optimal input experiment design is also discussed and substantiated with extensive experiments.

Keywords

Parallel manipulatorsDynamicsIdentificationOptimal experiment design
Type
Article
Information
Robotica , Volume 28 , Issue 3 , May 2010 , pp. 359 - 368
Copyright
Copyright © Cambridge University Press 2009

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References

1.An, C. H., Atkeson, C. G. and Hollerbach, J. M., “Model-Based Control of a Robot Manipulator,” In: The MIT Press Series in Artificial Intelligence (The MIT Press, 1988).Google Scholar
2.Kozlowski, K., “Modelling and Identification in Robotics,” In: Advances in Industrial Control (Springer-Verlag, 1998).Google Scholar
3.Khalil, W. and Dombre, E., Modeling, Identification and Control of Robots (Hermes, London, UK, 2002).Google Scholar
4.Gautier, M. and Khalil, W., “Exciting trajectories for the identification of base inertial parameters of robots,” Int. J. Robot. Res. 11 (4), 362–375 (1992).CrossRefGoogle Scholar
5.Swevers, J., Gansemann, C., Tükel, D., Schutter, J. D. and Brussel, H. V., “Optimal robot excitation and identification,” IEEE Trans. Robot. Autom. 13 (5), 730–740 (1997).CrossRefGoogle Scholar
6.Guegan, S., Khalil, W. and Lemoine, P., “Identification of the Dynamic Parameters of the Orthoglide,” Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003) pp. 3272–3277.Google Scholar
7.Vivas, A. et al. , “Experimental Dynamic Identification of a Fully Parallel Robot,” Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan (2003), pp. 3278–3283.Google Scholar
8.Ramdani, N. and Poignet, P., “Experimental Parallel Robot Dynamic Model Evaluation with set Membership Estimation,” Proceedings of the 14th IFAC Symposium on System Identification, Newcastle, Australia (2006).Google Scholar
9.Farhat, N., Mata, V., Page, À. lvaro and Valero, F., “Identification of dynamic parameters of a 3-dof rps parallel manipulator,” Mech. Mach. Theory 43 (1), 1–17 (2008).CrossRefGoogle Scholar
10.Merlet, J.-P., “Jacobian, manipulability, condition number, and accuracy of parallel robots,” J. Mech. Des. 128 (1), 199–206 (2006).CrossRefGoogle Scholar
11.Merlet, J.-P., “Parallel Robots,” In: Solid Mechanics and its Applications (Kluwer Academic Publishers, 2000).Google Scholar
12.Abdellatif, H. and Heimann, B., Industrial Robotics: Theory, Modeling and Control (Pro-Literatur Verlag, 2007) pp. 523–556.Google Scholar
13.Abdellatif, H., Benimeli, F., Heimann, B. and Grotjahn, M., “Direct Identification of Dynamic Parameters for Parallel Manipulators,” Proceedings of the International Conference on Mechatronics and Robotics 2004, MechRob2004, Aachen, Germany (2004) pp. 999–1005.Google Scholar
14.Abdellatif, H., Heimann, B., Hornung, O. and Grotjahn, M., “Identification and Appropriate Parametrization of Parallel Robot Dynamic Models by Using Estimation Statistical Properties,” Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS2005, Edmonton, Canada (2005) pp. 444–449.Google Scholar
15.Abdellatif, H., Grotjahn, M. and Heimann, B., “High Efficient Dynamics Calculation Approach for Computed-Force Control of Robots with Parallel Structures,” Proceedings of the 44th IEEE Conference on Decision and Control and the 2005 European Control Conference, CDC-ECC05, Seville, Spain (2005) pp. 2024–2029.Google Scholar
16.Olsen, M. M., Swevers, J. and Verdonck, W., “Maximum likelihood identification of a dynamic robot model: Implementation issues,” Int. J. Robot. Res. 21 (2), 89–96 (2002).CrossRefGoogle Scholar
17.Gautier, M. and Poignet, P., “Extended kalman filtering and weighted least squares dynamic identification of robot,” Control Eng. Practice 9 (12), 1361–1372 (2001).Google Scholar
18.Ljung, L., System Identification: Theory for the User, 2nd ed. (Prentice-Hall, Upper Saddle Hall, NJ, 1999).Google Scholar
19.Walter, E. and Pronzato, L., Identification of Parametric Models form Experimental Data (Springer-Verlag, London, UK: 1997).Google Scholar
20.Harib, K. and Srinivasan, K., “Kinematic and dynamics analysis of stewart platform-based machine tool structures,” Robotica 21, 541–554 (2003).CrossRefGoogle Scholar
21.Abdellatif, H. and Heimann, B., “On Compensation of Passive Joint Friction in Robotic Manipulators: Modeling, Detection and Identification,” Proceedings of the 2006 IEEE International Conference on Control Applications, CCA2006, Munich, Germany (2006) pp. 2510–2515.CrossRefGoogle Scholar
22.Khalil, W. and Guegan, S. D., “Inverse and direct dynamics modeling of gough-stewart robots,” IEEE Trans. Robot. 20 (4), 754–762 (2004).CrossRefGoogle Scholar
23.Kostic, D., de Jager, B., Steinbuch, M. and Hensen, R., “Modeling and identification for high-performance robot control: An rrr-robotic arm case study,” IEEE Trans. Control Syst. Technol. 12 (6), 904–919 (2004).CrossRefGoogle Scholar
24.Bona, B. and Curatella, A., “Identification of Industrial Robot Parameters for Advanced Model-Based Controllers Design,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain (2005) pp. 1693–1698.Google Scholar
25.Mata, V., Benimeli, F., Farhat, N. and Valera, A., “Dynamic parameter identification in industrial robots considering physical feasibility,” Adv. Robot. 19 (1), 101–119 (2005).CrossRefGoogle Scholar
26.Renders, J.-M., Rossignol, E., Becquet, M. and Hanus, R., “Kinematic calibration and geometrical parameter identification for robots,” IEEE Trans. Robot. Autom. 7 (6), 721–432 (1991).CrossRefGoogle Scholar
27.Olsen, M. M. and Petersen, H. G., “A new method for estimating parameters of a dynamic robot model,” IEEE Trans. Robot. Autom. 17 (1), 95–100 (2001).CrossRefGoogle Scholar
28.Gevers, M., “Identification for control: From the early achievements to the revival of experiment design,” Eur. J. Control 11 (4–5), 335–352 (2005).CrossRefGoogle Scholar
29.Emery, A. F. and Nenarokomov, A. V., “Optimal experiment design,” Meas. Sci. Technol. 49 (9), 864–876 (1998).CrossRefGoogle Scholar
30.Denkena, B., Heimann, B., Abdellatif, H. and Holz, C., “Design, Modeling and Advanced Control of the Innovative Parallel Manipulator Palida,” Proceedings of the 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM2005 Monterrey, CA (2005) pp. 632–637.Google Scholar