Grey-box modeling of friction: An experimental case-study

European Journal of Control

Volumn 6, Issue 3, 2000, Pages 258-267

  Hensen, R H A ^a   Angelis, G Z ^a   Van De Molengraft, M J G ^a   De Jager, A G ^a   Kok, J J ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Extended Kalman filtering; Friction models; Identification; Neural networks; Polytopic model

Indexed keywords

EID: 0000707725     PISSN: 09473580     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0947-3580(00)71134-4     Document Type: Article

References (29)

1. Armstrong-Hélouvry B. Control of machines with friction. Kluwer Academic Publishers, 1991
2. Armstrong-Hélouvry B, Dupont P, Canudas de Wit C. A survey of models, analysis tools and compensation methods for the control of machines with friction. Automatica, 1994; 30: 1083-1138
3. Bai E, Sastry SS. Parameter identification utilizing prior information. Int J Control 1986; 44: 455-473
4. Bliman P-A, Sorine M. Easy-to-use realistic dry friction models for automatic control. In: Proceedings of 3rd European Control Conference, Rome, Italy, 1995; 3788-3794
5. Bohlin T. A case study of grey box identification. Automatica 1994; 30(2): 307-31,8
6. Canudas de Wit C, Olsson H, Åström KJ, Lischinsky P. A new model for control of systems with friction. IEEE Trans on Automatic Control March 1995; 40(3): 419-425
7. Courtney-Pratt J, Eisner E. The effect of a tangential force on the contact of metallic bodies. In: Proceedings of Royal Society, vol. A238, 1997, pp 529-550
8. Dasgupta S, Anderson BDO, and Kaye RJ. Output-error identification methods for partially known systems. Int J Control 1986; 43: 177-191
9. dSPACE digital signal processing and control engineering GmbH. DS1102 User's Guide. Paderborn, Germany
10. Gawthrop PJ, Jezek J, Jones RW, Sroka I. Grey-box model identification. Control-Theory Advanced Technol March 1993; 9(1): 139-157
11. Haessig D, Friedland B. A method for simultaneous state and parameter estimation in nonlinear systems. In: Proceedings of the American Control Conference, Albuquerque, New Mexico, June 1997; 947-951
12. Gelb A. Applied optimal estimation. M.I.T. Press, 1978
13. Held D, Maron C. Estimation of friction characteristics, inertial and coupling coefficients in robotic joints based on current and speed measurements. In: Robot Control 1988, Selected Papers from the 2nd IFAC Symposium, 1988; 207-212
14. Haessig Jr DA, Friedland B. On the modeling and simulation of friction. J Dynamic Systems Measurements Control September 1991; 113: 354-362
15. Harnoy A, Friedland B, Semenock R, Rachoor H, Aly A. Apparatus for emperical determination of dynamic Friction. In: Procedings of the American Control Conference, Baltimore, Maryland, June 1994; 546-550
16. Hornik K, Stinchombe M, White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989; 2: 359-366
17. Horowitz I, Oldak S, Shapiro A. Extensions of dithered feedback systems. Int J Control 1991; 54(1): 83-109
18. Johansen TA, Foss BA. Nonlinear local model representation for adaptive systems. Int Conf Intelligent Control Instrument 1992; 2: 677-682
19. Johansen TA, Foss BA. State-space modeling using operating resime decomposition and local models. Preprints 12th IFAC World Congress 1993; 1: 431-434
20. Johnson CT, Lorenz RD. Experimental identification of friction and its compensation in precise, position controlled mechanisms. Proc Indus App Soc Annual Meeting 1991; 1400-1406
21. Lee HS, Tomizuka M. Robust motion controller design for high-accuracy positioning systems. IEEE Trans Industrial Electronics February 1996; 43(1)
22. Narendra KS, Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Trans Neural Networks 1990; 1: 4-27
23. Nijmeijer H, Schaft van der AJ. Nonlinear dynamical control systems. Springer, 1990
24. Olsson H, Åström KJ, Canudas de Wit C, Gäfvert M, Lischinsky P. Friction models and friction compensation. European J Control 1998; 4(3):
25. Selmic R, Lewis FL. Neural network approximation of piecewise continuous functions: application to friction compensation. In: Proceedings of International Symposium on Intelligent Control 1997; 227-232
26. Shorten R, Murray-Smith R. On normalising radial basis function networks. In: Irish Neural Networks Conference, University College Dublin, September 1994,
27. Sugeno M, Kang GT. Structure identification of fuzzy model. Fuzzy Sets Systems, 1998; 26: 15-33
28. Tafazoli F, Silva de CW, Lawrence PD. Tracking control of an electrohydraulic manipulator in the presence of friction. IEEE Trans Control Systems Technol May 1998; 6(3): 401-411
29. Wang LX, Mendel JM. Fuzzy basis functions, universal approximators and orthogonal least-squares learninc. IEEE Trans Neural Networks 1992; 3: 807-814