Friction identification and compensation design for precision positioning

doi:10.1007/s40436-017-0171-z

Abstract

Abstract:

Precision positioning systems driven by linear motors are vulnerable to force disturbances owing to the reduction of gear transmission. The friction, included in the disturbance, can be modeled and compensated to improve the servo performance. This paper proposes a modified Stribeck friction model (SFM) and an optimization algorithm for consistency with the positioning platform. The compensators based on the friction model and disturbance observer (DOB) are simulated. The simulation results show that as compared with the DOB compensator (the velocity recovers by 5.19%), the friction model based compensator (the velocity recovers by 10.66%) exhibits a better performance after adding the disturbance. Moreover, compensation comparisons among the Coulomb friction, traditional SFM, and modified SFM are performed. The experimental results show that the following error with modified SFM compensation improves by 67.67% and 51.63% at a speed of 0.005 m/s and 0.05 m/s, compared with the Coulomb friction compensation. This demonstrates that the proposed model, optimization algorithm, and compensator can reduce the following error effectively.

Key words: Precision positioning, Friction compensation, Identification, Modeling

Feng-Tian Li, Li Ma, Lin-Tao Mi, You-Xuan Zeng, Ning-Bo Jin, Ying-Long Gao. Friction identification and compensation design for precision positioning[J]. Advances in Manufacturing, 2017, 5(2): 120-129.

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URL: https://www.aim.shu.edu.cn/EN/10.1007/s40436-017-0171-z

https://www.aim.shu.edu.cn/EN/Y2017/V5/I2/120

1. Chen MY, Lu JS (2014) High-precision motion control for a linear permanent magnet iron core synchronous motor drive in position platform. IEEE Trans Ind Inform 10(1):99-108
2. Yao WH, Tung PC, Fuh CC et al (2011) A robust uncertainty controller with system delay compensation for an ILPMSM system with unknown system parameters. IEEE Trans Ind Electron 58(10):4727-4735
3. Wu Y, Jiang H, Zou M (2012) The research on fuzzy PID control of the permanent magnet linear synchronous motor. Phys Procedia 24:1311-1318
4. Ruderman M, Iwasaki M (2015) Observer of nonlinear friction dynamics for motion control. IEEE Trans Ind Electron 62(9):5941-5949
5. Yan M, Huang K, Shiu Y et al (2007) Disturbance observer and adaptive controller design for a linear-motor-driven table system. Int J Adv Manuf Technol 35(3-4):408-415
6. Zhang DL, Chen YP, Wu A et al (2007) Precision motion control of permanent magnet linear motors. Int J Adv Manuf Technol 35(3-4):301-308
7. Cho K, Kim J, Choi SB et al (2015) A high-precision motion control based on a periodic adaptive disturbance observer in a PMLSM. IEEE-ASME Trans Mechatron 20(5):2158-2171
8. Feng B, Zhang D, Yang J et al (2015) A novel time-varying friction compensation method for servomechanism. Math Probl Eng 1:1-16
9. Wu W (2011) Disturbance compensation using feedforward and feedback for scanner direct current motor mechanism low speed regulation. J Dyn Syst Meas Control-Trans ASME 137(4):1614-1619
10. Wang XJ, Wang SP (2012) High performance adaptive control of mechanical servo system with LuGre friction model: identification and compensation. J Dyn Syst Meas Control-Trans ASME 134(1):011021
11. Shen JC, Lu QZ, Wu CH et al (2014) Sliding-mode tracking control with DNLRX model-based friction compensation for the precision stage. IEEE-ASME Trans Mechatron 19(2):788-797
12. Kim J, Cho K, Jung H et al (2014) A novel method on disturbance analysis and feed-forward compensation in permanent magnet linear motor system. In: International conference on intelligent system, pp 394-399
13. Villegas FJ, Hecker L, Peña ME et al (2014) Modeling of a linear motor feed drive including pre-rolling friction and aperiodic cogging and ripple. Int J Adv Manuf Technol 73(1):267-277
14. Tan WB, Li XW, Xiang HB et al (2011) Parameter identification of LuGre model based on analysis of steady state error. Opt Precis Eng 3:664-671
15. Li YX, Meng HR, Zhang B et al (2015) Stribeck friction measure system of servo table based on Labview. Chin J Liq Cryst Disp 30(1):180-185
16. Chen Q, Tao L, Nan Y et al (2015) Adaptive nonlinear sliding mode control of mechanical servo system with LuGre friction compensation. J Dyn Syst Meas Control 138(2):021003
17. Yeh S, Su H (2011) Development of friction identification methods for feed drives of CNC machine tools. Int J Adv Manuf Technol 52(1-4):263-278
18. Harman JJ, Kaufman MR, Shrestha DK (2014) Compensation and estimation of friction by using on-line input estimation algorithm. J Tribol 136(2):325
19. Piatkowski T (2014) Dahl and LuGre dynamic friction models— the analysis of selected properties. Mech Mach Theory 73:91-100
20. Lin CJ, Yau HT, Tian YC (2013) Identification and compensation of nonlinear friction characteristics and precision control for a linear motor stage. IEEE-ASME Trans Mechatron 18(4):1385-1396
21. Kabziński J, Jastrzebski M (2014) Practical implementation of adaptive friction compensation based on partially identified LuGre model. In: the 19th international conference on methods and models in automation and robotics (MMAR), pp 699-704
22. Wang XJ, Wang SP (2015) Output torque tracking control of direct-drive rotary torque motor with dynamic friction compensation. J Frankl Inst 352(11):5361-5379

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Friction identification and compensation design for precision positioning

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