Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-10T15:10:33.506Z Has data issue: false hasContentIssue false

Uncertainty estimation in robust tracking control of robot manipulators using the Fourier series expansion

Published online by Cambridge University Press:  20 July 2015

Saeed Khorashadizadeh*
Affiliation:
Department of Electrical and Robotic Engineering, Shahrood University, 361995161 Shahrood, Iran
Mohammad Mehdi Fateh
Affiliation:
Department of Electrical and Robotic Engineering, Shahrood University, 361995161 Shahrood, Iran
*
*Corresponding author. E-mail: s_khorashadi@yahoo.com

Summary

This paper presents a novel control algorithm for electrically driven robot manipulators. The proposed control law is simple and model-free based on the voltage control strategy with the decentralized structure and only joint position feedback. It works for both repetitive and non-repetitive tasks. Recently, some control approaches based on the uncertainty estimation using the Fourier series have been presented. However, the proper value for the fundamental period duration has been left as an open problem. This paper addresses this issue and intuitively shows that in order to perform repetitive tasks; the least common multiple (LCM) of fundamental period durations of the desired trajectories of the joints is a proper value for the fundamental period duration of the Fourier series expansion. Selecting the LCM results in the least tracking error. Moreover, the truncation error is compensated by the proposed control law to make the tracking error as small as possible. Adaptation laws for determining the Fourier series coefficients are derived according to the stability analysis. The case study is an SCARA robot manipulator driven by permanent magnet DC motors. Simulation results and comparisons with a voltage-based controller using adaptive neuro-fuzzy systems show the effectiveness of the proposed control approach in tracking various periodic trajectories. Moreover, the experimental results on a real SCARA robot manipulator verify the successful practical implementation of the proposed controller.

Type
Articles
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Qu, Z. and Dawson, D. M., Robust Tracking Control of Robot Manipulators (IEEE Press, Inc., New York, 1996).Google Scholar
2. Sage, H. G., De Mathelin, M. F. and Ostertag, E., “Robust control of robot manipulators: A survey,” Int. J. Control 72 (16), 14981522 (1999).CrossRefGoogle Scholar
3. Abdallah, C., Dawson, D., Dorato, P. and Jamshidi, M., “Survey of robust control for rigid robots,” IEEE Control Syst. Mag. 11, 2430 (1991).Google Scholar
4. Corless, M. J., “Control of uncertain nonlinear systems,” ASME Trans. J. Dyn. Syst. Meas. Control 115 (2B), 362372 (1993).CrossRefGoogle Scholar
5. Fateh, M. M., “Proper uncertainty bound parameter to robust control of electrical manipulators using nominal model,” Nonlinear Dyn. 61 (4), 655666 (2010).CrossRefGoogle Scholar
6. Fateh, M. M. and Khorashadizadeh, S., “Optimal Robust voltage control of electrically driven robots,” Nonlinear Dyn. 70, 14451458 (2012).CrossRefGoogle Scholar
7. Nicosia, S. and Tomei, P., “Robot control by using only joint position measurements,” IEEE Trans. Autom. Control 35 (9), 10581061 (1990).CrossRefGoogle Scholar
8. Talole, S. E., Kolhe, J. P. and Phadke, S. B., “Extended-state-observer-based control of flexible-joint system with experimental validation,” IEEE Trans. Ind. Electron. 57 (4), 14111419 (2010).CrossRefGoogle Scholar
9. Chen, W. H., Ballance, D. J., Gawthrop, P. J. and O'Reilly, J., “A nonlinear disturbance observer for robotic manipulators,” IEEE Trans. Ind. Electron. 47 (4), 932938 (2000).CrossRefGoogle Scholar
10. Slotine, J. J. and Li, W., Applied Nonlinear Control (Prentice-Hall NJ, USA, 1991).Google Scholar
11. Uzmay, I. and Burkan, R., “Parameter estimation and upper bounding adaptation in adaptive-robust control approaches for trajectory control of robots,” Robotica 20 (6), 653660 (2002).CrossRefGoogle Scholar
12. Garcia-Hernandez, R., Sanchez, E., Bayro-Corrochano, E., Santibanez, V. and Ruz-Hernandez, J., “Real-time decentralized neural block control: Application to a two DOF robot manipulator,” Int. J. Innovative Comput. Inf. Control 7 (3), 10751085 (2011).Google Scholar
13. Peng, J., Wang, J. and Wang, Y., “Neural network based robust hybrid control for robotic system: An H ∞ approach,” Nonlinear Dyn. 65, 421431 (2011).CrossRefGoogle Scholar
14. Puga-Guzmán, S., Moreno-Valenzuela, J. and Santibáñez, V., “Adaptive neural network motion control of manipulators with experimental evaluations,” Sci. World J. 2014, 113. doi: 10.1155/2014/694706 (2014).CrossRefGoogle ScholarPubMed
15. Fateh, M. M. and Khorashadizadeh, S., “Robust control of electrically driven robots by adaptive fuzzy estimation of uncertainty,” Nonlinear Dyn. 69, 14651477 (2012).CrossRefGoogle Scholar
16. Fateh, M. M., Azargoshasb, S. and Khorashadizadeh, S., “Model-free discrete control for robot manipulators using a fuzzy estimator,” Int. J. Comput. Math. Electr. Electron. Eng. 33 (3), 10511067, (2014).CrossRefGoogle Scholar
17. Wang, L. X., A Course in Fuzzy Systems and Control (Prentice-Hall, New York, 1997).Google Scholar
18. Fateh, M. M., “On the voltage-based control of robot manipulators,” Int. J. Control. Autom. Syst. 6 (5), 702712 (2008).Google Scholar
19. Orrante-Sakanassi, J., Santibañez, V. and Moreno-Valenzuela, J., “Stability analysis of a voltage-based controller for robot manipulators,” Int. J. Adv. Robot. Syst. 10, 119. doi: 10.5772/53894 (2013).CrossRefGoogle Scholar
20. Fateh, M. M., “Nonlinear control of electrical flexible-joint robots,” Nonlinear Dyn. 67, 25492559 (2012).CrossRefGoogle Scholar
21. Khorashadizadeh, S. and Fateh, M. M., “Robust control of flexible-joint robots using voltage control strategy,” Nonlinear Dyn. 67, 15251537 (2012).Google Scholar
22. Fateh, M. M., “Robust voltage control of electrical manipulators in task-space,” Int. J. Innov. Comput. Info. Control 6 (6), 26912700 (2010).Google Scholar
23. Chien, M. C. and Huang, A. C., “Regressor-free Adaptive Impedance Control of Flexible-Joint Robots using FAT,” Proceedings of the 2006 American Control Conference, (2006) pp. 3904–3909.Google Scholar
24. Chien, M. C. and Huang, A. C., “Adaptive impedance controller design for flexible-joint electrically-driven robots without computation of the regressor matrix,” Robotica 30, 133144 (2012).CrossRefGoogle Scholar
25. Kreyszig, E., Advanced Engineering Mathematics (John Wiley & Sons, 2007).Google Scholar
26. Spong, M. W., Hutchinson, S. and Vidyasagar, M., Robot Modelling and Control (Wiley, Hoboken, 2006).Google Scholar
27. Hardy, G. H. and Wright, E. M., An Introduction to the Theory of Numbers (Oxford University Press, 1979).Google Scholar
28. Khorashadizadeh, S. and Fateh, M. M., “Robust task-space control of robot manipulators using Legendre polynomials for uncertainty estimation,” Nonlinear Dyn. 79 (2), 11511161 (2014), DOI: 10.1007/s11071-014-1730-5.CrossRefGoogle Scholar
29. Wai, R. J. and Chen, P. C., “Intelligent tracking control for robot manipulator including actuator dynamics via TSK-type fuzzy neural network,” IEEE Trans. Fuzzy Systs. 12 (4), 552559 (2004).CrossRefGoogle Scholar