Support vector machine networks for friction modeling

IEEE/ASME Transactions on Mechatronics

Volumn 9, Issue 3, 2004, Pages 601-606

  Wang, G L ^a   Li, Y F ^b   Bi, D X ^b  

^a SUN YAT SEN UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ACTUATORS; APPROXIMATION THEORY; CONTROL SYSTEM SYNTHESIS; DEGREES OF FREEDOM (MECHANICS); FRICTION; MANIPULATORS; MATHEMATICAL MODELS; MOTION CONTROL; OPTIMIZATION;

FRICTION MODELLING; SERVO MOTION SYSTEMS; STRUCTURAL RISK MINIMIZATION; SUPPORT VECTOR MACHINE NETWORKS; SUPPORT VECTOR MACHINE REGRESSION;

SERVOMECHANISMS;

EID: 4744362973     PISSN: 10834435     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMECH.2004.835345     Document Type: Article

References (12)

1. B. Armstrong-Helouvry, P. Dupont, and C. Canudas de Wit, "A survey of models, analysis tools, and compensation methods for the control of machines with frction," Automatica, vol. 30, no. 7, pp. 1083-11838, 1994.
2. B. Armstrong-Helouvry, Control of Machines with Friction. Norwell, MA: Kluwer, 1991.
3. C. Canudas de Wit, P. Noel, A. Aubin, and B. Brogliato, "Adaptive friction compensation in robot manipulators: Low-velocities," Int. J.Robotics Res., vol. 10, no. 3, pp. 189-199, 1991.
4. M. Feemster, P. Vedagarbha, D. M. Dawson, and D. Haste, "Adaptive control techniques for friction compensation," in Proc. American Control Conf., Philadelphia, PA, 1998, pp. 1488-1492.
5. H. Du and S. S. Nair, "Low velocity friction compensation," IEEE Contr. Syst. Mag., vol. 18, pp. 61-69, Apr. 1998.
6. R. R. Selmic and F. L. Lewis, "Neural-network approximation of piecewise continuous function: Application to friction compensation," IEEE Trans. Neural Networks, vol. 13, pp. 745-751, May 2002.
7. F. Girosi, M. Jones, and T. Poggio, "Regularization theory and neural network architectures," Neural Computation, vol. 7, no. 2, pp. 219-269, 1995.
8. T. Evgeniou, M. Pontil, and T. Poggio, "Regularization networks and support vector machines," Adv. Computational Math., vol. 13, pp. 1-50, 2000.
9. G. Wahba, Spline Models for Observational Data, ser. Series in Applied Mathematics. Philadelphia, PA: SIAM, 1990.
10. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
11. G. W. Flake and S. Lawrence, "Efficient SVM regression training with SMO," Mach. Learning, vol. 46, no. 1-3, pp. 271-290, 2002.
12. M. R. Popović and A. A. Goldenberg, "Modeling of friction using spectral analysis," IEEE Trans. Robot. Automat., vol. 14, pp. 114-122, Feb. 1998.